Tumor immune infiltrates and overall survival in non-small cell lung cancer are associated with an unsupervised radiomic signature.

Abstract

e20525 Background: We investigated whether radiomic features derived from computed tomography (CT) scans are predictive of survival and are correlated with immune cell types in non-small cell lung cancer (NSCLC), employing a mathematical network-based approach. Methods: DICOM data of pre-treatment CT scans and associated clinical variables for 116 NSCLC patients were downloaded from The Cancer Imaging Atlas (TCIA). RNA-Seq gene expression profiles for the matched cases were downloaded from the GEO database (GSE103584). CIBERSORT scores that consist of 22 immune cell types were generated using the R library (CIBERSORT) on the RNA-Seq gene expression profiles. From contoured tumors on CT scans, 83 radiomic features including tumor volume were extracted, utilizing the CERR radiomics toolbox. Radiomic networks were constructed with a threshold of Spearman correlation coefficient of 0.85 between each pair of radiomic features. The mathematical network-based k-means clustering method was applied to the largest connected network component that consists of 40 radiomic features, using the Wasserstein distance from optimal mass transport (OMT), to minimize the summed distance between k partitioned centroids and their respective data points. For the sub-groups identified, Kaplan-Meier survival analysis was conducted with log-rank test and CIBERSORT scores and clinical variables were then compared among the sub-groups using Kruskal-Wallis test. Results: The median age was 69 years (range: 46–85 years). Eighty-seven patients (75.0%) were males. Most patients were former (N = 75, 64.7%) or current smokers (N = 24, 20.7%). The median follow-up time (last follow-up or death) was 44 months and 75 patients were alive at last contact. The Wasserstein distance-based k-means method resulted in 3 sub-groups (high: N = 32, medium: N = 45, low-risk: N = 39). Kaplan-Meier analysis on the 3 sub-groups showed a significant difference in overall survival with log-rank p = 0.02. There were no significant differences for the variables mentioned above among the sub-groups. Two immune cell types showed statistically significant differences among the sub-groups: mast cells resting with p = 0.004 and M2 macrophages with p = 0.013 using Kruskal-Wallis test. The values for both immune cell types increased from high < medium < low-risk group. There was a significant volume difference with p < 0.001, but the difference did not fully account for the survival difference; the median volume was 30.8 cc, 2.7 cc, and 12.8 cc for high, medium, and low-risk groups, respectively. Conclusions: A radiomic network analysis, utilizing a mathematical network-based method, resulted in sub-groups with the survival difference in NSCLC patients. Furthermore, these sub-groups had significantly different immune cell types likely associated with the survival.