Diagnosis of Ovarian Neoplasms Using Nomogram in Combination With Ultrasound Image-Based Radiomics Signature and Clinical Factors - Qi, Lisha; Chen, Dandan; Li, Chunxiang; Li, Jinghan; Wang, Jingyi; Zhang, Chao; Li, Xiaofeng; Qiao, Ge; Wu, Haixiao; Zhang, Xiaofang; Ma, Wenjuan (Frontiers in Genetics, 2021)
Objectives To establish and validate a nomogram integrating radiomics signatures from ultrasound and clinical factors to discriminate between benign, borderline, and malignant serous ovarian tumors. Materials and methods In this study, a total of 279 pathology-confirmed serous ovarian tumors collected from 265 patients between March 2013 and December 2016 were used. The training cohort was generated by randomly selecting 70% of each of three types (benign, borderline, and malignant) of tumors, while the remaining 30% was included in the validation cohort. From the transabdominal ultrasound scanning of ovarian tumors, radiomics features were extracted, and a score was calculated. The ability of radiomics to differentiate between types of ovarian tumors was tested by comparing benign vs. borderline and malignant (task 1) and borderline vs. malignant (task 2). Results were compared with the diagnostic performance and subjective assessment by junior and senior sonographers. Finally, a clinical-feature alone model and combined clinical-radiomics (CCR) model were built using predictive nomograms for two tasks. Receiver operating characteristic (ROC) analysis, calibration curve, and decision curve analysis (DCA) were performed to evaluate the model performance. Results The US-based radiomics models perform satisfactorily in both tasks, showing higher accuracy in task 2 by successfully discriminating borderline and malignant tumors compared to the evaluations by senior sonographers (AUC=0.789 for seniors and 0.877 for radiomics models in task 1; AUC=0.612 for senior and 0.839 for radiomics model in task 2). We show that the CCR model, comprising CA125 level, lesion location, ascites, and radiomics signatures, performed the best (AUC = 0.937, 95%CI 0.905–0.969 in task 1, AUC = 0.924, 95%CI 0.876–0.971 in task 2) in the training as well as in validation cohorts (AUC = 0.914, 95%CI 0.851–0.976 in task 1, AUC = 0.890, 95%CI 0.794–0.987 in task 2). The calibration curve and DCA analysis of the CCR model more accurately predicted the classification of tumors than clinical features alone. Conclusion This study integrates novel radiomics signatures from ultrasound and clinical factors to create a nomogram to provide preoperative diagnostic information for differentiating between three types of tumors, thereby reducing unnecessary and risky biopsies and surgeries.