MRI-based radiomics to predict response in locally advanced rectal cancer: comparison of manual and automatic segmentation on external validation in a multicentre study

MRI-based radiomics to predict response in locally advanced rectal cancer: comparison of manual and automatic segmentation on external validation in a multicentre study

Abstract Background Pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer (LARC) is achieved in 15–30% of cases. Our aim was to implement and externally validate a magnetic resonance imaging (MRI)-based radiomics pipeline to predict response to treatmen...

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Bibliographic Details
Main Authors: Arianna Defeudis, Simone Mazzetti, Jovana Panic, Monica Micilotta, Lorenzo Vassallo, Giuliana Giannetto, Marco Gatti, Riccardo Faletti, Stefano Cirillo, Daniele Regge, Valentina Giannini
Format: Article
Language:English
Published: SpringerOpen 2022-05-01
Series:European Radiology Experimental
Subjects:
Artificial intelligence
Machine learning
Multiparametric magnetic resonance imaging
Neoadjuvant therapy
Rectal neoplasms
Online Access:https://doi.org/10.1186/s41747-022-00272-2

Internet

https://doi.org/10.1186/s41747-022-00272-2

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