Noninvasive prediction of residual disease for advanced high-grade serous ovarian carcinoma by MRI-based radiomic-clinical nomogram

• Published in: European radiology Vol. 31; no. 10; pp. 7855 - 7864
• Main Authors: Li, Haiming, Zhang, Rui, Li, Ruimin, Xia, Wei, Chen, Xiaojun, Zhang, Jiayi, Cai, Songqi, Li, Yong’ai, Zhao, Shuhui, Qiang, Jinwei, Peng, Weijun, Gu, Yajia, Gao, Xin
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2021; Springer Nature B.V
• Subjects: Diagnostic Radiology; Feature extraction; Imaging; Internal Medicine; Interventional Radiology; Magnetic resonance imaging; Medical imaging; Medicine; Medicine & Public Health; Neuroradiology; Nomograms; Oncology; Ovarian cancer; Ovarian carcinoma; Ovaries; Patients; Performance prediction; Predictions; Radiology; Radiomics; Regression analysis; Tumors; Ultrasound; Residual disease; Magnetic resonance imaging; Ovarian neoplasms; Nomogram; Radiomics
• ISSN: 0938-7994; 1432-1084
• DOI: 10.1007/s00330-021-07902-0

Objectives To develop a preoperative MRI-based radiomic-clinical nomogram for prediction of residual disease (RD) in patients with advanced high-grade serous ovarian carcinoma (HGSOC). Methods In total, 217 patients with advanced HGSOC were enrolled from January 2014 to June 2019 and randomly divided into a training set ( n = 160) and a validation set ( n = 57). Finally, 841 radiomic features were extracted from each tumor on T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) sequence, respectively. We used two fusion methods, the maximal volume of interest (MV) and the maximal feature value (MF), to fuse the radiomic features of bilateral tumors, so that patients with bilateral tumors have the same kind of radiomic features as patients with unilateral tumors. The radiomic signatures were constructed by using mRMR method and LASSO classifier. Multivariable logistic regression analysis was used to develop a radiomic-clinical nomogram incorporating radiomic signature and conventional clinico-radiological features. The performance of the nomogram was evaluated on the validation set. Results In total, 342 tumors from 217 patients were analyzed in this study. The MF-based radiomic signature showed significantly better prediction performance than the MV-based radiomic signature (AUC = 0.744 vs. 0.650, p = 0.047). By incorporating clinico-radiological features and MF-based radiomic signature, radiomic-clinical nomogram showed favorable prediction ability with an AUC of 0.803 in the validation set, which was significantly higher than that of clinico-radiological signature and MF-based radiomic signature (AUC = 0.623, 0.744, respectively). Conclusions The proposed MRI-based radiomic-clinical nomogram provides a promising way to noninvasively predict the RD status. Key Points • MRI-based radiomic-clinical nomogram is feasible to noninvasively predict residual disease in patients with advanced HGSOC. • The radiomic signature based on MF showed significantly better prediction performance than that based on MV. • The radiomic-clinical nomogram showed a favorable prediction ability with an AUC of 0.803.