Introduction: Decision-making regarding radiotherapy techniques for patients with nasopharyngeal cancer requires a comparison of photon and proton plans generated using planning software, which requires time and expertise. We developed a fully automated decision tool to select patients for proton therapy that predicts proton therapy (XT) and photon therapy (PT) dose distributions using only patient CT image data, predicts xerostomia and dysphagia probability using predicted critical organ mean doses, and makes decisions based on the Netherlands’ National Indication Protocol Proton therapy (NIPP) to select patients likely to benefit from proton therapy.Methods: This study used 48 nasopharyngeal patients treated at the Cancer Hospital of the Chinese Academy of Medical Sciences. We manually generated a photon plan and a proton plan for each patient. Based on this dose distribution, photon and proton dose prediction models were trained using deep learning (DL) models. We used the NIPP model to measure xerostomia levels 2 and 3, dysphagia levels 2 and 3, and decisions were made according to the thresholds given by this protocol.Results: The predicted doses for both photon and proton groups were comparable to those for manual plan (MP). The Mean Absolute Error (MAE) for each organ at risk in the photon and proton plans did not exceed 5% and showed a good performance of the dose prediction model. For proton, the normal tissue complication probability (NTCP) of xerostomia and dysphagia performed well,p> 0.05. There was no statistically significant difference. For photon, the NTCP of dysphagia performed well,p> 0.05. For xerostomiap< 0.05 but the absolute deviation was 0.85% and 0.75%, which would not have a great impact on the prediction result. Among the 48 patients’ decisions, 3 were wrong, and the correct rate was 93.8%. The area under curve (AUC) of operating characteristic curve (ROC) was 0.86, showing the good performance of the decision-making tool in this study.Conclusions: The decision tool based on DL and NTCP models can accurately select nasopharyngeal cancer patients who will benefit from proton therapy. The time spent generating comparison plans is reduced and the diagnostic efficiency of doctors is improved, and the tool can be shared with centers that do not have proton expertise.Trial registration: This study was a retrospective study, so it was exempt from registration.
引言：鼻咽癌患者放疗技术的决策需要借助计划软件生成光子与质子计划的对比分析，这一过程既耗时又依赖专业经验。本研究开发了一套全自动决策工具，仅基于患者CT影像数据即可预测质子治疗（XT）与光子治疗（PT）的剂量分布，通过预测的关键器官平均剂量推算口干症与吞咽困难发生概率，并依据荷兰国家质子治疗适应证指南（NIPP）的标准，筛选可能从质子治疗中获益的患者。 方法：本研究纳入中国医学科学院肿瘤医院收治的48例鼻咽癌患者。对每位患者分别手工制定光子计划与质子计划。基于实际剂量分布数据，采用深度学习模型训练光子与质子剂量预测模型。运用NIPP模型评估2-3级口干症与2-3级吞咽困难发生率，并依据该指南设定的阈值进行临床决策。 结果：光子组与质子组的预测剂量与手工计划（MP）结果具有可比性。两组计划中各危及器官的平均绝对误差（MAE）均未超过5%，表明剂量预测模型性能良好。质子组的口干症与吞咽困难正常组织并发症概率（NTCP）预测效果理想（p>0.05），无统计学显著差异。光子组的吞咽困难NTCP预测效果良好（p>0.05）；口干症预测虽存在统计学差异（p<0.05），但绝对偏差仅为0.85%与0.75%，对预测结果影响有限。48例患者的决策中仅出现3例误判，正确率达93.8%。受试者工作特征曲线（ROC）下面积（AUC）为0.86，显示本决策工具具有良好性能。 结论：基于深度学习与NTCP模型的决策工具能准确筛选可从质子治疗中获益的鼻咽癌患者。该工具显著缩短了对比计划生成时间，提升了临床诊断效率，并可推广至缺乏质子专业资源的医疗中心。 试验注册：本研究为回顾性研究，故免予注册。
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