{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T19:03:47Z","timestamp":1789067027698,"version":"build-2803163510"},"reference-count":29,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T00:00:00Z","timestamp":1779321600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>This study aimed to investigate the clinical feasibility of artificial intelligence-rapid on-site evaluation (AI-ROSE) based on exfoliated cell blotting from percutaneous puncture biopsy specimens for diagnosing pulmonary lesions and to provide a reference for intraoperative rapid diagnosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 266 patients with pulmonary lesions who underwent computed tomography (CT)-guided percutaneous core needle biopsy between June 11 and November 27, 2024, were enrolled. Exfoliated cell prints from biopsy specimens were stained with Diff-Quik, followed by diagnosis using AI-ROSE. Using the final histopathological diagnosis as the standard, the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of AI-ROSE and conventional cytological diagnoses were compared. The consistency between the methods and histopathological diagnosis was analyzed. The diagnostic times for AI-ROSE, cytology, and histopathology were recorded and compared. Postoperative complications were documented, and the correlation between lesion characteristics and complications was analyzed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Compared with histopathological results, AI-ROSE achieved a sensitivity of 95.67%, specificity of 79.31%, diagnostic accuracy of 92.11%, PPV of 94.31%, and NPV of 83.64% in diagnosing pulmonary lesions, with good consistency (\u03ba = 0.764, P &amp;lt; 0.001). No significant difference in the AI-ROSE diagnostic accuracy was observed among the different pathological types (P &amp;gt; 0.05). Conventional cytological diagnosis showed a sensitivity of 87.50%, specificity of 85.71%, and accuracy of 87.10%, with lower consistency and histopathology (\u03ba = 0.665, P &amp;lt; 0.001) than those of AI-ROSE. The mean diagnostic time of AI-ROSE was 254.60 \u00b1 13.88 s, which was significantly shorter than that of cytology (1.48 \u00b1 0.86 days) and histopathology (2.37 \u00b1 1.42 days). The overall incidence of postoperative complications was 22.93%, including pneumothorax (12.78 %) and needle tract bleeding\/mild hemoptysis (9.77%), with no fatal complications. A smaller nodule volume was associated with a higher risk of puncture bleeding (P = 0.004), whereas lesion size and puncture path length were not significantly correlated with pneumothorax risk (P &amp;gt; 0.05).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>AI-ROSE enables rapid intraoperative diagnosis of pulmonary lesions in percutaneous biopsy with high diagnostic performance and consistency with histopathological results, demonstrating its favorable value for clinical application and popularization.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1836366","type":"journal-article","created":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T04:46:38Z","timestamp":1779338798000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Feasibility of artificial intelligence-based rapid on-site evaluation for the diagnosis of pulmonary disease"],"prefix":"10.3389","volume":"16","author":[{"given":"Yuxian","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Radiology, Qilu Hospital of Shandong University","place":["Jinan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengcheng","family":"Du","sequence":"additional","affiliation":[{"name":"Department of Radiology, Qilu Hospital of Shandong University","place":["Jinan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengchen","family":"Gu","sequence":"additional","affiliation":[{"name":"Shanghai Aitrox Technology Corporation Limited","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunhai","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiology, Qilu Hospital of Shandong University","place":["Jinan, 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