Li Guiyuan, Chen Xinyuan, Ding Jialin, Shen Linyi, Li Mengyang, Yi Junlin, Dai Jianrong
National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
Cancers (Basel). 2025 Aug 11;17(16):2620. doi: 10.3390/cancers17162620.
: 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. : 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. : 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, > 0.05. There was no statistically significant difference. For photon, the NTCP of dysphagia performed well, > 0.05. For xerostomia < 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. : 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. : This study was a retrospective study, so it was exempt from registration.
鼻咽癌患者放疗技术的决策需要比较使用计划软件生成的光子和质子计划，这需要时间和专业知识。我们开发了一种全自动决策工具，用于选择适合质子治疗的患者，该工具仅使用患者CT图像数据预测质子治疗（XT）和光子治疗（PT）的剂量分布，使用预测的关键器官平均剂量预测口干和吞咽困难的概率，并根据荷兰国家质子治疗适应症协议（NIPP）做出决策，以选择可能从质子治疗中受益的患者。
本研究使用了中国医学科学院肿瘤医院治疗的48例鼻咽癌患者。我们为每位患者手动生成了一个光子计划和一个质子计划。基于此剂量分布，使用深度学习（DL）模型训练光子和质子剂量预测模型。我们使用NIPP模型测量2级和3级口干、2级和3级吞咽困难，并根据该协议给出的阈值做出决策。
光子组和质子组的预测剂量与手动计划（MP）的剂量相当。光子和质子计划中每个危及器官的平均绝对误差（MAE）不超过5%，表明剂量预测模型性能良好。对于质子，口干和吞咽困难的正常组织并发症概率（NTCP）表现良好，>0.05。无统计学显著差异。对于光子，吞咽困难的NTCP表现良好，>0.05。对于口干，<0.05，但绝对偏差为0.85%和0.75%，对预测结果影响不大。在48例患者的决策中，3例错误，正确率为93.8%。操作特征曲线（ROC）的曲线下面积（AUC）为0.86，表明本研究中的决策工具性能良好。
基于DL和NTCP模型的决策工具可以准确选择将从质子治疗中受益的鼻咽癌患者。减少了生成比较计划的时间，提高了医生的诊断效率，并且该工具可以与没有质子专业知识的中心共享。
本研究为回顾性研究，因此无需注册。
