Shun-Mao Yang1, Li-Wei Chen2, Hao-Jen Wang2, Leng-Rong Chen2, Kuo-Lung Lor2, Yi-Chang Chen3, Mong-Wei Lin4, Min-Shu Hsieh5, Jin-Shing Chen4, Yeun-Chung Chang6, Chung-Ming Chen7. 1. Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taiwan; Department of Surgery, National Taiwan University Hospital, Hsin-Chu Branch, Taiwan. 2. Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taiwan. 3. Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taiwan; Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taiwan. 4. Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taiwan. 5. Department of Pathology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taiwan. 6. Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taiwan. Electronic address: ycc5566@ntu.edu.tw. 7. Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taiwan. Electronic address: chung@ntu.edu.tw.
Abstract
INTRODUCTION: Histological subtypes of lung adenocarcinomas (ADCs) classified by the International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society (IASLC/ATS/ERS) system have been investigated using radiomic approaches. However, the results have had limitations since <80% of invasive lung ADCs were heterogeneous, with two or more subtypes. To reduce the influence of heterogeneity during radiomic analysis, computed tomography (CT) images of lung ADCs with near-pure ADC subtypes were analyzed to extract representative radiomic features of different subtypes. METHODS: We enrolled 95 patients who underwent complete resection for lung ADC and a pathological diagnosis of a "near-pure" (≥70%) IASLC/ATS/ERS histological subtype. Conventional histogram/morphological features and complex radiomic features (grey-level-based statistical features and component variance-based features) of thin-cut CT data of tumor regions were analyzed. A prediction model based on leave-one-out cross-validation (LOOCV) and logistic regression (LR) was used to classify all five subtypes and three pathologic grades (lepidic, acinar/papillary, micropapillary/solid) of ADCs. The validation was performed using 36 near-pure ADCs in a later cohort. RESULTS: A total of 31 lepidic, 14 papillary, 32 acinar, 10 micropapillary, and 8 solid ADCs were analyzed. With 21 conventional and complex radiomic features, for 5 subtypes and 3 pathological grades, the prediction models achieved accuracy rates of 84.2% (80/95) and 91.6% (87/95), respectively, while accuracy was 71.6% and 85.3%, respectively, if only conventional features were used. The accuracy rate for the validation set (n = 36) was 83.3% (30/36) and 94.4% (34/36) in 5 subtypes and 3 pathological grades, respectively, using conventional and complex features, while it was 66.7% and 77.8% only using conventional features, respectively. CONCLUSION: Lung ADC with high purity pathological subtypes demonstrates strong stratification of radiomic values, which provide basic information for accurate pathological subtyping and image parcellation of tumor sub-regions.
INTRODUCTION: Histological subtypes of lung adenocarcinomas (ADCs) classified by the International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society (IASLC/ATS/ERS) system have been investigated using radiomic approaches. However, the results have had limitations since <80% of invasive lung ADCs were heterogeneous, with two or more subtypes. To reduce the influence of heterogeneity during radiomic analysis, computed tomography (CT) images of lung ADCs with near-pure ADC subtypes were analyzed to extract representative radiomic features of different subtypes. METHODS: We enrolled 95 patients who underwent complete resection for lung ADC and a pathological diagnosis of a "near-pure" (≥70%) IASLC/ATS/ERS histological subtype. Conventional histogram/morphological features and complex radiomic features (grey-level-based statistical features and component variance-based features) of thin-cut CT data of tumor regions were analyzed. A prediction model based on leave-one-out cross-validation (LOOCV) and logistic regression (LR) was used to classify all five subtypes and three pathologic grades (lepidic, acinar/papillary, micropapillary/solid) of ADCs. The validation was performed using 36 near-pure ADCs in a later cohort. RESULTS: A total of 31 lepidic, 14 papillary, 32 acinar, 10 micropapillary, and 8 solid ADCs were analyzed. With 21 conventional and complex radiomic features, for 5 subtypes and 3 pathological grades, the prediction models achieved accuracy rates of 84.2% (80/95) and 91.6% (87/95), respectively, while accuracy was 71.6% and 85.3%, respectively, if only conventional features were used. The accuracy rate for the validation set (n = 36) was 83.3% (30/36) and 94.4% (34/36) in 5 subtypes and 3 pathological grades, respectively, using conventional and complex features, while it was 66.7% and 77.8% only using conventional features, respectively. CONCLUSION: Lung ADC with high purity pathological subtypes demonstrates strong stratification of radiomic values, which provide basic information for accurate pathological subtyping and image parcellation of tumor sub-regions.