Purpose: Pancreatic ductal adenocarcinoma (PDAC) is a heterogeneous disease with variable presentations and natural histories of disease. We hypothesized that different morphologic characteristics of PDAC tumors on diagnostic computed tomography (CT) scans would reflect their underlying biology. Experimental Design: We developed a quantitative method to categorize the PDAC morphology on pretherapy CT scans from multiple datasets of patients with resectable and metastatic disease and correlated these patterns with clinical/pathologic measurements. We modeled macroscopic lesion growth computationally to test the effects of stroma on morphologic patterns, hypothesizing that the balance of proliferation and local migration rates of the cancer cells would determine tumor morphology. Results: In localized and metastatic PDAC, quantifying the change in enhancement on CT scans at the interface between tumor and parenchyma (delta) demonstrated that patients with conspicuous (high-delta) tumors had significantly less stroma, higher likelihood of multiple common pathway mutations, more mesenchymal features, higher likelihood of early distant metastasis, and shorter survival times compared with those with inconspicuous (low-delta) tumors. Pathologic measurements of stromal and mesenchymal features of the tumors supported the mathematical model's underlying theory for PDAC growth. Conclusion: At baseline diagnosis, a visually striking and quantifiable CT imaging feature reflects the molecular and pathological heterogeneity of PDAC, and may be used to stratify patients into distinct subtypes. Moreover, growth patterns of PDAC may be described using physical principles, enabling new insights into diagnosis and treatment of this deadly disease. Clin Cancer Res; 1–12. ©2018 AACR.

Oral stents have been shown to reduce the deleterious effects of head and neck radiotherapy through the displacement of normal tissues away from the areas of high dose irradiation. While these stents are commonly used in the treatment of patients with head and neck cancer at many large academic cancer centers, their use is much more limited outside of these institutions due to the time and expertise required for their fabrication. In the study, we describe a novel method to design and manufacture oral stents from routine computed tomography (CT) imaging studies through the use of 3D printing technologies. Our proposed method may help to greatly expand access to these beneficial devices for patients undergoing radiation treatment at centers without access to dental and oral/maxillofacial specialists.

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.

We have previously shown that ablative radiotherapy (A-RT) with a biologically effective dose (BED₁₀) ≥ 80.5 Gy for patients with unresectable intrahepatic cholangiocarcinoma (ICC) is associated with longer survival. Despite recent large-scale sequencing efforts in ICC, outcomes following RT based on genetic alterations have not been described. We reviewed records of 156 consecutive patients treated with A-RT for unresectable ICC from 2008 to 2020. For 114 patients (73%), next-generation sequencing provided molecular profiles. The overall survival (OS), local control (LC), and distant metastasis-free survival (DMFS) were estimated using the Kaplan–Meier method. Univariate and multivariable Cox analyses were used to determine the associations with the outcomes. The median tumor size was 7.3 (range: 2.2–18.2) cm. The portal vein thrombus (PVT) was present in 10%. The RT median BED₁₀ was 98 Gy (range: 81–144 Gy). The median (95% confidence interval) follow-up was 58 (42–104) months from diagnosis and 39 (33–74) months from RT. The median OS was 32 (29–35) months after diagnosis and 20 (16–24) months after RT. The one-year OS, LC, and intrahepatic DMFS were 73% (65–80%), 81% (73–87%), and 34% (26–42%). The most common mutations were in IDH1 (25%), TP53 (22%), ARID1A (19%), and FGFR2 (13%). Upon multivariable analysis, the factors associated with death included worse performance status, larger tumor, metastatic disease, higher CA 19-9, PVT, satellitosis, and IDH1 and PIK3CA mutations. TP53 mutation was associated with local failure. Further investigation into the prognostic value of individual mutations and combinations thereof is warranted.