Dana-Farber Cancer Institute, Boston, MA.
Memorial-Sloan Kettering Cancer Center, New York, NY.
JCO Precis Oncol. 2024 Mar;8:e2300507. doi: 10.1200/PO.23.00507.
Precision oncology clinical trials often struggle to accrue, partly because it is difficult to find potentially eligible patients at moments when they need new treatment. We piloted deployment of artificial intelligence tools to identify such patients at a large academic cancer center.
Neural networks that process radiology reports to identify patients likely to start new systemic therapy were applied prospectively for patients with solid tumors that had undergone next-generation sequencing at our center. Model output was linked to the MatchMiner tool, which matches patients to trials using tumor genomics. Reports listing genomically matched patients, sorted by probability of treatment change, were provided weekly to an oncology nurse navigator (ONN) coordinating recruitment to nine early-phase trials. The ONN contacted treating oncologists when patients likely to change treatment appeared potentially trial-eligible.
Within weekly reports to the ONN, 60,199 patient-trial matches were generated for 2,150 patients on the basis of genomics alone. Of these, 3,168 patient-trial matches (5%) corresponding to 525 patients were flagged for ONN review by our model, representing a 95% reduction in review compared with manual review of all patient-trial matches weekly. After ONN review for potential eligibility, treating oncologists for 74 patients were contacted. Common reasons for not contacting treating oncologists included cases where patients had already decided to continue current treatment (21%); the trial had no slots (14%); or the patient was ineligible on ONN review (12%). Of 74 patients whose oncologists were contacted, 10 (14%) had a consult regarding a trial and five (7%) enrolled.
This approach facilitated identification of potential patients for clinical trials in real time, but further work to improve accrual must address the many other barriers to trial enrollment in precision oncology research.
精准肿瘤学临床试验常常难以入组，部分原因是很难在患者需要新治疗时及时找到潜在合格的患者。我们在一家大型学术癌症中心试用了人工智能工具来识别此类患者。
应用处理放射学报告以识别可能开始新系统治疗的患者的神经网络，前瞻性地对在我们中心进行下一代测序的实体瘤患者进行分析。模型输出与 MatchMiner 工具相关联，该工具使用肿瘤基因组学来匹配患者和试验。每周向负责协调 9 项早期试验入组的肿瘤学护士导航员（ONN）提供一份列出基于基因组匹配的患者、按治疗变化可能性排序的报告。当可能改变治疗的患者似乎有资格参加试验时，ONN 会联系主治肿瘤医生。
根据基因组学，在每周提供给 ONN 的报告中，为 2150 名患者生成了 60199 个患者-试验匹配。其中，有 3168 个患者-试验匹配（5%）对应 525 名患者，通过我们的模型进行了 ONN 审查，与每周手动审查所有患者-试验匹配相比，审查减少了 95%。在 ONN 对潜在资格进行审查后，联系了 74 名患者的主治肿瘤医生。未联系主治肿瘤医生的常见原因包括患者已决定继续当前治疗（21%）、试验无名额（14%）或患者在 ONN 审查中不具备资格（12%）。在联系主治肿瘤医生的 74 名患者中，有 10 名（14%）进行了关于试验的咨询，5 名（7%）入组。
这种方法实时促进了临床试验潜在患者的识别，但要提高入组率，还必须解决精准肿瘤学研究中试验入组的许多其他障碍。
