Importance Substantial heterogeneity exists in treatment recommendations across molecular tumor boards (MTBs), especially for biomarkers with low evidence levels; therefore, the learning program is essential. Objective To determine whether a learning program sharing treatment recommendations for biomarkers with low evidence levels contributes to the standardization of MTBs and to investigate the efficacy of an artificial intelligence (AI)-based annotation system. Design, Setting, and Participants This prospective quality improvement study used 50 simulated cases to assess concordance of treatment recommendations between a central committee and participants. Forty-seven participants applied from April 7 to May 13, 2021. Fifty simulated cases were randomly divided into prelearning and postlearning evaluation groups to assess similar concordance based on previous investigations. Participants included MTBs at hub hospitals, treating physicians at core hospitals, and AI systems. Each participant made treatment recommendations for each prelearning case from registration to June 30, 2021; participated in the learning program on July 18, 2021; and made treatment recommendations for each postlearning case from August 3 to September 30, 2021. Data were analyzed from September 2 to December 10, 2021. Exposures The learning program shared the methodology of making appropriate treatment recommendations, especially for biomarkers with low evidence levels. Main Outcomes and Measures The primary end point was the proportion of MTBs that met prespecified accreditation criteria for postlearning evaluations (approximately 90% concordance with high evidence levels and approximately 40% with low evidence levels). Key secondary end points were chronological enhancements in the concordance of treatment recommendations on postlearning evaluations from prelearning evaluations. Concordance of treatment recommendations by an AI system was an exploratory end point. Results Of the 47 participants who applied, 42 were eligible. The accreditation rate of the MTBs was 55.6% (95% CI, 35.3%-74.5%; P < .001). Concordance in MTBs increased from 58.7% (95% CI, 52.8%-64.4%) to 67.9% (95% CI, 61.0%-74.1%) (odds ratio, 1.40 [95% CI, 1.06-1.86]; P = .02). In postlearning evaluations, the concordance of treatment recommendations by the AI system was significantly higher than that of MTBs (88.0% [95% CI, 68.7%-96.1%]; P = .03). Conclusions and Relevance The findings of this quality improvement study suggest that use of a learning program improved the concordance of treatment recommendations provided by MTBs to central ones. Treatment recommendations made by an AI system showed higher concordance than that for MTBs, indicating the potential clinical utility of the AI system.

中文翻译：

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分子肿瘤委员会和人工智能提出治疗建议的学习计划。

重要性 分子肿瘤委员会 (MTB) 的治疗建议存在很大的异质性，特别是对于证据水平较低的生物标志物；因此，学习计划至关重要。目的 确定共享低证据水平生物标志物治疗建议的学习计划是否有助于 MTB 的标准化，并研究基于人工智能 (AI) 的注释系统的有效性。设计、设置和参与者 这项前瞻性质量改进研究使用 50 个模拟病例来评估中央委员会和参与者之间治疗建议的一致性。2021年4月7日至5月13日，47名参与者提出申请。50个模拟案例被随机分为学前评估组和学后评估组，以根据之前的调查评估类似的一致性。参与者包括中心医院的 MTB、核心医院的治疗医生以及人工智能系统。每位参与者从注册到2021年6月30日期间对每个学前病例提出治疗建议；2021年7月18日参加学习计划；并对2021年8月3日至9月30日的每个学习后病例提出治疗建议。对2021年9月2日至12月10日的数据进行分析。 暴露学习计划分享了提出适当治疗建议的方法，特别是针对证据水平较低的生物标志物。主要成果和措施 主要终点是符合预先设定的学习后评估认证标准的 MTB 比例（高证据水平的一致性约为 90%，低证据水平的一致性约为 40%）。关键的次要终点是学习后评估的治疗建议与学习前评估的一致性的时间顺序增强。人工智能系统对治疗建议的一致性是一个探索性终点。结果 在 47 名申请者中，42 人符合资格。MTB 的认证率为 55.6%（95% CI，35.3%-74.5%；P < .001）。MTB 的一致性从 58.7% (95% CI, 52.8%-64.4%) 增加到 67.9% (95% CI, 61.0%-74.1%)（比值比，1.40 [95% CI, 1.06-1.86]；P = . 02）。在学习后评估中，AI 系统治疗建议的一致性显着高于 MTB（88.0% [95% CI, 68.7%-96.1%]；P = .03）。结论和相关性 这项质量改进研究的结果表明，学习计划的使用提高了 MTB 提供的治疗建议与中心建议的一致性。人工智能系统提出的治疗建议比 MTB 的一致性更高，表明人工智能系统具有潜在的临床效用。