Evidence map›Paper›PMID 41774133›Full record

ReviewAbdominal radiology (New York)2026

Tumor metrics imaging core labs: primer for radiologists.

Rachana Gurudu, Dhruv Bansal, Anil Chauhan, Sree Harsha Tirumani

Abstract readReview
In one paragraph

Review in Abdominal radiology (New York), 2026. 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

4 authors.

Rachana GuruduCase Western Reserve University, Cleveland, USA.
Dhruv BansalDepartment of Medical Oncology, Endeavor Health, Chicago, USA.
Anil ChauhanDepartment of Radiology, University of Kansas Medical Center, Kansas City, USA.
Sree Harsha TirumaniDepartment of Radiology, University Hospitals Cleveland Medical Center, Case Western Reserve University, Cleveland, USA. sreeharsha.tirumani@uhhospitals.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Imaging biomarkers have emerged as increasingly important endpoints in cancer clinical trials. Incorporating tumor metric reads as part of routine clinical reads by on-site radiologists for cancer clinical trials has several challenges which can be addressed by tumor metrics imaging core lab. Despite the operational and financial challenges inherent in establishing and maintaining tumor metrics imaging core labs, including workflow complexities, infrastructure demands, and data security considerations, these facilities confer significant advantages including accelerated trial timelines, improved regulatory compliance, and the creation of interdisciplinary research environments. Moreover, the integration of artificial intelligence within tumor metrics imaging core labs offers enhanced image analysis, predictive modeling, and improved trial efficiency. This article provides a comprehensive review of the role of tumor metrics imaging core labs in clinical trials and provides an overview of the key components involved in setting up a core lab. We will also briefly present the challenges in the successful operation of a tumor metrics imaging core lab and delve into the potential solutions, including the integration of AI tools for clinical trials.

Indexed as

Biomarkers, TumorDiagnostic ImagingLaboratories, ClinicalNeoplasmsArtificial IntelligenceClinical Trials as TopicHumansBiomarkers, TumorCancer clinical trialsImaging biomarkersTumor metrics imaging core lab

Identifiers

PMID41774133
PMCPMC13476335

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.