Radiomics for prediction of response to EGFR-TKI based on metastasis/brain parenchyma (M/BP)-interface

• Published in: Radiologia medica 2022-12, Vol.127 (12), p.1342-1354
• Main Authors: Fan, Ying, Zhao, Zilong, Wang, Xingling, Ai, Hua, Yang, Chunna, Luo, Yahong, Jiang, Xiran
• Format: Article
• Language: English
• Subjects: Brain; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - drug therapy; Carcinoma, Non-Small-Cell Lung - diagnostic imaging; Carcinoma, Non-Small-Cell Lung - drug therapy; Clustering; Computer Application; Diagnostic Radiology; Edema; ErbB Receptors - genetics; Growth factors; Humans; Imaging; Interventional Radiology; Kinases; Lung Neoplasms - diagnostic imaging; Lung Neoplasms - drug therapy; Medicine; Medicine & Public Health; Metastasis; Mutation; Neuroradiology; Radiology; Radiomics; Retrospective Studies; Training; Tumors; Tyrosine; Ultrasound
• ISSN: 1826-6983; 0033-8362; 1826-6983
• DOI: 10.1007/s11547-022-01569-3

Purpose To evaluate the potential of subregional radiomics as a novel tumor marker in predicting epidermal growth factor receptor (EGFR) mutation status and response to EGFR-tyrosine kinase inhibitor (TKI) therapy in NSCLC patients with brain metastasis (BM). Materials and methods We included 230 patients from center 1, and 80 patients were included from center 2 to form a primary and external validation cohort, respectively. Patients underwent contrast-enhanced T1-weighted and T2-weighted MRI scans before treatment. The individual- and population-level clustering was used to partition the peritumoral edema area (POA) into phenotypically consistent subregions. Radiomics features were calculated and selected from the tumor active area (TAA), POA and subregions, and used to develop models. Prediction values of each region were investigated and compared with receiver operating characteristic curves and Delong test. Results For predicting EGFR mutations, a multi-region combined model (EGFR-Fusion) was developed based on joint of the partitioned metastasis/brain parenchyma (M/BP)-interface and TAA, and generated the highest prediction performance in the training (AUC = 0.945, SEN = 0.878, SPE = 0.937), internal validation (AUC = 0.880, SEN = 0.733, SPE = 0.969), and external validation (AUC = 0.895, SEN = 0.875, SPE = 0.800) cohorts. For predicting response to EGFR-TKI, the developed multi-region combined model (TKI-Fusion) yielded predictive AUCs of 0.869 (SEN = 0.717, SPE = 0.884), 0.786 (SEN = 0.708, SPE = 0.818), and 0.802 (SEN = 0.750, SPE = 0.800) in the training, internal validation and external validation cohort, respectively. Conclusion Our study revealed that complementary information regarding the EGFR status and response to EGFR-TKI can be provided by subregional radiomics. The proposed radiomics models may be new markers to guide treatment plans for NSCLC patients with BM.