Bin Wang1, Yuhong Tang2, Yinan Chen3, Preeti Hamal1, Yajing Zhu3, TingTing Wang1, Yangyang Sun4, Yang Lu3, Maheshkumar Satishkumar Bhuva1, Xue Meng4, Yang Yang1, Zisheng Ai2, Chunyan Wu5, Xiwen Sun6. 1. Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China. 2. Department of Medical Statistics, Tongji University School of Medicine, Shanghai, China. 3. 12 Sigma Technologies, Shanghai, China. 4. Department of Radiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China. 5. Department of pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China. Electronic address: wuchunyan581@sina.com. 6. Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China. Electronic address: sunxiwen5256@163.com.
Abstract
OBJECTIVES: To evaluate the diagnostic accuracy of radiomics method and frozen sections (FS) for the pathological classification of peripheral lung adenocarcinoma manifesting as ground-glass nodules (GGNs) in computer tomography (CT). MATERIALS AND METHODS: A dataset of 831 peripheral lung adenocarcinoma manifesting as GGNs in CT were divided into two cohorts: training cohort (n = 581) and validation cohort (n = 250). Combined with clinical features, the radiomics classifier was trained and validated to distinguish the pathological classification of these nodules. FS diagnoses in the validation cohort were collected. Diagnostic performance of the FS and radiomics methods was compared in the validation cohort. The predictive factors for the misdiagnosis of FS were determined via univariate and multivariate analyses. RESULTS: The accuracy of radiomics method in the training and validation cohorts was 72.5 % and 68.8 % respectively. The overall accuracy of FS in the validation cohort was 70.0 %. The concordance rate between FS and final pathology when FS had a different diagnosis from radiomics classifier was significantly lower than when FS had the same diagnosis as radiomics classifier (46 vs. 87 %, P < 0.001). Univariate and Multivariate analyses showed that different diagnosis between FS and radiomics classifier was the independent predictive factor for the misdiagnosis of FS (OR: 7.46; 95%CI: 4.00-13.91; P < 0.001). CONCLUSIONS: Radiomics classifier predictions may be a reliable reference for the classification of peripheral lung adenocarcinoma manifesting as GGNs when FS cannot provide a timely diagnosis. Intraoperative diagnoses of the cases where FS had a different diagnosis from radiomics method should be considered cautiously due to the higher misdiagnosis rate.
OBJECTIVES: To evaluate the diagnostic accuracy of radiomics method and frozen sections (FS) for the pathological classification of peripheral lung adenocarcinoma manifesting as ground-glass nodules (GGNs) in computer tomography (CT). MATERIALS AND METHODS: A dataset of 831 peripheral lung adenocarcinoma manifesting as GGNs in CT were divided into two cohorts: training cohort (n = 581) and validation cohort (n = 250). Combined with clinical features, the radiomics classifier was trained and validated to distinguish the pathological classification of these nodules. FS diagnoses in the validation cohort were collected. Diagnostic performance of the FS and radiomics methods was compared in the validation cohort. The predictive factors for the misdiagnosis of FS were determined via univariate and multivariate analyses. RESULTS: The accuracy of radiomics method in the training and validation cohorts was 72.5 % and 68.8 % respectively. The overall accuracy of FS in the validation cohort was 70.0 %. The concordance rate between FS and final pathology when FS had a different diagnosis from radiomics classifier was significantly lower than when FS had the same diagnosis as radiomics classifier (46 vs. 87 %, P < 0.001). Univariate and Multivariate analyses showed that different diagnosis between FS and radiomics classifier was the independent predictive factor for the misdiagnosis of FS (OR: 7.46; 95%CI: 4.00-13.91; P < 0.001). CONCLUSIONS: Radiomics classifier predictions may be a reliable reference for the classification of peripheral lung adenocarcinoma manifesting as GGNs when FS cannot provide a timely diagnosis. Intraoperative diagnoses of the cases where FS had a different diagnosis from radiomics method should be considered cautiously due to the higher misdiagnosis rate.
Authors: Arthur Jochems; Turkey Refaee; Henry C Woodruff; Philippe Lambin; Guangyao Wu; Abdalla Ibrahim; Chenggong Yan; Sebastian Sanduleanu Journal: Eur J Nucl Med Mol Imaging Date: 2021-03-11 Impact factor: 10.057