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ORIGINAL RESEARCH article

Front. Cell. Infect. Microbiol.
Sec. Clinical Microbiology
Volume 14 - 2024 | doi: 10.3389/fcimb.2024.1388991
This article is part of the Research Topic Comprehensive Insights into Respiratory Virus Pathogenesis, Prophylaxis, Clinical Manifestations, and Diagnostics View all 4 articles

A CT-Based Radiomics predictive nomogram to identify pulmonary tuberculosis from community-acquired pneumonia: a multicenter cohort study

Provisionally accepted
Pulin Li Pulin Li 1Jiling Wang Jiling Wang 2*Min Li Min Li 1*Min Tang Min Tang 1*Rui Han Rui Han 1Sijing Zhou Sijing Zhou 3*Xingwang Wu Xingwang Wu 1*Ran Wang Ran Wang 1*
  • 1 First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China
  • 2 The Second People's Hospital of Hefei, Hefei, Anhui Province, China
  • 3 Third People's Hospital of Hefei, Hefei, Anhui Province, China

The final, formatted version of the article will be published soon.

    Purpose: To develop a predictive nomogram based on computed tomography (CT) radiomics to distinguish pulmonary tuberculosis (PTB) from community-acquired pneumonia (CAP). Methods: A total of 195 PTB patients and 163 CAP patients were enrolled from three hospitals. It is divided into a training cohort, a testing cohort and validation cohort. Clinical models were established by using significantly correlated clinical features. Radiomics features were screened by the least absolute shrinkage and selection operator (LASSO) algorithm. Radiomics scores (Radscore) were calculated from the formula of radiomics features. Clinical radiomics conjoint nomogram was established according to Radscore and clinical features, and the diagnostic performance of the model was evaluated by receiver operating characteristic (ROC) curve analysis. Results: Two clinical features and 12 radiomic features were selected as optimal predictors for the establishment of clinical radiomics conjoint nomogram. The results showed that the predictive nomogram had an outstanding ability to discriminate between the two diseases, and the AUC of the training cohort was 0.947 (95% CI, 0.916-0.979), testing cohort was 0.888 (95% CI, 0.814-0.961) and that of the validation cohort was 0.850 (95% CI, 0.778-0.922). Decision curve analysis (DCA) indicated that the nomogram has outstanding clinical value. Conclusions: This study developed a clinical radiomics model that uses radiomics 4 features to identify PTB from CAP. This model provides valuable guidance to clinicians in identifying PTB.

    Keywords: pulmonary tuberculosis, Community-acquired pneumonia, computed tomography, Radiomics, nomogram

    Received: 20 Feb 2024; Accepted: 30 Aug 2024.

    Copyright: © 2024 Li, Wang, Li, Tang, Han, Zhou, Wu and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Jiling Wang, The Second People's Hospital of Hefei, Hefei, Anhui Province, China
    Min Li, First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui Province, China
    Min Tang, First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui Province, China
    Sijing Zhou, Third People's Hospital of Hefei, Hefei, Anhui Province, China
    Xingwang Wu, First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui Province, China
    Ran Wang, First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.