论文标题
在非小细胞肺癌试验中预测肺部病变收缩的深度学习促进放射学解决方案
A deep learning-facilitated radiomics solution for the prediction of lung lesion shrinkage in non-small cell lung cancer trials
论文作者
论文摘要
本文中，我们提出了一种基于深度学习的方法，以根据非小细胞肺癌试验中的患者的临床CT扫描提取的放射性特征来预测肺部病变反应。该方法始于各种解剖位置的原发性病变和转移性病变的肺部病变分类。为了关注肺部病变，我们进行自动分割以提取其3D体积。然后，从治疗前扫描的病变中提取放射素特征，并进行了首次随访扫描，以预测治疗过程中直径至少30％（pembrolizumab或化学疗法和pembrolizumab的组合）的直径至少缩小，这将定义为响应评估机构的部分响应定义的指南（reciation Criteria in Solid tamors in Solid tamors in Solid tamors in recisiss）（re recisiss）。对训练组的5倍交叉验证导致AUC为0.84 +/- 0.03，测试数据集的预测达到了AUC的AUC为0.73 +/- 0.02，直径收缩的结果收缩30％。
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.
