School of Intelligent Medicine, China Medical University, Liaoning 110122, People's Republic of China.
Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Liaoning 110042, People's Republic of China.
Phys Med Biol. 2022 Jun 8;67(12). doi: 10.1088/1361-6560/ac7192.
To develop and externally validate habitat-based MRI radiomics for preoperative prediction of the EGFR mutation status based on brain metastasis (BM) from primary lung adenocarcinoma (LA).We retrospectively reviewed 150 and 38 patients from hospital 1 and hospital 2 between January 2017 and December 2021 to form a primary and an external validation cohort, respectively. Radiomics features were calculated from the whole tumor (W), tumor active area (TAA) and peritumoral oedema area (POA) in the contrast-enhanced T1-weighted (T1CE) and T2-weighted (T2W) MRI image. The least absolute shrinkage and selection operator was applied to select the most important features and to develop radiomics signatures (RSs) based on W (RS-W), TAA (RS-TAA), POA (RS-POA) and in combination (RS-Com). The area under receiver operating characteristic curve (AUC) and accuracy analysis were performed to assess the performance of radiomics models.RS-TAA and RS-POA outperformed RS-W in terms of AUC, ACC and sensitivity. The multi-region combined RS-Com showed the best prediction performance in the primary validation (AUCs, RS-Com versus RS-W versus RS-TAA versus RS-POA, 0.901 versus 0.699 versus 0.812 versus 0.883) and external validation (AUCs, RS-Com versus RS-W versus RS-TAA versus RS-POA, 0.900 versus 0.637 versus 0.814 versus 0.842) cohort.The developed habitat-based radiomics models can accurately detect the EGFR mutation in patients with BM from primary LA, and may provide a preoperative basis for personal treatment planning.
为了基于脑转移（BM）来自原发性肺腺癌（LA），开发并外部验证基于栖息地的 MRI 放射组学，用于预测 EGFR 突变状态的术前预测。我们回顾性地审查了医院 1 和医院 2 分别于 2017 年 1 月至 2021 年 12 月之间的 150 例和 38 例患者，以形成主要和外部验证队列。从对比增强 T1 加权（T1CE）和 T2 加权（T2W）MRI 图像中的整个肿瘤（W），肿瘤活跃区（TAA）和瘤周水肿区（POA）计算放射组学特征。最小绝对收缩和选择算子（LASSO）用于选择最重要的特征，并基于 W（RS-W），TAA（RS-TAA），POA（RS-POA）和组合（RS-Com）开发放射组学特征（RS）。通过接收者操作特征曲线（AUC）和准确性分析来评估放射组学模型的性能。在 AUC、ACC 和灵敏度方面，RS-TAA 和 RS-POA 优于 RS-W。多区域联合 RS-Com 在主要验证（AUC，RS-Com 与 RS-W 与 RS-TAA 与 RS-POA，0.901 与 0.699 与 0.812 与 0.883）和外部验证（AUC，RS-Com 与 RS-W 与 RS-TAA 与 RS-POA，0.900 与 0.637 与 0.814 与 0.842）队列中表现出最佳的预测性能。基于栖息地的放射组学模型可以准确地检测出来自原发性 LA 的 BM 患者的 EGFR 突变，并且可能为术前个体化治疗计划提供依据。
