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

Jayasree Chakraborty; Liana Langdon-Embry; Joanna G. Escalon; Peter J. Allen; Maeve A. Lowery; Eileen M. O'Reilly; Richard K. G. Do; Amber L. Simpson

doi:10.1117/12.2214470

21 March 2016 Texture analysis for survival prediction of pancreatic ductal adenocarcinoma patients with neoadjuvant chemotherapy

Jayasree Chakraborty, Liana Langdon-Embry, Joanna G. Escalon, Peter J. Allen, Maeve A. Lowery, Eileen M. O'Reilly, Richard K. G. Do, Amber L. Simpson

Author Affiliations +

Proceedings Volume 9784, Medical Imaging 2016: Image Processing; 97841W (2016) https://doi.org/10.1117/12.2214470
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

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.

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Jayasree Chakraborty, Liana Langdon-Embry, Joanna G. Escalon, Peter J. Allen, Maeve A. Lowery, Eileen M. O'Reilly, Richard K. G. Do, and Amber L. Simpson "Texture analysis for survival prediction of pancreatic ductal adenocarcinoma patients with neoadjuvant chemotherapy", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97841W (21 March 2016); https://doi.org/10.1117/12.2214470

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