Application of artificial intelligence in assisting treatment of gynecologic tumors: a systematic review

In recent years, the application of artificial intelligence (AI) in medical image analysis has drawn increasing attention in clinical studies of gynecologic tumors. This study presents the development and prospects of AI applications to assist in the treatment of gynecological oncology. The Web of Science database was screened for articles published until August 2023. “artificial intelligence,” “deep learning,” “machine learning,” “radiomics,” “radiotherapy,” “chemoradiotherapy,” “neoadjuvant therapy,” “immunotherapy,” “gynecological malignancy,” “cervical carcinoma,” “cervical cancer,” “ovarian cancer,” “endometrial cancer,” “vulvar cancer,” “Vaginal cancer” were used as keywords. Research articles related to AI-assisted treatment of gynecological cancers were included. A total of 317 articles were retrieved based on the search strategy, and 133 were selected by applying the inclusion and exclusion criteria, including 114 on cervical cancer, 10 on endometrial cancer, and 9 on ovarian cancer. Among the included studies, 44 (33%) focused on prognosis prediction, 24 (18%) on treatment response prediction, 13 (10%) on adverse event prediction, five (4%) on dose distribution prediction, and 47 (35%) on target volume delineation. Target volume delineation and dose prediction were performed using deep Learning methods. For the prediction of treatment response, prognosis, and adverse events, 57 studies (70%) used conventional radiomics methods, 13 (16%) used deep Learning methods, 8 (10%) used spatial-related unconventional radiomics methods, and 3 (4%) used temporal-related unconventional radiomics methods. In cervical and endometrial cancers, target prediction mostly included treatment response, overall survival, recurrence, toxicity undergoing radiotherapy, lymph node metastasis, and dose distribution. For ovarian cancer, the target prediction included platinum sensitivity and postoperative complications. The majority of the studies were single-center, retrospective, and small-scale; 101 studies (76%) had single-center data, 125 studies (94%) were retrospective, and 127 studies (95%) included Less than 500 cases. The application of AI in assisting treatment in gynecological oncology remains limited. Although the results of AI in predicting the response, prognosis, adverse events, and dose distribution in gynecological oncology are superior, it is evident that there is no validation of substantial data from multiple centers for these tasks.