Illustration of concept of neural style transfer using original work [101].

Illustration of concept of neural style transfer using original work [101].

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Radiomics converts medical images into mineable data via a high-throughput extraction of quantitative features used for clinical decision support. However, these radiomic features are susceptible to variation across scanners, acquisition protocols, and reconstruction settings. Various investigations have assessed the reproducibility and validation...

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... groundbreaking work of Gatys et al. [101] paved the way for a new field of NST. Gatys et al. [101] first conducted a study that separates content from one image and style from another image and combines it into a new image using a neural network ( Figure 5). The paper demonstrated that transferring style from one image to the other can be modelled as an optimization problem that can further be solved by training a neural network, VGG-19 [102] in this case. ...

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... Moreover, integrating AI tools into clinical workflows requires compatibility with electronic health records, interoperability across healthcare systems, and secure data handling [40]. In this investigation, we used the ComBat method to harmonize the radiomic features and mitigate the impact of differences in acquisition modalities and scanners [44]. At the same time, we employed a five-fold cross-validation technique to reduce the risk of overfitting due to the small sample size, which may compromise the generalizability and reproducibility of the results [45]. ...
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    (1) Background: Oral tongue squamous cell carcinoma (OTSCC) is a prevalent malignancy with high loco-regional recurrence. Advanced imaging biomarkers are critical for stratifying patients at a high risk of recurrence. This study aimed to develop MRI-based radiomic models to predict loco-regional recurrence in OTSCC patients undergoing surgery. (2) Methods: We retrospectively selected 92 patients with OTSCC who underwent MRI, followed by surgery and cervical lymphadenectomy. A total of 31 patients suffered from a loco-regional recurrence. Radiomic features were extracted from preoperative post-contrast high-resolution MRI and integrated with clinical and pathological data to develop predictive models, including radiomic-only and combined radiomic–clinical approaches, trained and validated with stratified data splitting. (3) Results: Textural features, such as those derived from the Gray-Level Size-Zone Matrix, Gray-Level Dependence Matrix, and Gray-Level Run-Length Matrix, showed significant associations with recurrence. The radiomic-only model achieved an accuracy of 0.79 (95% confidence interval: 0.69, 0.87) and 0.74 (95% CI: 0.54, 0.89) in the training and validation set, respectively. Combined radiomic and clinical models, incorporating features like the pathological depth of invasion and lymph node status, provided comparable diagnostic performances. (4) Conclusions: MRI-based radiomic models demonstrated the potential for predicting loco-regional recurrence, highlighting their increasingly important role in advancing precision oncology for OTSCC.
    ... Nevertheless, in the context of outcome prediction models, where tumor grade has been histopathologically proven, incorporating grade as a covariate could potentially improve harmonization. Additionally, employing modified ComBat techniques, such as bootstrapped ComBat [22], or more advanced harmonization methods based on deep learning [38,39] could also lead to better results. ...
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    The WHO grading of pancreatic neuroendocrine neoplasms (PanNENs) is essential in patient management and an independent prognostic factor for patient survival. Radiomics features from CE-CT images hold promise for the outcome and tumor grade prediction. However, variations in reconstruction parameters can impact the predictive value of radiomics. 127 patients with histopathologically confirmed PanNENs underwent CT scans with filtered back projection (B20f) and iterative (I26f) reconstruction kernels. 3190 radiomic features were extracted from tumors and pancreatic volumes. Wilcoxon paired tests assessed the impact of reconstruction kernels and ComBat harmonization efficiency. SVM models were employed to predict tumor grade using the entire set of radiomics features or only those identified as harmonizable. The models’ performance was assessed on an independent dataset of 36 patients. Significant differences, after correction for multiple testing, were observed in 69% of features in the pancreatic volume and 51% in the tumor volume with B20f and I26f kernels. SVM models demonstrated accuracy ranging from 0.67 (95%CI: 0.50–0.81) to 0.83 (95%CI: 0.69–0.94) in distinguishing grade 1 cases from higher grades. Reconstruction kernels alter radiomics features and iterative kernel models trended towards higher performance. ComBat harmonization mitigates kernel impacts but addressing this effect is crucial in studies involving data from different kernels.
    ... Also, multiple data harmonization techniques exist to reduce batch effects in the image or feature domain to account for different image acquisition or reconstruction parameters [50]. These have not yet been applied extensively, as we suspect that large differences might already originate from the segmentation process. ...
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    Kirsten Rat Sarcoma viral oncogene homolog (KRAS) is a frequently occurring mutation in non-small-cell lung cancer (NSCLC) and influences cancer treatment and disease progression. In this study, a machine learning (ML) pipeline was applied to radiomic features extracted from public and internal CT images to identify KRAS mutations in NSCLC patients. Both datasets were analyzed using parametric (ttest) and non-parametric statistical tests (Mann–Whitney U test) and dimensionality reduction techniques. Afterwards, the proposed ML pipeline was applied to both datasets using a five-fold cross-validation on the training set (70/30 train/test split) before being validated on the other dataset. The results show that the radiomic features are significantly different (Mann–Whitney U test; p < 0.05) between the two datasets, despite the use of identical feature extraction methods. Model transferability is therefore difficult to achieve, which became evident during external testing (F1 score = 0.41). Oversampling, undersampling, clustering and harmonization techniques were applied to balance and harmonize the datasets, but did not improve the classification of KRAS mutation presence. In general, due to only a single moderate result (highest test F1 score = 0.67), the accuracy of KRAS prediction is not sufficient for clinical application. In future work, the complexity of KRAS mutation might be addressed by taking submutations into consideration. Larger multicentric datasets with balanced tumor stages, including multi-scanner datasets, seem to be necessary for building robust predictive models.
    ... Despite the promising applications of radiomics in BCa treatment response assessment, challenges remain, particularly in harmonizing imaging data owing to the variability in MR protocols and acquisition parameters across different vendors and devices, impacting the consistency of radiomic features [84,85]. Traditional and more advanced image harmonization methods, including Gaussian mixture-based ComBat normalization, have been proposed to address these issues, although they still depend on predefined groupings and feature sets [86]. ...
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    Bladder cancer is the 10th most common and 13th most deadly cancer worldwide, with urothelial carcinomas being the most common type. Distinguishing between non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) is essential due to significant differences in management and prognosis. MRI may play an important diagnostic role in this setting. The Vesical Imaging Reporting and Data System (VI-RADS), a multiparametric MRI (mpMRI)-based consensus reporting platform, allows for standardized preoperative muscle invasion assessment in BCa with proven diagnostic accuracy. However, post-treatment assessment using VI-RADS is challenging because of anatomical changes, especially in the interpretation of the muscle layer. MRI techniques that provide tumor tissue physiological information, including diffusion-weighted (DW)- and dynamic contrast-enhanced (DCE)-MRI, combined with derived quantitative imaging biomarkers (QIBs), may potentially overcome the limitations of BCa evaluation when predominantly focusing on anatomic changes at MRI, particularly in the therapy response setting. Delta-radiomics, which encompasses the assessment of changes (Δ) in image features extracted from mpMRI data, has the potential to monitor treatment response. In comparison to the current Response Evaluation Criteria in Solid Tumors (RECIST), QIBs and mpMRI-based radiomics, in combination with artificial intelligence (AI)-based image analysis, may potentially allow for earlier identification of therapy-induced tumor changes. This review provides an update on the potential of QIBs and mpMRI-based radiomics and discusses the future applications of AI in BCa management, particularly in assessing treatment response. Critical relevance statement Incorporating mpMRI-based quantitative imaging biomarkers, radiomics, and artificial intelligence into bladder cancer management has the potential to enhance treatment response assessment and prognosis prediction. Key Points Quantitative imaging biomarkers (QIBs) from mpMRI and radiomics can outperform RECIST for bladder cancer treatments. AI improves mpMRI segmentation and enhances radiomics feature extraction effectively. Predictive models integrate imaging biomarkers and clinical data using AI tools. Multicenter studies with strict criteria validate radiomics and QIBs clinically. Consistent mpMRI and AI applications need reliable validation in clinical practice. Graphical Abstract
    ... The main limitations of this systematic review are heterogeneity between studies, making direct comparison of results difficult. Variable methodological quality among the included studies also impacts the reliability of conclusions [48]. The reviewed studies exhibit significant heterogeneity in sample sizes, imaging techniques, and research designs, which complicates direct comparisons and consistency analysis. ...
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    Background/Objectives: Cervical cancer is a significant global health concern, with high incidence and mortality rates, especially in less-developed regions. [18F]FDG PET/CT is now indicated at various stages of management, but its analysis is essentially based on SUVmax, a measure of [18F]FDG uptake. Radiomics, by extracting a multitude of parameters, promises to improve the diagnostic and prognostic performance of the examination. However, studies remain heterogeneous, both in terms of patient numbers and methods, so a synthesis is needed. Methods: This systematic review was conducted following PRISMA-P guidelines and registered in PROSPERO (CRD42024584123). Eligible studies on PET/CT radiomics in cervical cancer were identified through PubMed and Scopus and assessed for quality using the Radiomics Quality Score (RQS v2.0), with data extraction focusing on study design, population characteristics, radiomic methods, and model performances. Results: The review identified 22 studies on radiomics in cervical cancer, 19 of which focused specifically on locally advanced cervical cancer (LACC) and assessed various clinical outcomes, such as survival, relapse, treatment response, and lymph node involvement prediction. They reported significant associations between prognostic indicators and radiomic features, indicating the potential of radiomics to improve the predictive accuracy for patient outcomes in LACC; however, the overall quality of the studies was relatively moderate, with a median RQS of 12/36. Conclusions: While radiomic analysis in cervical cancer presents promising opportunities for survival prediction and personalized care, further well-designed studies are essential to provide stronger evidence for its clinical utility.
    ... Previously, this effect was only hypothesized [15][16][17][18][19][20], but the impact of these variations was not explicitly assessed to our knowledge. Data harmonization was proposed as a possible solution to reduce the impact of different acquisition protocols across multiple centers [21], but we couldn't know whether data harmonization may at least partially mitigate the effect of different scanners since none of the studies included in this meta-analysis used it. Moreover, while several other studies suggested the promising role of COMBAT in mitigating the dependence of radiomic data by image acquisition and reconstruction parameters [20][21][22][23][24][25][26][27], concerns about its application to radiomics were highlighted by some authors [28,29]. ...
    ... Data harmonization was proposed as a possible solution to reduce the impact of different acquisition protocols across multiple centers [21], but we couldn't know whether data harmonization may at least partially mitigate the effect of different scanners since none of the studies included in this meta-analysis used it. Moreover, while several other studies suggested the promising role of COMBAT in mitigating the dependence of radiomic data by image acquisition and reconstruction parameters [20][21][22][23][24][25][26][27], concerns about its application to radiomics were highlighted by some authors [28,29]. Moreover, although the Imaging Biomarker Standardization Initiative (IBSI) consortium [30] has made significant strides toward the development of reproducible methodologies, there are still concerns about variations in radiomics signatures due to data origin. ...
    Article
    To investigate whether methodological aspects may influence the performance of MRI-radiomic models to predict response to neoadjuvant treatment (NAT) in breast cancer (BC) patients. We conducted a systematic review until March 2023. A random-effects meta-analysis was performed to combine the area under the receiver operating characteristic curve (AUC) values. Publication bias was assessed using Egger’s test and heterogeneity was estimated by I2. A meta-regression was conducted to investigate the impact of various factors, including scanner, features’ number/transformation/type, pixel/voxel scaling, etc. Forty-two studies were included. The summary AUC was 0.77 (95% CI: 0.74–0.81). Substantial heterogeneity was observed (I2 = 81%) with no publication bias (p = 0.35). Radiomic model accuracy was influenced by the scanner vendor, with lower AUCs in studies using mixed scanner vendors (AUC; 95% CI: 0.70; 0.61–0.78) compared to studies including images obtained from the same scanner (AUC (95% CI): 0.83 (0.77–0.88), 0.74 (0.67–0.82), 0.83 (0.78–0.89) for three different vendors; vendors 1, 2, and 3, respectively; p-value = 0.03 for comparison with vendor 1). Feature type also seemed to have an impact on the AUC, with higher prediction accuracy observed for studies using 3D than 2D/2.5D images (AUC; 95% CI: 0.81; 0.78–0.85 and 0.73; 0.65–0.81, respectively, p-value = 0.03). Non-significant between-study heterogeneity was observed in the studies including 3D images (I2 = 33%) and Vendor 1 scanners (I2 = 40%). MRI-radiomics has emerged as a potential method for predicting the response to NAT in BC patients, showing promising outcomes. Nevertheless, it is important to acknowledge the diversity among the methodological choices applied. Further investigations should prioritize achieving standardized protocols, and enhancing methodological rigor in MRI-radiomics. Question Do methodological aspects influence the performance of MRI-radiomic models in predicting response to NAT in BC patients? Findings Radiomic model accuracy was influenced by the scanner vendor and feature type. Clinical relevance Methodological discrepancies affect the performance of MRI-radiomic models. Developing standardized protocols and enhancing methodological rigor in these studies should be prioritized.
    ... This limitation hinders the use of invasive molecular analysis based on biopsies but offers great potential for medical imaging to capture tumor heterogeneity non-invasively. Furthermore, his paper had a large number of high-quality reviews [37]. Radiomics is a high-throughput mining technique that identifies quantitative image characteristics from standard medical imaging. ...
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    This research utilized the bibliometrics method to analyze the published literature related to prostate cancer (PCa) imaging. Furthermore, current knowledge and research hotspots of radiomics in PCa diagnosis and treatment were comprehensively reviewed, as well as progress and emerging trends in field were explored. In this investigation, the relevant literature on radiomics, and PCa was retrieved from Web of Science Core Collection (WoSCC) databases from 2000 and 2024. Furthermore, a comprehensive bibliometric analysis was carried out using advanced tools like CiteSpace6.2, VOS viewer, and the 'bibliometrix' package of R software to visualize the annual distribution of publications across various aspects such as authors, countries, journals, institutions, and keywords. This analysis included 593 from 58 countries including China and the United States. Chinese Academy of Sciences and Frontiers in Oncology were the institutions and journals that publish the most relevant articles, -while Radiology journal had the greatest number of co-cited publications. Furthermore, 3,621 authors published on this topic, of which Madabhushi Anant and Stoyanova Radka had the highest contributions. Moreover, Lambin, P. had the most co-citations. In addition, the diagnostic characteristics of radiomics in PCa imaging and treatment strategies are the current research focal points. The establishment of multi-functional imaging techniques and independent factor models warrants future investigation. In summary, this analysis revealed that the research on PCa imaging is developing vigorously, focusing on the diagnostic methods and intervention measures of imaging in PCa diagnosis and treatment. In the future, there is an urgent need for improved collaboration and communication among countries and institutions.
    ... It is important to evaluate the robustness of features across different scanners to make them more clinically useful. [61] The results of Stamoulou et al. suggested normalization techniques such as histogram matching, z-score normalization, or ComBat harmonization in multicenter studies can further reduce inter-scanner variability and improve radiomic analysis reliability for MRI, which is characterized by an absence of a standard intensity scale and well-defined units. [62] Overall, the strength of our radiomics approach is raised from the analysis of different regions of brain tissue including thalamus, putamen, hippocampus, and brain stem in clinical routine MRI images despite one single site VOI and potentiality of robust radiomic features to capture the brain structures. ...
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    Background Despite extensive research on various brain diseases, a few studies have focused on radiomic feature distribution in healthy brain images. The present study applied a novel radiomic framework to investigate the robustness and baseline values of radiomic features in normal brain magnetic resonance imaging (MRIs) regions. Materials and Methods Analyses were performed on T1 and T2 images including 276 normal brains and 14 healthy volunteers were scanned with three scanners using the same protocols. The images were divided into 1024 three-dimensional nonoverlap patches with the same pixel size. Seven patches located in the thalamus, putamen, hippocampus and brain stem were selected as volume of interest (VOI). Eighty-five radiomic features were generated. To investigate the variation of features across VOIs, the analysis of variance was performed and coefficient of variation (COV) and intraclass correlation coefficient (ICC) were explored to examine the features repeatability. Results Thalamus (right and left) and hippocampus (left) resulted in more stable features (COV ≤ 6%) in T1 and T2 images, respectively. The inter-scanner ICC analysis demonstrated the features of T2 sequences represented more repeatable results and the brain stem and thalamus (both T1 and T2) showed particularly high repeatability (higher ICC values). Robust results (ICC ≥ 0.9) were identified for energy and range features of the first order class and several textures features across different brain regions. Conclusion Our results indicated the baselines of the repeatable texture features in healthy brain structural MRI highlighting inter-scanner stability. According to the findings, MRI sequencing and VOI location impact feature robustness and should be considered in brain radiomic studies.
    ... A previous studies utilized radiomics models based on ultrasound (US) for non-invasive prediction of preoperative lymph node metastasis (LNM) in cervical cancer patients. 47 However, studies indicated that radiomics features are susceptible to variations in scanners, acquisition protocols, and reconstruction settings, which is unavoidable in retrospective and multicenter studies in the current clinical practice 10 . The influences of different scanners and automatic segmentation algorithms in US-based radiomics had also been reported [11][12][13] . ...
    ... In the image domain, methods of standardization of image acquisition, post-processing of raw sensor-level image data, data augmentation techniques, and style transfer were usually applied to harmonize radiomics features 50 . GAN and neural style transfer (NST) techniques, or a combination of both, had been investigated intensively with CT, MRI and PET images to address the variability across multi-centric radiomic studies 10 . Previously, Liu et al proposed a novel and general style transfer framework to remove the appearance shifts of US images to improve US image segmentation 51 . ...
    Article
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    Background: Ultrasound (US) based radiomics is susceptible to variations in scanners, sonographers. Objective: To retrospectively investigate the feasibility of an adapted cycle generative adversarial networks (CycleGAN) in the style transfer to improve US based radiomics in the prediction of lymph node metastasis (LNM) with images from multiple scanners for patients with early cervical cancer (ECC). Methods: The CycleGAN was firstly trained to transfer paired US phantom images from one US device to another one; the model was then further trained and tested with clinical US images of ECC by transferring images from four US devices to one specific device; finally, the adapted model was tested with its effects on the radiomics feature harmonization and accuracy of LNM prediction in US based radiomics for ECC patients. Results: Phantom study demonstrated an increased radiomics harmonization using CycleGAN with an average Pearson correlation coefficient of 0.60 and 0.81 for radiomics features extracted from original and generated images in correlation with the target phantom images, respectively. Additionally, the image quality metric Peak Signal-to-Noise Ratio (PSNR) was increased from 11.18 for the original images to 15.45 for the generated image. Clinical US images of 169 ECC patients were enrolled for style transfer model training and validation. The area under curve (AUC) of LNM prediction radiomics models with features extracted from generated images of different style transfer models ranged from 0.73 to 0.85. The AUC was improved from 0.78 with features extracted from original images to 0.85 with style transferred images. Conclusions: The adapted CycleGAN network is able to increase the radiomics feature harmonization for images from different ultrasound equipment based on image domain and improve the LNM prediction accuracy for ECC.
    ... Most radiomics models published to date are limited by the lack of large, standardized datasets and the absence of clinical validation [20]. Ideally, radiomics features should be independent of image acquisition parameters or protocols [20]. ...
    ... Most radiomics models published to date are limited by the lack of large, standardized datasets and the absence of clinical validation [20]. Ideally, radiomics features should be independent of image acquisition parameters or protocols [20]. However, several previous studies have pointed out the poor reproducibility of CT radiomics features across different CT acquisition parameters, reconstruction methods, and CT scanners [4,8,10,21]. ...
    ... Efforts to enhance the reproducibility of CT radiomics can be divided into image and feature domain strategies. [20]. The harmonization strategy in the image domain includes the development of standardization guidelines and the utilization of raw image datasets [20]. ...
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    We assessed the feasibility of using deep learning-based image harmonization to improve the reproducibility of radiomics features in abdominal CT scans. In CT imaging, harmonization adjusts images from different institutions to ensure consistency despite variations in scanners and acquisition protocols. This process is essential because such differences can lead to variability in radiomics features, affecting reproducibility and accuracy. Harmonizing images minimizes these inconsistencies, supporting more reliable and clinically applicable results across diverse settings. A pre-trained harmonization algorithm was applied to 63 dual-energy abdominal CT images, which were reconstructed into four different types, and 10 regions of interest (ROIs) were analyzed. From the original 455 radiomics features per ROI, 387 were used after excluding redundant features. Reproducibility was measured using the intraclass correlation coefficient (ICC), with a threshold of ICC ≥ 0.85 indicating acceptable reproducibility. The region-based analysis revealed significant improvements in reproducibility post-harmonization, especially in vessel features, which increased from 14% to 69%. Other regions, including the spleen, kidney, muscle, and liver parenchyma, also saw notable improvements, although air reproducibility slightly decreased from 95% to 94%, impacting only a few features. In patient-based analysis, reproducible features increased from 18% to 65%, with an average of 179 additional reproducible features per patient after harmonization. These results demonstrate that deep learning-based harmonization can significantly enhance the reproducibility of radiomics features in abdominal CT, offering promising potential for advancing radiomics development and its clinical applications.