Evidence map›Paper›PMID 41611812›Full record

ArticleNPJ precision oncology2026

Integrated predictive model for visceral pleural invasion in small NSCLC with high clinical utility.

Shuyi Yang, Ying Wei, Qingle Wang, Yaoyao Zhuo, Shan Yang, Weiya Shi, Yinwen Gan, Tingting Cai, Yichu He, Yi Zhan and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ precision oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

17 authors.

Shuyi Yang *Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Ying Wei *Department of Research and Development, United Imaging Intelligence, Shanghai, China.
Qingle WangDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Yaoyao ZhuoDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Shan YangDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Weiya ShiDepartment of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.
Yinwen GanDepartment of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.
Tingting CaiDepartment of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.
Yichu HeDepartment of Research and Development, United Imaging Intelligence, Shanghai, China.
Yi ZhanDepartment of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.
Haoling ZhangDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Yuxin ShiShanghai Institute of Medical Imaging, Shanghai, China.
Mengsu ZengDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Feng ShiDepartment of Research and Development, United Imaging Intelligence, Shanghai, China.
Zhong XueDepartment of Research and Development, United Imaging Intelligence, Shanghai, China. zhong.xue@uii-ai.com.
Zhiyong ZhangDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China. zhyzhang@fudan.edu.cn.
Fei ShanShanghai Institute of Medical Imaging, Shanghai, China. shanfei_2901@163.com.

Funding

National Natural Science Foundation of China 82172030Science and Technology Commission of Shanghai Municipality 22YF1443500Shanghai Municipal Hospital Development Center SHDC2020CR3080B
6 · The paper itself

Abstract

This study aims to develop and validate a multi-feature integrated imaging fusion (MIIF) model, incorporating deep learning, radiomics features, and computed tomography (CT) findings, for identifying visceral pleural invasion (VPI) in small non-small cell lung cancer (NSCLC). This multi-center retrospective analysis included 2822 small NSCLCs. These were divided into four datasets (training, validation, internal/external test). The MIIF model's diagnostic performance was compared against the assessments of six radiologists. Additionally, we evaluated the clinical utility of the MIIF model by comparing the diagnostic performance of radiologists, with/without the aid of the model. The MIIF model yielded AUCs of 0.869/0.785 in the internal/external test sets, respectively, which were comparable to the radiologists' (P > 0.05). With MIIF assistance, radiologists' accuracy and specificity increased to 0.845/0.828 and 0.836/0.841 in the internal/external test sets (P < 0.001). The MIIF model shows enhanced accuracy and specificity in detecting VPI in small NSCLC and may improve radiologist' diagnostic performance.

Identifiers

PMID41611812
PMCPMC12957428

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.