FLAIR-based Radiomics Signature from Brain-Tumor Interface for Early Prediction of Response to EGFR-TKI Therapy in NSCLC Patients with Brain Metastasis

Chunna Yang¹Yiyao Sun¹Mingchen Jiang¹Ying Fan¹Yanjun Hu²Qianhui Zhang¹Yan Zhang³Yan Wang¹Xiran Jiang¹Zekun Wang²Yang Zhiguang⁴Bo Sun⁵Wenyan Jiang^2*

• ¹China Medical University, Shenyang, Liaoning Province, China
• ²Liaoning Cancer Hospital, China Medical University, Shenyang, Liaoning Province, China
• ³Liaoning Provincial Center for Disease Control and Prevention, Shenyang, Liaoning Province, China
• ⁴Shengjing Hospital, Shenyang, Liaoning Province, China
• ⁵First Affiliated Hospital, Dalian Medical University, Dalian, Liaoning Province, China

AbstractObjectives. Evaluating 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.Approach. A primary cohort of 133 patients (January 2018 to March 2024) from center 1 and an external cohort of 52 patients (May 2017 to December 2022) from center 2 were established. Radiomics features were extracted from 4mm 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 Results. The 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.Significance. The 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.