Texture analysis for survival prediction of pancreatic ductal adenocarcinoma patients with neoadjuvant chemotherapy


Conference Paper

© 2016 SPIE. Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer-related death in the United States. The five-year survival rate for all stages is approximately 6%, and approximately 2% when presenting with distant disease.1 Only 10-20% of all patients present with resectable disease, but recurrence rates are high with only 5 to 15% remaining free of disease at 5 years. At this time, we are unable to distinguish between resectable PDAC patients with occult metastatic disease from those with potentially curable disease. Early classification of these tumor types may eventually lead to changes in initial management including the use of neoadjuvant chemotherapy or radiation, or in the choice of postoperative adjuvant treatments. Texture analysis is an emerging methodology in oncologic imaging for quantitatively assessing tumor heterogeneity that could potentially aid in the stratification of these patients. The present study derives several texture-based features from CT images of PDAC patients, acquired prior to neoadjuvant chemotherapy, and analyzes their performance, individually as well as in combination, as prognostic markers. A fuzzy minimum redundancy maximum relevance method with leave-one-image-out technique is included to select discriminating features from the set of extracted features. With a naive Bayes classifier, the proposed method predicts the 5-year overall survival of PDAC patients prior to neoadjuvant therapy and achieves the best results in terms of the area under the receiver operating characteristic curve of 0:858 and accuracy of 83:0% with four-fold cross-validation techniques.

Full Text

Duke Authors

Cited Authors

  • Chakraborty, J; Langdon-Embry, L; Escalon, JG; Allen, PJ; Lowery, MA; O'Reilly, EM; Do, RKG; Simpson, AL

Published Date

  • January 1, 2016

Published In

Volume / Issue

  • 9784 /

International Standard Serial Number (ISSN)

  • 1605-7422

International Standard Book Number 13 (ISBN-13)

  • 9781510600195

Digital Object Identifier (DOI)

  • 10.1117/12.2214470

Citation Source

  • Scopus