A multi-modality radiomics-based model for predicting recurrence in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is one of the leading causes of death worldwide. Medical imaging is used to determine cancer staging; however, these images may hold additional information which could be utilized to aid in outcome prediction. A multi-modality radiomics approach incorporating quantitative and qualitative features from the tumor and its surrounding regions, along with clinical features, has yet to be explored. Therefore, we hypothesize that a model containing CT and PET radiomic features, in addition to clinical and qualitative features, has the potential improve risk-stratification of NSCLC patients better than cancer stage alone. Our dataset consisted of 135 NSCLC patients (training: n=94, testing: n=41) who underwent surgical resection. Each region of interest was segmented using a semi-automatic approach on both the pre-treatment CT and PET images. Radiomic features were extracted using the Quantitative Image Feature Engine. A total of 1030 features were extracted including clinical, qualitative, and radiomic features. LASSO regression was used to identify the top features to predict time to recurrence in the training cohort and the model was evaluated in the testing cohort. A total of nine features were selected, including two clinical, one CT, and six PET radiomic features. The model achieved a concordance of 0.81 in the training cohort, which was validated in the testing cohort (concordance=0.79) and outperformed stage alone (concordances=0.68-0.69). This model has the potential to assist physicians in risk-stratifying patients with NSCLC and could be used to identify patients that may benefit from more aggressive or personalized treatment options.