AUTHOR=Yang Chunna , Sun Yiyao , Jiang Mingchen , Fan Ying , Hu Yanjun , Zhang Qianhui , Zhang Yan , Wang Yan , Jiang Xiran , Wang Zekun , Yang Zhiguang , Sun Bo , Jiang Wenyan 

TITLE=FLAIR-based radiomics signature from brain-tumor interface for early prediction of response to EGFR-TKI therapy in NSCLC patients with brain metastasis

JOURNAL=Frontiers in Cell and Developmental Biology

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2025.1525989

DOI=10.3389/fcell.2025.1525989

ISSN=2296-634X

ABSTRACT=ObjectivesEvaluating response to epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) is crucial in non-small cell lung cancer (NSCLC) patients with brain metastases (BM). To explore values of multi-sequence MRI in early assessing response to EGFR-TKIs in non-small cell lung cancer (NSCLC) patients with BM.ApproachA primary cohort of 133 patients (January 2018 to March 2024) from center one and an external cohort of 52 patients (May 2017 to December 2022) from center two were established. Radiomics features were extracted from 4 mm brain-tumor interface (BTI) and whole BM region across T1-weighted contrast enhanced (T1CE) and T2-weighted (T2W) and T2 fluid-attenuated inversion recovery (T2-FLAIR) MRI sequences. The most relevant features were selected using the U test and least absolute shrinkage and selection operator (LASSO) method to develop the multi-sequence models based on BTI (RS-BTI-COM) and BM (RS-BM-COM). By integrating RS-BTI-COM with peritumoral edema volume (VPE), the combined model was built using logistic regression. Model performance was evaluated using the area under the ROC curve (AUC), sensitivity (SEN), specificity (SPE) and accuracy (ACC).Main ResultsThe constructed RS-BTI-COM demonstrated a higher association with early response to EGFR-TKI therapy than RS-BM-COM. The combined RS-BTIplusVPE, incorporating BTI-based radiomics features and VPE, exhibited the highest AUCs (0.843–0.938), SPE (0.808–0.905) and ACC (0.712–0.875) in the training, internal validation, and external validation cohort, respectively.SignificanceThe study developed a validated non-invasive model (RS-BTIplusVPE) based on integrating BTI-based radiomics features and VPE, which showed improved prediction of EGFR-TKI response in NSCLC patients with BM compared to tumor-focused models.