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In particular, in non-small cell lung cancer (NSCLC), such features could potentially predict immune checkpoint biomarker status, such as PD-L1 expression, and histological subtype without the need for invasive tissue sampling.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We retrospectively analyzed pre-treatment\n                      <jats:sup>18<\/jats:sup>\n                      F-FDG PET\/CT scans of 115 patients with histologically confirmed NSCLC (adenocarcinoma or squamous cell carcinoma) and known PD-L1 status. Radiomic features were extracted from primary tumors and combined with basic clinical variables (\u201c\n                      <jats:italic>na\u00efve features<\/jats:italic>\n                      \u201d). Principal component analysis (PCA) and hierarchical clustering were performed to assess separability between the selected classes representing either PD-L1 subtype or histology. Multiple machine learning classifiers, including random forest (RF), were trained using radiomic-only, na\u00efve-only, and combined feature sets. Model performance was evaluated by area under the receiver operating characteristic curve (AUC-ROC).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      For PD-L1 prediction, the RF model trained on radiomic features achieved the highest accuracy (AUC-ROC\u2009=\u20090.83, 95% CI\u2009=\u2009[0.75 - 0.91]), outperforming the combined feature model (AUC-ROC\u2009=\u20090.81, 95% CI\u2009=\u2009[0.73 - 0.89]) and na\u00efve-only generalized linear and RF models (AUC-ROC\u2009~\u20090.5). Heatmap analysis of selected radiomic features showed significant clustering of PD-L1-positive and -negative tumors (Fisher\u2019s exact\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20097\u2009\u00d7\u200910\n                      <jats:sup>\u2212 4<\/jats:sup>\n                      ). For histological subtype classification, the best-performing RF model achieved an AUC-ROC of 0.76 using radiomic features. Although PCA demonstrated partial separation between adenocarcinoma and squamous cell carcinoma (PC1\u2009=\u200938%, PC2\u2009=\u200921%), the hierarchical clustering led to the significant class separation (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.0234).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      Our pilot project indicated that\n                      <jats:sup>18<\/jats:sup>\n                      F-FDG PET\/CT radiomics enables accurate non-invasive prediction of PD-L1 expression and provides moderate discrimination between major NSCLC histological subtypes. Clinical variables alone have negligible predictive value for PD-L1 status. Radiomic models may complement histopathology, particularly when tissue sampling is limited or repeated biomarker assessment is required. Prospective, multi-center validation is needed to confirm generalizability and facilitate clinical translation.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12967-026-08029-w","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T08:18:53Z","timestamp":1773821933000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Non-invasive assessment of PD-L1 status and histology in non-small cell lung cancer using 18F-FDG PET\/CT radiomics"],"prefix":"10.1186","volume":"24","author":[{"given":"Bart\u0142omiej","family":"Tomasik","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcin","family":"J\u0105kalski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Micha\u0142","family":"Bie\u0144kowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacek","family":"Teodorczyk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bartosz Kamil","family":"Sobocki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Konrad","family":"Stawiski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacek","family":"Burzynski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Grzegorz","family":"Romanowicz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rafa\u0142","family":"Dziadziuszko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wojciech","family":"Cytawa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2091-012X","authenticated-orcid":false,"given":"Jakub","family":"Mieczkowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,18]]},"reference":[{"issue":"1","key":"8029_CR1","first-page":"45","volume":"25","author":"KC Thandra","year":"2021","unstructured":"Thandra KC, Barsouk A, Saginala K, Aluru JS, Barsouk A. 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RD - Advisory\/consultancy fees: Roche, Foundation Medicine, Pfizer, AstraZeneca, Novartis, Merck Sharp & Dohme, Takeda, GlaxoSmithKline. Honoraria: Roche, AstraZeneca, Amgen. Participation in Data Safety Monitoring Boards\/advisory boards: Roche, AstraZeneca, Amgen, Pfizer, Merck Sharp & Dohme. Received drug sample from Novartis, Pfizer. Other Authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"587"}}