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Multiregional radiomics of brain metastasis can predict response to EGFR-TKI in metastatic NSCLC
Ying Fan1, Xinti Wang2, Yue Dong3
1School of Intelligent Medicine, China Medical University, Shenyang, 110122, People's Republic of China.
Objectives:
To develop radiomics signatures from multiparametric magnetic resonance imaging (MRI) scans to detect epidermal growth factor receptor (EGFR) mutations and predict the response to EGFR-tyrosine kinase inhibitors (EGFR-TKIs) in non-small cell lung cancer (NSCLC) patients with brain metastasis (BM).
Methods:
We included 230 NSCLC patients with BM treated at our hospital between January 2017 and December 2021 and 80 patients treated at another hospital between July 2014 and October 2021 to form the primary and external validation cohorts, respectively. All patients underwent contrast-enhanced T1-weighted (T1C) and T2-weighted (T2W) MRI, and radiomics features were extracted from both the tumor active area (TAA) and peritumoral edema area (POA) for each patient. The least absolute shrinkage and selection operator (LASSO) was used to identify the most predictive features. Radiomics signatures (RSs) were constructed using logistic regression analysis.
Results:
For predicting the EGFR mutation status, the created RS-EGFR-TAA and RS-EGFR- POA showed similar performance. By combination of TAA and POA, the multi-region combined RS (RS-EGFR-Com) achieved the highest prediction performance, with AUCs of 0.896, 0.856, and 0.889 in the primary training, internal validation, and external validation cohort, respectively. For predicting response to EGFR-TKI, the multi-region combined RS (RS-TKI-Com) generated the highest AUCs in the primary training (AUC = 0.817), internal validation (AUC = 0.788), and external validation (AUC = 0.808) cohort, respectively.
Conclusions:
Our findings suggested values of multiregional radiomics of BM for predicting EGFR mutations and response to EGFR-TKI.
Clinical Relevance Statement:
The application of radiomic analysis of multiparametric brain MRI has proven to be a promising tool to stratify which patients can benefit from EGFR-TKI therapy and to facilitate the precise therapeutics of NSCLC patients with brain metastases.
Key Points:
• Multiregional radiomics can improve efficacy in predicting therapeutic response to EGFR-TKI therapy in NSCLC patients with brain metastasis. • The tumor active area (TAA) and peritumoral edema area (POA) may hold complementary information related to the therapeutic response to EGFR-TKI. • The developed multi-region combined radiomics signature achieved the best predictive performance and may be considered as a potential tool for predicting response to EGFR-TKI.
Insights
Multiregional radiomics from brain MRI effectively predict epidermal growth factor receptor (EGFR) mutations and response to EGFR-tyrosine kinase inhibitors (EGFR-TKIs) in non-small cell lung cancer (NSCLC) patients with brain metastasis (BM). This approach aids in stratifying patients for precise therapeutics.
Area of Science:
- Radiomics and Medical Imaging
- Oncology and Cancer Therapeutics
- Neuro-oncology and Brain Metastasis
Background:
- Non-small cell lung cancer (NSCLC) with brain metastasis (BM) presents significant therapeutic challenges.
- Predicting epidermal growth factor receptor (EGFR) mutations and response to EGFR-tyrosine kinase inhibitors (EGFR-TKIs) is crucial for personalized treatment.
- Multiparametric MRI offers rich data for non-invasive tumor characterization.
Purpose of the Study:
- To develop radiomics signatures from multiparametric MRI to detect EGFR mutations in NSCLC patients with BM.
- To predict the response to EGFR-TKIs in this patient cohort.
- To evaluate the utility of radiomics in stratifying patients for targeted therapy.
Main Methods:
- Inclusion of 230 primary and 80 external validation NSCLC patients with BM.
- Extraction of radiomics features from contrast-enhanced T1-weighted (T1C) and T2-weighted (T2W) MRI scans, focusing on tumor active area (TAA) and peritumoral edema area (POA).
- Utilized Least Absolute Shrinkage and Selection Operator (LASSO) for feature selection and logistic regression for constructing radiomics signatures (RSs).
Main Results:
- Multi-region combined radiomics signature (RS-EGFR-Com) achieved high prediction performance for EGFR mutation status (AUCs: 0.896-0.856).
- Multi-region combined radiomics signature (RS-TKI-Com) demonstrated strong predictive capability for EGFR-TKI response (AUCs: 0.817-0.788).
- Both TAA and POA contributed complementary information for predicting therapeutic response.
Conclusions:
- Multiregional radiomics analysis of brain MRI is valuable for predicting EGFR mutations and TKI response in NSCLC with BM.
- The developed radiomics signatures show potential as non-invasive tools for patient stratification.
- This approach facilitates precise therapeutic strategies for NSCLC patients with brain metastases.
