{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T15:04:57Z","timestamp":1784646297971,"version":"3.55.0"},"reference-count":29,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T00:00:00Z","timestamp":1783555200000},"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>Introduction<\/jats:title>\n                    <jats:p>\n                      Differentiating\n                      <jats:italic>Mycobacterium tuberculosis lung disease<\/jats:italic>\n                      (MTB-LD) from nontuberculous mycobacterial lung disease (NTM-LD) remains clinically challenging because of overlapping symptoms and imaging features, as well as the slow turnaround of conventional culture-based diagnostics.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective study enrolled 102 patients with microbiologically confirmed mycobacterial lung disease, including 53 patients with MTB-LD and 49 patients with NTM-LD. We developed and validated an interpretable multimodal machine-learning framework integrating clinical symptoms, hematological biomarkers, and high-resolution computed tomography (HRCT) features. Three representative classifiers, including k-nearest neighbors, logistic regression, and random forest, were used to evaluate the discriminative contribution of different feature modalities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Multimodal integration of HRCT, clinical, and laboratory features showed better discriminative performance than single-modality approaches. Among the three representative classifiers, the random forest model achieved the best hold-out test performance, with an AUC of 0.92, sensitivity of 0.89, specificity of 0.93, and F1-score of 0.90. Key predictive contributors included cystic bronchiectasis, tree-in-bud sign, fever, and selected laboratory biomarkers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>These findings suggest that routinely available multimodal clinical data may provide preliminary decision support for MTB-LD\/NTM-LD differentiation. However, the proposed framework should be regarded as an exploratory decision-support tool, and external validation is required before clinical implementation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1858126","type":"journal-article","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T12:34:22Z","timestamp":1783600462000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Precise discrimination of mycobacterial pulmonary diseases via multimodal machine learning integrating chest CT and clinical markers"],"prefix":"10.3389","volume":"13","author":[{"given":"Yangyi","family":"Jin","sequence":"first","affiliation":[{"name":"Department of General Internal Medicine, Zhejiang Cancer Hospital","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jindun","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Respiratory and Critical Care Medicine, The People\u2019s Hospital of Yuhuan","place":["Yuhuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinsheng","family":"Ouyang","sequence":"additional","affiliation":[{"name":"Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University","place":["Wenzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiye","family":"Yao","sequence":"additional","affiliation":[{"name":"Wenzhou Institute, University of Chinese Academy of Sciences","place":["Wenzhou, China"]},{"name":"Shanghai Key Lab of Modern Optical System, Terahertz Spectrum and Imaging Technology Cooperative Innovation Center, Terahertz Technology Innovation Research Institute, University of Shanghai for Science and Technology","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liping","family":"Wang","sequence":"additional","affiliation":[{"name":"Wenzhou Institute, University of Chinese Academy of Sciences","place":["Wenzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruisong","family":"Xu","sequence":"additional","affiliation":[{"name":"Wenzhou Institute, University of Chinese Academy of Sciences","place":["Wenzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuewen","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Respiratory and Critical Care Medicine, The People\u2019s Hospital of Yuhuan","place":["Yuhuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,9]]},"reference":[{"key":"ref1","volume-title":"Global Tuberculosis Report 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