Preliminary study of tumor heterogeneity in imaging predicts two year survival in pancreatic cancer patients Journal Article
| Authors: | Chakraborty, J.; Langdon-Embry, L.; Cunanan, K. M.; Escalon, J. G.; Allen, P. J.; Lowery, M. A.; O’Reilly, E. M.; Gönen, M.; Do, R. G.; Simpson, A. L. |
| Article Title: | Preliminary study of tumor heterogeneity in imaging predicts two year survival in pancreatic cancer patients |
| Abstract: | Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers in the United States with a five-year survival rate of 7.2% for all stages. Although surgical resection is the only curative treatment, currently we are unable to differentiate between resectable patients with occult metastatic disease from those with potentially curable disease. Identification of patients with poor prognosis via early classification would help in initial management including the use of neoadjuvant chemotherapy or radiation, or in the choice of postoperative adjuvant therapy. PDAC ranges in appearance from homogeneously isoattenuating masses to heterogeneously hypovascular tumors on CT images; hence, we hypothesize that heterogeneity reflects underlying differences at the histologic or genetic level and will therefore correlate with patient outcome. We quantify heterogeneity of PDAC with texture analysis to predict 2-year survival. Using fuzzy minimum-redundancy maximum-relevance feature selection and a naive Bayes classifier, the proposed features achieve an area under receiver operating characteristic curve (AUC) of 0.90 and accuracy (Ac) of 82.86% with the leave-one-image-out technique and an AUC of 0.80 and Ac of 75.0% with three-fold cross-validation. We conclude that texture analysis can be used to quantify heterogeneity in CT images to accurately predict 2-year survival in patients with pancreatic cancer. From these data, we infer differences in the biological evolution of pancreatic cancer subtypes measurable in imaging and identify opportunities for optimized patient selection for therapy. © 2017 Chakraborty et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
| Keywords: | adult; clinical article; controlled study; aged; overall survival; gemcitabine; cancer patient; computer assisted tomography; multiple cycle treatment; bayesian learning; cohort analysis; retrospective study; prediction; algorithm; pancreas adenocarcinoma; neoadjuvant chemotherapy; oxaliplatin; genetic heterogeneity; tumor heterogeneity; support vector machine; human; male; female; article; fuzzy minimum redundancy maximum relevance |
| Journal Title: | PLoS ONE |
| Volume: | 12 |
| Issue: | 12 |
| ISSN: | 1932-6203 |
| Publisher: | Public Library of Science |
| Date Published: | 2017-12-07 |
| Start Page: | e0188022 |
| Language: | English |
| DOI: | 10.1371/journal.pone.0188022 |
| PROVIDER: | scopus |
| PMCID: | PMC5720792 |
| PUBMED: | 29216209 |
| DOI/URL: | |
| Notes: | Article -- Export Date: 2 January 2018 -- Source: Scopus |
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