Evidence map›Paper›PMID 41201567›Full record

ArticleLa Radiologia medica2026

The prognostic value of CT-measured body composition combined with radiomics in predicting the survival of patients with resectable colon cancer.

Xiaoling Zhi, Tong Nie, Mingming Song, Zhihao Liu, Yixin Heng, Jiaxin Xu, Xiaoyu Wu, Yinghao Cao, Feihong Wu, Chuansheng Zheng

Abstract read
In one paragraph

Article in La Radiologia medica, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

2 citing papers in PubMed.

  1. Article
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4 · The record

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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

10 authors.

Xiaoling ZhiDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
Tong NieDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
Mingming Song *Harbin Medical University Cancer Hospital, No. 150 Haping Road, Nangang District, Harbin, 150081, China.
Zhihao LiuChina Medical University, Shenyang, 110122, China.
Yixin HengDepartment of Gastrointestinal Surgery, The First Affiliated Hospital, Shihezi University, Shihezi, 832000, China.
Jiaxin XuDepartment of Gastrointestinal Surgery, The First Affiliated Hospital, Shihezi University, Shihezi, 832000, China.
Xiaoyu WuDepartment of Gastrointestinal Surgery, The First Affiliated Hospital, Shihezi University, Shihezi, 832000, China.
Yinghao CaoDepartment of Digestive Surgical Oncology, Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China. yinghaocao@hust.edu.cn.
Feihong WuDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China. wfh_wuhan@163.com.
Chuansheng ZhengDepartment of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China. hqzcsxh@sina.com.ORCID http://orcid.org/0000-0002-2435-1417

Funding

Innovative Research Group Project of the National Natural Science Foundation of China 82302313Innovative Research Group Project of the National Natural Science Foundation of China 82403144the Hubei Province Key Laboratory of Molecular Imaging No. 2024fzyx024the National Key Research and Development Program of China 2023YFC2413500
6 · The paper itself

Abstract

objectiveTo explore the prognostic value of body compositions and radiomics in patients with resectable colon cancer, and to develop and validate a clinical-radiomics model for predicting the postoperative overall survival of patients with resectable colon cancer.

methodsThis study included 296 patients (43 months of median follow-up) with resectable colon cancer. Non-contrast CT images were used to quantify the body composition at the level of the third lumbar vertebra. Radiomics features were extracted from portal venous-phase CT scans. The recursive feature elimination and the least absolute shrinkage and selection operator regression were used for feature selection and construction of radiomic signatures. Univariate and multivariate Cox regression analysis were used to identify body composition. Combined with radiomics features, clinical-radiomics prediction model was constructed and plotted by nomogram, with performance metrics including the area under the receiver operating characteristic curve, calibration curves, decision curve analysis, and integrated discrimination improvement index.

resultLow skeletal muscle density (HR = 0.398, 95%CI = 0.168-0.939, P = 0.035) and low visceral fat area (HR = 0.238, 95%CI = 0.108-0.524, P < 0.001) were significantly associated with poor OS. The integrated clinical-radiomics model achieved C-index of 0.802 and 0.786 in the training and test cohorts, with superior 3-year OS AUC values of 0.804 and 0.828. Furthermore, clinical-radiomics model has a significant improvement in performance compared with radiomics model (IDI: 23.2%, P < 0.001) and clinical model (IDI:5.2%, P = 0.008).

conclusionNomogram combining body composition and tumor radiomics features can help predict the long-term prognosis of patients with resectable colon cancer and may serve as an effective tool to promote individualized treatment.

Indexed as

Body CompositionColonic NeoplasmsTomography, X-Ray ComputedAdultAgedAged, 80 and overFemaleHumansMaleMiddle AgedNomogramsPredictive Value of TestsPrognosisRadiomicsRetrospective StudiesColon cancerRadiomicsSkeletal muscle densityVisceral fat

Identifiers

PMID41201567
PMCPMC12982315

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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.