探索个性化放射治疗策略 第一部分：利用类激活映射解锁与反应相关的肿瘤亚区
Exploring Strategies for Personalized Radiation Therapy Part I Unlocking Response-Related Tumor Subregions with Class Activation Mapping
AI总结：
本研究比较放射组学、梯度特征和CAM增强CNN三种方法，用于预测伽玛刀治疗脑转移瘤的反应，发现像素级CAM能识别反应相关肿瘤亚区，分类准确率最高，有助于个性化放疗。
摘要：
个性化精准放射治疗需要的不仅仅是简单的分类，它要求识别预后性的、具有空间信息性的特征，并能够根据个体反应调整治疗方案。本研究比较了三种预测治疗反应的方法：标准放射组学、基于梯度的特征，以及结合类激活映射的卷积神经网络。我们分析了接受伽玛刀放射外科治疗的39名患者的69个脑转移瘤。使用一个集成的自编码器分类器模型，将肿瘤体积在三个月随访时缩小超过20%的预测作为二分类任务。结果突显了它们在层次化特征提取和分类器判别能力方面的优势。在各类模型中，像素级CAM提供了最详细的空间洞察，识别出病变特定区域而非依赖固定模式，展现出强大的泛化能力。在无反应病灶中，激活区域可能指示放射抵抗区域。像素级CAM在分类准确性上优于放射组学和基于梯度的方法。此外，其细粒度的空间特征允许与细胞水平数据对齐，支持生物学验证和对异质性治疗反应的更深入理解。尽管仍需进一步验证，这些发现强调了其在指导光子和粒子治疗中个性化和适应性放疗策略方面的前景。
英文摘要：
Personalized precision radiation therapy requires more than simple classification, it demands the identification of prognostic, spatially informative features and the ability to adapt treatment based on individual response. This study compares three approaches for predicting treatment response: standard radiomics, gradient based features, and convolutional neural networks enhanced with Class Activation Mapping. We analyzed 69 brain metastases from 39 patients treated with Gamma Knife radiosurgery. An integrated autoencoder classifier model was used to predict whether tumor volume would shrink by more than 20 percent at a three months follow up, framed as a binary classification task. The results highlight their strength in hierarchical feature extraction and the classifiers discriminative capacity. Among the models, pixel wise CAM provides the most detailed spatial insight, identifying lesion specific regions rather than relying on fixed patterns, demonstrating strong generalization. In non responding lesions, the activated regions may indicate areas of radio resistance. Pixel wise CAM outperformed both radiomics and gradient based methods in classification accuracy. Moreover, its fine grained spatial features allow for alignment with cellular level data, supporting biological validation and deeper understanding of heterogeneous treatment responses. Although further validation is necessary, these findings underscore the promise in guiding personalized and adaptive radiotherapy strategies for both photon and particle therapies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
