CT Radiomics and Whole Genome Sequencing in Patients with Pancreatic Ductal Adenocarcinoma: Predictive Radiogenomics Modeling

SIMPLE SUMMARY: Linking imaging-derived radiomics features to underlying tumor biology and pathogenesis is a developing field of increasing interest, given the wide availability of imaging data in contrast to costs, expenses and logistical issues of molecular analyses. While invasive tissue sampling remains the gold standard for histologic characterization, the usage of noninvasive imaging techniques for diagnosis and detection of specific tumor characteristics could represent a potential additive or eventually an alternative, especially in patients with advanced, inoperable disease or when inaccessible to biopsy. Biomarkers are continuously expanding and several actionable targets have already been identified in pancreatic ductal adenocarcinoma (PDAC), in order to guide clinical decision making and help to develop novel treatment strategies. Our study indicates acceptable correlation of CT-derived radiomics features and driver gene mutations in PDAC. ABSTRACT: We investigate whether computed tomography (CT) derived radiomics may correlate with driver gene mutations in patients with pancreatic ductal adenocarcinoma (PDAC). In this retrospective study, 47 patients (mean age 64 ± 11 years; range: 42–86 years) with PDAC, who were treated surgically and who underwent preoperative CT imaging at our institution were included in the study. Image segmentation and feature extraction was performed semi-automatically with a commonly used open-source software platform. Genomic data from whole genome sequencing (WGS) were collected from our institution’s web-based resource. Two statistical models were then built, in order to evaluate the predictive ability of CT-derived radiomics feature for driver gene mutations in PDAC. 30/47 of all tumor samples harbored 2 or more gene mutations. Overall, 81% of tumor samples demonstrated mutations in KRAS, 68% of samples had alterations in TP53, 26% in SMAD4 and 19% in CDKN2A. Extended statistical analysis revealed acceptable predictive ability for KRAS and TP53 (Youden Index 0.56 and 0.67, respectively) and mild to acceptable predictive signal for SMAD4 and CDKN2A (Youden Index 0.5, respectively). Our study establishes acceptable correlation of radiomics features and driver gene mutations in PDAC, indicating an acceptable prognostication of genomic profiles using CT-derived radiomics. A larger and more homogenous cohort may further enhance the predictive ability.