BM of NSCLC

This study utilized data on the 3-year follow-up of patients with non-small cell lung cancer (NSCLC) from January 2018 to January 2021 on the status of brain metastases. Segmentation was performed by ITK-SNAP, Habitat-based radiomics were extracted from whole-tumor regions of interest (ROIs) that were initially diagnosed with enhanced lung CT from aortic phase CT imaging, and were used to develop predictive models for predicting brain metastases. This combined model is designed to improve the accuracy of predicting brain metastases in NSCLC.

本研究采用2018年1月至2021年1月期间收录的非小细胞肺癌（non-small cell lung cancer, NSCLC）患者3年随访数据，数据涵盖患者脑转移状态相关信息。本研究通过ITK-SNAP完成影像分割，从主动脉期增强肺部CT成像的初始诊断全肿瘤感兴趣区（regions of interest, ROIs）中提取基于肿瘤微生境的放射组学特征，并以此构建脑转移预测模型。该联合模型旨在提升非小细胞肺癌患者脑转移预测的准确率。