Department of Obstetrics and Gynecology, Tokyo Women's Medical University Medical Center East, Tokyo, Japan.
Department of Obstetrics and Gynecology, Tokyo Women's Medical University Medical Center East, Tokyo, Japan.
Artif Intell Med. 2021 Oct;120:102164. doi: 10.1016/j.artmed.2021.102164. Epub 2021 Sep 3.
Over the past years, the application of artificial intelligence (AI) in medicine has increased rapidly, especially in diagnostics, and in the near future, the role of AI in medicine will become progressively more important. In this study, we elucidated the state of AI research on gynecologic cancers.
A search was conducted in three databases-PubMed, Web of Science, and Scopus-for research papers dated between January 2010 and December 2020. As keywords, we used "artificial intelligence," "deep learning," "machine learning," and "neural network," combined with "cervical cancer," "endometrial cancer," "uterine cancer," and "ovarian cancer." We excluded genomic and molecular research, as well as automated pap-smear diagnoses and digital colposcopy.
Of 1632 articles, 71 were eligible, including 34 on cervical cancer, 13 on endometrial cancer, three on uterine sarcoma, and 21 on ovarian cancer. A total of 35 studies (49%) used imaging data and 36 studies (51%) used value-based data as the input data. Magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, cytology, and hysteroscopy data were used as imaging data, and the patients' backgrounds, blood examinations, tumor markers, and indices in pathological examination were used as value-based data. The targets of prediction were definitive diagnosis and prognostic outcome, including overall survival and lymph node metastasis. The size of the dataset was relatively small because 64 studies (90%) included less than 1000 cases, and the median size was 214 cases. The models were evaluated by accuracy scores, area under the receiver operating curve (AUC), and sensitivity/specificity. Owing to the heterogeneity, a quantitative synthesis was not appropriate in this review.
In gynecologic oncology, more studies have been conducted on cervical cancer than on ovarian and endometrial cancers. Prognoses were mainly used in the study of cervical cancer, whereas diagnoses were primarily used for studying ovarian cancer. The proficiency of the study design for endometrial cancer and uterine sarcoma was unclear because of the small number of studies conducted. The small size of the dataset and the lack of a dataset for external validation were indicated as the challenges of the studies.
在过去几年中，人工智能（AI）在医学中的应用迅速增加，尤其是在诊断方面，并且在不久的将来，AI 在医学中的作用将变得越来越重要。本研究旨在阐明妇科癌症领域 AI 研究的现状。
在 PubMed、Web of Science 和 Scopus 三个数据库中，对 2010 年 1 月至 2020 年 12 月期间发表的研究论文进行了检索。使用“人工智能”、“深度学习”、“机器学习”和“神经网络”作为关键词，结合“宫颈癌”、“子宫内膜癌”、“子宫癌”和“卵巢癌”。我们排除了基因组和分子研究，以及自动巴氏涂片诊断和数字阴道镜检查。
在 1632 篇文章中，有 71 篇符合条件，包括 34 篇宫颈癌、13 篇子宫内膜癌、3 篇子宫肉瘤和 21 篇卵巢癌。共有 35 项研究（49%）使用了影像学数据，36 项研究（51%）使用了基于价值的数据作为输入数据。磁共振成像（MRI）、计算机断层扫描（CT）、超声、细胞学和宫腔镜数据被用作影像学数据，而患者背景、血液检查、肿瘤标志物和病理检查中的指标则被用作基于价值的数据。预测的目标是明确诊断和预后结果，包括总生存率和淋巴结转移。由于数据集的规模相对较小，因为 64 项研究（90%）的病例数少于 1000 例，中位数为 214 例。模型的评估指标是准确率、接受者操作特征曲线下面积（AUC）和敏感性/特异性。由于存在异质性，因此在本综述中不适合进行定量综合分析。
在妇科肿瘤学中，宫颈癌的研究多于卵巢癌和子宫内膜癌。在宫颈癌的研究中，预后主要用于研究，而诊断主要用于研究卵巢癌。由于研究数量较少，子宫内膜癌和子宫肉瘤的研究设计的熟练程度尚不清楚。数据集的规模较小以及缺乏外部验证数据集被认为是研究的挑战。
