Rapid and noninvasive artificial intelligence-assisted diagnostic method for oral squamous cell carcinoma.

Yilan Sun, Xin Hu, Jing Han, Yujue Wang, Jiacheng Luo, Jiayi Yu, Yixiang Duan, Xu Wang, Jiannan Liu

Abstract read

In one paragraph

Article in NPJ digital 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.

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.

Yilan Sun *Department of Oral and Maxillofacial Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Xin Hu *Department of Oral and Maxillofacial Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Jing HanDepartment of Oral and Maxillofacial Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Yujue WangDepartment of Oral and Maxillofacial Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Jiacheng LuoDepartment of Oral and Maxillofacial Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Jiayi YuFaculty of Dentistry, Kanagawa Dental University, Yokosuka City, Japan.

Yixiang DuanSchool of Mechanical Engineering, Sichuan University, Chengdu, China.

Xu WangDepartment of Oral and Maxillofacial Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. wangx312016@sh9hospital.org.cn.

Jiannan LiuDepartment of Oral and Maxillofacial Head and Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. liujiannan@sh9hospital.org.cn.

Funding

The Medical Engineering Cross Foundation of Shanghai Jiao Tong University Grant No. YG2023LC06The National Natural Science Foundation of China Grant No. 82272815The National Natural Science Foundation of China Outstanding Youth Fund Grant No. 62322114

6 · The paper itself

Abstract

Oral squamous cell carcinoma (OSCC) remains the most common head and neck malignancy, for which early detection is critical yet challenging with current invasive methods. This study aimed to establish a comprehensive diagnostic framework for OSCC by integrating proton transfer reaction-time-of-flight mass spectrometry (PTR-TOF-MS) breath analysis and metagenomic sequencing with artificial intelligence (AI). Exhaled breath and saliva samples were collected from participants in a discovery cohort (n = 222) and an external validation cohort (n = 83). Samples were analyzed using PTR-TOF-MS and metagenomic sequencing, and multimodal diagnostic models were constructed and trained on the discovery cohort data. We identified OSCC-specific biomarkers, including methanethiol and Fusobacterium nucleatum, and developed an interactive online platform ( https://bio.futurecnn.com/ ) enabling real-time predictions and biomarker interpretability. The AI-driven diagnostic model achieved excellent accuracy (ROC-AUC: 0.92) in distinguishing OSCC patients from healthy controls in the external set. This approach offers a practical, noninvasive solution for OSCC screening and establishes an adaptable framework for other breath-based diagnostics.

Identifiers

PMID41917329

DOI10.1038/s41746-026-02527-3

PMCPMC13201731

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.