Advances in artificial intelligence for the diagnosis and treatment of ovarian cancer (Review)

Abstract

Artificial intelligence (AI) has emerged as a crucial technique for extracting high‑throughput information from various sources, including medical images, pathological images, and genomics, transcriptomics, proteomics and metabolomics data. AI has been widely used in the field of diagnosis, for the differentiation of benign and malignant ovarian cancer (OC), and for prognostic assessment, with favorable results. Notably, AI‑based radiomics has proven to be a non‑invasive, convenient and economical approach, making it an essential asset in a gynecological setting. The present study reviews the application of AI in the diagnosis, differentiation and prognostic assessment of OC. It is suggested that AI‑based multi‑omics studies have the potential to improve the diagnostic and prognostic predictive ability in patients with OC, thereby facilitating the realization of precision medicine.

Keywords: artificial intelligence; ovarian cancer; radiomics; whole‑slide imaging.

Figure 1.

Flow chart of radiomics. The process of radiomics includes image acquisition, ROI segmentation, feature extraction, feature screening and model building. ROC and calibration curves are often used to evaluate the model performance in the process of model building. ROI, region of interest; ROC, receiver operating characteristic. The statistical images (feature screening and model building) are from Dr Yanli Wang (unpublished data).

Figure 2.

Relationship of the AI algorithm. The internal inclusion relationship of the AI algorithm, and the paratactic relationship with radiomics. AI, artificial intelligence; ML, machine learning; DL, deep learning; CNN, convolutional neural network.

Figure 3.

AI in omics. AI is widely used in radiomics, pathomics, genomics, transcriptomics, proteomics and metabolomics. These omics can be used in a number of clinical applications, including differential diagnosis, pathological classification, predicting gene state, tumor metastasis, prognosis and drug response.