Deep Learning Radiomics Nomogram Based on Magnetic Resonance Imaging for Differentiating Type I/II Epithelial Ovarian Cancer

Mingxiang Wei; Guannan Feng; Xinyi Wang; Jianye Jia; Yu Zhang; Yao Dai; Cai Qin; Genji Bai; Shuangqing Chen

doi:10.1016/j.acra.2023.08.002

Deep Learning Radiomics Nomogram Based on Magnetic Resonance Imaging for Differentiating Type I/II Epithelial Ovarian Cancer

Acad Radiol. 2023 Aug 27:S1076-6332(23)00401-4. doi: 10.1016/j.acra.2023.08.002. Online ahead of print.

Authors

Mingxiang Wei¹, Guannan Feng², Xinyi Wang¹, Jianye Jia³, Yu Zhang⁴, Yao Dai⁵, Cai Qin⁶, Genji Bai³, Shuangqing Chen⁷

Affiliations

¹ Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China (M.W., X.W., S.C.).
² Department of Gynecology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China (G.F.).
³ Department of Radiology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, Jiangsu, China (J.J., G.B.).
⁴ Department of Radiology, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, Jiangsu, China (Y.Z.).
⁵ Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China (Y.D.).
⁶ Department of Radiology, Tumor Hospital Affiliated to Nantong University, Nantong, Jiangsu, China (C.Q.).
⁷ Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China (M.W., X.W., S.C.). Electronic address: sznaonao@163.com.

PMID: 37643927
DOI: 10.1016/j.acra.2023.08.002

Abstract

Rationale and objectives: To develop and validate a T2-weighted magnetic resonance imaging (MRI)-based deep learning radiomics nomogram (DLRN) to differentiate between type I and type II epithelial ovarian cancer (EOC).

Materials and methods: This multicenter study incorporated 437 patients from ﬁve centers, divided into training (n = 271), internal validation (n = 68), and external validation (n = 98) sets. The deep learning (DL) model was constructed using the largest orthogonal slices of the tumor area. The extracted radiomics features were employed in building the radiomics model. The clinical model was developed based on clinical characteristics. A DLRN was built by integrating the DL signature, radiomics signature, and independent clinical predictors. Model performances were evaluated through receiver operating characteristic (ROC) analysis, Brier score, calibration curve, and decision curve analysis (DCA). The areas under the ROC curve (AUCs) were compared using the DeLong test. A two-tailed P < 0.05 was considered signiﬁcantly different.

Results: The DLRN exhibited satisfactory discrimination between type I and type II EOC with the AUC of 0.888 (95% confidence interval [CI] 0.810, 0.966) and 0.866 (95% CI 0.786, 0.946) in the internal and external validation sets, respectively. These AUCs significantly exceeded those of the clinical model (P = 0.013 and 0.043, in the internal and external validation sets, respectively). The DLRN demonstrated optimal classification accuracy and clinical application value, according to Brier scores, calibration curves, and DCA.

Conclusion: A T2-weighted MRI-based DLRN showed promising potential in differentiating between type I and type II EOC, which could offer assistance in clinical decision-making.

Keywords: Deep learning; Epithelial ovarian cancer; Magnetic resonance imaging; Nomogram; Radiomics.