Evidence map›Paper›PMID 40621484›Full record

ArticleTherapeutic advances in medical oncology2025

A machine learning-based prognostic stratification of locoregional interventional therapies for patients with colorectal cancer liver metastases: a real-world study.

Jing Wang, Kai Wang, Kangjie Wang, Baogen Zhang, Siyu Zhu, Xuyang Zhang, Li Wang, Yingying Tong, Aiwei Feng, Haibin Zhu and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Therapeutic advances in medical oncology, 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

13 authors.

Jing WangDepartment of Oncology, Beijing Luhe Hospital Affiliated to Capital Medical University, Beijing, China.ORCID https://orcid.org/0000-0002-9996-0815
Kai WangDepartment of Oncology, Beijing Luhe Hospital Affiliated to Capital Medical University, Beijing, China.
Kangjie WangDepartment of Oncology, Beijing Luhe Hospital Affiliated to Capital Medical University, Beijing, China.
Baogen ZhangDepartment of Oncology, Beijing Luhe Hospital Affiliated to Capital Medical University, Beijing, China.
Siyu ZhuDepartment of Oncology, Beijing Luhe Hospital Affiliated to Capital Medical University, Beijing, China.
Xuyang ZhangDepartment of Oncology, Beijing Luhe Hospital Affiliated to Capital Medical University, Beijing, China.
Li WangDepartment of Oncology, Beijing Luhe Hospital Affiliated to Capital Medical University, Beijing, China.
Yingying TongDepartment of Oncology, Beijing Luhe Hospital Affiliated to Capital Medical University, Beijing, China.
Aiwei FengDepartment of Interventional Therapy, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China.
Haibin ZhuDepartment of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China.
Ting XuDepartment of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China.
Xu ZhuDepartment of Interventional Therapy, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, No.52 Fucheng Road, Haidian District, Beijing 100142, China.
Dong YanDepartment of Oncology, Beijing Luhe Hospital Affiliated to Capital Medical University, No. 82, Xinhua South Road, Tongzhou District, Beijing 101149, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Colorectal cancer liver metastases (CRLM) represent a major cause of mortality in advanced colorectal cancer, with intra-arterial interventional therapy (IAIT) playing an increasingly important role in multidisciplinary management. This study aims to develop a machine learning (ML)-based prognostic model to predict survival outcomes in unresectable colorectal cancer liver metastases (uCRLM) patients undergoing IAIT treatment, enabling improved risk assessment. Design: A retrospective study. Objectives: This study aims to explore the effect of IAIT on the survival of patients with uCRLM. Methods: Retrospective data were obtained from patients with CRLM who visited Luhe Hospital and Peking University Cancer Hospital from January 2018 to January 2023. The study population was divided into two groups: one group received IAIT sequence by systemic standard of care (SOC) therapy group (ISOC; Results: After PSM 1:1, 574 patients were divided into two groups. The median OS of patients who received ISOC was significantly higher than those who received systemic SOC therapy alone (40 vs 25 months, Conclusion: In this study, ISOC significantly improved the prognosis of patients. The ML model provides accurate prognostic stratification for uCRLM patients, which may aid in risk-based clinical decision-making.

Indexed as

colorectal cancer liver metastasisintra-arterial interventional therapymachine learningsurvival

Identifiers

PMID40621484
PMCPMC12227886

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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.