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Annals of Surgical Oncology
Published in: Annals of Surgical Oncology 4/2018

01-04-2018 | Pancreatic Tumors

Survival Prediction in Pancreatic Ductal Adenocarcinoma by Quantitative Computed Tomography Image Analysis

Authors: Marc A. Attiyeh, MD, Jayasree Chakraborty, PhD, Alexandre Doussot, MD, PhD, Liana Langdon-Embry, BA, Shiana Mainarich, Mithat Gönen, PhD, Vinod P. Balachandran, MD, Michael I. D’Angelica, MD, Ronald P. DeMatteo, MD, William R. Jarnagin, MD, T. Peter Kingham, MD, Peter J. Allen, MD, Amber L. Simpson, PhD, Richard K. Do, MD, PhD

Published in: Annals of Surgical Oncology | Issue 4/2018

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Abstract

Background

Pancreatic cancer is a highly lethal cancer with no established a priori markers of survival. Existing nomograms rely mainly on post-resection data and are of limited utility in directing surgical management. This study investigated the use of quantitative computed tomography (CT) features to preoperatively assess survival for pancreatic ductal adenocarcinoma (PDAC) patients.

Methods

A prospectively maintained database identified consecutive chemotherapy-naive patients with CT angiography and resected PDAC between 2009 and 2012. Variation in CT enhancement patterns was extracted from the tumor region using texture analysis, a quantitative image analysis tool previously described in the literature. Two continuous survival models were constructed, with 70% of the data (training set) using Cox regression, first based only on preoperative serum cancer antigen (CA) 19-9 levels and image features (model A), and then on CA19-9, image features, and the Brennan score (composite pathology score; model B). The remaining 30% of the data (test set) were reserved for independent validation.

Results

A total of 161 patients were included in the analysis. Training and test sets contained 113 and 48 patients, respectively. Quantitative image features combined with CA19-9 achieved a c-index of 0.69 [integrated Brier score (IBS) 0.224] on the test data, while combining CA19-9, imaging, and the Brennan score achieved a c-index of 0.74 (IBS 0.200) on the test data.

Conclusion

We present two continuous survival prediction models for resected PDAC patients. Quantitative analysis of CT texture features is associated with overall survival. Further work includes applying the model to an external dataset to increase the sample size for training and to determine its applicability.
Appendix
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Metadata
Title
Survival Prediction in Pancreatic Ductal Adenocarcinoma by Quantitative Computed Tomography Image Analysis
Authors
Marc A. Attiyeh, MD
Jayasree Chakraborty, PhD
Alexandre Doussot, MD, PhD
Liana Langdon-Embry, BA
Shiana Mainarich
Mithat Gönen, PhD
Vinod P. Balachandran, MD
Michael I. D’Angelica, MD
Ronald P. DeMatteo, MD
William R. Jarnagin, MD
T. Peter Kingham, MD
Peter J. Allen, MD
Amber L. Simpson, PhD
Richard K. Do, MD, PhD
Publication date
01-04-2018
Publisher
Springer International Publishing
Published in
Annals of Surgical Oncology / Issue 4/2018
Print ISSN: 1068-9265
Electronic ISSN: 1534-4681
DOI
https://doi.org/10.1245/s10434-017-6323-3

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