An Enhanced CT-based Radiomics Model for Predicting the Anaplastic Lymphoma Kinase Mutation Status in Lung Adenocarcinoma.

INTRODUCTION

This study aimed to explore the relationship between radiomics features and anaplastic lymphoma kinase (ALK) gene mutation status in lung adenocarcinoma and to develop a radiomics nomogram for preoperative prediction of ALK mutations.

METHODS

A retrospective analysis was conducted on 210 patients with histologically confirmed lung adenocarcinoma (50 ALK mutation-positive, 160 mutation-negative), divided into training (n=147) and validation (n=63) cohorts (7:3 ratio). Preoperative enhanced CT images were analyzed using ITK-SNAP for region-of-interest delineation, and radiomics features were extracted A.K. software. The least absolute shrinkage and selection operator algorithm selected features to generate a radiomics score. Multivariate logistic regression identified independent risk factors, and a radiomics nomogram combining clinical features and radiomics signatures was developed. Model performance was evaluated using AUC in both training and validation sets.

RESULTS

Nineteen radiomics features were selected to construct the radiomics signature. The signature achieved an AUC of 0.89 (95% CI: 0.84-0.95) in the training set and 0.79 (95% CI: 0.63-0.95) in the validation set. The radiomics nomogram demonstrated superior performance (AUC=0.80, 95% CI: 0.63-0.97) compared to the clinical model alone (AUC=0.66, 95% CI: 0.47-0.85) in the validation set. While the nomogram showed no statistically significant improvement over the radiomics signature alone (>0.05), it outperformed the clinical model significantly (<0.001 in training; =0.0337 in validation).

DISCUSSION

The radiomics nomogram integrating clinical and radiomics data demonstrated robust predictive capability for ALK mutations, highlighting the potential of non-invasive CT-based radiomics in guiding personalized treatment. However, the lack of significant difference between the nomogram and radiomics signature alone suggests limited incremental value from clinical variables in this cohort. Limitations include the retrospective design, single-center data, and class imbalance (fewer ALK-positive cases), which may affect generalizability. External validation is warranted to confirm clinical utility.

CONCLUSION

The CT-derived radiomics signature and nomogram show promise for preoperative ALK mutation prediction in lung adenocarcinoma. These tools could enhance clinical decision-making by identifying candidates for targeted therapies, though further validation is needed to optimize their application in diverse populations.