Predictive Modeling for Voxel-Based Quantification of Imaging-Based Subtypes of Pancreatic Ductal Adenocarcinoma (PDAC): A Multi-Institutional Study.

Abstract

Simple SummaryPreviously, we demonstrated that qualitative scoring of pancreatic ductal adenocarcinoma (PDAC) tumors on computed tomography (CT) scans (delta) is biologically and clinically relevant, whereby tumors with a conspicuous border (high delta) show more aggressive biology and are associated with worse clinical outcomes when compared to those with an inconspicuous border (low delta). However, in some cases, a visual classification can be challenging and subjective. Here, we used machine learning and quantitative approaches for a multi-institutional dataset to build a biologically and clinically relevant model that can quantitatively identify these imaging-based subtypes of PDAC from routine CT scans. Our results showed that the quantitative classification (q-delta) had high correlation with the gold standard qualitative scoring in internal and external datasets. Further, q-delta classification was demonstrated to be associated with the clinical outcome and the stromal heterogeneity of the PDAC tumors. High intra- and interrater agreement scores indicate the reproducibility of the results.Previously, we characterized qualitative imaging-based subtypes of pancreatic ductal adenocarcinoma (PDAC) on computed tomography (CT) scans. Conspicuous (high delta) PDAC tumors are more likely to have aggressive biology and poorer clinical outcomes compared to inconspicuous (low delta) tumors. Here, we developed a quantitative classification of this imaging-based subtype (quantitative delta; q-delta). Retrospectively, baseline pancreatic protocol CT scans of three cohorts (cohort#1 = 101, cohort#2 = 90 and cohort#3 = 16 [external validation]) of patients with PDAC were qualitatively classified into high and low delta. We used a voxel-based method to volumetrically quantify tumor enhancement while referencing normal-pancreatic-parenchyma and used machine learning-based analysis to build a predictive model. In addition, we quantified the stromal content using hematoxylin- and eosin-stained treatment-naïve PDAC sections. Analyses revealed that PDAC quantitative enhancement values are predictive of the qualitative delta scoring and were used to build a classification model (q-delta). Compared to high q-delta, low q-delta tumors were associated with improved outcomes, and the q-delta class was an independent prognostic factor for survival. In addition, low q-delta tumors had higher stromal content and lower cellularity compared to high q-delta tumors. Our results suggest that q-delta classification provides a clinically and biologically relevant tool that may be integrated into ongoing and future clinical trials.