{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T07:04:11Z","timestamp":1774940651502,"version":"3.50.1"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T00:00:00Z","timestamp":1774915200000},"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. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>To explore the feasibility of a combined model integrating radiomics and clinical-semantic features for differentiating pulmonary tuberculosis (PTB) from non-tuberculous solid lung lesions on contrast-enhanced CT.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this study, 900 patients enrolled before October 2016 were randomly partitioned into training and internal validation sets at a 3:1 ratio, while those recruited between October 2016 and October 2017 formed an independent temporal validation set. Clinical-semantic features were selected through univariate analysis followed by multivariate analysis, while predictive radiomics features were identified using analysis of variance, Spearman correlation analysis, least absolute shrinkage and selection operator regression. Binary logistic regression was then used to construct the clinical-semantic, radiomics, and combined models. Model performance was evaluated using average precision (AP) derived from the precision-recall curve, and differences between models were assessed using bootstrap resampling. Clinical utility was assessed using decision curve analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Following feature selection, two clinical-semantic and three radiomics features were incorporated into the combined model. This model achieved APs of 0.91, 0.85, and 0.62 in the training, internal validation, and temporal validation sets, respectively, outperforming the clinical-semantic model, which yielded APs of 0.64, 0.61, and 0.41 (\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.001,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.001, and\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.006). The radiomics model also outperformed the clinical-semantic model across the three sets with APs of 0.88, 0.82, and 0.45. Decision curve analysis showed that the combined model can provide good net benefit across varying threshold probabilities.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>By integrating clinical-semantic and radiomics features, the combined model enables accurate differentiation between PTB and non-tuberculous solid lung lesions, potentially facilitating non-invasive diagnosis and personalized treatment planning.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1754750","type":"journal-article","created":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T05:32:56Z","timestamp":1774935176000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Differentiation of pulmonary tuberculosis from non-tuberculous solid lung lesions using radiomics and clinical-semantic features on contrast-enhanced CT"],"prefix":"10.3389","volume":"13","author":[{"given":"Sunyi","family":"Zheng","sequence":"first","affiliation":[{"name":"National Clinical Research Center for Cancer, Tianjin\u2019s Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Digestive Cancer, Key Laboratory of Cancer Prevention and Therapy, Medical Artificial General Intelligence for Computation (MAGIC) Lab, Department of Radiology, Tianjin Medical University Cancer Institute and Hospital","place":["Tianjin, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Public Health, Tianjin University of Traditional Chinese Medicine","place":["Tianjin, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Liang","sequence":"additional","affiliation":[{"name":"National Clinical Research Center for Cancer, Tianjin\u2019s Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Digestive Cancer, Key Laboratory of Cancer Prevention and Therapy, Medical Artificial General Intelligence for Computation (MAGIC) Lab, Department of Radiology, Tianjin Medical University Cancer Institute and Hospital","place":["Tianjin, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaomeng","family":"Yang","sequence":"additional","affiliation":[{"name":"National Clinical Research Center for Cancer, Tianjin\u2019s Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Digestive Cancer, Key Laboratory of Cancer Prevention and Therapy, Medical Artificial General Intelligence for Computation (MAGIC) Lab, Department of Radiology, Tianjin Medical University Cancer Institute and Hospital","place":["Tianjin, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaxin","family":"Liu","sequence":"additional","affiliation":[{"name":"National Clinical Research Center for Cancer, Tianjin\u2019s Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Digestive Cancer, Key Laboratory of Cancer Prevention and Therapy, Medical Artificial General Intelligence for Computation (MAGIC) Lab, Department of Radiology, Tianjin Medical University Cancer Institute and Hospital","place":["Tianjin, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanju","family":"Li","sequence":"additional","affiliation":[{"name":"National Clinical Research Center for Cancer, Tianjin\u2019s Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Digestive Cancer, Key Laboratory of Cancer Prevention and Therapy, Medical Artificial General Intelligence for Computation (MAGIC) Lab, Department of Radiology, Tianjin Medical University Cancer Institute and Hospital","place":["Tianjin, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengcheng","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Radiology, Baoding No. 6 Hospital","place":["Baoding, Hebei, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianyu","family":"Xiao","sequence":"additional","affiliation":[{"name":"National Clinical Research Center for Cancer, Tianjin\u2019s Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Digestive Cancer, Key Laboratory of Cancer Prevention and Therapy, Medical Artificial General Intelligence for Computation (MAGIC) Lab, Department of Radiology, Tianjin Medical University Cancer Institute and Hospital","place":["Tianjin, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaeyoun","family":"Yi","sequence":"additional","affiliation":[{"name":"Coreline Soft","place":["Seoul, Republic of Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianwei","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, National Cancer Center, National, Clinical Research Center for Cancer\/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College","place":["Beijing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaoxiang","family":"Ye","sequence":"additional","affiliation":[{"name":"National Clinical Research Center for Cancer, Tianjin\u2019s Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Digestive Cancer, Key Laboratory of Cancer Prevention and Therapy, Medical Artificial General Intelligence for Computation (MAGIC) Lab, Department of Radiology, Tianjin Medical University Cancer Institute and Hospital","place":["Tianjin, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaonan","family":"Cui","sequence":"additional","affiliation":[{"name":"National Clinical Research Center for Cancer, Tianjin\u2019s Clinical Research Center for Cancer, State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin Key Laboratory of Digestive Cancer, Key Laboratory of Cancer Prevention and Therapy, Medical Artificial General Intelligence for Computation (MAGIC) Lab, Department of Radiology, Tianjin Medical University Cancer Institute and Hospital","place":["Tianjin, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,3,31]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"e20","DOI":"10.1016\/S2666-5247(22)00359-7","article-title":"WHO'S global tuberculosis report 2022","volume":"4","author":"Bagcchi","year":"2023","journal-title":"Lancet Microbe"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"834","DOI":"10.2214\/AJR.07.3896","article-title":"Pulmonary tuberculosis: up-to-date imaging and management","volume":"191","author":"Jeong","year":"2008","journal-title":"Am J Roentgenol"},{"key":"ref3","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":"Radiographics"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"867","DOI":"10.1007\/s10096-014-2105-z","article-title":"Performance of solid and liquid culture media for the detection of Mycobacterium tuberculosis in clinical materials: meta-analysis of recent studies","volume":"33","author":"Rageade","year":"2014","journal-title":"Eur J Clin Microbiol Infect Dis"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1378\/chest.126.3.748","article-title":"Risk factors of pneumothorax and bleeding: multivariate analysis of 660 CT-guided coaxial cutting needle lung biopsies","volume":"126","author":"Yeow","year":"2004","journal-title":"Chest"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"3310","DOI":"10.1007\/s00330-020-06661-8","article-title":"Dynamic contrast\u2013enhanced computed tomography for the diagnosis of solitary pulmonary nodules: a systematic review and meta-analysis","volume":"30","author":"Weir-McCall","year":"2020","journal-title":"Eur Radiol"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1016\/j.chest.2022.08.2227","article-title":"Diagnostic accuracy of a convolutional neural network assessment of solitary pulmonary nodules compared with PET with CT imaging and dynamic contrast-enhanced CT imaging using unenhanced and contrast-enhanced CT imaging","volume":"163","author":"Weir-McCall","year":"2023","journal-title":"Chest"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"e230171","DOI":"10.1148\/rg.230171","article-title":"Conventional and contrast-enhanced US of the lung: from performance to diagnosis","volume":"44","author":"Jim\u00e9nez-Serrano","year":"2024","journal-title":"Radiographics"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: images are more than pictures, they are 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multicenter setting","volume":"34","author":"Uhlig","year":"2024","journal-title":"Eur Radiol"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"1213","DOI":"10.2214\/AJR.19.21490","article-title":"Application of radiomics in predicting the malignancy of pulmonary nodules in different sizes","volume":"213","author":"Xu","year":"2019","journal-title":"Am J Roentgenol"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"1049","DOI":"10.1007\/s00330-020-07141-9","article-title":"Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives","volume":"31","author":"Chetan","year":"2021","journal-title":"Eur Radiol"},{"key":"ref15","doi-asserted-by":"publisher","first-page":"1245","DOI":"10.1016\/j.acra.2018.10.013","article-title":"Radiomics for classification of lung cancer histological subtypes based on nonenhanced computed tomography","volume":"26","author":"Linning","year":"2019","journal-title":"Acad 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Tumors\u201d","volume":"17","author":"Tsao","year":"2022","journal-title":"J Thorac Oncol"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"1193","DOI":"10.1213\/ane.0000000000004410","article-title":"Chi-square tests in medical research","volume":"129","author":"Schober","year":"2019","journal-title":"Anesth Analg"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1148\/radiol.2283021330","article-title":"Hypothesis testing III: counts and medians","volume":"228","author":"Applegate","year":"2003","journal-title":"Radiology"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"837","DOI":"10.2307\/2531595","article-title":"Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach","volume":"44","author":"DeLong","year":"1988","journal-title":"Biometrics"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"2052","DOI":"10.1097\/01.ccm.0000275267.64078.b0","article-title":"Assessing the calibration of mortality benchmarks in critical care: the Hosmer\u2013Lemeshow test revisited","volume":"35","author":"Kramer","year":"2007","journal-title":"Crit Care Med"},{"key":"ref26","author":"Lundberg","year":"2017"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1186\/s13244-023-01415-8","article-title":"CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII","volume":"14","author":"Kocak","year":"2023","journal-title":"Insights Imaging"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1186\/s13244-023-01572-w","article-title":"METhodological RadiomICs score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII","volume":"15","author":"Kocak","year":"2024","journal-title":"Insights Imaging"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1038\/s41571-025-01067-1","article-title":"Radiomics quality score 2.0: towards radiomics readiness levels and clinical translation for personalized medicine","volume":"22","author":"Lambin","year":"2025","journal-title":"Nat Rev Clin Oncol"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1186\/s13244-021-01138-8","article-title":"Active pulmonary tuberculosis: something old, something new, something borrowed, something blue","volume":"13","author":"Wetscherek","year":"2022","journal-title":"Insights Imaging"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"639","DOI":"10.2214\/ajr.184.2.01840639","article-title":"Pulmonary tuberculosis: another disease showing clusters of small nodules","volume":"184","author":"Heo","year":"2005","journal-title":"Am J Roentgenol"},{"key":"ref32","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":"ref33","doi-asserted-by":"publisher","first-page":"172","DOI":"10.2967\/jnumed.121.262464","article-title":"A guide to ComBat harmonization of imaging biomarkers in multicenter studies","volume":"63","author":"Orlhac","year":"2022","journal-title":"J Nucl Med"}],"container-title":["Frontiers in 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