Evidence map›Paper›PMID 41186849›Full record

ArticleDiscover oncology2025

Web-based explainable machine-learning tool for predicting five-year recurrence of colorectal cancer after curative resection: multicentre retrospective cohort study.

Chi-Sheng Chen, Tai-Han Lin, Hsing-Yi Chung, Ming-Jr Jian, Chih-Kai Chang, Cherng-Lih Perng, Ping-Ying Chang, Wen-Yen Huang, Chao-Yang Chen, Yu-Chun Lin and 1 more

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Article in Discover oncology, 2025. 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

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

2 citing papers in PubMed.

  1. Review
  2. Review
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

11 authors.

Chi-Sheng Chen *Department of Pathology, Tri-Service General Hospital, National Defense Medical University, Taipei, Taiwan, ROC.
Tai-Han Lin *Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical University, No. 161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei, 11490, Taiwan, ROC.
Hsing-Yi ChungDivision of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical University, No. 161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei, 11490, Taiwan, ROC.
Ming-Jr JianDivision of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical University, No. 161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei, 11490, Taiwan, ROC.
Chih-Kai ChangDivision of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical University, No. 161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei, 11490, Taiwan, ROC.
Cherng-Lih PerngDivision of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical University, No. 161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei, 11490, Taiwan, ROC.
Ping-Ying ChangDivision of Hematology-Oncology, Department of Internal Medicine, Tri- Service General Hospital, National Defense Medical University, Taipei, Taiwan, ROC.
Wen-Yen HuangDepartment of Radiation Oncology, Tri-Service General Hospital, National Defense Medical University, Taipei, Taiwan, ROC.
Chao-Yang Chen *Division of Colon and Rectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical University, Taipei, Taiwan, ROC.
Yu-Chun Lin *Department of Pathology, Tri-Service General Hospital, National Defense Medical University, Taipei, Taiwan, ROC.
Hung-Sheng ShangDivision of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical University, No. 161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei, 11490, Taiwan, ROC. iamkeith@mail.ndmctsgh.edu.tw.

Funding

Tri-Service General Hospital TSGH-D-114102
6 · The paper itself

Abstract

objectiveThe World Health Organization identifies colorectal cancer as the third-most diagnosed malignancy and second leading cause of cancer-related mortality worldwide. Up to 30% of patients relapse within 5 years postoperatively; however, conventional staging methods cannot reliably stratify individual risks, underscoring the need for precise, patient-centred decision-support tools.

methodsWe retrospectively analysed data on 1,789 colorectal cancer patients undergoing curative resection (2013-2023) from the Tri-Service General Hospital registry. Four tree-based machine learning algorithms were trained on demographic, tumour, immunohistochemical, and laboratory features. Patients were divided chronologically into training (January to September) and validation (October to December) sets. Feature importance was assessed using random forest impurity scores and Shapley additive explanations (SHAP). A web-based artificial intelligence-clinical decision support system (AI-CDSS) was developed to provide real-time, scenario-specific five-year recurrence-risk estimates.

resultsOf the 1,789 patients, 406 (22.7%) experienced recurrence. The top 10 predictors accounted for approximately 43% of the total model importance. SHAP analysis confirmed that tumour burden, biological markers, treatment intensity, and host factors were key drivers of recurrence risk. On the validation set, all models achieved area under the receiver operating characteristic curve values of 0.83-0.84. The random forest-based system demonstrated 87% accuracy, 85% positive predictive value, 87% negative predictive value, and an F1 score of 0.64, and was consequently selected as the AI-CDSS engine.

conclusionThe proposed AI-CDSS delivers personalised five-year recurrence-risk estimates for patients with colorectal cancer within 1 s via an intuitive web interface, facilitating evidence-based, patient-centred treatment decisions grounded in local population data.

Indexed as

Artificial intelligenceClinical decision-support systemColorectal cancerMachine learningRecurrence prediction

Identifiers

PMID41186849
PMCPMC12586746

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LicenceCC BY-NC-ND
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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.