Development and external evaluation of a self-learning auto-segmentation model for Colorectal Cancer Liver Metastases Assessment (COALA)

Developed colorectal liver metastases segmentation model to facilitate total tumor volume assessment. Model achieved high performance on internal and external test sets. Model can improve prognostic stratification and treatment planning for colorectal liver metastases.

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Publication Details

Journal
Insights into Imaging
Published
2024-11-22
DOI
https://doi.org/10.1186/s13244-024-01820-7
Citations
7
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
2.83

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article

Development and external evaluation of a self-learning auto-segmentation model for Colorectal Cancer Liver Metastases Assessment (COALA)

Koert P. de Jong, T. Chapelle, Efstratios Gavves, Marinde J. G. Bond et al.
7 citations
Insights into Imaging
Radiomics and Machine Learning in Medical Imaging
2.83
article

Development and external evaluation of a self-learning auto-segmentation model for Colorectal Cancer Liver Metastases Assessment (COALA)

Koert P. de Jong, T. Chapelle, Efstratios Gavves, Marinde J. G. Bond, Cornelis Verhoef, Leonard Bereska, Inez M. Verpalen, John J. Hermans, Kathryn L. Colborn, Marc G. Besselink, Nina J. Wesdorp, Gunter Kemmerich, Davit L. Aghayan, Henk A. Marquering, Håvard Bjørke Jenssen, Rutger‐Jan Swijnenburg, Mike S.L. Liem, Caroline S. Verbeke, Mirko D’Onofrio, Giuseppe Malleo, Giovanni Marchegiani, Shira Moos, Knut Jørgen Labori, Egidijus Pelanis, Benedict Kinny‐Köster, Michiel Zeeuw, Ronald M. van Dam, Cornelis J.A. Punt, Thomas F. Stoop, Matteo Fassan, Wouter K. G. Leclercq, Michael F. Gerhards, Philipp Mayer, Toshimasa Clark, Filippo Crimì, I. Nota, Arantza Fariña Sarasqueta, Joost Huiskens, Daniela Bassi, Claudio Luchini, Janneke van den Bergh, D. van der Meulen, Krijn P. van Lienden, Mattia Ballo, Geert Kazemier, Mugur Grasu, Joost M. Klaase, Riccardo Boetto, Roberto Salvia, Mihai Dan Pomohaci, Dirk J. Grünhagen, Alberto Balduzzi, Ioana Gabriela Lupescu, Riccardo Carandina, Åsmund Avdem Fretland, Arjen M. Rijken, Johannes H.W. de Wilt, Ammar A. Javed, Bjørn Edwin, Christoph Michalski, Boris V. Janssen, Trygve Syversveen, Jacqueline I. Bereska, Jeffrey B. Kaplan, Cristian Anghel, Marco Del Chiaro, Christopher L. Wolfgang, Martinus J. van Amerongen, Giulia Zamboni, Jaap Stoker, Theo Ruers, Lodewijk J. Wagenaar, Jan‐Hein T. M. van Waesberghe, Marc Engelbrecht, Thomas M. van Gulik, Gijs A Patijn, I. Quintus Molenaar, Niels F. M. Kok, Martin Loos, Finn Kristian Kolrud, Riccardo di Robertis
article en
7 citations

Abstract

Developed colorectal liver metastases segmentation model to facilitate total tumor volume assessment. Model achieved high performance on internal and external test sets. Model can improve prognostic stratification and treatment planning for colorectal liver metastases.

Insights into ImagingVol. 15(1)
University of Verona (IT), Oslo University Hospital (NO), Amsterdam University College (NL), University Medical Center Groningen (NL), University of Padua (IT), Radboud University Nijmegen (NL), University of Groningen (NL), Utrecht University (NL), Heidelberg University (DE), Dutch Cancer Society (NL), Clinical Emergency Hospital Bucharest (RO), Maastricht University Medical Centre (NL), Medisch Spectrum Twente (NL), University Hospital Heidelberg (DE), Radboud University Medical Center (NL), The Netherlands Cancer Institute (NL), University Medical Center Utrecht (NL), NYU Langone Health (US), Antwerp University Hospital (BE), St. Antonius Ziekenhuis (NL), OLVG (NL), Amphia Ziekenhuis (NL), Amsterdam Neuroscience (NL), Máxima Medisch Centrum (NL), Sint Maartenskliniek (NL), Isala (NL), Erasmus MC Cancer Institute (NL), Amsterdam University Medical Centers (NL), Cancer Center Amsterdam (NL), University of Colorado Anschutz Medical Campus (US), Amsterdam University of Applied Sciences (NL), Vrije Universiteit Amsterdam (NL), University of Amsterdam (NL)
KWF Kankerbestrijding, Cancer Center Amsterdam
Openalex Percentile: Top 9%
Radiomics and Machine Learning in Medical Imaging
2.83
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