Artificial intelligence for precision management of epithelial ovarian cancer: a comprehensive review

TL;DR

This paper reviews how artificial intelligence is being used to improve the treatment and prognosis of epithelial ovarian cancer.

Contribution

The paper focuses on recent AI applications in treatment and prognosis, areas less explored compared to diagnosis.

Findings

AI models help predict complete cytoreduction and chemotherapy effectiveness in EOC.

AI technologies using pathology and radiomics data improve prognosis assessment.

The review highlights opportunities for expanding AI use in EOC treatment strategies.

Abstract

Epithelial ovarian cancer (EOC) has a high rate of incidence and mortality, seriously threatening women’s health. Artificial intelligence (AI) possesses functions such as image recognition, data mining and pattern recognition, which can solve problems that traditional statistical methods cannot handle, such as large amounts of data and data missing. It has achieved breakthrough progress in the fields of risk prediction, diagnosis, treatment and response assessment of malignant tumors. Most AI technologies are mainly applied in the preoperative diagnosis of EOC, as well as in imaging and pathological genomics. However, their application in treatment and prognosis assessment studies is relatively limited. This article reviews the AI application in the treatment and prognosis assessment of EOC in recent years, including the establishment of prediction models for complete cytoreduction (R0 resection), the prediction of chemotherapy and targeted drug efficacy, and the application of different AI technologies based on pathology, radiomics, and clinical data for the prognosis assessment of EOC, with the aim of providing more ideas for the application of AI in EOC.

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