Development and external evaluation of a self-learning auto-segmentation model for Colorectal Cancer Liver Metastases Assessment (COALA)
Authors: Bereska Jacqueline I. , Zeeuw Michiel , Wagenaar Luuk , Jenssen Håvard Bjørke , Wesdorp Nina J. , van der Meulen Delanie , Bereska Leonard F. , Gavves Efstratios , Janssen Boris V. , Besselink Marc G. , Marquering Henk A. , van Waesberghe Jan-Hein T. M. , Aghayan Davit L. , Pelanis Egidijus , van den Bergh Janneke , Nota Irene I. M. , Moos Shira , Kemmerich Gunter , Syversveen Trygve , Kolrud Finn Kristian , Huiskens Joost , Swijnenburg Rutger-Jan , Punt Cornelis J. A. , Stoker Jaap , Edwin Bjørn , Fretland Åsmund A. , Kazemier Geert , Verpalen Inez M. ,   for the Pancreatobiliary and Hepatic Artificial Intelligence Research (PHAIR) consortium, Marchegiani Giovanni , Bassi Domenico , Boetto Riccardo , Ballo Mattia , Carandina Riccardo , Crimi Filippo , Fassan Matteo , Farina Arantza , Verbeke Caroline , Labori Knut Jørgen , Fretland Åsmund , D’Onofrio Mirko , Zamboni Giulia , di Robertis Riccardo , Luchini Claudio , Balduzzi Alberto , Malleo Giuseppe , Salvia Roberto , Wolfgang Christopher , Javed Ammar , Colborn Katie , Chiaro Marco Del , Kaplan Jeffrey , Clark Toshimasa , Stoop Thomas , Lupescu Ioana , Grasu Cristian Mugur , Anghel Cristian , Pomohaci Mihai Dan , Mayer Philipp , Kinny-Köster Benedict , Loos Martin , Michalski Christoph ,   the Dutch Colorectal Cancer Group Liver Expert Panel, van Amerongen Martinus J. , Bond Marinde J. G. , Chapelle Thiery , van Dam Ronald M. , Engelbrecht Marc R. W. , Gerhards Michael F. , Grunhagen Dirk J. , van Gulik Thomas M. , Hermans John J. , de Jong Koert P. , Klaase Joost M. , Kok Niels F. M. , Leclercq Wouter K. G. , Liem Mike S. L. , van Lienden Krijn P. , Molenaar I. Quintus , Patijn Gijs A. , Rijken Arjen M. , Ruers Theo M. , Verhoef Cornelis , de Wilt Johannes H. W. 
Published: November 2024

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 study aimed to develop and externally evaluate a CRLM auto-segmentation model on CT scans, to facilitate the clinical adoption of TTV.

Methods

We developed an auto-segmentation model to segment CRLM using 783 contrast-enhanced portal venous phase CTs (CT-PVP) of 373 patients. We used a self-learning setup whereby we first trained a teacher model on 99 manually segmented CT-PVPs from three radiologists. The teacher model was then used to segment CRLM in the remaining 663 CT-PVPs for training the student model. We used the DICE score and the intraclass correlation coefficient (ICC) to compare the student model’s segmentations and the TTV obtained from these segmentations to those obtained from the merged segmentations. We evaluated the student model in an external test set of 50 CT-PVPs from 35 patients from the Oslo University Hospital and an internal test set of 21 CT-PVPs from 10 patients from the Amsterdam University Medical Centers.

Results

The model reached a mean DICE score of 0.85 (IQR: 0.05) and 0.83 (IQR: 0.10) on the internal and external test sets, respectively. The ICC between the segmented volumes from the student model and from the merged segmentations was 0.97 on both test sets.

Conclusion

The developed colorectal cancer liver metastases auto-segmentation model achieved a high DICE score and near-perfect agreement for assessing TTV.

Critical relevance statement

AI model segments colorectal liver metastases on CT with high performance on two test sets. Accurate segmentation of colorectal liver metastases could facilitate the clinical adoption of total tumor volume as an imaging biomarker for prognosis and treatment response monitoring.

Key Points

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.

Graphical Abstract