Princess Margaret Cancer Centre, University Health Network, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
London Regional Cancer Program, London Health Sciences Centre, Schulich School of Medicine and Dentistry, University of Western Ontario, London, ON, Canada.
J Natl Cancer Inst. 2023 Apr 11;115(4):365-374. doi: 10.1093/jnci/djad013.
The aim of this study is to provide a comprehensive understanding of the current landscape of artificial intelligence (AI) for cancer clinical trial enrollment and its predictive accuracy in identifying eligible patients for inclusion in such trials.
Databases of PubMed, Embase, and Cochrane CENTRAL were searched until June 2022. Articles were included if they reported on AI actively being used in the clinical trial enrollment process. Narrative synthesis was conducted among all extracted data: accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. For studies where the 2x2 contingency table could be calculated or supplied by authors, a meta-analysis to calculate summary statistics was conducted using the hierarchical summary receiver operating characteristics curve model.
Ten articles reporting on more than 50 000 patients in 19 datasets were included. Accuracy, sensitivity, and specificity exceeded 80% in all but 1 dataset. Positive predictive value exceeded 80% in 5 of 17 datasets. Negative predictive value exceeded 80% in all datasets. Summary sensitivity was 90.5% (95% confidence interval [CI] = 70.9% to 97.4%); summary specificity was 99.3% (95% CI = 81.8% to 99.9%).
AI demonstrated comparable, if not superior, performance to manual screening for patient enrollment into cancer clinical trials. As well, AI is highly efficient, requiring less time and human resources to screen patients. AI should be further investigated and implemented for patient recruitment into cancer clinical trials. Future research should validate the use of AI for clinical trials enrollment in less resource-rich regions and ensure broad inclusion for generalizability to all sexes, ages, and ethnicities.
本研究旨在全面了解人工智能（AI）在癌症临床试验入组中的应用现状及其在识别合格患者纳入此类试验中的预测准确性。
检索 PubMed、Embase 和 Cochrane CENTRAL 数据库，截至 2022 年 6 月。如果文章报告 AI 正在积极用于临床试验入组过程，则将其纳入。对所有提取的数据进行叙述性综合：准确性、敏感度、特异性、阳性预测值和阴性预测值。对于可以计算或由作者提供 2x2 四格表的研究，使用分层总结接收者操作特征曲线模型对汇总统计数据进行荟萃分析。
纳入了 10 篇报告超过 50000 例患者的 19 个数据集的文章。除 1 个数据集外，所有数据集的准确性、敏感度和特异性均超过 80%。17 个数据集中有 5 个数据集的阳性预测值超过 80%。所有数据集的阴性预测值均超过 80%。汇总敏感度为 90.5%（95%置信区间 [CI] = 70.9%至 97.4%）；汇总特异性为 99.3%（95% CI = 81.8%至 99.9%）。
AI 在癌症临床试验患者入组方面的表现与手动筛查相当，如果不是更好的话。此外，AI 效率很高，筛选患者所需的时间和人力资源较少。应该进一步研究和实施 AI 用于癌症临床试验的患者招募。未来的研究应该验证 AI 在资源较少的地区用于临床试验入组的使用，并确保广泛纳入所有性别、年龄和种族，以实现普遍性。
