Survival prediction in pancreatic ductal adenocarcinoma by quantitative computed tomography image analysis Journal Article


Authors: Attiyeh, M. A.; Chakraborty, J.; Doussot, A.; Langdon-Embry, L.; Mainarich, S.; Gönen, M.; Balachandran, V. P.; D’Angelica, M. I.; DeMatteo, R. P.; Jarnagin, W. R.; Kingham, T. P.; Allen, P. J.; Simpson, A. L.; Do, R. K.
Article Title: Survival prediction in pancreatic ductal adenocarcinoma by quantitative computed tomography image analysis
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. © 2018, Society of Surgical Oncology.
Journal Title: Annals of Surgical Oncology
Volume: 25
Issue: 4
ISSN: 1068-9265
Publisher: Springer  
Date Published: 2018-04-01
Start Page: 1034
End Page: 1042
Language: English
DOI: 10.1245/s10434-017-6323-3
PROVIDER: scopus
PUBMED: 29380093
DOI/URL:
Notes: Article -- Export Date: 2 April 2018 -- Source: Scopus
Altmetric Score
MSK Authors
  1. Ronald P Dematteo
    581 Dematteo
  2. Mithat Gonen
    680 Gonen
  3. Peter Allen
    417 Allen
  4. William R Jarnagin
    561 Jarnagin
  5. Kinh Gian Do
    102 Do
  6. T Peter Kingham
    272 Kingham
  7. Amber L Simpson
    45 Simpson
  8. Alexandre Florent Doussot
    15 Doussot
  9. Marc   Attiyeh
    19 Attiyeh