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Updated: May 26, 2026
Strategy for Biobanking of Ovarian Cancer Organoids: Addressing the Interpatient Heterogeneity across Histological Subtypes and Disease Stages
Published on: February 23, 2024
Artificial intelligence in ovarian cancer: advancing in precision diagnosis and clinical management
Mingjun Shao1,2, Tong Wang3, Limei Ji1,2
1Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, Zhejiang, China.
Background:
Ovarian cancer remains one of the deadliest gynecologic malignancies. Poor outcomes largely reflect late diagnosis, marked inter- and intratumoral heterogeneity, and variable treatment response.
Methods:
This review summarizes recent advances in artificial intelligence (AI) for ovarian cancer research and clinical care, focusing on imagine-based radiology, digital pathology; longitudinal clinical data/Electronic Health Record (EHR), and spatial-temporal multi-omics.
Results:
AI approaches have been applied to tumor detection and classification, prognostic risk stratification, and treatment response prediction. Multimodal models that integrate imaging, molecular profiling, and clinical data enable more refined characterization of tumor heterogeneity and the tumor microenvironment, supporting improved diagnosis, risk assessment, and individualized management.
