Evidence map›Paper›PMID 40705461›Full record

ArticleJournal of computer assisted tomography

Minimal Ablative Margin Quantification Using Hepatic Arterial Versus Portal Venous Phase CT for Colorectal Metastases Segmentation: A Dual-center, Retrospective Analysis.

Noreen S Siddiqi, Yuan-Mao Lin, Jessica Albuquerque Marques Silva, Gregor Laimer, Peter Schullian, Yannick Scharll, Alexandra M Dunker, Caleb S O'Connor, Kyle A Jones, Kristy K Brock and 3 more

Abstract readMulticenter StudyComparative Study
In one paragraph

Article in Journal of computer assisted tomography. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Noreen S SiddiqiDepartment of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX.ORCID 0009-0002-9738-6431
Yuan-Mao LinDepartment of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX.
Jessica Albuquerque Marques SilvaDepartment of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX.
Gregor LaimerDepartment of Radiology, Interventional Oncology-Microinvasive Therapy, Medical University Innsbruck, Innsbruck, Austria.
Peter SchullianDepartment of Radiology, Interventional Oncology-Microinvasive Therapy, Medical University Innsbruck, Innsbruck, Austria.
Yannick ScharllDepartment of Radiology, Interventional Oncology-Microinvasive Therapy, Medical University Innsbruck, Innsbruck, Austria.
Alexandra M DunkerDepartment of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX.
Caleb S O'ConnorDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX.
Kyle A JonesDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX.
Kristy K BrockDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX.
Reto BaleDepartment of Radiology, Interventional Oncology-Microinvasive Therapy, Medical University Innsbruck, Innsbruck, Austria.
Bruno C OdisioDepartment of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX.
Iwan PaolucciDepartment of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX.ORCID 0000-0002-9393-3015

Funding

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
NCI NIH HHS R01 CA235564NCI NIH HHS T32 CA261856
6 · The paper itself

Abstract

objectiveTo compare the predictive value of minimal ablative margin (MAM) quantification using tumor segmentation on intraprocedural contrast-enhanced hepatic arterial (HAP) versus portal venous phase (PVP) CT on local outcomes following percutaneous thermal ablation of colorectal liver metastases (CRLM).

methodsThis dual-center retrospective study included patients undergoing thermal ablation of CRLM with intraprocedural preablation and postablation contrast-enhanced CT imaging between 2009 and 2021. Tumors were segmented in both HAP and PVP CT phases using an artificial intelligence-based auto-segmentation model and reviewed by a trained radiologist. The MAM was quantified using a biomechanical deformable image registration process. The area under the receiver operating characteristic curve (AUROC) was used to compare the prognostic value for predicting local tumor progression (LTP).

resultsAmong 81 patients (60 y±13, 53 men), 151 CRLMs were included. During 29.4 months of median follow-up, LTP was noted in 24/151 (15.9%). Median tumor volumes on HAP and PVP CT were 1.7 mL and 1.2 mL, respectively, with respective median MAMs of 2.3 and 4.0 mm (both P < 0.001). The AUROC for 1-year LTP prediction was 0.78 (95% CI: 0.70-0.85) on HAP and 0.84 (95% CI: 0.78-0.91) on PVP ( P = 0.002).

conclusionsDuring CT-guided percutaneous thermal ablation, MAM measured based on tumors segmented on PVP images conferred a higher predictive accuracy of ablation outcomes among CRLM patients than those segmented on HAP images, supporting the use of PVP rather than HAP images for segmentation during ablation of CRLMs.

Indexed as

Colorectal NeoplasmsHepatic ArteryLiver NeoplasmsPortal VeinTomography, X-Ray ComputedAgedContrast MediaFemaleHumansMaleMargins of ExcisionMiddle AgedRetrospective StudiesContrast Mediaablation techniquescolorectal neoplasmscomputed tomographyimage processingliver

Identifiers

PMID40705461
PMCPMC12404250

What OpenQuestion holds

Textmetadata
LicenceTDM
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.