Peritumoral and intratumoral magnetic resonance imaging-based radiomics of brain metastases for predicting the response to EGFR-tyrosine kinase inhibitors in metastatic non-small cell lung cancer

Abstract

Background: The early prediction of treatment response for EGFR-tyrosine kinase inhibitors (EGFR-TKIs) is critical to guiding therapy in patients with metastatic non-small cell lung cancer (NSCLC). This study aimed to develop a magnetic resonance imaging (MRI)-based radiomics model based on intratumoral and peritumoral regions to assess the response of patients with metastatic NSCLC to EGFR-TKIs.

Methods: We retrospectively recruited 418 and 160 patients with brain metastases (BMs) from EGFR-mutant NSCLC who received EGFR-TKI therapy from hospital 1 and hospital 2, respectively. The intratumoral region of interest (ROI_I) was manually segmented for contrast-enhanced T1-weighted (T1-CE) imaging. Five peritumoral ROIs (ROI_P) at 2-, 4-, 6-, 8-, and 10-mm expansions along ROI_I were defined, and combined ROIs (ROI_I and ROI_P) were automatically generated. The least absolute shrinkage and selection operator (LASSO) was used to select the most predictive features, which was followed by the construction of radiomics models (the ROI_I model, ROI_P model, and the combined model). The area under the curve (AUC) and Shapley method were used to validate the performance of the models and explain the best models.

Results: The combined intratumoral and peritumoral 6-mm regions achieved the best performance, with AUCs of 0.913 and 0.826 in the training and test cohort. The ROI_I model also demonstrated a degree of classification power in both the training and test cohort, with AUCs of 0.868 and 0.762, respectively.

Conclusions: As compared to models consisting of intratumoral or peritumoral radiomics features alone, the model combining intratumoral and peritumoral radiomics features achieved better performance in predicting therapeutic response to EGFR-TKIs. The optimal combined region model with 6-mm peritumoral expansion along the tumor may benefit the clinical treatment of NSCLC.

Keywords: EGFR; Radiomics; brain metastases (BMs); magnetic resonance imaging (MRI); peritumoral.

Figure 1

The flowchart of patient enrollment. BM, brain metastasis; EGFR, epidermal growth factor receptor; MRI, magnetic resonance imaging; NSCLC, non-small cell lung cancer; T1-CE, contrast-enhanced T1-weighted; TKI, tyrosine kinase inhibitor.

Figure 2

The workflow of the study. (A) Acquisition of T1-CE images of patients with EGFR-mutant NSCLC and BM. (B) Segmentation of intratumoral, peritumoral, and combined ROIs. (C) Feature extraction via the t-test, Pearson correlation analysis, and the LASSO method. (D) To select the best algorithm for each ROI to construct model. (E) ROC curves were used to evaluate the model’s performance, and the SHAP summary plot was used to explain the contribution of features. (F) Illustrative example of model prediction result interpretation. BM, brain metastasis; EGFR, epidermal growth factor receptor; extra trees, extremely randomized trees; KNN, k-nearest neighbors; LASSO, least absolute shrinkage and selection operator; LightGBM, light gradient boosting machine; LR, logistic regression; MLP, multilayer perceptron; NSCLC, non-small cell lung cancer; RF, random forest; ROC, receiver operating characteristic; ROI, region of interest; SHAP, Shapley additive explanation; T1-CE, contrast-enhanced T1-weighted; XGBoost, extreme gradient boosting.

Figure 3

ROC curves of radiomics models. ROC curves of peritumoral models in the (A) training and (B) test cohorts. ROC curves of the combined models in the (C) training and (D) test cohorts. AUC, area under the receiver operating characteristic curve; ROC, receiver operating characteristic; ROI, region of interest.

Figure 4

SHAP summary plots of the ROI6 model. The plot demonstrates the features’ importance and contribution to model performance. ROI, region of interest; SHAP, Shapley additive explanation.

Figure 5

SHAP force plots explaining how the ROI6 model discriminates the treatment response of two patients. (A) Patient A was placed in the responsive group, and (B) patient B was placed in the nonresponsive group. ROI, region of interest; SHAP, Shapley additive explanation.