Background: ComBat is a promising harmonization method for radiomic features, but it cannot harmonize simultaneously by multiple batch effects and shows reduced performance in the setting of bimodal distributions and unknown clinical/batch variables. In this study, we develop and evaluate two iterative ComBat approaches (Nested and Nested+GMM ComBat) to address these limitations and improve radiomic feature harmonization performance. Methods: In Nested ComBat, radiomic features are sequentially harmonized by multiple batch effects with order determined by the permutation associated with the smallest number of features with statistically significant differences due to batch effects. In Nested+GMM ComBat, a Gaussian mixture model is used to identify a scan grouping associated with a latent variable from the observed feature distributions to be added as a batch effect to Nested ComBat. These approaches were used to harmonize differences associated with contrast enhancement, spatial resolution due to reconstruction kernel, and manufacturer in radiomic datasets generated by using CapTK and PyRadiomics to extract features from lung CT datasets (Lung3 and Radiogenomics). Differences due to batch effects in the original data and data harmonized with standard ComBat, Nested ComBat, and Nested+GMM ComBat were assessed. Results: Nested ComBat exhibits similar or better performance compared to standard ComBat, likely due to bimodal feature distributions. Nested+GMM ComBat successfully harmonized features with bimodal distributions and in most cases showed superior harmonization performance when compared to Nested and standard ComBat. Conclusions: Our findings show that Nested ComBat can harmonize by multiple batch effects and that Nested+GMM ComBat can improve harmonization of bimodal features.
Prognosis plays a crucial role in the customization of lung cancer care. The effective prediction of treatment response is essential to tailor treatment decisions to lung cancer patients. Molecular characterization of tumors using genomics-based approaches is important for personalized treatment planning, however, repeated tumor biopsies should be performed to capture their molecular heterogeneity, putting patients at risk of procedural complications such as a pneumothorax. Furthermore, the recent addition of immunotherapy after chemoradiotherapy for patients with unresectable stage III NSCLC can improve survival outcomes. The survival benefit achieved by stage III NSCLC patients undergoing chemoradiation is of interest since currently available biomarkers are inadequate to predict which patients are most likely to benefit from immunotherapy for first-line treatment along with chemoradiation. In this study, we investigate the association between local failure-free survival and radiomic features extracted from CT scans of stage III NSCLC adenocarcinoma patients. We retrospectively analyzed a well-curated cohort of 89 non-contrast enhanced CT scans from patients receiving homogeneous chemoradiation treatment. A set of 107 radiomic features was extracted using the pyradiomics package. In univariate analysis we performed log-rank tests per feature to predict risk of local failure. In multivariate analysis we applied principal component analysis to fit a Cox model to predict local failure-free survival. Univariate analysis showed that no individual radiomic feature can predict local failure-free survival, while multivariate analysis gave a C-index = 0.70, 95% CI = [0.56,0.85]. We conclude that radiomic features from CT scans, can predict local failure-free survival in stage III NSCLC.
Background: Imaging biomarkers derived from quantitative computed tomography (QCT) enable to quantify lung diseases and to distinguish their phenotypes. However, variability in radiomic features can have an impact on their diagnosis and prognosis significance. We aim to assess the effect of CT image reconstruction parameters on radiomic features in the PROSPR lung cancer screening cohort (1); thereby identifying more robust imaging features across heterogeneous CT images. Methods: CT feature extraction analysis was performed using a lattice-based texture estimation for data (n = 330) collected from a single CT scanner (Siemens Healthineers, Erlangen, Germany) with two different sets of image reconstruction kernels (medium (I30f), sharp (I50f)). A total of 26 features from three major statistical approaches, graylevel histogram, co-occurrence, and run-length, were computed. Features were calculated and averaged within a range of window sizes (W) from 4mm to 20mm. Furthermore, an unsupervised hierarchal clustering was applied to the features to identify distinct phenotypic patterns for the two kernels. The difference across phenotypes by age, sex, and Lung-Rads was assessed. Results: The results showed two distinct subtypes for two kernels across different window sizes. The heat map generated by radiomic features of the sharper kernel provided more distinct patterns compared to the medium kernel. The extracted features across the two kernels and their corresponding clusters were compared based on different clinical features. Conclusions: Our results suggest a set of radiomic features across different kernels can distinguish distinct phenotypes and can also help to assess the sensitivity of texture analysis to CT variabilities; helping for a better characterization of CT heterogeneity.
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