Jose Arimateia Batista Araujo-Filho1, Maria Mayoral2, Junting Zheng3, Kay See Tan3, Peter Gibbs4, Annemarie Fernandes Shepherd5, Andreas Rimner5, Charles B Simone5, Gregory Riely6, James Huang7, Michelle S Ginsberg4. 1. Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York; Department of Radiology, Hospital Sirio-Libanes, São Paulo, Brazil. Electronic address: ariaraujocg@gmail.com. 2. Diagnostic Imaging Center, Hospital Clínic of Barcelona, University of Barcelona, Barcelona, Catalonia, Spain. 3. Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York. 4. Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York. 5. Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center New York, New York. 6. Division of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York. 7. Department of Surgery, Memorial Sloan Kettering Cancer Center New York, New York.
Abstract
BACKGROUND: To explore the performance of a computed tomography based radiomics model in the preoperative prediction of resectability status and TNM staging in thymic epithelial tumors. METHODS: We reviewed the last preoperative computed tomography scan of patients with thymic epithelial tumors prior to resection and pathology evaluation at our institution between February 2008 and June 2019. A total of 101 quantitative features were extracted and a radiomics model was trained using elastic net penalized logistic regressions for each aim. In the set-aside testing sets, discriminating performance of each model was assessed with area under receiver operating characteristic curve. RESULTS: Our final population consisted of 243 patients with: 153 (87%) thymomas, 23 (9%) thymic carcinomas, and 9 (4%) thymic carcinoids. Incomplete resections (R1 or R2) occurred in 38 (16%) patients, and 67 (28%) patients had more advanced stage tumors (stage III or IV). In the set-aside testing sets, the radiomics model achieved good performance in preoperatively predicting incomplete resections (area under receiver operating characteristic curve: 0.80) and advanced stage tumors (area under receiver operating characteristic curve: 0.70). CONCLUSIONS: Our computed tomography radiomics model achieved good performance to predict resectability status and staging in thymic epithelial tumors, suggesting a potential value for the evaluation of radiomic features in the preoperative prediction of surgical outcomes in thymic malignancies.
BACKGROUND: To explore the performance of a computed tomography based radiomics model in the preoperative prediction of resectability status and TNM staging in thymic epithelial tumors. METHODS: We reviewed the last preoperative computed tomography scan of patients with thymic epithelial tumors prior to resection and pathology evaluation at our institution between February 2008 and June 2019. A total of 101 quantitative features were extracted and a radiomics model was trained using elastic net penalized logistic regressions for each aim. In the set-aside testing sets, discriminating performance of each model was assessed with area under receiver operating characteristic curve. RESULTS: Our final population consisted of 243 patients with: 153 (87%) thymomas, 23 (9%) thymic carcinomas, and 9 (4%) thymic carcinoids. Incomplete resections (R1 or R2) occurred in 38 (16%) patients, and 67 (28%) patients had more advanced stage tumors (stage III or IV). In the set-aside testing sets, the radiomics model achieved good performance in preoperatively predicting incomplete resections (area under receiver operating characteristic curve: 0.80) and advanced stage tumors (area under receiver operating characteristic curve: 0.70). CONCLUSIONS: Our computed tomography radiomics model achieved good performance to predict resectability status and staging in thymic epithelial tumors, suggesting a potential value for the evaluation of radiomic features in the preoperative prediction of surgical outcomes in thymic malignancies.
Authors: Sara A Hayes; James Huang; Andrew J Plodkowski; Janine Katzen; Junting Zheng; Chaya S Moskowitz; Michelle S Ginsberg Journal: J Thorac Oncol Date: 2014-07 Impact factor: 15.609