Proceedings Article | 3 April 2024
KEYWORDS: Radiomics, Tumors, Tumor growth modeling, Brain-machine interfaces, Image segmentation, Feature extraction, Computed tomography, Lung cancer, Statistical analysis, Matrices
Tumor expression of PDL1 is a suboptimal biomarker to predict response to anti-PD1/PDL1 therapy, including pembrolizumab (PEMBRO). We hypothesize that radiomic features, high-throughput descriptors of tumor heterogeneity, can improve performance of models built using established clinical biomarkers for prediction of overall survival and presence of relevant genetic mutations KRAS and STK11 (significant in patients selected for PEMBRO-based therapy). Our study includes baseline CT scans from 308 patients with stage 4 NSCLC, treated with first line PEMBRO, from a single-center retrospective study conducted at Hospital of the University of Pennsylvania (November 2016-December 2020). Region of interest (ROI) segmentation on 3D tumor CT volumes was performed using ITK-SNAP. Feature extraction on ROIs was performed using CaPTk. Scan parameter heterogeneity (presence/absence of contrast enhancement, reconstruction kernel and voxel spacing) was mitigated using a nested ComBat harmonization technique, while protecting the variable BMI to prevent removal of heterogeneity arising from biological variation during harmonization, noting BMI had statistically significant correlation with first radiomic principal component (PC). Dimensionality of resultant set was reduced using an unsupervised hierarchical clustering technique. These identified radiomic phenotypes, when combined with clinical variables (longest tumor diameter at baseline, tumor PDL1 expression, age, sex, race, smoking status, BMI), increased the five-fold cross-validated c-score in the prediction of overall survival from 0.60 [0.54,0.63] to 0.64 [0.57,0.66]. For a subset of 287 patients (further sub-divided based on histology: squamous vs. non-squamous), the radiomic phenotypes were used to identify patients with mutations in KRAS and STK11, using linear SVM classifier: KRAS (squamous: 65% (AUC-0.61), non-squamous: 74% (AUC-0.63)), STK11 (squamous: 76% (AUC-0.62), non-squamous: 82% (AUC-0.68)). The radiomic phenotypes improve the prognostic value of clinical biomarkers in the prediction of overall survival and can identify patients with gene mutations that are significant in patient selection for PEMBRO-based therapy. Our future work involves building a multi-omic (radiomic, genomic, clinical) predictor of progression-free survival, to study patient response to PEMBRO-based therapy.
