Evidence map›Paper›PMID 40727884›Full record

ArticleTherapeutic advances in medical oncology2025

Establishment and validation of a convenient and efficient screening tool for active pulmonary tuberculosis in lung cancer patients based on common parameters.

Fan Zhang, Fei Qi, Mengyan Sun, Peng Jiang, Minghang Zhang, Xiaomi Li, Yujie Dong, Juan Du, Liang Li, Tongmei Zhang

Abstract read
In one paragraph

Article in Therapeutic advances in medical oncology, 2025. 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

10 authors.

Fan ZhangGeneral Department, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
Fei QiGeneral Department, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.ORCID https://orcid.org/0000-0002-4853-2845
Mengyan SunDepartment of Stomatology, Shenzhen Guangming District People's Hospital, Shenzhen, Guangdong, China.
Peng JiangGeneral Department, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
Minghang ZhangGeneral Department, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
Xiaomi LiGeneral Department, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
Yujie DongDepartment of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
Juan DuDepartment of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.
Liang LiBeijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, No. 9 Beiguan Street, Tongzhou District, Beijing 101149, China.ORCID https://orcid.org/0000-0001-5212-5890
Tongmei ZhangGeneral Department, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, No. 9 Beiguan Street, Tongzhou District, Beijing 101149, China.ORCID https://orcid.org/0000-0003-4271-3773

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Coexistent pulmonary tuberculosis and lung cancer (PTB-LC) is a rare type of disease with frequent under- and/or mis-diagnosis. Establishment of a reliable screening model for PTB-LC holds considerable medical and economic significance. Objectives: We aimed to develop an efficient and convenient tool to identify high-risk individuals for tuberculosis (TB) infection among LC patients based on commonly available parameters in clinical practice. Design: This study consisted of a primary retrospective patient cohort for model construction and verification, and a prospective patient cohort for prospective validation. Methods: Patients with active PTB-LC and LC diagnosed in Beijing Chest Hospital from 2018 to 2022 were collected and 1:1 matched according to time of admission and were classified into a training set ( Results: Through multivariable analysis, gender, pleural effusion, cavitation, monocyte count (MONO), and plasma adenosine deaminase (ADA) levels were identified as independent predictors of PTB-LC and included in model construction. LR, RF, SVM, DT, and NN were used to construct the screening or pre-diagnosis models. The RF demonstrated the best performance with an area under the curve of 0.966 in the training set, 0.817 in the testing set, and 0.805 in the prospective dataset. The accuracy, precision, recall, and F1 score of the RF model of the training set were 0.88, 0.87, 0.89, and 0.88, respectively, and these indicators of the testing set were 0.71, 0.75, 0.72, and 0.74, respectively, which were superior to those of other methods. The prospective cohort further validated the good performance of the screening model. We also established a nomogram with gender, pleural effusion, cavitation, MONO, and serum ADA in assessing high-risk patients of developing TB infection. Further TB-related diagnostic tests were recommended for these high-risk patients. Conclusion: The RF screening model constructed with gender, pleural effusion, cavitation, MONO, and ADA may help identify high-risk patients of PTB-LC from LC alone cases.

Indexed as

coexistent pulmonary tuberculosis and lung cancerlung cancermachine learningrandom forestscreening model

Identifiers

PMID40727884
PMCPMC12301598

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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.