Petrila Octavia, Stefan Anca-Elena, Gafitanu Dumitru, Scripcariu Viorel, Nistor Ionut
Faculty of Medicine, University of Medicine and Pharmacy "Grigore T. Popa", 700115 Iasi, Romania.
Department of Radiology, "Sfantul Spiridon" Hospital, 700111 Iasi, Romania.
Diagnostics (Basel). 2023 Aug 3;13(15):2592. doi: 10.3390/diagnostics13152592.
(1) Objective: Artificial intelligence (AI) has become an important tool in medicine in diagnosis, prognosis, and treatment evaluation, and its role will increase over time, along with the improvement and validation of AI models. We evaluated the applicability of AI in predicting the depth of myometrial invasion in MRI studies in women with endometrial cancer. (2) Methods: A systematic search was conducted in PubMed, SCOPUS, Embase, and clinicaltrials.gov databases for research papers from inception to May 2023. As keywords, we used: "endometrial cancer artificial intelligence", "endometrial cancer AI", "endometrial cancer MRI artificial intelligence", "endometrial cancer machine learning", and "endometrial cancer machine learning MRI". We excluded studies that did not evaluate myometrial invasion. (3) Results: Of 1651 screened records, eight were eligible. The size of the dataset was between 50 and 530 participants among the studies. We evaluated the models by accuracy scores, area under the curve, and sensitivity/specificity. A quantitative analysis was not appropriate for this study due to the high heterogeneity among studies. (4) Conclusions: High accuracy, sensitivity, and specificity rates were obtained among studies using different AI systems. Overall, the existing studies suggest that they have the potential to improve the accuracy and efficiency of the myometrial invasion evaluation of MRI images in endometrial cancer patients.
(1) 目的：人工智能（AI）已成为医学诊断、预后和治疗评估中的重要工具，随着AI模型的改进和验证，其作用将与日俱增。我们评估了AI在预测子宫内膜癌女性MRI研究中肌层浸润深度方面的适用性。(2) 方法：在PubMed、SCOPUS、Embase和clinicaltrials.gov数据库中进行了系统检索，以查找从数据库建立至2023年5月的研究论文。作为关键词，我们使用了：“子宫内膜癌人工智能”、“子宫内膜癌AI”、“子宫内膜癌MRI人工智能”、“子宫内膜癌机器学习”以及“子宫内膜癌机器学习MRI”。我们排除了未评估肌层浸润的研究。(3) 结果：在筛选出的1651条记录中，有8条符合要求。各研究中数据集的规模在50至530名参与者之间。我们通过准确率得分、曲线下面积以及敏感度/特异度来评估这些模型。由于各研究之间存在高度异质性，因此本研究不适合进行定量分析。(4) 结论：在使用不同AI系统的研究中均获得了较高的准确率、敏感度和特异度。总体而言，现有研究表明它们有潜力提高子宫内膜癌患者MRI图像肌层浸润评估的准确性和效率。
