Evidence map›Paper›PMID 38123148›Full record

ArticleJournal of cachexia, sarcopenia and muscle2024

Three-dimensional body composition parameters using automatic volumetric segmentation allow accurate prediction of colorectal cancer outcomes.

Aiya Bimurzayeva, Min Jung Kim, Jong-Sung Ahn, Ga Yoon Ku, Dokyoon Moon, Jinsun Choi, Hyo Jun Kim, Han-Ki Lim, Rumi Shin, Ji Won Park and 6 more

Abstract read
In one paragraph

Article in Journal of cachexia, sarcopenia and muscle, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
–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

18 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

16 authors.

Aiya BimurzayevaDepartment of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea.
Min Jung KimDepartment of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea.ORCID 0000-0002-3105-9633
Jong-Sung AhnDepartment of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea.
Ga Yoon KuDepartment of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea.
Dokyoon MoonDepartment of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea.
Jinsun ChoiDepartment of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea.
Hyo Jun KimDepartment of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea.
Han-Ki LimDepartment of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea.
Rumi ShinDepartment of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea.
Ji Won ParkDepartment of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea.
Seung-Bum RyooDepartment of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea.
Kyu Joo ParkDepartment of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea.
Han-Jae ChungResearch and Science Division, MEDICAL IP Co., Ltd., Seoul, Republic of Korea.
Jong-Min KimResearch and Science Division, MEDICAL IP Co., Ltd., Seoul, Republic of Korea.
Sang Joon ParkResearch and Science Division, MEDICAL IP Co., Ltd., Seoul, Republic of Korea.
Seung-Yong JeongDepartment of Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea.

Funding

Seoul National University Hospital 2920190030Seoul National University Hospital 3020200060
6 · The paper itself

Abstract

backgroundParameters obtained from two-dimensional (2D) cross-sectional images have been used to determine body composition. However, data from three-dimensional (3D) volumetric body images reflect real body composition more accurately and may be better predictors of patient outcomes in cancer. This study aimed to assess the 3D parameters and determine the best predictive factors for patient prognosis.

methodsPatients who underwent surgery for colorectal cancer (CRC) between 2010 and 2016 were included in this study. Preoperative computed tomography images were analysed using an automatic segmentation program. Body composition parameters for muscle, muscle adiposity, subcutaneous fat (SF) and abdominal visceral fat (AVF) were assessed using 2D images at the third lumbar (L3) level and 3D images of the abdominal waist (L1-L5). The cut-off points for each parameter were determined using X-tile software. A Cox proportional hazards regression model was used to identify the association between the parameters and the treatment outcomes, and the relative influence of each parameter was compared using a gradient boosting model.

resultsOverall, 499 patients were included in the study. At a median follow-up of 59 months, higher 3D parameters of the abdominal muscles and SF from the abdominal waist were found to be associated with longer overall survival (OS) and disease-free survival (all P < 0.001). Although the 3D parameters of AVF were not related to survival outcomes, patients with a high AVF volume and mass experienced higher rate of postoperative complications than those with low AVF volume (27.4% vs. 18.7%, P = 0.021, for mass; 27.1% vs. 19.0%, P = 0.028, for volume). Low muscle mass and volume (hazard ratio [HR] 1.959, P = 0.016; HR 2.093, P = 0.036, respectively) and low SF mass and volume (HR 1.968, P = 0.008; HR 2.561, P = 0.003, respectively), both in the abdominal waist, were identified as independent prognostic factors for worse OS. Along with muscle mass and volume, SF mass and volume in the abdominal waist were negatively correlated with mortality (all P < 0.001). Both AVF mass and volume in the abdominal waist were positively correlated with postoperative complications (P < 0.05); 3D muscle volume and SF at the abdominal waist were the most influential factors for OS.

conclusions3D volumetric parameters generated using an automatic segmentation program showed higher correlations with the short- and long-term outcomes of patients with CRC than conventional 2D parameters.

Indexed as

Colorectal NeoplasmsMuscle, SkeletalBody CompositionBody Mass IndexHumansPostoperative Complicationsbody compositioncolorectal cancercomplicationsegmentationsurvival

Identifiers

PMID38123148
PMCPMC10834310

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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.