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

Abstract Objectives Total tumor volume (TTV) is associated with overall and recurrence-free survival in patients with colorectal cancer liver metastases (CRLM). However, the labor-intensive nature of such manual assessments has hampered the clinical adoption of TTV as an imaging biomarker. This stud...

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Автори:	Jacqueline I. Bereska, Michiel Zeeuw, Luuk Wagenaar, Håvard Bjørke Jenssen, Nina J. Wesdorp, Delanie van der Meulen, Leonard F. Bereska, Efstratios Gavves, Boris V. Janssen, Marc G. Besselink, Henk A. Marquering, Jan-Hein T. M. van Waesberghe, Davit L. Aghayan, Egidijus Pelanis, Janneke van den Bergh, Irene I. M. Nota, Shira Moos, Gunter Kemmerich, Trygve Syversveen, Finn Kristian Kolrud, Joost Huiskens, Rutger-Jan Swijnenburg, Cornelis J. A. Punt, Jaap Stoker, Bjørn Edwin, Åsmund A. Fretland, Geert Kazemier, Inez M. Verpalen, for the Pancreatobiliary and Hepatic Artificial Intelligence Research (PHAIR) consortium, the Dutch Colorectal Cancer Group Liver Expert Panel
Формат:	Artigo
Мова:	Inglês
Опубліковано:	SpringerOpen 2024-11-01
Серія:	Insights into Imaging
Предмети:	Colorectal neoplasms Liver Biomarkers Tumor Artificial intelligence
Онлайн доступ:	https://doi.org/10.1186/s13244-024-01820-7
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Development and external evaluation of a self-learning auto-segmentation model for Colorectal Cancer Liver Metastases Assessment (COALA)

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