用校准预测提升放疗质量保证效率，减少测量工作量。
Training-Aware Risk Control for Intensity Modulated Radiation Therapies Quality Assurance with Conformal Prediction
- 结合训练与风险控制的校准预测方法，动态筛选放疗计划。
- 敏感性高、特异性好，99%以上计划可免测量，不扩大置信区间。
- 适合临床放疗质控团队，提升流程效率，保障患者安全。
剂量调制放射治疗（IMRT）的质量保证（QA）对癌症治疗安全至关重要。当前测量型IMRT QA失败率已低于1%，但该流程耗时耗力，影响患者治疗进度。本研究探索校准预测方法在计划分诊中的应用，提出一种新型训练感知的校准风险控制方法，融合伽马通过率决策阈值及临床评估中使用的风险函数，构建风险控制框架。实验结果表明，该方法具备高灵敏度与高特异性，能显著减少需进行物理测量的计划数量，同时避免产生过大的置信区间。结果验证了校准预测在提升IMRT QA效率、减轻工作负荷方面的有效性与实用性。
原文摘要 · Abstract (English)
Measurement quality assurance (QA) practices play a key role in the safe use of Intensity Modulated Radiation Therapies (IMRT) for cancer treatment. These practices have reduced measurement-based IMRT QA failure below 1%. However, these practices are time and labor intensive which can lead to delays in patient care. In this study, we examine how conformal prediction methodologies can be used to robustly triage plans. We propose a new training-aware conformal risk control method by combining the benefit of conformal risk control and conformal training. We incorporate the decision making thresholds based on the gamma passing rate, along with the risk functions used in clinical evaluation, into the design of the risk control framework. Our method achieves high sensitivity and specificity and significantly reduces the number of plans needing measurement without generating a huge confidence interval. Our results demonstrate the validity and applicability of conformal prediction methods for improving efficiency and reducing the workload of the IMRT QA process.
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