{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T10:39:21Z","timestamp":1788172761211,"version":"build-2803163510"},"reference-count":39,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T00:00:00Z","timestamp":1726704000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cell. Infect. Microbiol."],"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>To develop a predictive nomogram based on computed tomography (CT) radiomics to distinguish pulmonary tuberculosis (PTB) from community-acquired pneumonia (CAP).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>This study developed a clinical radiomics model that uses radiomics features to identify PTB from CAP. This model provides valuable guidance to clinicians in identifying PTB.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fcimb.2024.1388991","type":"journal-article","created":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T05:10:37Z","timestamp":1726722637000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["A CT-based radiomics predictive nomogram to identify pulmonary tuberculosis from community-acquired pneumonia: a multicenter cohort study"],"prefix":"10.3389","volume":"14","author":[{"given":"Pulin","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiling","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sijing","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingwang","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ran","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,9,19]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"104665","DOI":"10.1016\/j.compbiomed.2021.104665","article-title":"Artificial intelligence-driven assessment of radiological images for COVID-19","volume":"136","author":"Bouchareb","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"B2","doi-asserted-by":"publisher","first-page":"1320","DOI":"10.1111\/crj.2018.12.issue-4","article-title":"Diagnosis and treatment of community-acquired pneumonia in adults: 2016 clinical practice guidelines by the Chinese Thoracic Society, Chinese Medical Association","volume":"12","author":"Cao","year":"2018","journal-title":"Clin. Respir. J."},{"key":"B3","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1183\/09031936.00017813","article-title":"Predicting Mycobacterium tuberculosis in patients with community-acquired pneumonia","volume":"43","author":"Cavallazzi","year":"2014","journal-title":"Eur. Respir. J."},{"key":"B4","doi-asserted-by":"publisher","first-page":"1214","DOI":"10.1183\/09031936.00167113","article-title":"Risk factors for pulmonary tuberculosis in community-acquired pneumonia","volume":"43","author":"Cavallazzi","year":"2014","journal-title":"Eur. Respir. J."},{"key":"B5","first-page":"1626","article-title":"Pulmonary tuberculosis among patients hospitalised with community-acquired pneumonia in a tuberculosis-prevalent area","volume":"17","author":"Chon","year":"2013","journal-title":"Int. J. Tuberculosis. Lung Dis."},{"key":"B6","doi-asserted-by":"publisher","first-page":"1211","DOI":"10.1016\/S0140-6736(15)00151-8","article-title":"Tuberculosis","volume":"387","author":"Dheda","year":"2016","journal-title":"Lancet (London. England)."},{"key":"B7","doi-asserted-by":"publisher","first-page":"e00021","DOI":"10.1128\/CMR.00021-18","article-title":"Incipient and subclinical tuberculosis: a clinical review of early stages and progression of infection","volume":"31","author":"Drain","year":"2018","journal-title":"Clin. Microbiol. Rev."},{"key":"B8","doi-asserted-by":"publisher","first-page":"6497","DOI":"10.1007\/s00330-020-07024-z","article-title":"Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas","volume":"30","author":"Feng","year":"2020","journal-title":"Eur. Radiol."},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2023.e18056","article-title":"Novel clinical radiomic nomogram method for differentiating Malignant from non-malignant pleural effusions","volume":"9","author":"Han","year":"2023","journal-title":"Heliyon"},{"key":"B10","doi-asserted-by":"publisher","first-page":"jitc","DOI":"10.1136\/jitc-2020-000550","article-title":"Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker","volume":"8","author":"He","year":"2020","journal-title":"J. Immunother. Cancer"},{"key":"B11","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/s00259-020-04924-6","article-title":"Value of (18)F-FDG PET\/CT radiomic features to distinguish solitary lung adenocarcinoma from tuberculosis","volume":"48","author":"Hu","year":"2021","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"B12","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1183\/09031936.00004312","article-title":"Pulmonary tuberculosis in the differential diagnosis of community-acquired pneumonia","volume":"40","author":"Jolobe","year":"2012","journal-title":"Eur. Respir. J."},{"key":"B13","doi-asserted-by":"publisher","first-page":"3643","DOI":"10.1007\/s00259-021-05371-7","article-title":"Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer","volume":"48","author":"Kirienko","year":"2021","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"B14","doi-asserted-by":"publisher","first-page":"TNMI7","DOI":"10.1128\/microbiolspec","article-title":"Pulmonary tuberculosis","volume":"5","author":"Lyon","year":"2017","journal-title":"Microbiol. Spectr."},{"key":"B15","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1186\/s12879-019-4597-8","article-title":"Clinical effect of T-SPOT.TB test for the diagnosis of tuberculosis","volume":"19","author":"Ma","year":"2019","journal-title":"BMC Infect. Dis."},{"key":"B16","doi-asserted-by":"publisher","first-page":"488","DOI":"10.2967\/jnumed.118.222893","article-title":"Introduction to radiomics","volume":"61","author":"Mayerhoefer","year":"2020","journal-title":"J. Nucl. Med."},{"key":"B17","doi-asserted-by":"publisher","first-page":"145","DOI":"10.3949\/ccjm.87a.19067","article-title":"Community-acquired pneumonia: Strategies for triage and treatment","volume":"87","author":"Modi","year":"2020","journal-title":"Cleveland. Clin. J. Med."},{"key":"B18","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1007\/s00259-018-4138-5","article-title":"Correlations between metabolic texture features, genetic heterogeneity, and mutation burden in patients with lung cancer","volume":"46","author":"Moon","year":"2019","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"B19","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1148\/rg.2017160032","article-title":"Pulmonary tuberculosis: role of radiology in diagnosis and management","volume":"37","author":"Nachiappan","year":"2017","journal-title":"Radiograph.: Rev. Publ. Radiol. Soc. North America. Inc."},{"key":"B20","doi-asserted-by":"publisher","first-page":"1373","DOI":"10.1017\/S1431927614012811","article-title":"Application of fractal and grey level co-occurrence matrix analysis in evaluation of brain corpus callosum and cingulum architecture","volume":"20","author":"Pantic","year":"2014","journal-title":"Microscopy. Microanalysis."},{"key":"B21","doi-asserted-by":"publisher","first-page":"1455","DOI":"10.1002\/cpt.v110.6","article-title":"The treatment of tuberculosis","volume":"110","author":"Peloquin","year":"2021","journal-title":"Clin. Pharmacol. Ther."},{"key":"B22","doi-asserted-by":"publisher","first-page":"1608","DOI":"10.1515\/med-2021-0349","article-title":"Clinical features of atypical tuberculosis mimicking bacterial pneumonia","volume":"16","author":"Qi","year":"2021","journal-title":"Open Med. (Warsaw. Poland)."},{"key":"B23","doi-asserted-by":"publisher","first-page":"3922","DOI":"10.1158\/0008-5472.CAN-17-0122","article-title":"Somatic Mutations Drive Distinct Imaging Phenotypes in Lung Cancer","volume":"77","author":"Rios Velazquez","year":"2017","journal-title":"Cancer Res."},{"key":"B24","doi-asserted-by":"publisher","first-page":"S102","DOI":"10.1093\/cid\/civ609","article-title":"Perspectives on advances in tuberculosis diagnostics, drugs, and vaccines","author":"Schito","year":"2015","journal-title":"Clin. Infect. Dis."},{"key":"B25","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.ijid.2014.12.007","article-title":"Imaging in tuberculosis","volume":"32","author":"Skoura","year":"2015","journal-title":"Int. J. Infect. Dis.: IJID."},{"key":"B26","doi-asserted-by":"publisher","first-page":"729","DOI":"10.3238\/arztebl.2019.0729","article-title":"The diagnosis and treatment of tuberculosis","volume":"116","author":"Su\u00e1rez","year":"2019","journal-title":"Deutsches. Arzteblatt. Int."},{"key":"B27","doi-asserted-by":"publisher","first-page":"a039537","DOI":"10.1101\/cshperspect.a039537","article-title":"Application of radiomics and artificial intelligence for lung cancer precision medicine","volume":"11","author":"Tunali","year":"2021","journal-title":"Cold Spring Harbor Perspect. Med."},{"key":"B28","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1080\/23744235.2019.1663363","article-title":"Clinical features in active tuberculosis patients with effects on T-SPOT.TB assay results","volume":"52","author":"Uda","year":"2020","journal-title":"Infect. Dis. (London. England)."},{"key":"B29","doi-asserted-by":"publisher","first-page":"754","DOI":"10.1016\/j.ajodo.2023.08.003","article-title":"Support vector machines","volume":"164","author":"Valkenborg","year":"2023","journal-title":"Am. J. Orthod. Dentofacial. Orthop."},{"key":"B30","doi-asserted-by":"publisher","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","article-title":"Computational radiomics system to decode the radiographic phenotype","volume":"77","author":"van Griethuysen","year":"2017","journal-title":"Cancer Res."},{"key":"B31","doi-asserted-by":"publisher","first-page":"e199","DOI":"10.1016\/S1473-3099(18)30111-7","article-title":"Tuberculosis: advances and challenges in development of new diagnostics and biomarkers","volume":"18","author":"Walzl","year":"2018","journal-title":"Lancet Infect. Dis."},{"key":"B32","doi-asserted-by":"publisher","first-page":"1197","DOI":"10.1186\/s12879-021-06901-2","article-title":"Clinical characteristics and chest computed tomography findings related to the infectivity of pulmonary tuberculosis","volume":"21","author":"Wang","year":"2021","journal-title":"BMC Infect. Dis."},{"key":"B33","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.lungcan.2018.09.013","article-title":"CT-based radiomics signature for differentiating solitary granulomatous nodules from solid lung adenocarcinoma","volume":"125","author":"Yang","year":"2018","journal-title":"Lung Cancer (Amsterdam. Netherlands)."},{"key":"B34","doi-asserted-by":"publisher","first-page":"1538","DOI":"10.1007\/s00330-021-08277-y","article-title":"CT-based radiomics signatures can predict the tumor response of non-small cell lung cancer patients treated with first-line chemotherapy and targeted therapy","volume":"32","author":"Yang","year":"2022","journal-title":"Eur. Radiol."},{"key":"B35","doi-asserted-by":"publisher","first-page":"2372","DOI":"10.1007\/s00330-014-3279-6","article-title":"A high-resolution computed tomography-based scoring system to differentiate the most infectious active pulmonary tuberculosis from community-acquired pneumonia in elderly and non-elderly patients","volume":"24","author":"Yeh","year":"2014","journal-title":"Eur. Radiol."},{"key":"B36","doi-asserted-by":"publisher","first-page":"105","DOI":"10.3343\/alm.2013.33.2.105","article-title":"Role of the neutrophil-lymphocyte count ratio in the differential diagnosis between pulmonary tuberculosis and bacterial community-acquired pneumonia","volume":"33","author":"Yoon","year":"2013","journal-title":"Ann. Lab. Med."},{"key":"B37","doi-asserted-by":"publisher","first-page":"1651","DOI":"10.21037\/qims-20-1323","article-title":"Illustration of a number of atypical computed tomography manifestations of active pulmonary tuberculosis","volume":"11","author":"Zeng","year":"2021","journal-title":"Quantitative. Imaging Med. Surg."},{"key":"B38","doi-asserted-by":"publisher","first-page":"713","DOI":"10.3978\/j.issn.2072-1439.2014.04.38","article-title":"The performance and limitation of T-SPOT.TB for the diagnosis of TB in a high prevalence setting","volume":"6","author":"Zhu","year":"2014","journal-title":"J. Thorac. Dis."},{"key":"B39","doi-asserted-by":"publisher","first-page":"6511","DOI":"10.2147\/JIR.S344563","article-title":"A novel clinical radiomics nomogram to identify crohn\u2019s disease from intestinal tuberculosis","volume":"14","author":"Zhu","year":"2021","journal-title":"J. Inflammation Res."}],"container-title":["Frontiers in Cellular and Infection Microbiology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcimb.2024.1388991\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T05:10:41Z","timestamp":1726722641000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcimb.2024.1388991\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,19]]},"references-count":39,"alternative-id":["10.3389\/fcimb.2024.1388991"],"URL":"https:\/\/doi.org\/10.3389\/fcimb.2024.1388991","relation":{},"ISSN":["2235-2988"],"issn-type":[{"value":"2235-2988","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,19]]},"article-number":"1388991"}}