Prediction of Early Recurrence After Bronchial Arterial Chemoembolization in Non-small Cell Lung Cancer Patients Using Dual-energy CT: An Interpretable Model Based on SHAP Methodology

Abstract

Rationale and Objectives

Bronchial artery chemoembolization (BACE) is a new treatment method for lung cancer. This study aimed to investigate the ability of dual-energy computed tomography (DECT) to predict early recurrence (ER) after BACE among patients with non-small cell lung cancer (NSCLC) who failed first-line therapy.

Materials and Methods

Clinical and imaging data from NSCLC patients undergoing BACE at Wenzhou Medical University Affiliated Fifth *** Hospital (10/2023–06/2024) were retrospectively analyzed. Logistic regression (LR) machine learning models were developed using 5 arterial-phase (AP) virtual monoenergetic images (VMIs; 40, 70, 100, 120, and 150 keV), while deep learning models utilized ResNet50/101/152 architectures with iodine maps. A combined model integrating optimal Rad-score, DL-score, and clinical features was established. Model performance was assessed via area under the receiver operating characteristic curve analysis (AUC), with SHapley Additive exPlanations (SHAP) framework applied for interpretability.

Results

A total of 196 patients were enrolled in this study (training cohort: n = 158; testing cohort: n = 38). The 100 keV machine learning model demonstrated superior performance (AUC = 0.751) compared to other VMIs. The deep learning model based on the ResNet101 method (AUC = 0.791) performed better than other approaches. The hybrid model combining Rad-score-100keV-A, Rad-score-100keV-V, DL-score-ResNet101-A, DL-score-ResNet101-V, and clinical features exhibited the best performance (AUC = 0.798) among all models.

Conclusion

DECT holds promise for predicting ER after BACE among NSCLC patients who have failed first-line therapy, offering valuable guidance for clinical treatment planning.