Radiomic and Interpretable Machine Learning for Non-Invasive Classification of Histological Subtypes of Non-Small Cell Lung Cancer
DOI:
https://doi.org/10.24191/jcrinn.v11i2.593Keywords:
Radiomics, Adenocarcinoma, Squamous Cell Carcinoma, Explainability, Random ForestAbstract
Telling apart the main histological subtypes of non-small cell lung cancer (NSCLC), like adenocarcinoma (ADC) and squamous cell carcinoma (SCC), is key for guiding treatment and making personalized medicine better. This study looks to build and test an interpretable machine learning model using radiomic features pulled from 3D CT scans. A retrospective cohort of 527 patients with non-small cell lung cancer (344 men and 183 women) was analyzed and randomly divided into a training set (75%) and a test set (25%). A total of 1,409 radiomic features were extracted from CT images. Three feature selection methods were evaluated, and class rebalancing using the SMOTE technique was applied only to the training set. Several classifiers, including random forest, were trained to distinguish adenocarcinoma from squamous cell carcinoma. Model performance was evaluated using the area under the ROC curve (AUC), precision and sensitivity, while interpretability was ensured using the SHAP (Shapley Additive Explanations) method. The consensus feature selection method, which was the best, selected 17 features for the SCC class and 13 for ADC. The Random Forest model stood out from seven other classifiers with superior performance in predicting ADC and SCC, with AUC values of 0.937 and 0.907, accuracy of 0.891 and 0.774, and sensitivity of 0.755 and 0.889, respectively. This study shows that CT-based radiomics combined with an interpretable Random Forest model allows reliable discrimination between ADC and SCC in NSCLC. SHAP analysis improves understanding of the decision-making process and supports the clinical applicability of the model.
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Copyright (c) 2026 Christy Ntambwe Kabamba, Tacite Mazoba Kpanya, Pierre Kafunda Katalayi, Angel Torrado Carvajal, Eugène Mbuyi Mukendi (Author)
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