Predicting the progressive-free survival of ALK-positive lung cancer: machine learning algorithm based on habitat imaging.

BACKGROUND

Lung cancer associated with the harbor oncogenic driver mutations or fusions, such as anaplastic lymphoma kinase (ALK), could benefit from representative clinical first-line treatment. And patients with ALK-positive (ALK+) show longer median survival than ALK-negative patients. This study aimed to develop a machine-learning model based on habitat analysis derived from computed tomography (CT) lung images to predict disease progression and explore its prognostic value for progression-free survival (PFS) in patients with (ALK+) lung cancer.

METHODS

A total of 79 patients with ALK-positive lung cancer and advanced TNM stage (III-IV) at The First Affiliated Hospital of Nanjing Medical University between April 2016 and August 2022 were divided into training (n=63) and testing (n=16) cohorts. Radiomics features were extracted from CT images using a habitat-based method with clustering analysis. Subsequently, the performance of eight machine learning algorithms was compared to build a habitat-radiomics (rad_habitat) model, yielding a rad_habitat_score. Using regression, we developed both a clinical model and a combined model that integrated clinical variables with the rad_habitat_score. Subsequently, these models' performance were evaluated. Univariate and multivariate Cox regression analyses were performed on clinical variables and the rad_habitat_score.

RESULTS

Three distinct habitats were identified. Among the eight machine learning algorithms, the multilayer perceptron (MLP) model demonstrated superior performance in handling non-linearity, achieving a higher area under the curve (AUC) of 0.836 [95% confidence interval (CI): 0.628-1.000] in the testing cohort. The combined model (AUC =0.836, 95% CI: 0.628-1.000) exhibited a higher AUC than the clinical model but a similar AUC to the rad_habitat model in the testing set. Delong test revealed a significant difference in the AUC values between the clinical model and the combined model and between the clinical model and rad_habitat model in the training cohort (P=0.002 and P=0.007, respectively), while the AUC values of the other models were not statistically different. Cox regression analysis identified the rad_habitat_score as the only independent predictor of PFS (P<0.001) for patients with high-risk ALK+ lung cancer.

CONCLUSIONS

The habitat-radiomics approach, utilizing three regional habitats, shows promise for accurately and interpretably identifying disease progression in ALK+ lung cancer. The rad_habitat_score, when combined with clinical features, demonstrated improved predictive accuracy for prognostic stratification compared with the clinical model alone.