Bi Cong Yan1, Ying Li1, Feng Hua Ma2, Feng Feng3, Ming Hua Sun4, Guang Wu Lin5, Guo Fu Zhang2, Jin Wei Qiang1. 1. Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China. 2. Departments of Radiology, Obstetrics & Gynecology Hospital, Fudan University, Shanghai, China. 3. Departments of Radiology, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, China. 4. Departments of Radiology, Huadong Hospital of Fudan University, Fudan University, Shanghai, China. 5. Departments of Radiology, Cancer Hospital of Nantong University, Nantong, China.
Abstract
BACKGROUND: High- and low-risk endometrial cancer (EC) differ in whether lymphadenectomy is performed. Assessment of high-risk EC is essential for planning surgery appropriately. PURPOSE: To develop a radiomics nomogram for high-risk EC prediction preoperatively. STUDY TYPE: Retrospective. POPULATION: In all, 717 histopathologically confirmed EC patients (mean age, 56 years ± 9) divided into a primary group (394 patients from Center A), validation groups 1 and 2 (146 patients from Center B and 177 patients from Centers C-E). FIELD STRENGTH/SEQUENCE: 1.5/3T scanners; T2 -weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient, and contrast enhancement sequences. ASSESSMENT: A radiomics nomogram was generated by combining the selected radiomics features and clinical parameters (metabolic syndrome, cancer antigen 125, age, tumor grade following curettage, and tumor size). The area under the curve (AUC) of the receiver operator characteristic was used to evaluate the predictive performance of the radiomics nomogram for high-risk EC. The surgical procedure suggested by the nomogram was compared with the actual procedure performed for the patients. Net benefit of the radiomics nomogram was evaluated by a clinical decision curve (CDC), net reclassification index (NRI), and integrated discrimination improvement (IDI). STATISTICAL TESTS: Binary least absolute shrinkage and selection operator (LASSO) logistic regression, linear regression, and multivariate binary logistic regression were used to select radiomics features and clinical parameters. RESULTS: The AUC for prediction of high-risk EC for the radiomics nomogram in the primary group, validation groups 1 and 2 were 0.896 (95% confidence interval [CI]: 0.866-0.926), 0.877 (95% CI: 0.825-0.930), and 0.919 (95% CI: 0.879-0.960), respectively. The nomogram achieved good net benefit by CDC analysis for high-risk EC. NRIs were 1.17, 1.28, and 1.51, and IDIs were 0.41, 0.60, and 0.61 in the primary group, validation groups 1 and 2, respectively. DATA CONCLUSION: The radiomics nomogram exhibited good performance in the individual prediction of high-risk EC, and might be used for surgical management of EC. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 2 J. MAGN. RESON. IMAGING 2020;52:1872-1882.
BACKGROUND: High- and low-risk endometrial cancer (EC) differ in whether lymphadenectomy is performed. Assessment of high-risk EC is essential for planning surgery appropriately. PURPOSE: To develop a radiomics nomogram for high-risk EC prediction preoperatively. STUDY TYPE: Retrospective. POPULATION: In all, 717 histopathologically confirmed EC patients (mean age, 56 years ± 9) divided into a primary group (394 patients from Center A), validation groups 1 and 2 (146 patients from Center B and 177 patients from Centers C-E). FIELD STRENGTH/SEQUENCE: 1.5/3T scanners; T2 -weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient, and contrast enhancement sequences. ASSESSMENT: A radiomics nomogram was generated by combining the selected radiomics features and clinical parameters (metabolic syndrome, cancer antigen 125, age, tumor grade following curettage, and tumor size). The area under the curve (AUC) of the receiver operator characteristic was used to evaluate the predictive performance of the radiomics nomogram for high-risk EC. The surgical procedure suggested by the nomogram was compared with the actual procedure performed for the patients. Net benefit of the radiomics nomogram was evaluated by a clinical decision curve (CDC), net reclassification index (NRI), and integrated discrimination improvement (IDI). STATISTICAL TESTS: Binary least absolute shrinkage and selection operator (LASSO) logistic regression, linear regression, and multivariate binary logistic regression were used to select radiomics features and clinical parameters. RESULTS: The AUC for prediction of high-risk EC for the radiomics nomogram in the primary group, validation groups 1 and 2 were 0.896 (95% confidence interval [CI]: 0.866-0.926), 0.877 (95% CI: 0.825-0.930), and 0.919 (95% CI: 0.879-0.960), respectively. The nomogram achieved good net benefit by CDC analysis for high-risk EC. NRIs were 1.17, 1.28, and 1.51, and IDIs were 0.41, 0.60, and 0.61 in the primary group, validation groups 1 and 2, respectively. DATA CONCLUSION: The radiomics nomogram exhibited good performance in the individual prediction of high-risk EC, and might be used for surgical management of EC. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 2 J. MAGN. RESON. IMAGING 2020;52:1872-1882.
Authors: Gulzhanat Aimagambetova; Sanja Terzic; Antonio Simone Laganà; Gauri Bapayeva; Philip la Fleur; Milan Terzic Journal: J Clin Med Date: 2021-12-30 Impact factor: 4.241
Authors: Erling A Hoivik; Erlend Hodneland; Julie A Dybvik; Kari S Wagner-Larsen; Kristine E Fasmer; Hege F Berg; Mari K Halle; Ingfrid S Haldorsen; Camilla Krakstad Journal: Commun Biol Date: 2021-12-06