Noninvasive prediction of ALK fusion status in non-small cell lung cancer by a machine learning model combining CT images and clinical information.

BACKGROUND: As the primary contributor to global cancer mortality, lung cancer requires precise anaplastic lymphoma kinase (ALK) genotyping to implement personalized targeted treatment for non-small cell lung cancer (NSCLC) patients. Invasive biopsy-based ALK detection is clinically limited by sampling deviation, procedural complications, and insufficient tumor specimens. The objective of this study was to develop and validate a machine learning model that integrates computed tomography (CT) radiomics features with clinicopathological data to non-invasively predict ALK fusion status in patients with NSCLC. METHODS: This retrospective multi-center study enrolled 722 NSCLC patients (291 ALK-positive, 431 ALK-negative). From segmented tumor regions, 2,264 radiomics features were derived. After feature selection using the least absolute shrinkage and selection operator (LASSO) regression, three predictive models were constructed and compared: a clinical & region of interest (ROI) model, a radiomics model, and a combined model integrating both feature types. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity in training, test, and validation cohorts. RESULTS: The final models included 14, 67, and 53 features in the clinical & ROI model, radiomics model, and combined model, respectively. The combined model demonstrated superior predictive performance, achieving an AUC of 0.997 in the training cohort, 0.988 in the test cohort, and 0.973 in the validation cohort. It significantly outperformed the clinical & ROI model (AUC: 0.997 vs. 0.923 in the training cohort, P<0.001) and showed a superior performance compared to the radiomics model (AUC: 0.997 vs. 0.995 in the training cohort, P=0.18), though not statistically significant. CONCLUSIONS: A machine learning model combining CT radiomics and clinical data exhibited robust performance in predicting ALK fusion status in NSCLC patients. This non-invasive approach shows significant potential as a clinical tool for pre-therapeutic selection of patients who may benefit from ALK-targeted therapies.