Evidence map›Paper›PMID 42491413›Full record

ArticleFrontiers in medicine2026

A prediction model for urological tumor metastasis using liquid biopsy-derived biomarkers.

Jiandong Qu, Jing Zhang, Xiaoli Huang

Abstract read
In one paragraph

Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Jiandong QuDepartment of Urology, Qingdao Municipal Hospital, Qingdao, Shandong, China.
Jing ZhangDepartment of Urology, Qingdao Municipal Hospital, Qingdao, Shandong, China.
Xiaoli HuangDepartment of Urology, Qingdao Municipal Hospital, Qingdao, Shandong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To construct and validate a prediction model for tumor metastasis in patients with urological tumors based on liquid biopsy biomarkers and clinical characteristics, to facilitate early clinical identification of metastasis risk and formulation of individualized diagnosis and treatment plans. Methods: A total of 360 patients with urological tumors admitted to our hospital from January 2021 to December 2024 were retrospectively included. They were randomly divided into a training set ( Results: There was no significant difference in baseline data between the training set and the validation set ( Conclusion: The random forest model constructed based on liquid biopsy predictive indicators can effectively predict tumor metastasis in patients with urological tumors. Neutrophil count, platelet count, and white blood cell count are key prediction indicators for urological tumor metastasis.

Indexed as

inflammation-related parametersliquid biopsymetastasispredictive indicatorsrandom forest modelurological tumors

Identifiers

PMID42491413
PMCPMC13377351

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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.