T Zhang1, M Yuan1, Y Zhong1, Y-D Zhang1, H Li2, J-F Wu3, T-F Yu4. 1. Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China. 2. Department of Pathology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China. 3. GE Healthcare, Shanghai, 210000, China. 4. Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China. Electronic address: njmu_ytf@163.com.
Abstract
AIM: To evaluate the predictive role of radiomics based on computed tomography (CT) in discriminating focal organising pneumonia (FOP) from peripheral lung adenocarcinoma (LA). MATERIALS AND METHODS: Institutional research board approval was obtained for this retrospective study. One hundred and seventeen patients with FOP and 109 patients with LA who underwent thin-section CT from January 2011 to August 2017 were reviewed systematically and analysed. The clinical and radiological features were established as model A and multi-feature-based radiomics as model B. The diagnostic performance of model A, model B, and model A+B were evaluated and compared via receiver operating characteristic (ROC) curve analysis and logistic regression analysis. RESULTS: Sex, symptoms, necrosis, and the halo sign were identified as independent predictors of LA. The area under the ROC curve (Az value), accuracy, sensitivity, and specificity of model A were 0.839, 75.7%, 82.6%, and 69.2% respectively. Model B showed significantly higher accuracy than model A (83.6% versus 75.7%, p=0.032). The top four best-performing features, WavEnLH_s-3, WavEnHH_s-3, Teta3, and Volume, performed as independent factors for discriminating LA. Regression analysis indicated that model B had superior model fit than model A with Akaike information criterion (AIC) values of 73.6% versus 59.1%, respectively. Combining model A with model B is useful in achieving better diagnostic performance in discriminating FOP from LA: the Az value, accuracy, sensitivity, and specificity were 0.956, 87.6%, 85.3%, and 89.7% respectively. CONCLUSIONS: Radiomics based on CT exhibited better diagnostic accuracy and model fit than clinical and radiological features in discriminating FOP from LA. Combination of both achieved better diagnostic performance.
AIM: To evaluate the predictive role of radiomics based on computed tomography (CT) in discriminating focal organising pneumonia (FOP) from peripheral lung adenocarcinoma (LA). MATERIALS AND METHODS: Institutional research board approval was obtained for this retrospective study. One hundred and seventeen patients with FOP and 109 patients with LA who underwent thin-section CT from January 2011 to August 2017 were reviewed systematically and analysed. The clinical and radiological features were established as model A and multi-feature-based radiomics as model B. The diagnostic performance of model A, model B, and model A+B were evaluated and compared via receiver operating characteristic (ROC) curve analysis and logistic regression analysis. RESULTS: Sex, symptoms, necrosis, and the halo sign were identified as independent predictors of LA. The area under the ROC curve (Az value), accuracy, sensitivity, and specificity of model A were 0.839, 75.7%, 82.6%, and 69.2% respectively. Model B showed significantly higher accuracy than model A (83.6% versus 75.7%, p=0.032). The top four best-performing features, WavEnLH_s-3, WavEnHH_s-3, Teta3, and Volume, performed as independent factors for discriminating LA. Regression analysis indicated that model B had superior model fit than model A with Akaike information criterion (AIC) values of 73.6% versus 59.1%, respectively. Combining model A with model B is useful in achieving better diagnostic performance in discriminating FOP from LA: the Az value, accuracy, sensitivity, and specificity were 0.956, 87.6%, 85.3%, and 89.7% respectively. CONCLUSIONS: Radiomics based on CT exhibited better diagnostic accuracy and model fit than clinical and radiological features in discriminating FOP from LA. Combination of both achieved better diagnostic performance.
Authors: Radouane El Ayachy; Nicolas Giraud; Paul Giraud; Catherine Durdux; Philippe Giraud; Anita Burgun; Jean Emmanuel Bibault Journal: Front Oncol Date: 2021-05-05 Impact factor: 6.244