Evidence map›Paper›PMID 42024509›Full record

ArticleSTAR protocols2026

Protocol for an interpretable self-attention artificial neural network framework to predict cancer risk in oral potentially malignant disorders.

Siting Luo, Xuewen Zou, Zihan Lin, Jingwen Li, Mengjing Zhang, Yafang Zhou, Liuyang Zhao, Liwu Zheng, Dian Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in STAR protocols, 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

9 authors.

Siting LuoCollege of Computer Science, Computer Science Graduate Program, University of California, Davis, CA, USA.
Xuewen ZouCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.
Zihan LinCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.
Jingwen LiDivision of Oral & Maxillofacial Surgery, Faculty of Dentistry, University of Hong Kong, Hong Kong SAR, China.
Mengjing ZhangCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.
Yafang ZhouCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.
Liuyang ZhaoCollege of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.
Liwu ZhengDivision of Oral & Maxillofacial Surgery, Faculty of Dentistry, University of Hong Kong, Hong Kong SAR, China.
Dian ZhangSchool of Data Science, Lingnan University, Hong Kong, China. Electronic address: dianzhang@ln.edu.hk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Oral potentially malignant disorders (OPMDs) can directly progress to cancer, necessitating accurate risk prediction to guide clinical intervention. Here, we present a protocol for predicting cancer risk in OPMDs using a customized self-attention artificial neural network (SANN). We describe steps for full preprocessing, defining and training the model, and model validation. We then detail procedures for evaluation and visualization. The model is trained and validated on 1,094 cases and compared with artificial neural networks, random forests, and DeepSurv. For complete details on the use and execution of this protocol, please refer to Li et al.

Indexed as

Mouth NeoplasmsNeural Networks, ComputerHumansPrediction AlgorithmsCancerComputer sciencesHealth Sciences

Identifiers

PMID42024509
PMCPMC13126006

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.