Background:Radiation therapy is a primary and cornerstone treatment modality for brain metastasis. However, it can result in complications like necrosis, which may lead to significant neurological deficits. This study aims to develop and validate an ensemble model with radiomics to predict radiation necrosis.Method:This study retrospectively collected and analyzed MRI images and clinical information from 209 stereotactic radiosurgery sessions involving 130 patients with brain metastasis. An ensemble model integrating gradient boosting, random forest, decision tree, and support vector machine was developed and validated using selected radiomic features and clinical factors to predict the likelihood of necrosis. The model performance was evaluated and compared with other machine learning algorithms using metrics, including the area under the curve (AUC), sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). SHapley Additive exPlanations (SHAP) analysis and local interpretable model-agnostic explanations (LIME) analysis were applied to explain the model’s prediction.Results:The ensemble model achieved strong performance in the validation cohort, with the highest AUC. Compared to individual models and the stacking ensemble model, it consistently outperformed. The model demonstrated superior accuracy, generalizability, and reliability in predicting radiation necrosis. SHAP and LIME were used to interpret a complex predictive model for radiation necrosis. Both analyses highlighted similar significant factors, enhancing our understanding of prediction dynamics.Conclusions:The ensemble model using radiomic features exhibited high accuracy and robustness in predicting the occurrence of radiation necrosis. It could serve as a novel and valuable tool to facilitate radiotherapy for patients with brain metastasis.
背景：放射治疗是脑转移瘤的主要和核心治疗手段，但其可能导致放射性坏死等并发症，引发严重的神经功能缺损。本研究旨在开发并验证一种结合影像组学的集成模型，用于预测放射性坏死的发生。 方法：本研究回顾性收集并分析了130例脑转移瘤患者共209次立体定向放射外科治疗的磁共振成像数据及临床信息。通过整合梯度提升、随机森林、决策树和支持向量机算法，利用筛选的影像组学特征及临床因素构建并验证了预测放射性坏死发生概率的集成模型。采用受试者工作特征曲线下面积、敏感性、特异性、阴性预测值和阳性预测值等指标评估模型性能，并与其他机器学习算法进行比较。同时应用SHAP可加性解释和局部可解释模型无关解释方法对模型预测结果进行解释。 结果：在验证队列中，集成模型表现出优异的预测性能，其曲线下面积值最高。与单一模型及堆叠集成模型相比，该模型始终具有更优的表现，在预测放射性坏死方面展现出更高的准确性、泛化能力和可靠性。SHAP和LIME分析成功解释了这一复杂的放射性坏死预测模型，两种方法均凸显出相似的重要预测因素，深化了我们对预测机制的理解。 结论：基于影像组学特征的集成模型在预测放射性坏死发生方面表现出较高的准确性和稳健性，可作为辅助脑转移瘤患者放射治疗的新型实用工具。
肿瘤(癌症)患者之家