Next Article in Journal
Molecular Alterations in Dog Pheochromocytomas and Paragangliomas
Next Article in Special Issue
Role of c-MET Inhibitors in Overcoming Drug Resistance in Spheroid Models of Primary Human Pancreatic Cancer and Stellate Cells
Previous Article in Journal
Amino Acid Deprivation-Induced Autophagy Requires Upregulation of DIRAS3 through Reduction of E2F1 and E2F4 Transcriptional Repression
Previous Article in Special Issue
Clinical Trials Targeting the Stroma in Pancreatic Cancer: A Systematic Review and Meta-Analysis
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle

Use of Machine-Learning Algorithms in Intensified Preoperative Therapy of Pancreatic Cancer to Predict Individual Risk of Relapse

1
Department of Medical Oncology, Clínica Universidad de Navarra, Pamplona, 31008 Navarra, Spain
2
Department of Mathematics and Statistics, Pharmamodelling, Noain, 31110 Navarra, Spain
3
Gastrointestinal Oncology Service, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
4
Weill Cornell Medical College, New York, NY 10065, USA
5
Human Oncology and Pathogenesis Program, Collaborative Research Centers, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
6
Department of Radiation Oncology, Clínica Universidad de Navarra, Pamplona, 31008 Navarra, Spain
7
Department of Pathology, Hospital Universitario Rey Juan Carlos, Móstoles, 28933 Madrid, Spain
8
Department of HPB Surgery, Clínica Universidad de Navarra, Pamplona, 31008 Navarra, Spain
9
Department of Gastroenterology, Clínica Universidad de Navarra, Pamplona, 31008 Navarra, Spain
10
Department of Radiology, Clínica Universidad de Navarra, Pamplona, 31008 Navarra, Spain
*
Authors to whom correspondence should be addressed.
Cancers 2019, 11(5), 606; https://doi.org/10.3390/cancers11050606
Received: 8 April 2019 / Revised: 24 April 2019 / Accepted: 26 April 2019 / Published: 30 April 2019
(This article belongs to the Special Issue Advances in Pancreatic Cancer Research)
  |  
PDF [1333 KB, uploaded 30 April 2019]
  |  

Abstract

Background: Although surgical resection is the only potentially curative treatment for pancreatic cancer (PC), long-term outcomes of this treatment remain poor. The aim of this study is to describe the feasibility of a neoadjuvant treatment with induction polychemotherapy (IPCT) followed by chemoradiation (CRT) in resectable PC, and to develop a machine-learning algorithm to predict risk of relapse. Methods: Forty patients with resectable PC treated in our institution with IPCT (based on mFOLFOXIRI, GEMOX or GEMOXEL) followed by CRT (50 Gy and concurrent Capecitabine) were retrospectively analyzed. Additionally, clinical, pathological and analytical data were collected in order to perform a 2-year relapse-risk predictive population model using machine-learning techniques. Results: A R0 resection was achieved in 90% of the patients. After a median follow-up of 33.5 months, median progression-free survival (PFS) was 18 months and median overall survival (OS) was 39 months. The 3 and 5-year actuarial PFS were 43.8% and 32.3%, respectively. The 3 and 5-year actuarial OS were 51.5% and 34.8%, respectively. Forty-percent of grade 3-4 IPCT toxicity, and 29.7% of grade 3 CRT toxicity were reported. Considering the use of granulocyte colony-stimulating factors, the number of resected lymph nodes, the presence of perineural invasion and the surgical margin status, a logistic regression algorithm predicted the individual 2-year relapse-risk with an accuracy of 0.71 (95% confidence interval [CI] 0.56–0.84, p = 0.005). The model-predicted outcome matched 64% of the observed outcomes in an external dataset. Conclusion: An intensified multimodal neoadjuvant approach (IPCT + CRT) in resectable PC is feasible, with an encouraging long-term outcome. Machine-learning algorithms might be a useful tool to predict individual risk of relapse. A small sample size and therapy heterogeneity remain as potential limitations. View Full-Text
Keywords: pancreatic; resectable; neoadjuvant chemotherapy; neoadjuvant chemoradiation; machine-learning; model-based prediction pancreatic; resectable; neoadjuvant chemotherapy; neoadjuvant chemoradiation; machine-learning; model-based prediction
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Sala Elarre, P.; Oyaga-Iriarte, E.; Yu, K.H.; Baudin, V.; Arbea Moreno, L.; Carranza, O.; Chopitea Ortega, A.; Ponz-Sarvise, M.; Mejías Sosa, L.D.; Rotellar Sastre, F.; Larrea Leoz, B.; Iragorri Barberena, Y.; Subtil Iñigo, J.C.; Benito Boíllos, A.; Pardo, F.; Rodríguez Rodríguez, J. Use of Machine-Learning Algorithms in Intensified Preoperative Therapy of Pancreatic Cancer to Predict Individual Risk of Relapse. Cancers 2019, 11, 606.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Cancers EISSN 2072-6694 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top