Park Jung Hyun, Choi Byung Se, Han Jung Ho, Kim Chae-Yong, Cho Jungheum, Bae Yun Jung, Sunwoo Leonard, Kim Jae Hyoung
Department of Radiology, Ajou University School of Medicine, Ajou University Medical Center, Suwon 443-380, Korea.
Department of Radiology, Seoul National University Bundang Hospital, 82, Gumi-ro 173beon-gil, Bundang-gu, Seongnam 13620, Korea.
J Clin Med. 2021 Jan 11;10(2):237. doi: 10.3390/jcm10020237.
This study aims to evaluate the utility of texture analysis in predicting the outcome of stereotactic radiosurgery (SRS) for brain metastases from lung cancer. From 83 patients with lung cancer who underwent SRS for brain metastasis, a total of 118 metastatic lesions were included. Two neuroradiologists independently performed magnetic resonance imaging (MRI)-based texture analysis using the Imaging Biomarker Explorer software. Inter-reader reliability as well as univariable and multivariable analyses were performed for texture features and clinical parameters to determine independent predictors for local progression-free survival (PFS) and overall survival (OS). Furthermore, Harrell's concordance index (C-index) was used to assess the performance of the independent texture features. The primary tumor histology of small cell lung cancer (SCLC) was the only clinical parameter significantly associated with local PFS in multivariable analysis. Run-length non-uniformity (RLN) and short-run emphasis were the independent texture features associated with local PFS. In the non-SCLC (NSCLC) subgroup analysis, RLN and local range mean were associated with local PFS. The C-index of independent texture features was 0.79 for the all-patients group and 0.73 for the NSCLC subgroup. In conclusion, texture analysis on pre-treatment MRI of lung cancer patients with brain metastases may have a role in predicting SRS response.
本研究旨在评估纹理分析在预测肺癌脑转移立体定向放射外科治疗（SRS）疗效方面的作用。在83例接受SRS治疗脑转移的肺癌患者中，共纳入118个转移病灶。两名神经放射科医生使用影像生物标志物探索者软件独立进行基于磁共振成像（MRI）的纹理分析。对纹理特征和临床参数进行阅片者间可靠性以及单变量和多变量分析，以确定局部无进展生存期（PFS）和总生存期（OS）的独立预测因素。此外，使用哈雷尔一致性指数（C指数）评估独立纹理特征的性能。在多变量分析中，小细胞肺癌（SCLC）的原发肿瘤组织学是与局部PFS显著相关的唯一临床参数。游程长度不均匀性（RLN）和短程强调是与局部PFS相关的独立纹理特征。在非小细胞肺癌（NSCLC）亚组分析中，RLN和局部范围均值与局部PFS相关。全患者组独立纹理特征的C指数为0.79，NSCLC亚组为0.73。总之，对肺癌脑转移患者治疗前的MRI进行纹理分析可能有助于预测SRS反应。
