Application of multimodal integration to develop preoperative diagnostic models for borderline and malignant ovarian tumors.

ABSTRACT: Malignant ovarian tumors (MOTs) and borderline ovarian tumors (BOTs) differ in treatment strategies and prognosis. However, accurate preoperative diagnosis remains challenging, and improving diagnostic accuracy is crucial. We developed and validated a system using artificial intelligence (AI) to integrate machine learning (ML) models based on blood test data and deep learning (DL) models based on magnetic resonance imaging (MRI) findings to distinguish between MOT and BOT. We analyzed 78 patients with malignant serous ovarian tumors and 31 with borderline serous ovarian tumors treated at our institution. A classification model was developed using ML for blood test data, and a DL model was constructed using MRI data. By integrating these models, we developed three fusion models as multimoda

SUBMITTER: Kunishima A 

PROVIDER: S-EPMC12550037 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature