Evidence map›Paper›PMID 40090348›Full record

Trial reportThe lancet. Gastroenterology & hepatology2025

Software-based versus visual assessment of the minimal ablative margin in patients with liver tumours undergoing percutaneous thermal ablation (COVER-ALL): a randomised phase 2 trial.

Bruno C Odisio, Jessica Albuquerque, Yuan-Mao Lin, Brian M Anderson, Caleb S O'Connor, Bastien Rigaud, Maria Briones-Dimayuga, Aaron K Jones, Bryan M Fellman, Steven Y Huang and 20 more

Registry-linked trialAbstract readClinical Trial, Phase IIRandomized Controlled TrialEquivalence Trial
In one paragraph

Trial report in The lancet. Gastroenterology & hepatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04083378 (Clinical Impact of a Volumetric Image Method for Confirming Tumor Coverage With Ablation on Patients With Malignant Liver Lesions), which is not on this map. Cited by 50 papers.

0numbers the graph read from it
0cells of the map it votes in
50citing 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.

NCT04083378 phase2active not recruitingnot on this map

Clinical Impact of a Volumetric Image Method for Confirming Tumor Coverage With Ablation on Patients With Malignant Liver Lesions (COVER-ALL)

TypeinterventionalSponsorM.D. Anderson Cancer CenterRan2020 to 2026Enrolled107ConditionsMalignant Liver NeoplasmArmsAblation Therapy, Image-Guided Therapy, Quality-of-Life Assessment, Questionnaire Administration
3 · Its place in the literature

Who cites it

50 citing papers in PubMed.

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  15. A Real-World Evaluation of Failure Detection for Liver CT Segmentation.medRxiv : the preprint server for health sciences · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

30 authors.

Bruno C OdisioDepartment of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. Electronic address: bcodisio@mdanderson.org.
Jessica AlbuquerqueDepartment of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Yuan-Mao LinDepartment of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Brian M AndersonDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Caleb S O'ConnorDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Bastien RigaudDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Maria Briones-DimayugaDepartment of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Aaron K JonesDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Bryan M FellmanDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Steven Y HuangDepartment of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Joshua KubanDepartment of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Zeyad A MetwalliDepartment of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Rahul ShethDepartment of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Peiman HabibollahiDepartment of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Milan PatelDepartment of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Ketan Y ShahDepartment of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Veronica L CoxDepartment of Abdominal Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
HyunSeon C KangDepartment of Abdominal Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Van K MorrisDepartment of Gastrointestinal Medical Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Scott KopetzDepartment of Gastrointestinal Medical Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Milind M JavleDepartment of Gastrointestinal Medical Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Ahmed KasebDepartment of Gastrointestinal Medical Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Ching-Wei TzengDepartment of Surgical Oncology, Division of Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Hop-Tran CaoDepartment of Surgical Oncology, Division of Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Timothy NewhookDepartment of Surgical Oncology, Division of Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Yun Shin ChunDepartment of Surgical Oncology, Division of Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Jean-Nicolas VautheyDepartment of Surgical Oncology, Division of Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Sanjay GuptaDepartment of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Iwan PaolucciDepartment of Interventional Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Kristy K BrockDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI DIANE BODURKA · 1985 to 2026
$290.8M
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
NCI NIH HHS P30 CA016672NCI NIH HHS R01 CA235564
6 · The paper itself

Abstract

backgroundTumour coverage with an optimal minimal ablative margin is crucial for improving local control of liver tumours following thermal ablation. The minimal ablative margin has traditionally been assessed through visual inspection of co-registered CT images. However, rates of local tumour control after thermal ablation are highly variable with visual assessment. We aimed to assess the use of a novel software-based method for minimal ablative margin assessment that incorporates biomechanical deformable image registration and artificial intelligence (AI)-based autosegmentation.

methodsThe COVER-ALL randomised, phase 2, superiority trial was conducted at The University of Texas MD Anderson Cancer Center (Houston, TX, USA). Patients aged 18 years or older with up to three histology-agnostic liver tumours measuring 1-5 cm and undergoing CT-guided thermal ablation were enrolled. Thermal ablation was performed with the aim of achieving a minimal ablative margin of 5 mm or greater. Patients were randomly assigned (1:1) to the experimental group (software-based assessment) or the control group (visual assessment) by use of dynamic minimisation to balance covariates. Randomisation was performed intraprocedurally after placement of the ablation applicator. Assessment of oncological outcomes and adverse events were masked to treatment allocation. All analyses were conducted on an intention-to-treat basis. The primary endpoint was the minimal ablative margin on post-ablation intraprocedural CT. A preplanned interim analysis for superiority was done at 50% patient enrolment. Adverse events were recorded with the Common Terminology Criteria for Adverse Events. This trial is registered with ClinicalTrials.gov (NCT04083378), and recruitment is complete.

findingsPatients were enrolled and treated with thermal ablation between June 15, 2020, and Oct 5, 2023. 26 patients were randomly assigned to the control group (mean age 58·1 [SD 14·8] years; 18 [69%] male and eight [31%] female; 11 [42%] colorectal cancer liver metastasis; median tumour diameter 1·7 cm [IQR 1·3-2·3]) and 24 to the experimental group (mean age 60·5 [14·4] years; 16 [67%] male and eight [33%] female; ten [42%] colorectal cancer liver metastasis; median tumour diameter 1·8 cm [1·5-2·5]). The interim analysis showed a mean minimal ablative margin of 2·2 mm (SD 2·8) in the control group and 5·9 mm (2·7) in the experimental group (p<0·0001), prompting halting of enrolment in the control group. A further 50 patients were enrolled to a non-randomised experimental group (mean age 56·5 [SD 11·7] years; 27 [54%] male and 23 [46%] female; 30 [60%] colorectal cancer liver metastasis; median tumour diameter 1·5 cm [IQR 1·3-2·2]); among these patients, the mean minimal ablative margin was 7·2 mm (SD 2·8). Grade 1-3 adverse events were reported in five (5%) of 100 patients: three (12%) of 26 in the control group and two (3%) of 74 in the experimental groups. No grade 4-5 adverse events or treatment-related deaths were reported.

interpretationSoftware-based assessment during CT-guided thermal ablation of liver tumours is safe and significantly improves the minimal ablative margin compared to visual assessment. Adoption of software-based assessment as a standard component of thermal ablation should be considered to achieve the intended minimal ablative margin.

fundingUS National Institutes of Health and US National Cancer Institute.

Indexed as

Liver NeoplasmsMargins of ExcisionSoftwareAgedArtificial IntelligenceFemaleHumansMaleMiddle AgedTomography, X-Ray ComputedTreatment Outcome

Identifiers

PMID40090348
PMCPMC12043112

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Registered trials

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.