文章：

基于多组学的个性化超分割立体定向自适应放疗（PULSAR）疗效预测研究

Multiomics-Based Outcome Prediction in Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy (PULSAR)

原文发布日期：9 October 2024

DOI: 10.3390/cancers16193425

类型: Article

开放获取: 是

英文摘要：

Objectives: This retrospective study aims to develop a multiomics approach that integrates radiomics, dosiomics, and delta features to predict treatment responses in brain metastasis (BM) patients undergoing PULSAR.Methods: A retrospective study encompassing 39 BM patients with 69 lesions treated with PULSAR was undertaken. Radiomics, dosiomics, and delta features were extracted from both pre-treatment and intra-treatment MRI scans alongside dose distributions. Six individual models, alongside an ensemble feature selection (EFS) model, were evaluated. The classification task focused on distinguishing between two lesion groups based on whether they exhibited a volume reduction of more than 20% at follow-up. Performance metrics, including sensitivity, specificity, accuracy, precision, F1 score, and the area under the receiver operating characteristic (ROC) curve (AUC), were assessed.Results: The EFS model integrated the features from pre-treatment radiomics, pre-treatment dosiomics, intra-treatment radiomics, and delta radiomics. It outperformed six individual models, achieving an AUC of 0.979, accuracy of 0.917, and F1 score of 0.821. Among the top nine features of the EFS model, six features came from post-wavelet transformation and three from original images.Conclusions: The study demonstrated the feasibility of employing a data-driven multiomics approach to predict treatment outcomes in BM patients receiving PULSAR treatment. Integrating multiomics with intra-treatment decision support in PULSAR shows promise for optimizing patient management and reducing the risks of under- or over-treatment.

摘要翻译： 

目的：本研究旨在开发一种整合放射组学、剂量组学及增量特征的多组学方法，用于预测接受PULSAR治疗的脑转移瘤患者的治疗反应。 方法：回顾性纳入39例接受PULSAR治疗的脑转移瘤患者（共69个病灶）。从治疗前及治疗中磁共振影像及剂量分布数据中提取放射组学、剂量组学及增量特征。评估了六个独立模型及一个集成特征选择模型。分类任务聚焦于鉴别随访期病灶体积缩小是否超过20%的两组病灶。评估指标包括灵敏度、特异度、准确率、精确率、F1分数及受试者工作特征曲线下面积。 结果：集成特征选择模型融合了治疗前放射组学、治疗前剂量组学、治疗中放射组学及增量放射组学特征，其表现优于六个独立模型，曲线下面积达0.979，准确率为0.917，F1分数为0.821。在该模型筛选出的前九个重要特征中，六个来源于小波变换后特征，三个来源于原始图像特征。 结论：本研究证实了采用数据驱动的多组学方法预测PULSAR治疗脑转移瘤患者疗效的可行性。将多组学特征与PULSAR治疗中的决策支持系统相结合，有望优化患者管理并降低治疗不足或过度治疗的风险。

原文链接：

Multiomics-Based Outcome Prediction in Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy (PULSAR)