Skip to main content

Advanced Search

Projected result for your current search:: 0

Search Operators:

You can use operators in your search query to narrow down your results even more. Click on the search operator to see an explanation of how it works.
Finds documents with exactly this word group, in exactly this word order and spelling (e.g. "employer branding").
Finds documents with both search terms (e.g. sales AND bonus).
Finds documents with one or the other search term (e.g. porsche OR volkswagen).
Finds documents with all search terms. Blank space is understood as AND (e.g. man robot production).
Finds documents with no appearance of the word behind NOT (e.g. ford NOT "harrison ford").
Finds documents where the search term is mentioned at least "n" times."n" may be any number (e.g. COUNT(gear)>8).
Finds documents with both search terms in any word order, permitting "n" words as a maximum distance between them. Best choose between 15 and 30 (e.g. NEAR(recruit, professionals, 20)).
Finds documents with the search term in word versions or composites. The asterisk * marks whether you wish them BEFORE, BEHIND, or BEFORE and BEHIND the search term (e.g. lightweight*, *lightweight, *lightweight*).
Finds documents with the search term in different spellings. "?" allows only one character (e.g. organi?ation).
Special characters are understood as AND (e.g. Miller Bros. & Sons).
 Log in
Top
Insights into Imaging

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

Published in:
Authors
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
Published in
Insights into Imaging | Issue 1/2024

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 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

Title
Development and external evaluation of a self-learning auto-segmentation model for Colorectal Cancer Liver Metastases Assessment (COALA)
Authors
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
Publication date
01-12-2024
Publisher
Springer Vienna
Published in
Insights into Imaging / Issue 1/2024
Electronic ISSN: 1869-4101
DOI
https://doi.org/10.1186/s13244-024-01820-7
This content is only visible if you are logged in and have the appropriate permissions.
This content is only visible if you are logged in and have the appropriate permissions.

Other articles of this Issue 1/2024

Femoral head decentration on hip MRI: comparison between imaging planes, methods of contrast administration, and hip deformities

Clinical implications of CT-detected ascites in gastric cancer: association with peritoneal metastasis and systemic inflammatory response

  • Open Access
  • Ascites
  • Original Article

Stage IA papillary and chromophobe renal cell carcinoma: effectiveness of cryoablation and partial nephrectomy

Advancements in diagnostic and interventional radiology for stroke treatment: the path from trial to bedside through the pre-MR CLEAN, MR CLEAN, and MR CLEAN II eras

Inter-platform reproducibility of ultrasound-based fat fraction for evaluating hepatic steatosis in nonalcoholic fatty liver disease

Effect of spin-lock frequency on quantitative myocardial T1ρ mapping

  • Open Access
  • Original Article

How can your team use biomarkers to improve management of AD? (Link opens in a new window)

0.5 AMA PRA Category 1 Credit(s)™

Our experts explore using biomarker tests and interpreting results, establishing a shared decision-making approach with patients and caregivers, and applying biomarker testing to guide treatment strategies.

This content is intended for healthcare professionals outside of the UK.

Supported by:
  • Lilly
Developed by: Springer Health+ IME
Learn more

How can you integrate PET into your practice? (Link opens in a new window)

1.5 AMA PRA Category 1 Credit(s)™

PET imaging is playing an increasingly critical role in managing AD. Our expert-led program will empower you with practical strategies and real-world case studies to effectively integrate it into clinical practice.

This content is intended for healthcare professionals outside of the UK.

Supported by:
  • Lilly
Developed by: Springer Health+ IME
Learn more
Image Credits
Alzheimer’s disease in the biomarker era: preparing the multidisciplinary care team/© Springer Healthcare IME, Navigating neuroimaging in Alzheimer’s care: Practical applications and strategies for integration/© Springer Health+ IME