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

Autor: Zaid M; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA., Widmann L; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA., Dai A; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA., Sun K; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA., Zhang J; Department of Experimental Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA., Zhao J; Sheikh Ahmed Center for Pancreatic Cancer Research, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA., Hurd MW; Sheikh Ahmed Center for Pancreatic Cancer Research, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA., Varadhachary GR; Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA., Wolff RA; Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA., Maitra A; Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA., Katz MHG; Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA., Herman JM; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA., Wang H; Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA., Knopp MV; Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA., Williams TM; Department of Radiation Oncology, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA., Bhosale P; Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA., Tamm EP; Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA., Koay EJ; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Jazyk: angličtina
Zdroj: Cancers [Cancers (Basel)] 2020 Dec 05; Vol. 12 (12). Date of Electronic Publication: 2020 Dec 05.
DOI: 10.3390/cancers12123656
Abstrakt: 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.
Databáze: MEDLINE
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