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Deep Learning Radiomics Nomogram Based on MRI for Differentiating between Borderline Ovarian Tumors and Stage I
Xinyi Wang1, Tao Quan2, Xiao Chu1
1Department of Radiology, the Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China (X.W., X.C., Y.C., S.C., M.W.).
Rationale And Objectives:
To develop and validate a deep learning radiomics nomogram (DLRN) based on T2-weighted MRI to distinguish between borderline ovarian tumors (BOTs) and stage I epithelial ovarian cancer (EOC) preoperatively.
Materials And Methods:
This retrospective multicenter study enrolled 279 patients from three centers, divided into a training set (n = 207) and an external test set (n = 72). The intra- and peritumoral radiomics analysis was employed to develop a combined radiomics model. A deep learning model was constructed based on the largest orthogonal slices of the tumor volume, and a clinical model was constructed using independent clinical predictors. The DLRN was then constructed by integrating deep learning, intra- and peritumoral radiomics, and clinical predictors. For comparison, an original radiomics model based solely on tumor volume (excluding the peritumoral area) was also constructed. All models were validated through 10-fold cross-validation and external testing, and their predictive performance was evaluated by the area under the receiver operating characteristic curve (AUC).
Results:
The DLRN demonstrated superior performance across the 10-fold cross-validation, with the highest AUC of 0.825±0.082. On the external test set, the DLRN significantly outperformed the clinical model and the original radiomics model (AUC = 0.819 vs. 0.708 and 0.670, P = 0.047 and 0.015, respectively). Furthermore, the combined radiomics model performed significantly better than the original radiomics model (AUC = 0.778 vs. 0.670, P = 0.043).
Conclusion:
The DLRN exhibited promising performance in distinguishing BOTs from stage I EOC preoperatively, thus potentially assisting clinical decision-making.
