基于多组学的个性化超分割立体定向自适应放疗（PULSAR）治疗脑转移瘤的疗效预测
Multiomics-based Outcome Prediction for the Treatment of Brain Metastases with Personalized Ultra-fractionated Stereotactic Adaptive Radiotherapy (PULSAR)
AI总结：
本研究开发了整合影像组学、剂量组学和delta特征的数据驱动多组学方法，用于PULSAR治疗脑转移瘤的早期疗效预测，所构建的集成特征选择模型AUC达0.979，显著优于单独模型。
摘要：
目的：我们旨在开发一种数据驱动的多组学方法，整合影像组学、剂量组学和delta特征，以在治疗早期（治疗中）预测接受PULSAR治疗的脑转移瘤（BMs）患者的治疗反应。方法：我们对39例接受PULSAR治疗的69个脑转移瘤患者进行了回顾性研究。从治疗前和治疗中的MRI扫描及剂量分布中提取影像组学、剂量组学和delta特征。使用支持向量机（SVM）构建了六个单独模型和一个集成特征选择（EFS）模型，并通过分层5折交叉验证进行评估。分类任务区分随访时体积减少>20%的病灶。我们评估了包括灵敏度、特异度、准确率、精确率、F1分数和AUC在内的性能指标。探索了各种特征提取和集成选择方案，以增强模型鲁棒性并减少过拟合。结果：整合了治疗前影像组学和剂量组学、治疗中影像组学以及delta影像组学特征的EFS模型优于六个单独模型。其AUC达到0.979，准确率为0.917，F1分数为0.821。在排名前9的特征中，6个来自小波变换后，3个来自原始图像。离散小波变换将体积图像分解为多分辨率分量，提供了对底层结构更全面的表征。结论：我们的研究证明了采用数据驱动的多组学方法预测接受PULSAR治疗的脑转移瘤患者肿瘤体积变化的可行性。与六个单独模型相比，EFS模型表现出更优的性能，强调了整合治疗前和治疗中数据的重要性。该应用有望优化脑转移瘤管理，可能降低与治疗不足或过度治疗相关的风险。
英文摘要：
Purpose: We aimed to develop a data-driven multiomics approach integrating radiomics, dosiomics, and delta features to predict treatment response at an earlier stage (intra-treatment) for brain metastases (BMs) patients treated with PULSAR. Methods: We conducted a retrospective study of 39 patients with 69 BMs treated with PULSAR. Radiomics, dosiomics, and delta features were extracted from pretreatment and intra-treatment MRI scans and dose distributions. Six individual models and an ensemble feature selection (EFS) model were constructed using SVM and evaluated via stratified 5-fold cross-validation. The classification task distinguished lesions with >20% volume reduction at follow-up. We assessed performance metrics including sensitivity, specificity, accuracy, precision, F1 score, and AUC. Various feature extraction and ensemble selection scenarios were explored to enhance model robustness and reduce overfitting. Results: The EFS model, integrating features from pre-treatment radiomics and dosiomics, intra-treatment radiomics, and delta-radiomics, outperformed six individual models. It achieved an AUC of 0.979, accuracy of 0.917, and F1 score of 0.821. Among the top 9 features, six were derived from post-wavelet transformation, and three from original images. The discrete wavelet transform decomposes volumetric images into multi-resolution components, offering a more comprehensive characterization of underlying structures. Conclusion: Our study demonstrated the feasibility of employing a data-driven multiomics approach to predict tumor volume changes in BMs patients with PULSAR. The EFS model demonstrates enhanced performance compared with six individual models, emphasizing the importance of integrating both pretreatment and intra-treatment data. This application holds promise for optimizing BMs management, potentially mitigating risks associated with under- or over-treatment.
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