Computed tomography imaging radiomics: a novel approach to early-stage non-small cell lung cancer prediction

Abstract

Radiomics shows promise as non-invasive method for enhancing clinical staging of non-small cell lung cancer (NSCLC) by using quantitative information from computed tomography (CT) scans. This study presents radiomics-based machine learning (ML) approach for staging NSCLC patients into clinical stages I, II, and III based on shape, intensity, and texture features. CT images of 369 NSCLC patients are collected from the cancer imaging archive (TCIA), and extracted 107 radiomic features following image biomarker standardization initiative (IBSI) protocol. The analysis of the sources of variability due to different imaging protocols, using principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), showed that these effects were resolved through ComBat harmonization. Recursive feature elimination (RFE) and least absolute shrinkage and selection operator (LASSO) are used for feature selection. Five ML algorithms: logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost) were used, with an 80:20 train-test split and 10-fold cross-validation. The classifier is assessed using accuracy, sensitivity, specificity, F1-score, and area under the receiver operating characteristic (AUROC) curve. The RFE and RF classifier combination performed the best with AUROC of 0.9307 and accuracy of 0.8114. This study illustrates the use of radiomics models in non-invasive classification of NSCLC stages and it is role in clinical decision making.