A machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma from preoperative diffusion-weighted imaging

A machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma from preoperative diffusion-weighted imaging

Abstract Background To develop a supervised machine learning (ML) algorithm predicting above- versus below-median overall survival (OS) from diffusion-weighted imaging-derived radiomic features in patients with pancreatic ductal adenocarcinoma (PDAC). Methods One hundred two patients with histopatho...

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Bibliographic Details
Main Authors: Georgios Kaissis, Sebastian Ziegelmayer, Fabian Lohöfer, Hana Algül, Matthias Eiber, Wilko Weichert, Roland Schmid, Helmut Friess, Ernst Rummeny, Donna Ankerst, Jens Siveke, Rickmer Braren
Format: Article
Language:English
Published: SpringerOpen 2019-10-01
Series:European Radiology Experimental
Subjects:
Machine learning
Diffusion magnetic resonance imaging
Pancreatic carcinoma
Radiomics
Survival analysis
Online Access:http://link.springer.com/article/10.1186/s41747-019-0119-0

Internet

http://link.springer.com/article/10.1186/s41747-019-0119-0

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