结合剂量学与影像组学形状特征的多模态机器学习模型用于预测鼻咽癌放疗后中重度口干症

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中文摘要

目的： 放射性口干症仍是接受放射治疗的鼻咽癌患者常见且使人衰弱的并发症，严重影响生活质量。准确预测口干症严重程度对于个体化治疗计划至关重要。本研究旨在开发并验证一种机器学习模型，该模型整合了计划计算机断层扫描图像中的剂量学参数与影像组学形状特征，以预测中重度口干症。
方法与方法： 在这项回顾性研究中，我们纳入了100例接受调强放射治疗的鼻咽癌患者。从计划靶区与左侧腮腺之间的重叠体积中提取影像组学形状特征。这些特征与标准剂量学参数（例如V20 Gy和V30 Gy）相结合。实施了严格的基于相关性的特征选择以处理多重共线性。开发了随机森林分类器以预测口干症分级（1级 vs ≥2级），并通过100次随机训练-测试拆分迭代严格评估其稳定性。使用受试者工作特征曲线下面积、准确率和混淆矩阵分析评估模型性能。
结果： 该模型在预测中重度口干症方面表现出中等性能且变异性适中，在100次随机训练-测试拆分中，平均受试者工作特征曲线下面积为0.78（标准差，0.09），准确率为0.71（标准差，0.09）。特征选择稳定性分析确定了6个持续重要的预测因子：“伸长率”、“最大二维直径切片”、“V20”、“平坦度”、“最小轴长度”和“主轴长度”。影像组学形状特征在最常被选择和最具影响力的参数中占主导地位。稳健的重要性量化进一步确定“V30”、“网格体积”和“球形度”为模型预测的首要贡献因素。
结论： 本研究提出了一种用于预测中重度口干症的稳健机器学习框架，表明影像组学形状特征对传统剂量学指标具有补充价值。这些特征的整合可能有助于更全面的毒性风险评估，并值得在临床决策背景下进一步研究。

英文摘要

PURPOSE: Radiation-induced xerostomia remains a frequent and debilitating complication in patients with nasopharyngeal carcinoma undergoing radiation therapy, severely impacting quality of life. Accurate prediction of xerostomia severity is crucial for personalized treatment planning. This study aimed to develop and validate a machine learning model that integrates dosimetric parameters with radiomic shape features from planning computed tomography images to predict moderate‑to‑severe xerostomia.
METHODS AND MATERIALS: In this retrospective study, we included 100 patients with nasopharyngeal carcinoma treated with intensity modulated radiation therapy. Radiomic shape features were extracted from the overlap volume between the planning target volume and the left parotid gland. These were combined with standard dosimetric parameters (eg, V20 Gy and V30 Gy). A rigorous correlation-based feature selection was implemented to address multicollinearity. A random forest classifier was developed to predict xerostomia grade (grade 1 vs ≥grade 2), and its stability was rigorously assessed through 100 iterations of random train-test splitting. Model performance was evaluated using the area under the receiver operating characteristic curve, accuracy, and confusion matrix analysis.
RESULTS: The model demonstrated moderate performance with modest variability in predicting moderate‑to‑severe xerostomia, achieving a mean area under the receiver operating characteristic curve of 0.78 (SD, 0.09) and an accuracy of 0.71 (SD, 0.09) across 100 random train‑test splits. Feature selection stability analysis identified 6 consistently important predictors: "Elongation," "Maximum2DDiameterSlice," "V20," "Flatness," "LeastAxisLength," and "MajorAxisLength." Radiomic shape features predominated among the most frequently selected and most influential parameters. Robust importance quantification further identified "V30," "MeshVolume," and "Sphericity" as the top contributors to the model's predictions.
CONCLUSIONS: This study suggests a robust machine learning framework for predicting moderate‑to‑severe xerostomia, indicating the complementary value of radiomic shape features to traditional dosimetric metrics. The integration of these features may contribute to more comprehensive toxicity risk assessment and warrants further investigation in clinical decision-making contexts.