Evidence map›Paper›PMID 40695290›Full record

ArticleCell reports. Medicine2025

Deep learning for detection and diagnosis of intrathoracic lymphadenopathy from endobronchial ultrasound multimodal videos: A multi-center study.

Junxiang Chen, Jin Li, Chunxi Zhang, Xinxin Zhi, Lei Wang, Quncheng Zhang, Pengfei Yu, Fei Tang, Xiankui Zha, Limin Wang and 3 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell reports. Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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.

Junxiang ChenDepartment of Respiratory Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Engineering Research Center of Respiratory Endoscopy, Shanghai, China.
Jin LiDepartment of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China.
Chunxi ZhangDepartment of Respiratory Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Engineering Research Center of Respiratory Endoscopy, Shanghai, China.
Xinxin ZhiDepartment of Respiratory Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Engineering Research Center of Respiratory Endoscopy, Shanghai, China.
Lei WangDepartment of Ultrasound, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Quncheng ZhangDepartment of Respiratory and Critical Care Medicine, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Zhengzhou, China.
Pengfei YuDepartment of Respiratory and Critical Care Medicine, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China.
Fei TangDepartment of Interventional Pulmonology and Endoscopic Diagnosis and Treatment Center, Anhui Chest Hospital, Hefei, China.
Xiankui ZhaDepartment of Interventional Pulmonology and Endoscopic Diagnosis and Treatment Center, Anhui Chest Hospital, Hefei, China.
Limin WangDepartment of Respiratory Medicine, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, China.
Wenrui DaiDepartment of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China. Electronic address: daiwenrui@sjtu.edu.cn.
Hongkai XiongDepartment of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China. Electronic address: xionghongkai@sjtu.edu.cn.
Jiayuan SunDepartment of Respiratory Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Engineering Research Center of Respiratory Endoscopy, Shanghai, China. Electronic address: xkyyjysun@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Convex probe endobronchial ultrasound (CP-EBUS) ultrasonographic features are important for diagnosing intrathoracic lymphadenopathy. Conventional methods for CP-EBUS imaging analysis rely heavily on physician expertise. To overcome this obstacle, we propose a deep learning-aided diagnostic system (AI-CEMA) to automatically select representative images, identify lymph nodes (LNs), and differentiate benign from malignant LNs based on CP-EBUS multimodal videos. AI-CEMA is first trained using 1,006 LNs from a single center and validated with a retrospective study and then demonstrated with a prospective multi-center study on 267 LNs. AI-CEMA achieves an area under the curve (AUC) of 0.8490 (95% confidence interval [CI], 0.8000-0.8980), which is comparable to experienced experts (AUC, 0.7847 [95% CI, 0.7320-0.8373]; p = 0.080). Additionally, AI-CEMA is successfully transferred to a pulmonary lesion diagnosis task and obtains a commendable AUC of 0.8192 (95% CI, 0.7676-0.8709). In conclusion, AI-CEMA shows great potential in clinical diagnosis of intrathoracic lymphadenopathy and pulmonary lesions by providing automated, noninvasive, and expert-level diagnosis.

Indexed as

Deep LearningEndosonographyLymphadenopathyAdultAgedBronchoscopyFemaleHumansLymph NodesMaleMiddle AgedProspective StudiesRetrospective StudiesUltrasonographydeep learningdiagnosisendobronchial ultrasoundintrathoracic lymphadenopathymultimodal imaging

Identifiers

PMID40695290
PMCPMC12432367

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.