Bao Feng1, Xiangmeng Chen2, Yehang Chen3, Kunfeng Liu4, Kunwei Li4, Xueguo Liu4, Nan Yao2, Zhi Li3, Ronggang Li5, Chaotong Zhang2, Jianbo Ji3, Wansheng Long6. 1. The Department of Radiology, The Affiliated Jiangmen Hospital of Sun Yat-sen University, Jiangmen, Guangdong Province, China; School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin, Guangxi Province, China. 2. The Department of Radiology, The Affiliated Jiangmen Hospital of Sun Yat-sen University, Jiangmen, Guangdong Province, China. 3. School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin, Guangxi Province, China. 4. The Department of Radiology, The Fifth Affiliated Hospital Sun Yat-sen University, Zhuhai, Guangdong Province, China. 5. The Department of Pathology, The Affiliated Jiangmen Hospital of Sun Yat-sen University, Jiangmen, Guangdong Province, China. 6. The Department of Radiology, The Affiliated Jiangmen Hospital of Sun Yat-sen University, Jiangmen, Guangdong Province, China. Electronic address: jmlws2@163.com.
Abstract
PURPOSE: To investigate the preoperative differential diagnostic performance of a radiomics nomogram in tuberculous granuloma (TBG) and lung adenocarcinoma (LAC) appearing as solitary pulmonary solid nodules (SPSNs). METHOD: We retrospectively recruited 426 patients with SPSNs from two centers and assigned them to training (n = 123), internal validation (n = 121), and external validation cohorts (n = 182). A model of deep learning (DL) was built for tumor segmentation from routine computed tomography (CT) images and extraction of 3D radiomics features. We used the least absolute shrinkage and selection operator (LASSO) logistic regression to build a radiomics signature. A clinical model was developed with clinical factors, including age, gender, and CT-based subjective findings (eg, lesion size, lesion location, lesion margin, lobulated sharp, and spiculation sign). We constructed individualized radiomics nomograms incorporating the radiomics signature and clinical factors to validate the diagnostic ability. RESULTS: Three factors - radiomics signature, age, and spiculation sign - were found to be independent predictors and were used to build the radiomics nomogram, which showed better diagnostic accuracy than any single model (all net reclassification improvement p < 0.05). The area under curve yielded was 0.9660 (95% confidence interval [CI], 0.9390-0.9931), 0.9342 (95% CI, 0.8944-0.9739), and 0.9064 (95% CI, 0.8639-0.9490) for the training, internal validation, and external validation cohorts, respectively. Decision curve analysis (DCA) and stratification analysis showed the nomogram has potential for generalizability. CONCLUSION: The radiomics nomogram we developed can preoperatively distinguish between LAC and TBG in patient with a SPSN.
PURPOSE: To investigate the preoperative differential diagnostic performance of a radiomics nomogram in tuberculous granuloma (TBG) and lung adenocarcinoma (LAC) appearing as solitary pulmonary solid nodules (SPSNs). METHOD: We retrospectively recruited 426 patients with SPSNs from two centers and assigned them to training (n = 123), internal validation (n = 121), and external validation cohorts (n = 182). A model of deep learning (DL) was built for tumor segmentation from routine computed tomography (CT) images and extraction of 3D radiomics features. We used the least absolute shrinkage and selection operator (LASSO) logistic regression to build a radiomics signature. A clinical model was developed with clinical factors, including age, gender, and CT-based subjective findings (eg, lesion size, lesion location, lesion margin, lobulated sharp, and spiculation sign). We constructed individualized radiomics nomograms incorporating the radiomics signature and clinical factors to validate the diagnostic ability. RESULTS: Three factors - radiomics signature, age, and spiculation sign - were found to be independent predictors and were used to build the radiomics nomogram, which showed better diagnostic accuracy than any single model (all net reclassification improvement p < 0.05). The area under curve yielded was 0.9660 (95% confidence interval [CI], 0.9390-0.9931), 0.9342 (95% CI, 0.8944-0.9739), and 0.9064 (95% CI, 0.8639-0.9490) for the training, internal validation, and external validation cohorts, respectively. Decision curve analysis (DCA) and stratification analysis showed the nomogram has potential for generalizability. CONCLUSION: The radiomics nomogram we developed can preoperatively distinguish between LAC and TBG in patient with a SPSN.
Authors: Zoran Stojanovic; Filipe Gonçalves-Carvalho; Alicia Marín; Jorge Abad Capa; Jose Domínguez; Irene Latorre; Alicia Lacoma; Cristina Prat-Aymerich Journal: ERJ Open Res Date: 2022-09-12
Authors: Elizabeth P V Le; Leonardo Rundo; Jason M Tarkin; Nicholas R Evans; Mohammed M Chowdhury; Patrick A Coughlin; Holly Pavey; Chris Wall; Fulvio Zaccagna; Ferdia A Gallagher; Yuan Huang; Rouchelle Sriranjan; Anthony Le; Jonathan R Weir-McCall; Michael Roberts; Fiona J Gilbert; Elizabeth A Warburton; Carola-Bibiane Schönlieb; Evis Sala; James H F Rudd Journal: Sci Rep Date: 2021-02-10 Impact factor: 4.379