Feng Li

University of Chicago, Chicago, IL, United States

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Publications (60)138.03 Total impact

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    ABSTRACT: OBJECTIVE: To evaluate radiologists' ability to detect focal pneumonia by use of standard chest radiographs alone compared with standard plus bone-suppressed chest radiographs. METHODS: Standard chest radiographs in 36 patients with 46 focal airspace opacities due to pneumonia (10 patients had bilateral opacities) and 20 patients without focal opacities were included in an observer study. A bone suppression image processing system was applied to the 56 radiographs to create corresponding bone suppression images. In the observer study, eight observers, including six attending radiologists and two radiology residents, indicated their confidence level regarding the presence of a focal opacity compatible with pneumonia for each lung, first by use of standard images, then with the addition of bone suppression images. Receiver operating characteristic (ROC) analysis was used to evaluate the observers' performance. RESULTS: The mean value of the area under the ROC curve (AUC) for eight observers was significantly improved from 0.844 with use of standard images alone to 0.880 with standard plus bone suppression images (P < 0.001) based on 46 positive lungs and 66 negative lungs. CONCLUSION: Use of bone suppression images improved radiologists' performance for detection of focal pneumonia on chest radiographs. KEY POINTS : • Bone suppression image processing can be applied to conventional digital radiography systems. • Bone suppression imaging (BSI) produces images that appear similar to dual-energy soft tissue images. • BSI improves the conspicuity of focal lung disease by minimizing bone opacity. • BSI can improve the accuracy of radiologists in detecting focal pneumonia.
    European Radiology 07/2012; · 4.34 Impact Factor
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    ABSTRACT: Managing and supervising the complex imaging examinations performed for clinical research in an academic medical center can be a daunting task. Coordinating with both radiology and research staff to ensure that the necessary imaging is performed, analyzed, and delivered in accordance with the research protocol is nontrivial. The purpose of this communication is to report on the establishment of a new Human Imaging Research Office (HIRO) at our institution that provides a dedicated infrastructure to assist with these issues and improve collaborations between radiology and research staff. The HIRO was created with three primary responsibilities: 1) coordinate the acquisition of images for clinical research per the study protocol, 2) facilitate reliable and consistent assessment of disease response for clinical research, and 3) manage and distribute clinical research images in a compliant manner. The HIRO currently provides assistance for 191 clinical research studies from 14 sections and departments within our medical center and performs quality assessment of image-based measurements for six clinical research studies. The HIRO has fulfilled 1806 requests for medical images, delivering 81,712 imaging examinations (more than 44.1 million images) and related reports to investigators for research purposes. The ultimate goal of the HIRO is to increase the level of satisfaction and interaction among investigators, research subjects, radiologists, and other imaging professionals. Clinical research studies that use the HIRO benefit from a more efficient and accurate imaging process. The HIRO model could be adopted by other academic medical centers to support their clinical research activities; the details of implementation may differ among institutions, but the need to support imaging in clinical research through a dedicated, centralized initiative should apply to most academic medical centers.
    Academic radiology 04/2012; 19(6):762-71. · 2.09 Impact Factor
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    ABSTRACT: To determine whether use of bone suppression (BS) imaging, used together with a standard radiograph, could improve radiologists' performance for detection of small lung cancers compared with use of standard chest radiographs alone and whether BS imaging would provide accuracy equivalent to that of dual-energy subtraction (DES) radiography. Institutional review board approval was obtained. The requirement for informed consent was waived. The study was HIPAA compliant. Standard and DES chest radiographs of 50 patients with 55 confirmed primary nodular cancers (mean diameter, 20 mm) as well as 30 patients without cancers were included in the observer study. A new BS imaging processing system that can suppress the conspicuity of bones was applied to the standard radiographs to create corresponding BS images. Ten observers, including six experienced radiologists and four radiology residents, indicated their confidence levels regarding the presence or absence of a lung cancer for each lung, first by using a standard image, then a BS image, and finally DES soft-tissue and bone images. Receiver operating characteristic (ROC) analysis was used to evaluate observer performance. The average area under the ROC curve (AUC) for all observers was significantly improved from 0.807 to 0.867 with BS imaging and to 0.916 with DES (both P < .001). The average AUC for the six experienced radiologists was significantly improved from 0.846 with standard images to 0.894 with BS images (P < .001) and from 0.894 to 0.945 with DES images (P = .001). Use of BS imaging together with a standard radiograph can improve radiologists' accuracy for detection of small lung cancers on chest radiographs. Further improvements can be achieved by use of DES radiography but with the requirement for special equipment and a potential small increase in radiation dose.
    Radiology 09/2011; 261(3):937-49. · 6.34 Impact Factor
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    ABSTRACT: To determine whether liver function correlating with indocyanine green (ICG) clearance could be estimated quantitatively from gadoxetate disodium-enhanced magnetic resonance (MR) images. This retrospective study was approved by the institutional review board, and the requirement for informed consent was waived. Twenty-three consecutive patients who underwent an ICG clearance test and gadoxetate disodium-enhanced MR imaging with the same parameters as were used for a preoperative examination were chosen. The hepatocellular uptake index (HUI) from liver volume (V(L))and mean signal intensity of the liver on contrast-enhanced T1-weighted images with fat suppression (L(20)) and mean signal intensity of the spleen on contrast-enhanced T1-weighted images with fat suppression (S(20)) on 3D gradient-echo T1-weighted images with fat suppression obtained at 20 minutes after gadoxetate disodium (0.025 mmol per kilogram of body weight) administration was determined with the following equation: V(L)[(L(20)/S(20)) - 1]. The correlation of the plasma disappearance rate of ICG (ICG-PDR) and various factors derived from MR imaging, including HUI, iron and fat deposition in the liver and spleen, and spleen volume (V(S)), were evaluated with stepwise multiple regression analysis. The difference between the ratio of the remnant HUI to the HUI of the total liver (rHUI/HUI) and ratio of the liver remnant V(L) to the total V(L) (rV(L)/V(L)) was evaluated in four patients who had segmental heterogeneity of liver function. HUI and V(S) were the factors significantly correlated with ICG-PDR (R = 0.87). The mean value and its 95% confidence interval were 0.18 and 0.01 to 0.34, respectively, for the following calculation: (rHUI/HUI) - (rV(L)/V(L)). The liver function correlating with ICG-PDR can be estimated quantitatively from the signal intensities and the volumes of the liver and spleen on gadoxetate disodium-enhanced MR images, which may improve the estimation of segmental liver function.
    Radiology 06/2011; 260(3):727-33. · 6.34 Impact Factor
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    ABSTRACT: The purpose of this article is to evaluate radiologists' ability to detect subtle nodules by use of standard chest radiographs alone compared with bone suppression imaging used together with standard radiographs. The cases used in this observer study comprised radiographs of 72 patients with a subtle nodule and 79 patients without nodules taken from the Japanese Society of Radiological Technology nodule database. A new image-processing system was applied to the 151 radiographs to create corresponding bone suppression images. Two image reading sets were used with an independent test method. The first reading included half of the patients (a randomly selected subset A) showing only the standard image and the remaining half (subset B) showing the standard image plus bone suppression images. The second reading entailed the same subsets; however, subset A was accompanied by bone suppression images, whereas subset B was shown with only the standard image. The two image sets were read by three experienced radiologists, with an interval of more than 2 weeks between the sessions. Receiver operating characteristic (ROC) curves, with and without localization, were obtained to evaluate the observers' performance. The mean value of the area under the ROC curve for the three observers was significantly improved, from 0.840 with standard radiographs alone to 0.863 with additional bone suppression images (p = 0.01). The area under the localization ROC curve was also improved with bone suppression imaging. The use of bone suppression images improved radiologists' performance in the detection of subtle nodules on chest radiographs.
    American Journal of Roentgenology 05/2011; 196(5):W535-41. · 2.90 Impact Factor
  • Jiahui Wang, Feng Li, Qiang Li
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    ABSTRACT: We developed an automated method for the segmentation of lungs with severe diffuse interstitial lung disease (DILD) in multi-detector CT. In this study, we would like to compare the performance levels of this method and a thresholdingbased segmentation method for normal lungs, moderately abnormal lungs, severely abnormal lungs, and all lungs in our database. Our database includes 31 normal cases and 45 abnormal cases with severe DILD. The outlines of lungs were manually delineated by a medical physicist and confirmed by an experienced chest radiologist. These outlines were used as reference standards for the evaluation of the segmentation results. We first employed a thresholding technique for CT value to obtain initial lungs, which contain normal and mildly abnormal lung parenchyma. We then used texture-feature images derived from co-occurrence matrix to further segment lung regions with severe DILD. The segmented lung regions with severe DILD were combined with the initial lungs to generate the final segmentation results. We also identified and removed the airways to improve the accuracy of the segmentation results. We used three metrics, i.e., overlap, volume agreement, and mean absolute distance (MAD) between automatically segmented lung and reference lung to evaluate the performance of our segmentation method and the thresholding-based segmentation method. Our segmentation method achieved a mean overlap of 96.1%, a mean volume agreement of 98.1%, and a mean MAD of 0.96 mm for the 45 abnormal cases. On the other hand the thresholding-based segmentation method achieved a mean overlap of 94.2%, a mean volume agreement of 95.8%, and a mean MAD of 1.51 mm for the 45 abnormal cases. Our new method obtained higher performance level than the thresholding-based segmentation method.
    Proc SPIE 03/2010;
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    ABSTRACT: Indocyanine green (ICG) is widely used for its clearance test in the evaluation of liver function. Gadoxetate disodium (Gd-EOB-DTPA) is a targeted MR contrast agent partially taken up by hepatocytes. The objective of this study was to evaluate the feasibility of an estimation of the liver function corresponding to plasma disappearance rate of indocyanine green (ICG-PDR) by use of the signal intensity of the liver alone in Gd-EOB-DTPA enhanced MR imaging (EOB-MRI). We evaluated fourteen patients who had EOB-MRI and ICG clearance test within 1 month. 2D-GRE T1 weighted images were obtained at pre contrast, 3 min (equilibrium phase) and 20 min (hepatobiliary phase) after the intravenous administration of Gd-EOB-DTPA, and the mean signal intensity of the liver was measured. The correlation between ICG-PDR and many parameters derived from the signal intensity of the liver in EOB-MRI was evaluated. The correlation coefficient between ICG-PDR and many parameters derived from the signal intensity of the liver in EOBMRI was low and not significant. The estimation of the liver function corresponding to ICG-PDR by use of the signal intensity of the liver alone in EOB-MRI would not be reliable.
    Proc SPIE 01/2010; 7626(1):762604.
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    ABSTRACT: Accurate detection of diffuse lung disease is an important step for computerized diagnosis and quantification of this disease. It is also a difficult clinical task for radiologists. We developed a computerized scheme to assist radiologists in the detection of diffuse lung disease in multi-detector computed tomography (CT). Two radiologists selected 31 normal and 37 abnormal CT scans with ground glass opacity, reticular, honeycombing and nodular disease patterns based on clinical reports. The abnormal cases in our database must contain at least an abnormal area with a severity of moderate or severe level that was subjectively rated by the radiologists. Because statistical texture features may lack the power to distinguish a nodular pattern from a normal pattern, the abnormal cases that contain only a nodular pattern were excluded. The areas that included specific abnormal patterns in the selected CT images were then delineated as reference standards by an expert chest radiologist. The lungs were first segmented in each slice by use of a thresholding technique, and then divided into contiguous volumes of interest (VOIs) with a 64 x 64 x 64 matrix size. For each VOI, we determined and employed statistical texture features, such as run-length and co-occurrence matrix features, to distinguish abnormal from normal lung parenchyma. In particular, we developed new run-length texture features with clear physical meanings to considerably improve the accuracy of our detection scheme. A quadratic classifier was employed for distinguishing between normal and abnormal VOIs by the use of a leave-one-case-out validation scheme. A rule-based criterion was employed to further determine whether a case was normal or abnormal. We investigated the impact of new and conventional texture features, VOI size and the dimensionality for regions of interest on detecting diffuse lung disease. When we employed new texture features for 3D VOIs of 64 x 64 x 64 voxels, our system achieved the highest performance level: a sensitivity of 86% and a specificity of 90% for the detection of abnormal VOIs, and a sensitivity of 89% and a specificity of 90% for the detection of abnormal cases. Our computerized scheme would be useful for assisting radiologists in the diagnosis of diffuse lung disease.
    Physics in Medicine and Biology 11/2009; 54(22):6881-99. · 2.70 Impact Factor
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    Jiahui Wang, Feng Li, Qiang Li
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    ABSTRACT: Accurate segmentation of lungs with severe interstitial lung disease (ILD) in thoracic computed tomography (CT) is an important and difficult task in the development of computer-aided diagnosis (CAD) systems. Therefore, we developed in this study a texture analysis-based method for accurate segmentation of lungs with severe ILD in multidetector CT scans. Our database consisted of 76 CT scans, including 31 normal cases and 45 abnormal cases with moderate or severe ILD. The lungs in three selected slices for each CT scan were first manually delineated by a medical physicist, and then confirmed or revised by an expert chest radiologist, and they were used as the reference standard for lung segmentation. To segment the lungs, we first employed a CT value thresholding technique to obtain an initial lung estimate, including normal and mild ILD lung parenchyma. We then used texture-feature images derived from the co-occurrence matrix to further identify abnormal lung regions with severe ILD. Finally, we combined the identified abnormal lung regions with the initial lungs to generate the final lung segmentation result. The overlap rate, volume agreement, mean absolute distance (MAD), and maximum absolute distance (dmax) between the automatically segmented lungs and the reference lungs were employed to evaluate the performance of the segmentation method. Our segmentation method achieved a mean overlap rate of 96.7%, a mean volume agreement of 98.5%, a mean MAD of 0.84 mm, and a mean dmax of 10.84 mm for all the cases in our database; a mean overlap rate of 97.7%, a mean volume agreement of 99.0%, a mean MAD of 0.66 mm, and a mean dmax of 9.59 mm for the 31 normal cases; and a mean overlap rate of 96.1%, a mean volume agreement of 98.1%, a mean MAD of 0.96 mm, and a mean dmax of 11.71 mm for the 45 abnormal cases with ILD. Our lung segmentation method provided accurate segmentation results for abnormal CT scans with severe ILD and would be useful for developing CAD systems for quantification, detection, and diagnosis of ILD.
    Medical Physics 10/2009; 36(10):4592-9. · 2.91 Impact Factor
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    ABSTRACT: To evaluate the number of actual detections versus "accidental" detections by a computer-aided detection (CAD) system for small nodular lung cancers (<or=30 mm) on chest radiographs, using two different criteria for measuring performance. A Food-and-Drug-Administration-approved CAD program (version 1.0; Riverain Medical) was applied to 34 chest radiographs with a "radiologist-missed" nodular cancer and 36 radiographs with a radiologist-mentioned nodule (a newer version 3.0 was also applied to the 36-case database). The marks applied by this CAD system consisted of 5-cm-diameter circles. A strict "nodule-in-center" criterion and a generous "nodule-in-circle" criterion were compared as methods for the calculation of CAD sensitivity. The increased sensitivities by the nodule-in-circle criterion were considered as nodules detected by chance. The number of false-positive (FP) marks was also analyzed. For the 34 radiologist-missed cancers, the nodule-in-circle criterion caused eight more cancers (24%) to be detected by chance, as compared to the nodule-in-center criterion, when using the version 1.0 results. For the 36 radiologist-mentioned nodules, the nodule-in-circle criterion caused seven more lesions (19%) to be detected by chance, as compared to the nodule-in-center criterion, when using the version 1.0 results, and three more lesions (8%) to be detected by chance when using the version 3.0 results. Version 1.0 yielded a mean of six FP marks per image, while version 3.0 yielded only three FP marks per image. The specific criteria used to define true- and false-positive CAD detections can substantially influence the apparent accuracy of a CAD system.
    Journal of Digital Imaging 05/2009; 23(1):66-72. · 1.10 Impact Factor
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    ABSTRACT: The aim of this study was to investigate the subjective similarity for pairs of images with various abnormal patterns of diffuse interstitial lung disease on thin-section computed tomography by experienced radiologists to explore a basis for selecting similar images to assist radiologists' interpretation. Four major patterns (ground-glass opacity, nodular opacity, reticular opacity, and honeycombing) on thin-section computed tomographic images were identified by at least two of three radiologists. One radiologist manually selected 104 image pairs, in which the images in each pair had the same pattern and were similar in appearance. An additional 208 image pairs were randomly selected and evenly divided among the four patterns. These pairs were then rated for subjective similarity (on a continuous scale ranging from 0 = not similar at all to 1.0 = almost identical) by 12 radiologists. For radiologist-selected pairs, the mean similarity rated by the 12 radiologists was 0.72. For randomly selected pairs, the mean similarity was higher for the same pattern (0.47) than for the varying patterns (0.27) (P < .001), and among the same pattern, the mean similarity was 0.63 for ground-glass opacity, 0.58 for honeycombing, 0.45 for nodular opacity, and 0.32 for reticular opacity. The mean standard deviation for similarity ratings on all pairs given by the 12 radiologists was 0.05 (rang, 0.01-0.09). Subjective similarity ratings for pairs of abnormal images can be measured reliably and reproducibly by radiologists and will provide a basis for the selection of similar images to assist radiologists' interpretation.
    Academic radiology 04/2009; 16(4):477-85. · 2.09 Impact Factor
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    ABSTRACT: The successful development of high performance computer-aided-diagnostic systems has potential to assist radiologists in the detection and diagnosis of diffuse lung disease. We developed in this study an automated scheme for the detection of diffuse lung disease on multi-detector computed tomography (MDCT). Our database consisted of 68 CT scans, which included 31 normal and 37 abnormal cases with three kinds of abnormal patterns, i.e., ground glass opacity, reticular, and honeycombing. Two radiologists first selected the CT scans with abnormal patterns based on clinical reports. The areas that included specific abnormal patterns in the selected CT images were then delineated as reference standards by an expert chest radiologist. To detect abnormal cases with diffuse lung disease, the lungs were first segmented from the background in each slice by use of a texture analysis technique, and then divided into contiguous volumes of interest (VOIs) with a 64×64×64 matrix size. For each VOI, we calculated many statistical texture features, including the mean and standard deviation of CT values, features determined from the run length matrix, and features from the co-occurrence matrix. A quadratic classifier was employed for distinguishing between normal and abnormal VOIs by use of a leave-one-case-out validation scheme. A rule-based criterion was employed to further determine whether a case was normal or abnormal. For the detection of abnormal VOIs, our CAD system achieved a sensitivity of 86% and a specificity of 90%. For the detection of abnormal cases, it achieved a sensitivity of 89% and a specificity of 90%. This preliminary study indicates that our CAD system would be useful for the detection of diffuse lung disease.
    Proc SPIE 02/2009;
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    ABSTRACT: We conducted an observer study to investigate whether radiologists can judge similarities in pairs of breast masses and lung nodules consistently and reproducibly. Institutional review board approval and informed observer consent were obtained. This study was compliant with the Health Insurance Portability and Accountability Act. We used eight pairs of breast masses on mammograms and eight pairs of lung nodules on computed tomographic images, for which subjective similarity ratings ranging from 0 to 1 were determined in our previous studies. From these, four sets of image pairs were created (ie, a set of eight mass pairs, a set of eight nodule pairs, and two mixed sets of four mass and four nodule pairs). Eight radiologists, including four breast radiologists and four chest radiologists, compared all combinations of the eight pairs in each set using a two-alternative forced-choice (2AFC) method to determine the similarity ranking scores by identifying which pair was more similar than the other pair based on the overall impression for diagnosis. In the mass set and nodule set, the relationship between the average subjective similarity ratings and the average similarity ranking scores by 2AFC indicated very high correlations (r = 0.91 and 0.88). Moreover, in the two mixed sets, the correlations between the average subjective similarity ratings and the average similarity ranking scores were also very high (r = 0.90 and 0.98). Thus, radiologists were able to compare the similarities for pairs of lesions consistently, even in the unusual comparison of pairs of completely different types of lesions. The subjective similarity of a pair of lesions in medical images can be quantified consistently by a group of radiologists. The concept of similarity of lesions in medical images can be subjected to rigorous scientific research and investigation in the future.
    Academic Radiology 08/2008; 15(7):887-94. · 1.91 Impact Factor
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    ABSTRACT: We retrospectively evaluated the usefulness of computer-aided diagnosis (CAD) schemes to radiologist performance in the simultaneous detection of vertebral fractures and lung nodules on chest radiographs. We evaluated posteroanterior and lateral chest images of 21 patients with vertebral fractures, 31 patients with lung nodules, and 10 persons acting as controls. The total number of subjects was 60 because both lesions were present in four patients. Eighteen radiologists were asked to detect vertebral fractures and nodules simultaneously on posteroanterior and lateral images. The radiologists indicated their confidence level ratings regarding the presence or absence of lesions and the most likely location of each lesion on either posteroanterior or lateral images, first without and then with CAD output. The observers' performance was evaluated with use of receiver operating characteristic (ROC) and jackknife free-response ROC curves. With the CAD scheme, the average area under the ROC curve for detection of vertebral fractures improved from 0.906 to 0.951 (p = 0.002). That for lung nodules also improved, but the improvement was not statistically significant (0.804-0.816, p = 0.297). The figure-of-merit values obtained with the jackknife free-response ROC program improved from 0.585 to 0.680 (p < 0.001) for vertebral fractures and from 0.622 to 0.650 (p = 0.017) for nodules, both results having statistical significance. Average sensitivity in the detection of lesions improved from 59.8% to 69.3% for vertebral fractures and from 64.9% to 67.6% for nodules. In the detection of vertebral fractures and lung nodules on chest images, diagnostic accuracy among radiologists improves with the use of CAD.
    American Journal of Roentgenology 07/2008; 191(1):260-5. · 2.90 Impact Factor
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    ABSTRACT: Digital radiography and display systems have revolutionized radiologic practice in recent years and have enabled clinical application of advanced image processing techniques. These include dual energy subtraction and temporal subtraction, both of which can improve diagnostic accuracy for abnormal findings in chest radiographs, especially for subtle lesions such as early lung cancer or focal pneumonia. Dual energy radiography exploits the differential attenuation of low-energy x-ray photons by calcium to produce separate images on the bones and soft tissues, which provides improved detection and characterization of both calcified and noncalcified lung lesions. Dual energy subtraction radiography is currently available from 2 of the major vendors and is in clinical use at many institutions in the United States. Temporal subtraction is a complementary technique that enhances interval change, by using a previous radiograph as a subtraction mask, so that unchanged normal anatomy is suppressed, whereas new abnormalities are enhanced. Though it is not yet a product in the United States, temporal subtraction is available for clinical use in Japan. Temporal subtraction can be combined with energy subtraction to reduce misregistration artifacts, and also has potential to improve computer-aided detection of nodules and other types of lung disease.
    Journal of Thoracic Imaging 05/2008; 23(2):77-85. · 1.26 Impact Factor
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    ABSTRACT: The objective of our study was to retrospectively evaluate whether the use of dual-energy subtraction chest radiographs can improve radiologists' performance for the detection of small previously missed lung cancers. Dual-energy subtraction chest radiographs of 19 patients with previously missed nodular cancers, in which the radiology report did not mention a nodule that was visible in retrospect, were selected. Dual-energy subtraction radiographs of 19 patients with cancer and 16 patients without cancer were used for an observer study. Six radiologists indicated their confidence level regarding the presence of a lung cancer and, if they thought a cancer was present, also marked the most likely position for each lung, first using standard posteroanterior and lateral chest radiographs and then using both soft-tissue and bone dual-energy subtraction images along with standard radiographs. Receiver operating characteristic (ROC) curves were used to evaluate the observers' performance. The indicated locations of cancers and false-positives were also analyzed. The average area under the ROC curve (A(z)) value for the six radiologists was improved from 0.718 to 0.816, a statistically significant amount (p = 0.004), and the average sensitivity (correct localizations) for 19 previously missed cancers was also significantly improved from 40% to 59% (p = 0.008) with the aid of dual-energy subtraction images. The average number of false-positive (incorrect) localizations on 70 lungs was 10 without and nine with dual-energy subtraction images (p = 0.785). Dual-energy subtraction chest radiography has the potential to improve radiologists' performance for the detection of small missed lung cancers.
    American Journal of Roentgenology 05/2008; 190(4):886-91. · 2.90 Impact Factor
  • Qiang Li, Feng Li, Kunio Doi
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    ABSTRACT: We developed a computer-aided diagnostic (CAD) scheme for detection of lung nodules in CT, and investigated its performance levels for nodules in different size and pattern groups. Our database consisted of 117 thin-slice CT scans with 153 nodules. There were 68 (44.4%) small, 52 (34.0%) medium-sized, and 33 (21.6%) large nodules; 101 (66.0%) solid and 52 (34.0%) nodules with ground glass opacity (GGO) in the database. Our CAD scheme consisted of lung segmentation, selective nodule enhancement, initial nodule detection, accurate nodule segmentation, and feature extraction and analysis techniques. We employed a case-based four-fold cross-validation method to evaluate the performance levels of our CAD scheme. We detected 87% of nodules (small: 74%, medium-sized: 98%, large: 94%; solid: 85%, GGO: 90%) with 6.5 false positives per scan; 82% of nodules (small: 68%, medium-sized: 94%, large: 91%; solid: 78%, GGO: 89%) with 2.8 false positives per scan; and 77% of nodules (small: 63%, medium-sized: 90%, large: 89%; solid: 71%, GGO: 89%) with 1.5 false positives per scan. Our CAD scheme achieved a higher sensitivity for GGO nodules than for solid nodules, because most of small nodules were solid. In conclusion, our CAD scheme achieved a low false positive rate and a relatively high detection rate for nodules with a large variation in size and pattern.
    Proc SPIE 04/2008;
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    Qiang Li, Feng Li, Kunio Doi
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    ABSTRACT: We have been developing a computer-aided diagnostic (CAD) scheme for lung nodule detection in order to assist radiologists in the detection of lung cancer in thin-section computed tomography (CT) images. Our database consisted of 117 thin-section CT scans with 153 nodules, obtained from a lung cancer screening program at a Japanese university (85 scans, 91 nodules) and from clinical work at an American university (32 scans, 62 nodules). The database included nodules of different sizes (4-28 mm, mean 10.2 mm), shapes, and patterns (solid and ground-glass opacity (GGO)). Our CAD scheme consisted of modules for lung segmentation, selective nodule enhancement, initial nodule detection, feature extraction, and classification. The selective nodule enhancement filter was a key technique for significant enhancement of nodules and suppression of normal anatomic structures such as blood vessels, which are the main sources of false positives. Use of an automated rule-based classifier for reduction of false positives was another key technique; it resulted in a minimized overtraining effect and an improved classification performance. We used a case-based four-fold cross-validation testing method for evaluation of the performance levels of our computerized detection scheme. Our CAD scheme achieved an overall sensitivity of 86% (small: 76%, medium-sized: 94%, large: 95%; solid: 86%, mixed GGO: 89%, pure GGO: 81%) with 6.6 false positives per scan; an overall sensitivity of 81% (small: 69%, medium-sized: 91%, large: 91%; solid: 79%, mixed GGO: 88%, pure GGO: 81%) with 3.3 false positives per scan; and an overall sensitivity of 75% (small: 60%, medium-sized: 88%, large: 87%; solid: 70%, mixed GGO: 87%, pure GGO: 81%) with 1.6 false positives per scan. The experimental results indicate that our CAD scheme with its two key techniques can achieve a relatively high performance for nodules presenting large variations in size, shape, and pattern.
    Academic Radiology 03/2008; 15(2):165-75. · 1.91 Impact Factor
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    ABSTRACT: Early diagnosis and treatment are important for improvement of the low survival rate of patients with lung cancer. The objective of this study was to evaluate the long-term survival rate of patients identified to have lung cancer by our population-based baseline and annual repeat low-radiation dose computed tomography (low-dose CT) screenings, conducted in 1996-1998. A total of 13,037 CT scans were obtained from 5480 subjects (2969 men, 2511 women) aged 40-74 years at the initial CT screening. Lung cancer was detected in 63 subjects (57 were detected by CT scans and underwent surgery; 1 was detected by sputum cytology and underwent surgery; 3 rejected treatment; and 2 were interval cases that developed symptoms prior to the next annual repeat CT screening). Follow-up study included review of medical records. Death certificates were examined to check for any deceased interval case among participants. Postoperative follow-up of the 50 survived patients ranged from 70 to 117 (median, 101) months. Eight patients died during follow-up (6 due to lung cancer from 20 to 67 months after surgery and 2 deaths unrelated to lung cancer, each 7 and 60 months following surgery). Three patients who rejected treatment died 14 months to 6 years after positive screening CT scans, and the 2 interval cases died at each 17 and 30 months, respectively, following negative screening CT scans. Survival was analysed in 59 patients with lung cancer detected by low-dose CT screening (excluding two patients; one was detected by sputum cytology and the other had mass lesion already noted on the chest radiograph of the previous year). The 10-year survival calculated by the Kaplan-Meier method was 83.1% (95% CI: 0.735-0.927) for death from all causes and 86.2% (95% CI: 0.773-0.951) for death from lung cancer. The survival rate was excellent for never-smokers, patients with BAC and adenocarcinoma/mixed types with non-solid CT density pattern, associated with Noguchi's type A or B and pathologic stage IA. A poorer prognosis was noted in smokers with adenocarcinomas/mixed types, associated with part-solid or solid CT density pattern and Noguchi's type C or D. All patients with non-solid tumours measuring 6-13.5mm at presentation are alive, patients with part-solid tumours, measuring 17mm or more, or solid tumours, measuring 13mm or more at presentation were associated with increased risk of lung cancer-related morbidity or mortality. The estimated rate of possible over-diagnosis was 13% in total and we failed to cure 17% of patients encountered in the programme. Low-dose CT screening substantially improves the 10-year survival for lung cancer with minimal use of invasive treatment procedures.
    Lung Cancer 01/2008; 58(3):329-41. · 3.39 Impact Factor
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    ABSTRACT: We have developed a new computer-aided diagnostic (CAD) scheme for detection of diffuse lung disease in computed radiographic (CR) chest images. One hundred ninety-four chest images (56 normals and 138 abnormals with diffuse lung diseases) were used. The 138 abnormal cases were classified into three levels of severity (34 mild, 60 moderate, and 44 severe) by an experienced chest radiologist with use of five different patterns, i.e., reticular, reticulonodular, nodular, air-space opacity, and emphysema. In our computerized scheme, the first moment of the power spectrum, the root-mean-square variation, and the average pixel value were determined for each region of interest (ROI), which was selected automatically in the lung fields. The average pixel value and its dependence on the location of the ROI were employed for identifying abnormal patterns due to air-space opacity or emphysema. A rule-based method was used for determining three levels of abnormality for each ROI (0: normal, 1: mild, 2: moderate, and 3: severe). The distinction between normal lungs and abnormal lungs with diffuse lung disease was determined based on the fractional number of abnormal ROIs by taking into account the severity of abnormalities. Preliminary results indicated that the area under the ROC curve was 0.889 for the 44 severe cases, 0.825 for the 104 severe and moderate cases, and 0.794 for all cases. We have identified a number of problems and reasons causing false positives on normal cases, and also false negatives on abnormal cases. In addition, we have discussed potential approaches for improvement of our CAD scheme. In conclusion, the CAD scheme for detection of diffuse lung diseases based on texture features extracted from CR chest images has the potential to assist radiologists in their interpretation of diffuse lung diseases.
    Proc SPIE 01/2008;

Publication Stats

1k Citations
138.03 Total Impact Points

Institutions

  • 2002–2011
    • University of Chicago
      • Department of Radiology
      Chicago, IL, United States
  • 2009
    • Duke University Medical Center
      • Department of Radiology
      Durham, NC, United States