Evidence map›Paper›PMID 42063542›Full record

ArticleiScience2026

Machine learning based prognostic model for oral squamous cell carcinoma using SEER data and external validation.

Yangxiao Zhang, Hongsheng Liu, Yixuan Liao, Zhenxing Su, Luwen Song, Zhenghao Ma, Zhi Qiang Pan, Yunqi Chen, Mingxun Xia, Jiancheng Li and 1 more

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PubMed Full text at PMC Publisher
In one paragraph

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

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Yangxiao ZhangSchool of Basic Medicine, Bengbu Medical University, Bengbu, China.
Hongsheng LiuSchool of Stomatology, Bengbu Medical University, Bengbu, China.
Yixuan LiaoSchool of Basic Medicine, Bengbu Medical University, Bengbu, China.
Zhenxing SuSchool of Basic Medicine, Bengbu Medical University, Bengbu, China.
Luwen SongThe First Affiliated Hospital of Bengbu Medical University, Bengbu, China.
Zhenghao MaThe First Affiliated Hospital of Bengbu Medical University, Bengbu, China.
Zhi Qiang PanThe First Affiliated Hospital of Bengbu Medical University, Bengbu, China.
Yunqi ChenThe First Affiliated Hospital of Bengbu Medical University, Bengbu, China.
Mingxun XiaSchool of Stomatology, Bengbu Medical University, Bengbu, China.
Jiancheng LiThe First Affiliated Hospital of Bengbu Medical University, Bengbu, China.
Lina JiangSchool of Stomatology, Bengbu Medical University, Bengbu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Oral squamous cell carcinoma (OSCC) is characterized by an insidious onset, pronounced aggressiveness, and a substantial impact on patient survival. Current prognostication relies heavily on the TNM staging system, which often lacks precision. To address this, we developed and validated a machine learning (ML)-based prognostic nomogram. Analyzing 8,927 patients from the SEER database, we employed ML algorithms (LASSO, XGBoost, random forest (RF), support vector machine (SVM)) to identify key determinants, including age, race, sex, grade, and TNM stage, and constructed a Cox-based risk model. Crucially, benchmarking analysis demonstrated that our model achieved a C-index of 0.714, significantly outperforming the traditional TNM staging system (C-index: 0.665,

Indexed as

CancerMachine learning

Identifiers

PMID42063542
PMCPMC13127388

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.