Conference PaperPDF Available

کاربرد هوش مصنوعی در تشخیص و درمان سرطان و تومور

Authors:

Abstract

در سا¬ل¬های اخیر، فناوری هوش مصنوعی به سرعت توسعه یافته است. به ویژه، در بینایی کامپیوتر، پردازش تصویر و تجزیه و تحلیل داده‌ها، هوش مصنوعی دستاوردهای مهمی را به همراه داشته است. کاربردهای هوش مصنوعی در حوزه تشخیص بیماریها و سرطان بسیار حائز اهمیت است زیرا با استفاده از الگوریتم¬های هوش مصنوعی می‌توان شناسایی بیماری‌ها را با دقت زیاد، تعداد بالا و به صورت 24ساعته انجام داد. همچنین الگوریتم¬های مبتنی بر هوش مصنوعی، به ویژه آنهایی که مبتنی بر یادگیری عمیق هستند، دارای برتری هایی هستند که سبب می‌شود تا توسعه این سیستم های تشخیص بیماری بهتر و بیشتر توسعه داده شود. با پیشرفت صنعت تصویربرداری و ظهور فناوری¬های جدیدی مانند مانند مایکروویو و ترموگرافی، پردازش تصویر نیز از الگوریتم¬های جدیدی برای تشخیص بیماری استفاده می‌کند. فناوری تشخیص هوش مصنوعی قادر به انجام تجزیه و تحلیل خوشه چند پارامتری و ساده کردن تصویر می¬باشد، بنابراین به پزشکان در غربالگری اولیه سرطان‌ها کمک بسیاری می¬کند. به طور کلی، هوش مصنوعی رشد قابل توجهی در سلامت پزشکی نشان داده است و در تصویربرداری های مختلف و تجزیه و تحلیل بیماری‌ها نقش بسیار موثری دارد. تشخیص سرطان از روی تصویر نیز مشکلات زیادی دارد که می توان از الگوریتم¬های هوش مصنوعی استفاده کرد و با ایجاد یک مدل هوشمند برای تجزیه و تحلیل تصاویر سرطان استفاده کرد.



*


m.vandjalili@maragheh.ac.ir

h.pezeshki@m-iau.ac.ir

  
     




        
  .  





    
 [1]
 

 

     
[2]
              
(Artificial Intelligence) 
.[3]   
        
 (CAD) [4-6]

[7]
      



[8] 


 (AI) 
  



[10]


                


               

 
[24]


 ANN 



           
[25]
 (ML) 
 (DL)   ML  DL 
       HI         
 ML 
            
HI  ML  DL 
 DL  ML 






            
        
       
 .
    (AI)            
[47] 

 (ANN) 
 (ANN) 
4  (ANN) 
(ANN)

              
(ANN)  
   AUC 
.

-.
               

            





     







 multi-omics 

 DNA RNA 
 FL  AI 
 FL 




ResNet-V2

٪

[56]  spline 
. 
 .  K-mean  C-means 
 .    
  SVM 


 31 (DCNN) 
 ٪

 [58] 

CCNI-BCC         

 SL 

 BRAF MSI 
SL SL  

  SL 

             

  [62]           
AlexNet  

SL

SL

SLBRAF
SL

                




 

)
E
(CAD )
x
(CAD
[70]
 

 [73]  

[74]

  


 

[75]
 (AI)  
 





 
ROI ROI



[76]

 (AI) 
 IHC  
 IHC IHC(IF)


[77]
(WSI) 
(WSI)
 (WSI) 



[80,81]      

 




[86]
 T 
.[87]

      
     
 
                 
             



        

[94]


 [95]

[96] 


 TME 


 I-O 
[97]
-
                 

  
 (AI) 
               


[100]

[100]

[101] 
 CT [102] 
 
 CT [103]
.[101]
 [PET]/CT 
 PET 

[104]
-

  
 
.
    (SVM)   
[105] 


     (AI)             
[106] CAD 




 

[110] 

 
                 
[114]



 




 



""
AI

SL
 








HIPAA



1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018:
GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer
journal for clinicians. 2018 Nov;68(6):394-424.
2. Mariotto AB, Noone AM, Howlader N, Cho H, Keel GE, Garshell J, Woloshin S, Schwartz LM. Cancer
survival: an overview of measures, uses, and interpretation. Journal of the National Cancer Institute Monographs.
2014 Nov 1;2014(49):145-86.
3. Giger ML, Doi K, MacMahon H. Computerized detection of lung nodules in digital chest radiographs.
InMedical Imaging 1987 Jan 1 (Vol. 767, pp. 384-387). SPIE.
4. Carmody DP, Nodine CF, Kundel HL. An analysis of perceptual and cognitive factors in radiographic
interpretation. Perception. 1980 Jun;9(3):339-44.
5. KUNDEL HL, HENDEE WR. The Perception of Radiologic Image Information Report of an NCI
Workshop on April 1516, 1985. Investigative Radiology. 1985 Nov 1;20(8):874-7.
6. Rao VM, Levin DC, Parker L, Cavanaugh B, Frangos AJ, Sunshine JH. How widely is computer-aided
detection used in screening and diagnostic mammography?. Journal of the American College of Radiology. 2010
Oct 1;7(10):802-5.
7. McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, Back T, Chesus M, Corrado
GS, Darzi A, Etemadi M. International evaluation of an AI system for breast cancer screening. Nature. 2020
Jan;577(7788):89-94.
8. Bejnordi BE, Veta M, Van Diest PJ, Van Ginneken B, Karssemeijer N, Litjens G, Van Der Laak JA,
Hermsen M, Manson QF, Balkenhol M, Geessink O. Diagnostic assessment of deep learning algorithms for
detection of lymph node metastases in women with breast cancer. Jama. 2017 Dec 12;318(22):2199-210.
9. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017 Apr 1;69:S36-40.
10. Bera K, Braman N, Gupta A, Velcheti V, Madabhushi A. Predicting cancer outcomes with radiomics and
artificial intelligence in radiology. Nature Reviews Clinical Oncology. 2022 Feb;19(2):132-46.
11. LeCun Y, Bengio Y. Convolutional networks for images, speech, and time series. The handbook of brain
theory and neural networks. 1995 Apr;3361(10):1995.
12. Paul TK, Iba H. Gene selection for classification of cancers using probabilistic model building genetic
algorithm. BioSystems. 2005 Dec 1;82(3):208-25.
13. Rajaguru H, SR SC. Analysis of decision tree and k-nearest neighbor algorithm in the classification of breast
cancer. Asian Pacific journal of cancer prevention: APJCP. 2019;20(12):3777.
14. Sherafatian M, Arjmand F. Decision tree-based classifiers for lung cancer diagnosis and subtyping using
TCGA miRNA expression data. Oncology letters. 2019 Aug 1;18(2):2125-31.
15. Aguirre-Allende I, Enriquez-Navascues JM, Elorza-Echaniz G, Etxart-Lopetegui A, Borda-Arrizabalaga N,
Ansorena YS, Placer-Galan C. Early-rectal cancer treatment: a decision-tree making based on systematic review
and meta-analysis. Cirugía Española. 2021 Feb 1;99(2):89-107.
16. Ghiasi MM, Zendehboudi S. Application of decision tree-based ensemble learning in the classification of
breast cancer. Computers in Biology and Medicine. 2021 Jan 1;128:104089.
17. Berger AC, Korkut A, Kanchi RS, Hegde AM, Lenoir W, Liu W, Liu Y, Fan H, Shen H, Ravikumar V, Rao
A. A comprehensive pan-cancer molecular study of gynecologic and breast cancers. Cancer cell. 2018 Apr
9;33(4):690-705.
18. Duan X, Yang Y, Tan S, Wang S, Feng X, Cui L, Feng F, Yu S, Wang W, Wu Y. Application of artificial
neural network model combined with four biomarkers in auxiliary diagnosis of lung cancer. Medical &
biological engineering & computing. 2017 Aug;55(8):1239-48.
19. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJ. Artificial intelligence in radiology. Nature
Reviews Cancer. 2018 Aug;18(8):500-10.
20. Hricak H, Abdel-Wahab M, Atun R, Lette MM, Paez D, Brink JA, Donoso-Bach L, Frija G, Hierath M,
Holmberg O, Khong PL. Medical imaging and nuclear medicine: a Lancet Oncology Commission. The Lancet
Oncology. 2021 Apr 1;22(4):e136-72.
21. Grover S, Xu MJ, Yeager A, Rosman L, Groen RS, Chackungal S, Rodin D, Mangaali M, Nurkic S,
Fernandes A, Lin LL. A systematic review of radiotherapy capacity in low-and middle-income countries.
Frontiers in oncology. 2015 Jan 22;4:380.
22. Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in
cancer prognosis and prediction. Computational and structural biotechnology journal. 2015 Jan 1;13:8-17.
23. Hart GR, Roffman DA, Decker R, Deng J. A multi-parameterized artificial neural network for lung cancer
risk prediction. PLoS One. 2018 Oct 24;13(10):e0205264.

24. Robertson S, Azizpour H, Smith K, Hartman J. Digital image analysis in breast pathologyfrom image
processing techniques to artificial intelligence. Translational Research. 2018 Apr 1;194:19-35.
25. Binczyk F, Prazuch W, Bozek P, Polanska J. Radiomics and artificial intelligence in lung cancer screening.
Translational lung cancer research. 2021 Feb;10(2):1186.
26. Huang CH, Kalaw EM. Automated classification for pathological prostate images using AdaBoost-based
Ensemble Learning. In2016 IEEE Symposium Series on Computational Intelligence (SSCI) 2016 Dec 6 (pp. 1-
4). IEEE.
27. Naik S, Doyle S, Agner S, Madabhushi A, Feldman M, Tomaszewski J. Automated gland and nuclei
segmentation for grading of prostate and breast cancer histopathology. In2008 5th IEEE International
Symposium on Biomedical Imaging: From Nano to Macro 2008 May 14 (pp. 284-287). IEEE.
28. Singh M, Kalaw EM, Giron DM, Chong KT, Tan CL, Lee HK. Gland segmentation in prostate
histopathological images. Journal of medical imaging. 2017 Jun;4(2):027501.
29. Ali T, Masood K, Irfan M, Draz U, Nagra AA, Asif M, Alshehri BM, Glowacz A, Tadeusiewicz R,
Mahnashi MH, Yasin S. Multistage segmentation of prostate cancer tissues using sample entropy texture
analysis. Entropy. 2020 Dec 4;22(12):1370.
30. Salman S, Ma Z, Mohanty S, Bhele S, Chu YT, Knudsen B, Gertych A. A machine learning approach to
identify prostate cancer areas in complex histological images. InInformation Technologies in Biomedicine,
Volume 3 2014 (pp. 295-306). Springer, Cham.
31. DiFranco MD, O’Hurley G, Kay EW, Watson RW, Cunningham P. Ensemble based system for whole-slide
prostate cancer probability mapping using color texture features. Computerized medical imaging and graphics.
2011 Oct 1;35(7-8):629-45.
32. Doyle S, Madabhushi A, Feldman M, Tomaszeweski J. A boosting cascade for automated detection of
prostate cancer from digitized histology. InInternational conference on medical image computing and computer-
assisted intervention 2006 Oct 1 (pp. 504-511). Springer, Berlin, Heidelberg.
33. Ayyad SM, Shehata M, Shalaby A, Abou El-Ghar M, Ghazal M, El-Melegy M, Abdel-Hamid NB, Labib
LM, Ali HA, El-Baz A. Role of AI and histopathological images in detecting prostate cancer: a survey. Sensors.
2021 Apr 7;21(8):2586.
34. Sanghavi FM, Agaian SS. Automated classification of histopathology images of prostate cancer using a Bag-
of-Words approach. InMobile Multimedia/Image Processing, Security, and Applications 2016 2016 May 19
(Vol. 9869, pp. 169-179). SPIE.
35. Gurav SB, Kulhalli KV, Desai VV. Prostate cancer detection using histopathology images and classification
using improved RideNN. Biomedical Engineering: Applications, Basis and Communications. 2019 Dec
17;31(06):1950042.
36. Li W, Li J, Sarma KV, Ho KC, Shen S, Knudsen BS, Gertych A, Arnold CW. Path R-CNN for prostate
cancer diagnosis and gleason grading of histological images. IEEE transactions on medical imaging. 2018 Oct
12;38(4):945-54.
37. Cui Y, Zhang G, Liu Z, Xiong Z, Hu J. A deep learning algorithm for one-step contour aware nuclei
segmentation of histopathology images. Medical & biological engineering & computing. 2019 Sep;57(9):2027-
43.
38. García G, Colomer A, Naranjo V. First-stage prostate cancer identification on histopathological images:
Hand-driven versus automatic learning. Entropy. 2019 Apr 2;21(4):356.
39. Arvaniti E, Fricker KS, Moret M, Rupp N, Hermanns T, Fankhauser C, Wey N, Wild PJ, Rueschoff JH,
Claassen M. Automated Gleason grading of prostate cancer tissue microarrays via deep learning. Scientific
reports. 2018 Aug 13;8(1):1-1.
40. Bulten W, Pinckaers H, van Boven H, Vink R, de Bel T, van Ginneken B, van der Laak J, de Kaa CH,
Litjens G. Automated gleason grading of prostate biopsies using deep learning. arXiv preprint arXiv:1907.07980.
2019 Jul 18.
41. Källén H, Molin J, Heyden A, Lundström C, Åström K. Towards grading gleason score using generically
trained deep convolutional neural networks. In2016 IEEE 13th International symposium on biomedical imaging
(ISBI) 2016 Apr 13 (pp. 1163-1167). IEEE.
42. Litjens G, Sánchez CI, Timofeeva N, Hermsen M, Nagtegaal I, Kovacs I, Hulsbergen-Van De Kaa C, Bult P,
Van Ginneken B, Van Der Laak J. Deep learning as a tool for increased accuracy and efficiency of
histopathological diagnosis. Scientific reports. 2016 May 23;6(1):1-1.
43. Tolkach Y, Dohmgörgen T, Toma M, Kristiansen G. High-accuracy prostate cancer pathology using deep
learning. Nature Machine Intelligence. 2020 Jul;2(7):411-8.
44. Tolkach Y, Dohmgörgen T, Toma M, Kristiansen G. High-accuracy prostate cancer pathology using deep
learning. Nature Machine Intelligence. 2020 Jul;2(7):411-8.

45. Duran-Lopez L, Dominguez-Morales JP, Rios-Navarro A, Gutierrez-Galan D, Jimenez-Fernandez A,
Vicente-Diaz S, Linares-Barranco A. Performance evaluation of deep learning-based prostate cancer screening
methods in histopathological images: measuring the impact of the model’s complexity on its processing speed.
Sensors. 2021 Feb 5;21(4):1122.
46. Nir G, Hor S, Karimi D, Fazli L, Skinnider BF, Tavassoli P, Turbin D, Villamil CF, Wang G, Wilson RS,
Iczkowski KA. Automatic grading of prostate cancer in digitized histopathology images: Learning from multiple
experts. Medical image analysis. 2018 Dec 1;50:167-80.
47. Mendelson EB. Artificial intelligence in breast imaging: potentials and limitations. American Journal of
Roentgenology. 2019 Feb;212(2):293-9.
48. Amato F, López A, Peña-Méndez EM, Vaňhara P, Hampl A, Havel J. Artificial neural networks in medical
diagnosis. Journal of applied biomedicine. 2013 Jan 1;11(2):47-58.
49. Beura S, Majhi B, Dash R. Mammogram classification using two dimensional discrete wavelet transform
and gray-level co-occurrence matrix for detection of breast cancer. Neurocomputing. 2015 Apr 22;154:1-4.
50. Kumar Y, Gupta S, Singla R, Hu YC. A systematic review of artificial intelligence techniques in cancer
prediction and diagnosis. Archives of Computational Methods in Engineering. 2021a Sep 27:1-
51. Kumar Y, Gupta S, Singla R, Hu YC. A systematic review of artificial intelligence techniques in cancer
prediction and diagnosis. Archives of Computational Methods in Engineering. 2021b Sep 27:1
52. Binczyk F, Prazuch W, Bozek P, Polanska J. Radiomics and artificial intelligence in lung cancer screening.
Translational lung cancer research. 2021 Feb;10(2):1186.
53. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, Grody WW, Hegde M, Lyon E, Spector E,
Voelkerding K. Standards and guidelines for the interpretation of sequence variants: a joint consensus
recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular
Pathology. Genetics in medicine. 2015 May;17(5):405-23.
54. Konečný J, McMahan HB, Yu FX, Richtárik P, Suresh AT, Bacon D. Federated learning: Strategies for
improving communication efficiency. arXiv preprint arXiv:1610.05492. 2016 Oct 18.
55. Ferreira CA, Melo T, Sousa P, Meyer MI, Shakibapour E, Costa P, Campilho A. Classification of breast
cancer histology images through transfer learning using a pre-trained inception resnet v2. InInternational
conference image analysis and recognition 2018 Jun 27 (pp. 763-770). Springer, Cham.
56. Joon P, Bajaj SB, Jatain A. Segmentation and detection of lung cancer using image processing and clustering
techniques. InProgress in advanced computing and intelligent engineering 2019 (pp. 13-23). Springer, Singapore.
57. Dabass M, Vig R, Vashisth S. Five-grade cancer classification of colon histology images via deep learning.
InCommunication and Computing Systems 2019 Oct 22 (pp. 18-24). CRC Press.
58. Ting FF, Tan YJ, Sim KS. Convolutional neural network improvement for breast cancer classification.
Expert Systems with Applications. 2019 Apr 15;120:103-15.
59. Bilal M, Raza SE, Azam A, Graham S, Ilyas M, Cree IA, Snead D, Minhas F, Rajpoot NM. Development
and validation of a weakly supervised deep learning framework to predict the status of molecular pathways and
key mutations in colorectal cancer from routine histology images: a retrospective study. The Lancet Digital
Health. 2021 Dec 1;3(12):e763-72.
60. Kather JN, Pearson AT, Halama N, Jäger D, Krause J, Loosen SH, Marx A, Boor P, Tacke F, Neumann UP,
Grabsch HI. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer.
Nature medicine. 2019 Jul;25(7):1054-6.
61. De Matos J, Britto Jr AD, Oliveira LE, Koerich AL. Histopathologic image processing: A review. arXiv
preprint arXiv:1904.07900. 2019 Apr 16.
62. Jasti V, Zamani AS, Arumugam K, Naved M, Pallathadka H, Sammy F, Raghuvanshi A, Kaliyaperumal K.
Computational technique based on machine learning and image processing for medical image analysis of breast
cancer diagnosis. Security and Communication Networks. 2022 Mar 9;2022.
63. Saldanha OL, Quirke P, West NP, James JA, Loughrey MB, Grabsch HI, Salto-Tellez M, Alwers E, Cifci D,
Ghaffari Laleh N, Seibel T. Swarm learning for decentralized artificial intelligence in cancer histopathology.
Nature Medicine. 2022 Apr 25:1-8.
64. Zhang N, Lou W, Ji F, Qiu L, Tsang BK, Di W. Low molecular weight heparin and cancer survival: clinical
trials and experimental mechanisms. Journal of cancer research and clinical oncology. 2016 Aug;142(8):1807-16.
65. Zhang R, Zheng Y, Mak TW, Yu R, Wong SH, Lau JY, Poon CC. Automatic detection and classification of
colorectal polyps by transferring low-level CNN features from nonmedical domain. IEEE journal of biomedical
and health informatics. 2016 Dec 5;21(1):41-7.
66. e Gonçalves WG, Dos Santos MH, Lobato FM, Ribeiro-dos-Santos Â, de Araújo GS. Deep learning in
gastric tissue diseases: a systematic review. BMJ open gastroenterology. 2020 Mar 1;7(1):e000371.

67. Nahar VK, Allison Ford M, Brodell RT, Boyas JF, Jacks SK, Biviji-Sharma R, Haskins MA, Bass MA. Skin
cancer prevention practices among malignant melanoma survivors: a systematic review. Journal of Cancer
Research and Clinical Oncology. 2016 Jun;142(6):1273-83.
68. Nakano K, Nemoto H, Nomura R, Inaba H, Yoshioka H, Taniguchi K, Amano A, Ooshima T. Detection of
oral bacteria in cardiovascular specimens. Oral microbiology and immunology. 2009 Feb;24(1):64-8. Nakano K,
Nemoto H, Nomura R, Inaba H, Yoshioka H, Taniguchi K, Amano A, Ooshima T. Detection of oral bacteria in
cardiovascular specimens. Oral microbiology and immunology. 2009 Feb;24(1):64-8.
69. He Z, Liu H, Moch H, Simon HU. Machine learning with autophagy-related proteins for discriminating renal
cell carcinoma subtypes. Scientific reports. 2020 Jan 20;10(1):1-7.
70. He Z, Liu H, Moch H, Simon HU. Machine learning with autophagy-related proteins for discriminating renal
cell carcinoma subtypes. Scientific reports. 2020 Jan 20;10(1):1-7.
71. Devi MA, Ravi S, Vaishnavi J, Punitha S. Classification of cervical cancer using artificial neural networks.
Procedia Computer Science. 2016 Jan 1;89:465-72.
72. Liu B, Chi W, Li X, Li P, Liang W, Liu H, Wang W, He J. Evolving the pulmonary nodules diagnosis from
classical approaches to deep learning-aided decision support: three decades’ development course and future
prospect. Journal of cancer research and clinical oncology. 2020 Jan;146(1):153-85.
73. Liu J, Ke F, Chen T, Zhou Q, Weng L, Tan J, Shen W, Li L, Zhou J, Xu C, Cheng H. MicroRNAs that
regulate PTEN as potential biomarkers in colorectal cancer: a systematic review. Journal of cancer research and
clinical oncology. 2020 Apr;146(4):809-20.
74. Nartowt BJ, Hart GR, Muhammad W, Liang Y, Stark GF, Deng J. Robust machine learning for colorectal
cancer risk prediction and stratification. Frontiers in big Data. 2020 Mar 10;3:6.
75. Slaoui M, Fiette L. Drug Safety Evaluation. Methods in Molecular Biology (Methods and Protocols).
76. Basurto-Hurtado JA, Cruz-Albarran IA, Toledano-Ayala M, Ibarra-Manzano MA, Morales-Hernandez LA,
Perez-Ramirez CA. Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification
Strategies Using Artificial Intelligence Algorithms. Cancers. 2022 Jul 15;14(14):3442.
77. Gown AM. Diagnostic immunohistochemistry: what can go wrong and how to prevent it. Archives of
pathology & laboratory medicine. 2016 Sep;140(9):893-8.
78. Pantanowitz L, Sinard JH, Henricks WH, Fatheree LA, Carter AB, Contis L, Beckwith BA, Evans AJ, Lal
A, Parwani AV. Validating whole slide imaging for diagnostic purposes in pathology: guideline from the College
of American Pathologists Pathology and Laboratory Quality Center. Archives of Pathology and Laboratory
Medicine. 2013 Dec;137(12):1710-22.
79. Zarella MD, Bowman D, Aeffner F, Farahani N, Xthona A, Absar SF, Parwani A, Bui M, Hartman DJ. A
practical guide to whole slide imaging: a white paper from the digital pathology association. Archives of
pathology & laboratory medicine. 2019 Feb;143(2):222-34.
80. Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. Artificial intelligence in digital pathology
new tools for diagnosis and precision oncology. Nature reviews Clinical oncology. 2019 Nov;16(11):703-15.
81. Tumeh PC, Hellmann MD, Hamid O, Tsai KK, Loo KL, Gubens MA, Rosenblum M, Harview CL, Taube
JM, Handley N, Khurana N. Liver metastasis and treatment outcome with anti-PD-1 monoclonal antibody in
patients with melanoma and NSCLC. Cancer immunology research. 2017 May;5(5):417-24.
82. Barisoni L, Lafata KJ, Hewitt SM, Madabhushi A, Balis UG. Digital pathology and computational image
analysis in nephropathology. Nature Reviews Nephrology. 2020 Nov;16(11):669-85.
83. Neltner JH, Abner EL, Schmitt FA, Denison SK, Anderson S, Patel E, Nelson PT. Digital pathology and
image analysis for robust high-throughput quantitative assessment of Alzheimer disease neuropathologic
changes. Journal of Neuropathology & Experimental Neurology. 2012 Dec 1;71(12):1075-85.
84. Dixon AR, Bathany C, Tsuei M, White J, Barald KF, Takayama S. Recent developments in multiplexing
techniques for immunohistochemistry. Expert review of molecular diagnostics. 2015 Sep 2;15(9):1171-86.
85. Blom S, Paavolainen L, Bychkov D, Turkki R, Mäki-Teeri P, Hemmes A, Välimäki K, Lundin J,
Kallioniemi O, Pellinen T. Systems pathology by multiplexed immunohistochemistry and whole-slide digital
image analysis. Scientific reports. 2017 Nov 14;7(1):1-3.
86. Carstens JL, Correa de Sampaio P, Yang D, Barua S, Wang H, Rao A, Allison JP, LeBleu VS, Kalluri R.
Spatial computation of intratumoral T cells correlates with survival of patients with pancreatic cancer. Nature
communications. 2017 Apr 27;8(1):1-3.
87. Feng Z, Puri S, Moudgil T, Wood W, Hoyt CC, Wang C, Urba WJ, Curti BD, Bifulco CB, Fox BA.
Multispectral imaging of formalin-fixed tissue predicts ability to generate tumor-infiltrating lymphocytes from
melanoma. Journal for immunotherapy of cancer. 2015 Dec;3(1):1-1.
88. Lopès A, Cassé AH, Billard E, Boulcourt-Sambou E, Roche G, Larois C, Barnich N, Naimi S, Bonnet M,
Dumas B. Deciphering the immune microenvironment of a tissue by digital imaging and cognition network.
Scientific reports. 2018 Nov 12;8(1):1-6.

89. Aeffner F, Zarella MD, Buchbinder N, Bui MM, Goodman MR, Hartman DJ, Lujan GM, Molani MA,
Parwani AV, Lillard K, Turner OC. Introduction to digital image analysis in whole-slide imaging: a white paper
from the digital pathology association. Journal of pathology informatics. 2019 Jan 1;10(1):9.
90. Heindl A, Nawaz S, Yuan Y. Mapping spatial heterogeneity in the tumor microenvironment: a new era for
digital pathology. Laboratory investigation. 2015 Apr;95(4):377-84.
91. Yuan J, Hegde PS, Clynes R, Foukas PG, Harari A, Kleen TO, Kvistborg P, Maccalli C, Maecker HT, Page
DB, Robins H. Novel technologies and emerging biomarkers for personalized cancer immunotherapy. Journal for
immunotherapy of cancer. 2016 Dec;4(1):1-25.
92. Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, Li B, Madabhushi A, Shah P, Spitzer
M, Zhao S. Applications of machine learning in drug discovery and development. Nature reviews Drug
discovery. 2019 Jun;18(6):463-77.
93. Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. Artificial intelligence in digital pathology
new tools for diagnosis and precision oncology. Nature reviews Clinical oncology. 2019 Nov;16(11):703-15.
94. Serag A, Ion-Margineanu A, Qureshi H, McMillan R, Saint Martin MJ, Diamond J, O'Reilly P, Hamilton P.
Translational AI and deep learning in diagnostic pathology. Frontiers in medicine. 2019 Oct 1;6:185.
95. Ganesan S, Madabhushi A, Basavanhally A, Xu J, Bhanot G, Barnard N, Toppmeyer D. Computerized
Histologic Image-Based Risk Score (IbRiS) Classifier for ER+ Breast Cancer. Cancer Research. 2009 Dec
15;69(24_Supplement):3046-.
96. Beck AH, Sangoi AR, Leung S, Marinelli RJ, Nielsen TO, Van De Vijver MJ, West RB, Van De Rijn M,
Koller D. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival.
Science translational medicine. 2011 Nov 9;3(108):108ra113-.
97. Baxi V, Edwards R, Montalto M, Saha S. Digital pathology and artificial intelligence in translational
medicine and clinical practice. Modern Pathology. 2022 Jan;35(1):23-32.
98. Zhang J, Wang G, Ren J, Yang Z, Li D, Cui Y, Yang X. Multiparametric MRI-based radiomics nomogram
for preoperative prediction of lymphovascular invasion and clinical outcomes in patients with breast invasive
ductal carcinoma. European Radiology. 2022 Jun;32(6):4079-89.
99. Schaffter T, Buist DS, Lee CI, Nikulin Y, Ribli D, Guan Y, Lotter W, Jie Z, Du H, Wang S, Feng J.
Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms.
JAMA network open. 2020 Mar 2;3(3):e200265-.
100. Grimm LJ, Mazurowski MA. Breast cancer radiogenomics: current status and future directions. Academic
Radiology. 2020 Jan 1;27(1):39-46.
101. Zhang Y, Jiang B, Zhang L, Greuter MJ, de Bock GH, Zhang H, Xie X. Lung nodule detectability of
artificial intelligence-assisted CT image reading in lung cancer screening. Current Medical Imaging. 2022 Mar
1;18(3):327-34.
102. Ardila D, Kiraly AP, Bharadwaj S, Choi B, Reicher JJ, Peng L, Tse D, Etemadi M, Ye W, Corrado G,
Naidich DP. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed
tomography. Nature medicine. 2019 Jun;25(6):954-61.
103. Zhao W, Yang J, Sun Y, Li C, Wu W, Jin L, Yang Z, Ni B, Gao P, Wang P, Hua Y. 3D deep learning from
CT scans predicts tumor invasiveness of subcentimeter pulmonary adenocarcinomas. Cancer research. 2018 Dec
15;78(24):6881-9.
104. Ravenel JG, Rosenzweig KE, Kirsch J, Ginsburg ME, Kanne JP, Kestin LL, Parker JA, Rimner A, Saleh
AG, Mohammed TL. ACR Appropriateness Criteria non-invasive clinical staging of bronchogenic carcinoma.
Journal of the American College of Radiology. 2014 Sep 1;11(9):849-56.
105. Kumar Y, Gupta S, Singla R, Hu YC. A systematic review of artificial intelligence techniques in cancer
prediction and diagnosis. Archives of Computational Methods in Engineering. 2021 Sep 27:1-28.
106. Serag A, Ion-Margineanu A, Qureshi H, McMillan R, Saint Martin MJ, Diamond J, O'Reilly P, Hamilton P.
Translational AI and deep learning in diagnostic pathology. Frontiers in medicine. 2019 Oct 1;6:185.
107. Madabhushi A, Agner S, Basavanhally A, Doyle S, Lee G. Computer-aided prognosis: predicting patient and
disease outcome via quantitative fusion of multi-scale, multi-modal data. Computerized medical imaging and
graphics. 2011 Oct 1;35(7-8):506-14.
108. Amiri Z, Mohammad K, Mahmoudi M, Parsaeian M, Zeraati H. Assessing the effect of quantitative and
qualitative predictors on gastric cancer individuals survival using hierarchical artificial neural network models.
Iranian Red Crescent Medical Journal. 2013 Jan;15(1):42.
109. Afshar S, Afshar S, Warden E, Manochehri H, Saidijam M. Application of artificial neural network in
miRNA biomarker selection and precise diagnosis of colorectal cancer. Iranian biomedical journal. 2019
May;23(3):175.
110. Vogel L. Rise of medical AI poses new legal risks for doctors.

111. Bejnordi BE, Veta M, Van Diest PJ, Van Ginneken B, Karssemeijer N, Litjens G, Van Der Laak JA,
Hermsen M, Manson QF, Balkenhol M, Geessink O. Diagnostic assessment of deep learning algorithms for
detection of lymph node metastases in women with breast cancer. Jama. 2017 Dec 12;318(22):2199-210.
112. Bandi P, Geessink O, Manson Q, Van Dijk M, Balkenhol M, Hermsen M, Bejnordi BE, Lee B, Paeng K,
Zhong A, Li Q. From detection of individual metastases to classification of lymph node status at the patient level:
the camelyon17 challenge. IEEE transactions on medical imaging. 2018 Aug 26;38(2):550-60.
113. Caicedo JC, Goodman A, Karhohs KW, Cimini BA, Ackerman J, Haghighi M, Heng C, Becker T, Doan M,
McQuin C, Rohban M. Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl. Nature
methods. 2019 Dec;16(12):1247-53.
114. Altini N, Prencipe B, Cascarano GD, Brunetti A, Brunetti G, Triggiani V, Carnimeo L, Marino F, Guerriero
A, Villani L, Scardapane A. Liver, kidney and spleen segmentation from CT scans and MRI with deep learning:
A survey. Neurocomputing. 2022 Jun 14;490:30-53.
115. Robertson S, Azizpour H, Smith K, Hartman J. Digital image analysis in breast pathologyfrom image
processing techniques to artificial intelligence. Translational Research. 2018 Apr 1;194:19-35.
116. Luchini C, Pea A, Scarpa A. Artificial intelligence in oncology: current applications and future perspectives.
British Journal of Cancer. 2022 Jan;126(1):4-9.
117. Echle A, Rindtorff NT, Brinker TJ, Luedde T, Pearson AT, Kather JN. Deep learning in cancer pathology: a
new generation of clinical biomarkers. British journal of cancer. 2021 Feb;124(4):686-96.
118. Campanella G, Hanna MG, Geneslaw L, Miraflor A, Werneck Krauss Silva V, Busam KJ, Brogi E, Reuter
VE, Klimstra DS, Fuchs TJ. Clinical-grade computational pathology using weakly supervised deep learning on
whole slide images. Nature medicine. 2019 Aug;25(8):1301-9.
119. Echle A, Grabsch HI, Quirke P, van den Brandt PA, West NP, Hutchins GG, Heij LR, Tan X, Richman SD,
Krause J, Alwers E. Clinical-grade detection of microsatellite instability in colorectal tumors by deep learning.
Gastroenterology. 2020 Oct 1;159(4):1406-16.
120. Kaissis GA, Makowski MR, Rückert D, Braren RF. Secure, privacy-preserving and federated machine
learning in medical imaging. Nature Machine Intelligence. 2020 Jun;2(6):305-11.
ResearchGate has not been able to resolve any citations for this publication.
Article
Full-text available
Breast cancer is one the main death causes for women worldwide, as 16% of the diagnosed malignant lesions worldwide are its consequence. In this sense, it is of paramount importance to diagnose these lesions in the earliest stage possible, in order to have the highest chances of survival. While there are several works that present selected topics in this area, none of them present a complete panorama, that is, from the image generation to its interpretation. This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the image generation and processing are presented and discussed. Novel methodologies should consider the adroit integration of artificial intelligence-concepts and the categorical data to generate modern alternatives that can have the accuracy, precision and reliability expected to mitigate the misclassifications.
Article
Full-text available
Artificial intelligence (AI) can predict the presence of molecular alterations directly from routine histopathology slides. However, training robust AI systems requires large datasets for which data collection faces practical, ethical and legal obstacles. These obstacles could be overcome with swarm learning (SL), in which partners jointly train AI models while avoiding data transfer and monopolistic data governance. Here, we demonstrate the successful use of SL in large, multicentric datasets of gigapixel histopathology images from over 5,000 patients. We show that AI models trained using SL can predict BRAF mutational status and microsatellite instability directly from hematoxylin and eosin (H&E)-stained pathology slides of colorectal cancer. We trained AI models on three patient cohorts from Northern Ireland, Germany and the United States, and validated the prediction performance in two independent datasets from the United Kingdom. Our data show that SL-trained AI models outperform most locally trained models, and perform on par with models that are trained on the merged datasets. In addition, we show that SL-based AI models are data efficient. In the future, SL can be used to train distributed AI models for any histopathology image analysis task, eliminating the need for data transfer.
Article
Full-text available
Breast cancer is the most lethal type of cancer for all women worldwide. At the moment, there are no effective techniques for preventing or curing breast cancer, as the source of the disease is unclear. Early diagnosis is a highly successful means of detecting and managing breast cancer, and early identification may result in a greater likelihood of complete recovery. Mammography is the most effective method of detecting breast cancer early. Additionally, this instrument enables the detection of additional illnesses and may provide information about the nature of cancer, such as benign, malignant, or normal. is article discusses an evolutionary approach for classifying and detecting breast cancer that is based on machine learning and image processing. is model combines image preprocessing, feature extraction, feature selection, and machine learning techniques to aid in the classification and identification of skin diseases. To enhance the image's quality, a geometric mean filter is used. AlexNet is used for extracting features. Feature selection is performed using the relief algorithm. For disease categorization and detection, the model makes use of the machine learning techniques such as least square support vector machine, KNN, random forest, and Naïve Bayes. e experimental investigation makes use of MIAS data collection. is proposed technology is advantageous for accurately identifying breast cancer disease using image analysis.
Article
Full-text available
Artificial intelligence (AI) is concretely reshaping the landscape and horizons of oncology, opening new important opportunities for improving the management of cancer patients. Analysing the AI-based devices that have already obtained the official approval by the Federal Drug Administration (FDA), here we show that cancer diagnostics is the oncology-related area in which AI is already entered with the largest impact into clinical practice. Furthermore, breast, lung and prostate cancers represent the specific cancer types that now are experiencing more advantages from AI-based devices. The future perspectives of AI in oncology are discussed: the creation of multidisciplinary platforms, the comprehension of the importance of all neoplasms, including rare tumours and the continuous support for guaranteeing its growth represent in this time the most important challenges for finalising the ‘AI-revolution’ in oncology.
Article
Full-text available
Background: Determining the status of molecular pathways and key mutations in colorectal cancer is crucial for optimal therapeutic decision making. We therefore aimed to develop a novel deep learning pipeline to predict the status of key molecular pathways and mutations from whole-slide images of haematoxylin and eosin-stained colorectal cancer slides as an alternative to current tests. Methods: In this retrospective study, we used 502 diagnostic slides of primary colorectal tumours from 499 patients in The Cancer Genome Atlas colon and rectal cancer (TCGA-CRC-DX) cohort and developed a weakly supervised deep learning framework involving three separate convolutional neural network models. Whole-slide images were divided into equally sized tiles and model 1 (ResNet18) extracted tumour tiles from non-tumour tiles. These tumour tiles were inputted into model 2 (adapted ResNet34), trained by iterative draw and rank sampling to calculate a prediction score for each tile that represented the likelihood of a tile belonging to the molecular labels of high mutation density (vs low mutation density), microsatellite instability (vs microsatellite stability), chromosomal instability (vs genomic stability), CpG island methylator phenotype (CIMP)-high (vs CIMP-low), BRAFmut (vs BRAFWT), TP53mut (vs TP53WT), and KRASWT (vs KRASmut). These scores were used to identify the top-ranked titles from each slide, and model 3 (HoVer-Net) segmented and classified the different types of cell nuclei in these tiles. We calculated the area under the convex hull of the receiver operating characteristic curve (AUROC) as a model performance measure and compared our results with those of previously published methods. Findings: Our iterative draw and rank sampling method yielded mean AUROCs for the prediction of hypermutation (0·81 [SD 0·03] vs 0·71), microsatellite instability (0·86 [0·04] vs 0·74), chromosomal instability (0·83 [0·02] vs 0·73), BRAFmut (0·79 [0·01] vs 0·66), and TP53mut (0·73 [0·02] vs 0·64) in the TCGA-CRC-DX cohort that were higher than those from previously published methods, and an AUROC for KRASmut that was similar to previously reported methods (0·60 [SD 0·04] vs 0·60). Mean AUROC for predicting CIMP-high status was 0·79 (SD 0·05). We found high proportions of tumour-infiltrating lymphocytes and necrotic tumour cells to be associated with microsatellite instability, and high proportions of tumour-infiltrating lymphocytes and a low proportion of necrotic tumour cells to be associated with hypermutation. Interpretation: After large-scale validation, our proposed algorithm for predicting clinically important mutations and molecular pathways, such as microsatellite instability, in colorectal cancer could be used to stratify patients for targeted therapies with potentially lower costs and quicker turnaround times than sequencing-based or immunohistochemistry-based approaches. Funding: The UK Medical Research Council.
Article
Full-text available
The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the application of AI-based cancer imaging analysis to address other, more complex, clinical needs. In this Perspective, we discuss the next generation of challenges in clinical decision-making that AI tools can solve using radiology images, such as prognostication of outcome across multiple cancers, prediction of response to various treatment modalities, discrimination of benign treatment confounders from true progression, identification of unusual response patterns and prediction of the mutational and molecular profile of tumours. We describe the evolution of and opportunities for AI in oncology imaging, focusing on hand-crafted radiomic approaches and deep learning-derived representations, with examples of their application for decision support. We also address the challenges faced on the path to clinical adoption, including data curation and annotation, interpretability, and regulatory and reimbursement issues. We hope to demystify AI in radiology for clinicians by helping them to understand its limitations and challenges, as well as the opportunities it provides as a decision-support tool in cancer management.
Article
Full-text available
Traditional pathology approaches have played an integral role in the delivery of diagnosis, semi-quantitative or qualitative assessment of protein expression, and classification of disease. Technological advances and the increased focus on precision medicine have recently paved the way for the development of digital pathology-based approaches for quantitative pathologic assessments, namely whole slide imaging and artificial intelligence (AI)–based solutions, allowing us to explore and extract information beyond human visual perception. Within the field of immuno-oncology, the application of such methodologies in drug development and translational research have created invaluable opportunities for deciphering complex pathophysiology and the discovery of novel biomarkers and drug targets. With an increasing number of treatment options available for any given disease, practitioners face the growing challenge of selecting the most appropriate treatment for each patient. The ever-increasing utilization of AI-based approaches substantially expands our understanding of the tumor microenvironment, with digital approaches to patient stratification and selection for diagnostic assays supporting the identification of the optimal treatment regimen based on patient profiles. This review provides an overview of the opportunities and limitations around implementing AI-based methods in biomarker discovery and patient selection and discusses how advances in digital pathology and AI should be considered in the current landscape of translational medicine, touching on challenges this technology may face if adopted in clinical settings. The traditional role of pathologists in delivering accurate diagnoses or assessing biomarkers for companion diagnostics may be enhanced in precision, reproducibility, and scale by AI-powered analysis tools.
Article
Deep Learning approaches for automatic segmentation of organs from CT scans and MRI are providing promising results, leading towards a revolution in the radiologists’ workflow. Precise delineations of abdominal organs boundaries reveal fundamental for a variety of purposes: surgical planning, volumetric estimation (e.g. Total Kidney Volume – TKV – assessment in Autosomal Dominant Polycystic Kidney Disease – ADPKD), diagnosis and monitoring of pathologies. Fundamental imaging techniques exploited for these tasks are Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), which enable clinicians to perform 3D analyses of all Regions of Interests (ROIs). In the realm of existing methods for segmentation and classification of these zones, Convolutional NeuralNetworks (CNNs) are emerging as the reference approach. In the last five years an enormous research effort has been done about the possibility of applying CNNs in Medical Imaging, resulting in more than 8000 documents on Scopus and more than 80000 results on Google Scholar. The high accuracy provided by those systems cannot be denied as motivation of all obtained results, though there are still problems to be addressed with. In this survey, major article databases, as Scopus, for instance, were systematically investigated for different kinds of Deep Learning approaches in segmentation of abdominal organs with a particular focus on liver, kidney and spleen. In this work, approaches are accurately classified, both by relevance of each organ (for instance, segmentation of liver has specific properties, if compared to other organs) and by type of computational approach, as well as the architecture of the employed network. For this purpose, a case study of segmentation for each of these organs is presented.
Article
Objective: To develop a multiparametric MRI-based radiomics nomogram for predicting lymphovascular invasion (LVI) status and clinical outcomes in patients with breast invasive ductal carcinoma (IDC). Methods: A total of 160 patients with pathologically confirmed breast IDC (training cohort: n = 112; validation cohort: n = 48) who underwent preoperative breast MRI were included. Imaging features were extracted from T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC) maps, and contrast-enhanced T1-weighted imaging (cT1WI) sequences. A four-step procedure was applied for feature selection and radiomics signature building. Univariate and multivariate logistic regression analyses were conducted to identify the features associated with LVI, which were then incorporated into the radiomics nomogram. The performance of the nomogram was evaluated by its discrimination, calibration, and clinical usefulness. Kaplan-Meier survival curves based on the two radiomics models were used to estimate disease-free survival (DFS). Results: The fusion radiomics signature of the T2WI, cT1WI, and ADC maps achieved a better predictive efficacy for LVI than either of them alone. The proposed radiomics nomogram, incorporating the fusion radiomics signature and MRI-reported peritumoral edema, showed satisfactory capabilities of calibration and discrimination in both training and validation datasets, with AUCs of 0.919 (95% CI: 0.871-0.967) and 0.863 (95% CI: 0.726-0.999), respectively. The radiomics signature and nomogram-defined high-risk groups had a shorter DFS than those in the low-risk groups (both p < 0.05). Higher Rad-scores were independently associated with a worse DFS in the whole cohort (p < 0.05). Conclusions: The proposed nomogram, incorporating multiparametric MRI-based radiomics signature and MRI-reported peritumoral edema, achieved a satisfactory preoperative prediction of LVI and clinical outcomes in IDC patients. Key points: • The fusion radiomics signature of the T2WI, cT1WI, and ADC maps achieved a better predictive efficacy for LVI than either of them alone. • The proposed nomogram achieved a favorable prediction of LVI in IDC patients with AUCs of 0.919 and 0.863 in the training and validation datasets, respectively. • The radiomics model could classify patients into high- and low-risk groups with significant differences in DFS.
Article
Artificial intelligence has aided in the advancement of healthcare research. The availability of open-source healthcare statistics has prompted researchers to create applications that aid cancer detection and prognosis. Deep learning and machine learning models provide a reliable, rapid, and effective solution to deal with such challenging diseases in these circumstances. PRISMA guidelines had been used to select the articles published on the web of science, EBSCO, and EMBASE between 2009 and 2021. In this study, we performed an efficient search and included the research articles that employed AI-based learning approaches for cancer prediction. A total of 185 papers are considered impactful for cancer prediction using conventional machine and deep learning-based classifications. In addition, the survey also deliberated the work done by the different researchers and highlighted the limitations of the existing literature, and performed the comparison using various parameters such as prediction rate, accuracy, sensitivity, specificity, dice score, detection rate, area undercover, precision, recall, and F1-score. Five investigations have been designed, and solutions to those were explored. Although multiple techniques recommended in the literature have achieved great prediction results, still cancer mortality has not been reduced. Thus, more extensive research to deal with the challenges in the area of cancer prediction is required.