Loufei Guo, Shuaitong Zhang, Hongbo Chen, Yifu Li, Yang Liu, Wancheng Liu, Qiang Wang, Zhenchao Tang, Ping Jiang, Junjie Wang
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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.</p>","PeriodicalId":29931,"journal":{"name":"Visual Computing for Industry Biomedicine and Art","volume":"8 1","pages":"23"},"PeriodicalIF":6.0000,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of artificial intelligence in assisting treatment of gynecologic tumors: a systematic review.\",\"authors\":\"Loufei Guo, Shuaitong Zhang, Hongbo Chen, Yifu Li, Yang Liu, Wancheng Liu, Qiang Wang, Zhenchao Tang, Ping Jiang, Junjie Wang\",\"doi\":\"10.1186/s42492-025-00201-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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. 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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. 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引用次数: 0
摘要
近年来,人工智能(AI)在医学图像分析中的应用在妇科肿瘤的临床研究中越来越受到重视。本研究介绍了人工智能在辅助妇科肿瘤治疗中的应用进展和前景。Web of Science数据库筛选了2023年8月之前发表的文章。关键词是“人工智能”、“深度学习”、“机器学习”、“放射组学”、“放疗”、“放化疗”、“新辅助治疗”、“免疫治疗”、“妇科恶性肿瘤”、“宫颈癌”、“宫颈癌”、“卵巢癌”、“子宫内膜癌”、“外阴癌”、“阴道癌”。纳入了与人工智能辅助妇科癌症治疗相关的研究文章。根据检索策略共检索到317篇文献,应用纳入和排除标准筛选出133篇文献,其中宫颈癌114篇、子宫内膜癌10篇、卵巢癌9篇。纳入的研究中,44项(33%)研究集中于预后预测,24项(18%)研究集中于治疗反应预测,13项(10%)研究集中于不良事件预测,5项(4%)研究集中于剂量分布预测,47项(35%)研究集中于靶体积描绘。使用深度学习方法进行靶体积描绘和剂量预测。对于治疗反应、预后和不良事件的预测,57项研究(70%)使用常规放射组学方法,13项(16%)使用深度学习方法,8项(10%)使用与空间相关的非常规放射组学方法,3项(4%)使用与时间相关的非常规放射组学方法。在宫颈癌和子宫内膜癌中,目标预测主要包括治疗反应、总生存期、复发、放疗毒性、淋巴结转移和剂量分布。对于卵巢癌,目标预测包括铂敏感性和术后并发症。大多数研究为单中心、回顾性和小规模研究;101项研究(76%)为单中心数据,125项研究(94%)为回顾性研究,127项研究(95%)纳入病例少于500例。人工智能在妇科肿瘤辅助治疗中的应用仍然有限。虽然人工智能在预测妇科肿瘤的反应、预后、不良事件和剂量分布方面的结果是优越的,但很明显,没有来自多个中心的大量数据验证这些任务。
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.