Background: Non-small cell lung cancer (NSCLC) histological subtypes impact treatment decisions. While pre-surgical histopathological examination is ideal, it's not always possible. CT radiomic analysis shows promise to predict NSCLC histological subtypes. Objective: To use CT scan radiomic analysis from NSCLC-Radiomics data to predict NSCLC histological subtypes using machine learning and deep learning models. Methods: 422 CT scans from The Cancer Imaging Archive (TCIA) were analyzed. Primary neoplasms were segmented by expert radiologists. Using PyRadiomics, 2446 radiomic features were extracted; post-selection, 179 features remained. Machine learning models like logistic regression, SVM, random forest, XGBoost, LightGBM, and CatBoost were employed, alongside a deep neural network (DNN) model. Results: Random forest demonstrated the highest accuracy at 78% (95% CI: 70%-84%) and AUC-ROC at 94% (95% CI: 90%-96%). LightGBM, XGBoost, and CatBoost had AUC-ROC values of 95%, 93%, and 93% respectively. The DNN's AUC was 94.4% (95% CI: 94.1% to 94.6%). Logistic regression had the least efficacy. For histological subtype prediction, random forest, boosting models, and DNN were superior. Conclusions: Quantitative radiomic analysis with machine learning can accurately determine NSCLC histological subtypes. Random forest, ensemble models, and DNNs show significant promise for pre-operative NSCLC classification, which can streamline therapy decisions. Keywords: Lung Cancer, Computed Tomography, Radiomics, Histopathology, Artificial Intelligence, Classification 

The authors have declared no competing interest.

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Images and segmentation data are available from NSCLC-Radiomics in The Cancer Imaging Archive. Aerts HJWL, Wee L, Rios Velazquez E, Leijenaar RTH, Parmar C, Grossmann P, et al. Data From NSCLC-Radiomics [Internet]. The Cancer Imaging Archive; 2019 [cited 2023 Aug 15]. Available from: https://wiki.cancerimagingarchive.net/x/FgL1

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