Evidence map›Paper›PMID 42527786›Full record

ArticleJournal of imaging informatics in medicine2026

Automated Body Composition from Computed Tomography Scans Improves Survival Prediction in Colorectal Cancer Patients.

Mushfiqus Salehin, Hyunwoo Lee, Vincent Tze Yang Chow, Erin K Weltzien, Long Nguyen, Jia Ming Li, Varun Akella, Bette J Caan, Elizabeth M Cespedes Feliciano, Da Ma and 2 more

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Article in Journal of imaging informatics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

12 authors.

Mushfiqus SalehinDepartment of Computer Science, Faculty of Science, Memorial University of Newfoundland, St. John's, NL, Canada. mushfiquss@mun.ca.
Hyunwoo LeeDivision of Neurology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada.
Vincent Tze Yang ChowSchool of Engineering, Simon Fraser University, Burnaby, BC, Canada.
Erin K WeltzienDivision of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
Long NguyenSchool of Engineering, Simon Fraser University, Burnaby, BC, Canada.
Jia Ming LiSchool of Engineering, Simon Fraser University, Burnaby, BC, Canada.
Varun AkellaSchool of Engineering, Simon Fraser University, Burnaby, BC, Canada.
Bette J CaanDivision of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
Elizabeth M Cespedes FelicianoDivision of Research, Kaiser Permanente Northern California, Oakland, CA, USA.
Da MaDepartment of Internal Medicine Section of Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Mirza Faisal BegSchool of Engineering, Simon Fraser University, Burnaby, BC, Canada.ORCID http://orcid.org/0009-0001-9952-5768
Karteek PopuriDepartment of Computer Science, Faculty of Science, Memorial University of Newfoundland, St. John's, NL, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Colorectal cancer is one of the most common cancers, but the current staging system is limited by the variability and paradoxical survival outcomes. Body composition is an accurate predictor of survival and can be extracted from routine computed tomography (CT) scans used in cancer diagnosis. However, despite its potential, the adoption of body composition analysis has been limited due to the challenges in generating the data. In this study, we propose a deep learning-based model that combines clinical and body composition biomarkers to predict the survival of colorectal cancer patients. Our best model, which integrates both clinical and body composition features, achieved a time-dependent concordance-index score of 0.7298 (

Indexed as

Automatic segmentationCancer survivalColorectalCT

Identifiers

PMID42527786

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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.