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Sub-solid lung nodules present unique diagnostic challenges. Pre-surgical characterization of the degree of invasiveness may be inaccurate even with tissue sampling. We hypothesize that the high-throughput information captured by radiomic descriptors on CT images can characterize the invasiveness of sub-solid nodules. In this study, we analyzed immediate pre-surgical CT scans of 72 nodules from 68 patients. Region of interest (ROI) segmentation on the scans was performed by our fellowship trained chest radiologists using the ITK-SNAP software. Feature extraction on ROIs was performed using the CaPTk toolkit. Scan parameter heterogeneity can affect radiomic features. To overcome this, image parameters including scanner manufacturer and voxel spacing parameters were harmonized at each time point using a nested ComBat harmonization technique. Clinical variables of ethnicity, BMI, smoking status and pathological category were protected during harmonization, to prevent the removal of biological variables of interest. Features with negative Mean Decrease in Accuracy (MDA) metric (non-optimal prognostic value) were dropped. Dimensionality of the feature set was reduced using the first radiomic principal component (PC) as a representative feature. Multiple logistic regression analysis using radiomic PC and clinical factors revealed only radiomic PC to be a significant predictor of nodule invasiveness (p < 0.05). A model containing clinical variables gave an accuracy of 73% (AUC=0.58) in identifying invasive sub-solid nodules. The accuracy increased to 93% (AUC-0.88) with the addition of radiomic PC. We further wanted to investigate if the change in the radiomic descriptors of the nodule properties over time, can improve the diagnosis of nodule invasiveness. Thus, from our original set of patients, we identified a subset of 40 nodules from 37 patients, with a total of 2 CT scans (immediate pre-surgical scan and 1 additional time point) and a subset of 34 nodules from 29 patients, with a total of 3 CT scans (immediate pre-surgical scan and 2 additional time points), each scan separated by at least a 12-month from each other. The features were harmonized at each time point. Delta radiomic features were computed (features from immediate pre-surgical scan- previous time point). Dimensionality of the delta radiomics feature set was reduced using the first radiomic principal component (PC) as a representative feature. In the case of longitudinal analysis using two scans, the model containing clinical variables gave an accuracy of 70% (AUC-0.55) in the diagnosis of nodule invasiveness. The accuracy increased to 82% (AUC- 0.65) upon the addition of delta radiomic PC (immediate pre-surgicalfirst time point). In the case of longitudinal analysis using three scans, the model containing clinical variables gave an accuracy of 68% (AUC-0.54) in the diagnosis of nodule invasiveness. The accuracy increased to 79% (AUC- 0.63) upon the addition of the first delta radiomic PC (immediate pre-surgical-first time point) and the second delta radiomic PC (immediate pre-surgical-second time point). Thus, the representative radiomic PCs, describing the change in the properties of the nodules over time, were found to be significant predictors of nodule invasiveness and augmented the performance of standard prognostic clinical factors. Thus, the ability of radiomic descriptors to predict nodule invasiveness could potentially be utilized to facilitate management of sub-solid nodules identified on chest CT imaging.
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