Evidence map›Paper›PMID 40500528›Full record

ArticleEuropean radiology2025

Non-enhanced CT deep learning model for differentiating lung adenocarcinoma from tuberculoma: a multicenter diagnostic study.

Guojin Zhang, Lan Shang, Shenglin Li, Jing Zhang, Zhuoli Zhang, Xin Zhang, Rong Qian, Ke Yang, Xin Li, Yiming Liu and 5 more

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
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

15 authors.

Guojin Zhang *Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China. zhangguojin@med.uestc.edu.cn.ORCID http://orcid.org/0000-0002-4312-5212
Lan Shang *Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Shenglin Li *Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Jing Zhang *Department of Radiology, The Fifth Affiliated Hospital of Zunyi Medical University, Zhuhai, China.
Zhuoli ZhangDepartment of Radiological Sciences, School of Medicine, University of California Irvine, Irvine, USA.
Xin ZhangDepartment of Pharmaceuticals Diagnosis, GE Healthcare, Beijing, China.
Rong QianDepartment of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Ke YangDepartment of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Xin LiDepartment of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Yiming LiuDepartment of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Ying WuDepartment of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Hong PuDepartment of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Yuntai CaoDepartment of Radiology, Qinghai University Affiliated Hospital, Xining, China. caoyt18@lzu.edu.cn.
Qiong ManSchool of Pharmacy, Chengdu Medical College, Chengdu, China. gretam@163.com.
Weifang KongDepartment of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China. kongweifang@med.uestc.edu.cn.

Funding

the National Natural Science Foundation of China No. 82202147
6 · The paper itself

Abstract

objectivesTo develop and validate a deep learning model based on three-dimensional features (DL_3D) for distinguishing lung adenocarcinoma (LUAD) from tuberculoma (TBM). MATERIALS AND

methodsA total of 1160 patients were collected from three hospitals. A vision transformer network-based DL_3D model was trained, and its performance in differentiating LUAD from TBM was evaluated using validation and external test sets. The performance of the DL_3D model was compared with that of two-dimensional features (DL_2D), radiomics, and six radiologists. Diagnostic performance was assessed using the area under the receiver operating characteristic curves (AUCs) analysis.

resultsThe study included 840 patients in the training set (mean age, 54.8 years [range, 19-86 years]; 514 men), 210 patients in the validation set (mean age, 54.3 years [range, 18-86 years]; 128 men), and 110 patients in the external test set (mean age, 54.7 years [range, 22-88 years]; 51 men). In both the validation and external test sets, DL_3D exhibited excellent diagnostic performance (AUCs, 0.895 and 0.913, respectively). In the test set, the DL_3D model showed better performance (AUC, 0.913; 95% CI: 0.854, 0.973) than the DL_2D (AUC, 0.804, 95% CI: 0.722, 0.886; p < 0.001), radiomics (AUC, 0.676, 95% CI: 0.574, 0.777; p < 0.001), and six radiologists (AUCs, 0.692 to 0.810; p value range < 0.001-0.035).

conclusionThe DL_3D model outperforms expert radiologists in distinguishing LUAD from TBM. KEY POINTS: Question Can a deep learning model perform in differentiating LUAD from TBM on non-enhanced CT images? Findings The DL_3D model demonstrated higher diagnostic performance than the DL_2D model, radiomics model, and six radiologists in differentiating LUAD and TBM. Clinical relevance The DL_3D model could accurately differentiate between LUAD and TBM, which can help clinicians make personalized treatment plans.

Indexed as

Adenocarcinoma of LungDeep LearningLung NeoplasmsTomography, X-Ray ComputedTuberculomaAdolescentAdultAgedAged, 80 and overDiagnosis, DifferentialFemaleHumansMaleMiddle AgedRadiographic Image Interpretation, Computer-AssistedYoung AdultComputed tomographyDeep learningLung adenocarcinomaTuberculoma

Identifiers

PMID40500528

What OpenQuestion holds

Textmetadata
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.