Evidence map›Paper›PMID 40000449›Full record

ArticleInternational journal of colorectal disease2025

Predicting extensive metastasis in postoperative oligometastatic colorectal cancer.

Rencai Fan, Chenkai Mao, Jiaqi Zhang, Min Dai, Rong Zhang, Xinran Wang, Jiaxin Dai, Shicheng Li, Zhixiang Zhuang

Abstract read
In one paragraph

Article in International journal of colorectal disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

9 authors.

Rencai FanCenter for Cancer Diagnosis and Treatment, The Second Affiliated Hospital of Soochow University, No.1055, Sanxiang Road, Gusu District, Soochow, 215004, Jiangsu Province, P.R. China.
Chenkai MaoCenter for Cancer Diagnosis and Treatment, The Second Affiliated Hospital of Soochow University, No.1055, Sanxiang Road, Gusu District, Soochow, 215004, Jiangsu Province, P.R. China.
Jiaqi ZhangCenter for Cancer Diagnosis and Treatment, The Second Affiliated Hospital of Soochow University, No.1055, Sanxiang Road, Gusu District, Soochow, 215004, Jiangsu Province, P.R. China.
Min DaiCenter for Cancer Diagnosis and Treatment, The Second Affiliated Hospital of Soochow University, No.1055, Sanxiang Road, Gusu District, Soochow, 215004, Jiangsu Province, P.R. China.
Rong ZhangCenter for Cancer Diagnosis and Treatment, The Second Affiliated Hospital of Soochow University, No.1055, Sanxiang Road, Gusu District, Soochow, 215004, Jiangsu Province, P.R. China.
Xinran WangDepartment of Respiratory Medicine, Wu Zhong People's Hospital, No. 61 Dongwu North Road, Wu Zhong District, Soochow, 215100, Jiangsu Province, P.R. China.
Jiaxin DaiDepartment of Oncology, The Nuclear Industry 417 Hospital, No. 5 Kangfu Road, Lintong District, Xi'an, Shaanxi Province, 710600, P.R. China.
Shicheng LiCenter for Cancer Diagnosis and Treatment, The Second Affiliated Hospital of Soochow University, No.1055, Sanxiang Road, Gusu District, Soochow, 215004, Jiangsu Province, P.R. China. lishcheng@126.com.
Zhixiang ZhuangCenter for Cancer Diagnosis and Treatment, The Second Affiliated Hospital of Soochow University, No.1055, Sanxiang Road, Gusu District, Soochow, 215004, Jiangsu Province, P.R. China. 13951106391@139.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeOligometastatic colorectal cancer (OMCRC) patients can achieve long-term disease control with multidisciplinary treatment. However, the development of extensive metastasis worsens prognosis and restricts treatment options. This study aims to develop a predictive model for extensive metastasis in OMCRC to assist in clinical decision-making.

methodsClinical and pathological data for OMCRC patients were collected from the Second Affiliated Hospital of Soochow University. Patients were randomly divided into training and testing cohorts. Risk factors for extensive metastasis were identified through LASSO regression analysis and COX regression analysis. Three predictive models were developed in the training cohort and validated in the testing cohort: COX regression analysis, Extreme Gradient Boosting (XGBoost), and Survival Support Vector Machine (SurvSVM). Finally, the optimal model was visualized with the nomogram.

resultsA total of 214 patients with OMCRC were enrolled in the study. Four independent risk factors were identified: whether surgery has been undertaken following oligometastasis (WST), histological type (HT), carcinoembryonic antigen at the last follow-up (CAE at last-FU), and preoperative albumin to globulin ratio (Preop-AGR). In the testing cohort, the COX model (1-year AUC = 0.82, 3-year AUC = 0.72, 5-year AUC = 0.85, mean AUC = 0.80) performed best. Decision curve analysis (DCA) confirmed the net benefit of the Cox model, and the nomogram provided accurate predictions of metastasis risk.

conclusionCAE at last-FU, Preop-AGR, HT, and WST are independent risk factors for extensive metastasis in OMCRC. The nomogram model incorporating risk factors can assist clinicians in developing optimal treatment for OMCRC patients.

Indexed as

Colorectal NeoplasmsAgedFemaleHumansMaleMiddle AgedNeoplasm MetastasisNomogramsPostoperative PeriodPrognosisProportional Hazards ModelsRisk FactorsColorectal cancerExtensive metastasisOligometastasisPrediction modelRisk factors

Identifiers

PMID40000449
PMCPMC11861249

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LicenceCC BY
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Registered trials

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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.