Evidence map›Paper›PMID 39213397›Full record

ArticleThe British journal of surgery2024

Identification of A0 minimum ablative margins for colorectal liver metastases: multicentre, retrospective study using deformable CT registration and artificial intelligence-based autosegmentation.

Iwan Paolucci, Jessica Albuquerque Marques Silva, Yuan-Mao Lin, Gregor Laimer, Valentina Cignini, Francesca Menchini, Marcio Meira, Alexander Shieh, Caleb O'Connor, Kyle A Jones and 7 more

Erratum issuedAbstract readMulticenter Study
In one paragraph

Article in The British journal of surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Article
  4. [Interventional therapeutic strategies for metastatic liver cancer].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2026
    Review
  5. Article
  6. Article
  7. Review
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Review
  17. Article
  18. Review
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors.

Iwan PaolucciDepartment of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.ORCID 0000-0002-9393-3015
Jessica Albuquerque Marques SilvaDepartment of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Yuan-Mao LinDepartment of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Gregor LaimerDepartment of Radiology, Interventional Oncology/Stereotaxy and Robotics, Medical University of Innsbruck, Innsbruck, Austria.
Valentina CigniniDepartment of Surgical Sciences, University of Turin, Turin, Italy.
Francesca MenchiniDepartment of Surgical Sciences, University of Turin, Turin, Italy.
Marcio MeiraDepartment of Radiology, Hospital das Clinicas, Faculdade de Medicina, Universidade de Sao Paulo, São Paulo, Brazil.
Alexander ShiehDepartment of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Caleb O'ConnorDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Kyle A JonesDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Carlo GazzeraDepartment of Diagnostic Imaging and Interventional Radiology, Città della Salute e della Scienza, Turin, Italy.
Paolo FonioDepartment of Diagnostic Imaging and Interventional Radiology, Città della Salute e della Scienza, Turin, Italy.ORCID 0000-0002-1106-2990
Kristy K BrockDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Marco CalandriDepartment of Diagnostic Imaging and Interventional Radiology, Città della Salute e della Scienza, Turin, Italy.
Marcos MenezesDepartment of Radiology, Hospital das Clinicas, Faculdade de Medicina, Universidade de Sao Paulo, São Paulo, Brazil.
Reto BaleDepartment of Radiology, Interventional Oncology/Stereotaxy and Robotics, Medical University of Innsbruck, Innsbruck, Austria.
Bruno C OdisioDepartment of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.ORCID 0000-0002-1771-8233

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Fabien Delerue · 1985 to 2026
$290.8M
Optimization and Evaluation of Anatomical Models of Liver Radiation ResponseR01CA221971 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI BROCK, KRISTY · 2018 to 2022
$1.8M
Anatomical Modeling to Improve the Precision of Image Guided Liver AblationR01CA235564 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI BROCK, KRISTY, ODISIO, BRUNO C · 2019 to 2023
$1.7M
Image Guided Cancer Therapy Training ProgramT32CA261856 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Kristy Brock, Clifton David Fuller · 2022 to 2026
$1.2M
Apache Corporation GrantGFI CALM_GFI_22_01_FLiechtensteinNCI NIH HHS P30 CA016672NCI NIH HHS R01 CA221971NCI NIH HHS R01 CA235564NCI NIH HHS R01CA235564NCI NIH HHS T32 CA261856NIH HHSProper Homines StiftungSwiss National Science Foundation P2BEP3_195444University of Texas MD Anderson Cancer CenterVaduz
6 · The paper itself

Abstract

backgroundSeveral ablation confirmation software methods for minimum ablative margin assessment have recently been developed to improve local outcomes for patients undergoing thermal ablation of colorectal liver metastases. Previous assessments were limited to single institutions mostly at the place of development. The aim of this study was to validate the previously identified 5 mm minimum ablative margin (A0) using autosegmentation and biomechanical deformable image registration in a multi-institutional setting.

methodsThis was a multicentre, retrospective study including patients with colorectal liver metastases undergoing CT- or ultrasound-guided microwave or radiofrequency ablation during 2009-2022, reporting 3-year local disease progression (residual unablated tumour or local tumour progression) rates by minimum ablative margin across all institutions and identifying an intraprocedural contrast-enhanced CT-based minimum ablative margin associated with a 3-year local disease progression rate of less than 1%.

resultsA total of 400 ablated colorectal liver metastases (median diameter of 1.5 cm) in 243 patients (145 men; median age of 62 [interquartile range 54-70] years) were evaluated, with a median follow-up of 26 (interquartile range 17-40) months. A total of 119 (48.9%) patients with 186 (46.5%) colorectal liver metastases were from international institutions B, C, and D that were not involved in the software development. Three-year local disease progression rates for 0 mm, >0 and <5 mm, and 5 mm or larger minimum ablative margins were 79%, 15%, and 0% respectively for institution A (where the software was developed) and 34%, 19%, and 2% respectively for institutions B, C, and D combined. Local disease progression risk decreased to less than 1% with an intraprocedurally confirmed minimum ablative margin greater than 4.6 mm.

conclusionA minimum ablative margin of 5 mm or larger demonstrates optimal local oncological outcomes. It is proposed that an intraprocedural minimum ablative margin of 5 mm or larger, confirmed using biomechanical deformable image registration, serves as the A0 for colorectal liver metastasis thermal ablation.

Indexed as

Artificial IntelligenceColorectal NeoplasmsLiver NeoplasmsMargins of ExcisionTomography, X-Ray ComputedAgedDisease ProgressionFemaleHumansMaleMiddle AgedRadiofrequency AblationRetrospective Studies

Identifiers

PMID39213397
PMCPMC11364140

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.