Preoperative Assessment for High‐Risk Endometrial Cancer by Developing an MRI‐ and Clinical‐Based Radiomics Nomogram: A Multicenter Study

列线图 无线电技术 医学 子宫内膜癌 置信区间 逻辑回归 放射科 接收机工作特性 Lasso(编程语言) 淋巴结切除术 核医学 内科学 癌症 计算机科学 万维网
作者
Bi Cong Yan,Ying Li,Hua Feng,Feng Feng,Ming Sun,Guangwu Lin,Guofu Zhang,Jin Wei Qiang
出处
期刊:Journal of Magnetic Resonance Imaging [Wiley]
卷期号:52 (6): 1872-1882 被引量:59
标识
DOI:10.1002/jmri.27289
摘要

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; T 2 ‐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.
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