To explore the value of machine learning models constructed based on CT habitat radiomics in non-invasive preoperative prediction of anaplastic lymphoma kinase (ALK) gene fusion expression in lung adenocarcinoma.

A total of 130 patients with lung adenocarcinoma who completed ALK gene testing and preoperative chest CT examination in the Imaging Department of the First People′s Hospital of Zhangjiagang from March 2015 to November 2023 were retrospectively included (45 cases were ALK positive and 85 cases were ALK negative). They were randomly divided into the training set (n=90) and the test set (n=40) in a 7∶3 ratio. The lesions were divided into two habitat subregions (Habitat 1 and Habitat 2) by K-means clustering, and 14 key habitat radiomics features were extracted and screened. Subsequently, models were constructed respectively using 6 algorithms: autoencoder (AE), genetic programming (GP), linear discriminant analysis (LDA), logistic regression (LR), Lasso logistic regression (LRLasso), and support vector machine (SVM). The receiver operator characteristic (ROC) curve was used to evaluate the model efficacy, and the DeLong test was used to compare the differences in area under the curve (AUC).

The AUCs of the machine learning model training set constructed based on the LR and LRLasso algorithms were 0.862 (0.788-0.935) and 0.854 (0.778-0.930), respectively, and the AUCs of the test set were 0.830 (0.678-0.930) and 0.802 (0.646-0.911), respectively. There were no statistically significant differences in the AUC between the LR model and the LRLasso model or the AE model (P=0.182, 0.104), but there were statistically significant differences in the AUC between the LR model and the remaining models (all P<0.05). In the test set, the sensitivity and specificity of the LR model were 71.4% and 96.2%, respectively, while those of the LRLasso model were 64.3% and 88.5%, respectively.

The CT image-based habitat radiomics shows a certain predictive capability and potential clinical utility for identifying ALK gene fusion in lung adenocarcinoma. The machine learning model based on LR has a good generalization ability and a potential clinical applicability, and may be used as a new non-invasive imaging tool for predicting ALK gene fusion in lung adenocarcinoma.