A Multimodal Model Based on Transvaginal Ultrasound-Based Radiomics to Predict the Risk of Peritoneal Metastasis in Ovarian Cancer: A Multicenter Study

Yi Zhou; Yayang Duan; Qiwei Zhu; Siyao Li; Chaoxue Zhang

doi:10.1016/j.acra.2024.12.074

A Multimodal Model Based on Transvaginal Ultrasound-Based Radiomics to Predict the Risk of Peritoneal Metastasis in Ovarian Cancer: A Multicenter Study

Acad Radiol. 2025 Jan 14:S1076-6332(24)01062-6. doi: 10.1016/j.acra.2024.12.074. Online ahead of print.

Authors

Yi Zhou¹, Yayang Duan¹, Qiwei Zhu¹, Siyao Li², Chaoxue Zhang³

Affiliations

¹ Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, Anhui, China (Y.Z., Y.D., Q.Z., C.Z.).
² Department of Ultrasound, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, Anhui, China (S.L.).
³ Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, Anhui, China (Y.Z., Y.D., Q.Z., C.Z.). Electronic address: zcxtgzs@163.com.

PMID: 39814662
DOI: 10.1016/j.acra.2024.12.074

Abstract

Rationale and objectives: This study aimed to develop a predictive model for peritoneal metastasis (PM) in ovarian cancer using a combination radiomics and clinical biomarkers to improve diagnostic accuracy.

Patients and methods: This retrospective cohort study of 619 ovarian cancer patients involved demographic data, radiomics, O-RADS standardized description, clinical biomarkers, and histological findings. Radiomics features were extracted using 3D Slicer and Pyradiomics, with selective feature extraction using Least Absolute Shrinkage and Selection Operator regression. Model development and validation were carried out using logistic regression and machine learning methods RESULTS: Interobserver agreement was high for radiomics features, with 1049 features initially extracted and 7 features selected through regression analysis. Multi-modal information such as Ascites, Fallopian tube invasion, Greatest diameter, HE4 and D-dimer levels were significant predictors of PM. The developed radiomics nomogram demonstrated strong discriminatory power, with AUC values of 0.912, 0.883, and 0.831 in the training, internal test, and external test sets respectively. The nomogram displayed superior diagnostic performance compared to single-modality models.

Conclusion: The integration of multimodal information in a predictive model for PM in ovarian cancer shows promise for enhancing diagnostic accuracy and guiding personalized treatment. This multi-modal approach offers a potential strategy for improving patient outcomes in ovarian cancer management with PM.

Keywords: Oncology; Ovarian cancer; Peritoneal metastasis; Radiomics; Ultrasound.