一种深度学习促进的放射组学解决方案，用于预测非小细胞肺癌试验中的肺部病变缩小
A deep learning-facilitated radiomics solution for the prediction of lung lesion shrinkage in non-small cell lung cancer trials
摘要：
在本文中，我们提出了一种基于深度学习的方法，用于基于从非小细胞肺癌试验中患者的临床CT扫描中提取的放射组学特征来预测肺部病变反应。该方法首先从不同解剖位置的原发性和转移性病变的集合中对肺病变进行分类。针对肺部病变，我们进行自动分割，以提取其3D体积。然后在治疗前扫描和第一次随访扫描时从病变中提取放射组学特征，以预测哪些病变在治疗期间直径将缩小至少30%（Pembrolizumab或化疗和Pembrolizumab的组合），这被实体瘤疗效评价标准（RECIST）指南定义为部分缓解。对训练集的5倍交叉验证导致AUC为0.84 +/- 0.03，并且对于30%直径收缩的结果，对测试数据集的预测达到AUC为0.73 +/- 0.02。
英文摘要：
Herein we propose a deep learning-based approach for the prediction of lung lesion response based on radiomic features extracted from clinical CT scans of patients in non-small cell lung cancer trials. The approach starts with the classification of lung lesions from the set of primary and metastatic lesions at various anatomic locations. Focusing on the lung lesions, we perform automatic segmentation to extract their 3D volumes. Radiomic features are then extracted from the lesion on the pre-treatment scan and the first follow-up scan to predict which lesions will shrink at least 30% in diameter during treatment (either Pembrolizumab or combinations of chemotherapy and Pembrolizumab), which is defined as a partial response by the Response Evaluation Criteria In Solid Tumors (RECIST) guidelines. A 5-fold cross validation on the training set led to an AUC of 0.84 +/- 0.03, and the prediction on the testing dataset reached AUC of 0.73 +/- 0.02 for the outcome of 30% diameter shrinkage.
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