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Deep learning-based CT radiomics for ALK rearrangement status prediction in lung adenocarcinoma
Cheng Li1, Jiabao Zhong2, Jiawei Pan3
1Department of Radiology, School of Medicine, the First People's Hospital of Foshan (Foshan Hospital, Southern University of Science and Technology), Southern University of Science and Technology, Foshan, China.
Background:
Current clinical guidelines mandate routine evaluation of anaplastic lymphoma kinase (ALK) rearrangement in lung adenocarcinoma prior to ALK-targeted therapy initiation. This study aimed to develop and validate a non-invasive predictive model integrating deep learning radiomic (DLR) features from pre-treatment computed tomography (CT) images with clinical data to improve pretherapeutic ALK rearrangement prediction.
Methods:
We retrospectively analyzed 502 patients with histologically confirmed lung adenocarcinoma (153 ALK-positive, 349 ALK-negative), randomly split into training (80%) and validation (20%) cohorts. DLR features were extracted from pre-treatment CT images, and eight machine learning algorithms were compared. The optimal-performing algorithm was used to develop a combined clinical and deep learning radiomics (CDLR) model. Performance was evaluated via receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Gradient-weighted Class Activation Mapping (Grad-CAM) and Shapley Additive Explanations (SHAP) enhanced model visualization and interpretability.
Results:
The support vector machine (SVM)-based DLR model yielded the best performance (training area under the curve (AUC): 0.971, 95% confidence interval (CI): 0.9528-0.9888; validation AUC: 0.877, 95% CI: 0.8071-0.9463). The CDLR model exhibited comparable efficacy (training AUC: 0.971, 95% CI: 0.9527-0.9889; validation AUC: 0.887, 95% CI: 0.8203-0.9530), with both significantly outperforming the clinical-only model (training AUC: 0.669, 95% CI: 0.6110-0.7273; validation AUC: 0.660, 95% CI: 0.5443-0.7757). Calibration analysis confirmed good agreement between predicted and observed outcomes.
Conclusions:
Our CT-based deep learning radiomics model holds promise for non-invasive detection of ALK rearrangements in lung adenocarcinoma, yet remains investigational and necessitates prospective multicenter validation before clinical implementation.