{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T18:27:44Z","timestamp":1785522464960,"version":"3.56.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T00:00:00Z","timestamp":1779926400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T00:00:00Z","timestamp":1785196800000},"content-version":"vor","delay-in-days":61,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Withers and Peters Grant, Royal Australian and New Zealand College of Radiologists"},{"name":"Cancer Council Victoria Colebatch Fellowship"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-53520-5","type":"journal-article","created":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T05:24:20Z","timestamp":1779427460000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep learning and radiomics models in patients with advanced non-small cell lung cancer treated with immunotherapy combined with stereotactic radiotherapy"],"prefix":"10.1038","volume":"16","author":[{"given":"G.","family":"Kothari","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"N.","family":"Hardcastle","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R.","family":"Perera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P.","family":"Jackson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S.","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J. D.","family":"Silver","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M.","family":"Gaudreault","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J.","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C.","family":"Brown","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P. L. R.","family":"Mitchell","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"T.","family":"Kron","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J. W.","family":"Welsh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"W. S. M. E.","family":"Theelen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S.","family":"Siva","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,28]]},"reference":[{"issue":"6","key":"53520_CR1","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1200\/JCO.21.01497","volume":"40","author":"M Reck","year":"2022","unstructured":"Reck, M., Remon, J. & Hellmann, M. D. First-line immunotherapy for non\u2013small-cell lung cancer. J. Clin. Oncol. 40(6), 586\u2013597 (2022).","journal-title":"J. Clin. Oncol."},{"issue":"5","key":"53520_CR2","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1016\/S2213-2600(20)30391-X","volume":"9","author":"WSME Theelen","year":"2020","unstructured":"Theelen, W. S. M. E. et al. Pembrolizumab with or without radiotherapy for metastatic non-small-cell lung cancer: a pooled analysis of two randomised trials. Lancet Respiratory Med. 9(5), 467\u2013475 (2020).","journal-title":"Lancet Respiratory Med."},{"issue":"16","key":"53520_CR3","doi-asserted-by":"publisher","first-page":"1611","DOI":"10.1200\/JCO.2017.76.2229","volume":"36","author":"JJ Luke","year":"2018","unstructured":"Luke, J. J. et al. Safety and clinical activity of pembrolizumab and multisite stereotactic body radiotherapy in patients with advanced solid tumors. J. Clin. Oncol. 36(16), 1611\u20131618 (2018).","journal-title":"J. Clin. Oncol."},{"issue":"8","key":"53520_CR4","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1038\/s41571-023-00782-x","volume":"20","author":"L Galluzzi","year":"2023","unstructured":"Galluzzi, L., Aryankalayil, M. J., Coleman, C. N. & Formenti, S. C. Emerging evidence for adapting radiotherapy to immunotherapy. Nat. Rev. Clin. Oncol. 20(8), 543\u2013557 (2023).","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"53520_CR5","doi-asserted-by":"publisher","DOI":"10.1001\/jamaoncol.2019.1478","author":"W Theelen","year":"2019","unstructured":"Theelen, W. et al. Effect of pembrolizumab after stereotactic body radiotherapy vs pembrolizumab alone on tumor response in patients with advanced non-small cell lung cancer: Results of the PEMBRO-RT phase 2 randomized clinical trial. JAMA Oncol. https:\/\/doi.org\/10.1001\/jamaoncol.2019.1478 (2019).","journal-title":"JAMA Oncol."},{"issue":"4","key":"53520_CR6","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1016\/j.jtho.2019.12.107","volume":"15","author":"S Lantuejoul","year":"2020","unstructured":"Lantuejoul, S. et al. PD-L1 testing for lung cancer in 2019: Perspective from the IASLC pathology committee. J. Thorac. Oncol. 15(4), 499\u2013519 (2020).","journal-title":"J. Thorac. Oncol."},{"issue":"12","key":"53520_CR7","doi-asserted-by":"publisher","first-page":"1335","DOI":"10.1016\/j.jtho.2022.09.109","volume":"17","author":"M Mino-Kenudson","year":"2022","unstructured":"Mino-Kenudson, M. et al. Predictive biomarkers for immunotherapy in lung cancer: Perspective from the international association for the study of lung cancer pathology committee. J. Thorac. Oncol. 17(12), 1335\u20131354 (2022).","journal-title":"J. Thorac. Oncol."},{"issue":"1","key":"53520_CR8","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1038\/s41746-021-00438-z","volume":"4","author":"R Aggarwal","year":"2021","unstructured":"Aggarwal, R. et al. Diagnostic accuracy of deep learning in medical imaging: A systematic review and meta-analysis. NPJ Digit Med. 4(1), 65 (2021).","journal-title":"NPJ Digit Med."},{"issue":"3","key":"53520_CR9","doi-asserted-by":"publisher","first-page":"S10","DOI":"10.1016\/j.ijrobp.2021.07.056","volume":"111","author":"P Mitchell","year":"2021","unstructured":"Mitchell, P. et al. A randomized phase 2 trial of Nivolumab and stereotactic ablative body radiotherapy (SABR) in advanced non-small cell lung cancer, progressing after first-or second-line chemotherapy (NIVORAD). Int. J. Radiat. Oncol. Biol. Phys. 111(3), S10\u2013S11 (2021).","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"issue":"3","key":"53520_CR10","doi-asserted-by":"publisher","first-page":"S10","DOI":"10.1016\/j.ijrobp.2021.07.056","volume":"111","author":"P Mitchell","year":"2021","unstructured":"Mitchell, P. et al. A randomized phase 2 trial of Nivolumab and stereotactic ablative body radiotherapy (SABR) in advanced non-small cell lung cancer, progressing after first- or second-line chemotherapy (NIVORAD). Int. J. Radiat. Oncol. Biol. Phys. 111(3), S10\u2013S11 (2021).","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"53520_CR11","doi-asserted-by":"publisher","DOI":"10.1136\/jitc-2020-001001","author":"J Welsh","year":"2020","unstructured":"Welsh, J. et al. Pembrolizumab with or without radiation therapy for metastatic non-small cell lung cancer: A randomized phase I\/II trial. J. Immunother. Cancer. https:\/\/doi.org\/10.1136\/jitc-2020-001001 (2020).","journal-title":"J. Immunother. Cancer."},{"issue":"9","key":"53520_CR12","doi-asserted-by":"publisher","first-page":"1180","DOI":"10.1016\/S1470-2045(18)30413-3","volume":"19","author":"R Sun","year":"2018","unstructured":"Sun, R. et al. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: An imaging biomarker, retrospective multicohort study. Lancet Oncol. 19(9), 1180\u20131191 (2018).","journal-title":"Lancet Oncol."},{"issue":"21","key":"53520_CR13","doi-asserted-by":"publisher","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","volume":"77","author":"JJM van Griethuysen","year":"2017","unstructured":"van Griethuysen, J. J. M. et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 77(21), e104\u2013e107 (2017).","journal-title":"Cancer Res."},{"issue":"2","key":"53520_CR14","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1148\/radiol.2020191145","volume":"295","author":"A Zwanenburg","year":"2020","unstructured":"Zwanenburg, A. et al. The image biomarker standardization initiative: Standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295(2), 328\u2013338 (2020).","journal-title":"Radiology"},{"issue":"1","key":"53520_CR15","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L. Random forests. Mach. Learn. 45(1), 5\u201332 (2001).","journal-title":"Mach. Learn."},{"key":"53520_CR16","unstructured":"Brownlee, J. Xgboost with python2019."},{"key":"53520_CR17","unstructured":"Ronneberger, O., Fischer, P., Brox, T., editors. U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5\u20139, 2015, Proceedings, part III 18; 2015: Springer."},{"key":"53520_CR18","doi-asserted-by":"crossref","unstructured":"Ishwaran, H., Kogalur, U.B., Blackstone, E.H., Lauer, M.S. Random survival forests. 2008.","DOI":"10.1214\/08-AOAS169"},{"key":"53520_CR19","unstructured":"Ishwaran, H., Kogalur, U.B. Fast unified random forests for survival, regression, and classification (RF-SRC). R package version. 2019;2(1)."},{"key":"53520_CR20","doi-asserted-by":"publisher","DOI":"10.32614\/RJ-2021-041","author":"RA Peterson","year":"2021","unstructured":"Peterson, R. A. Finding optimal normalizing transformations via best normalize. R J. https:\/\/doi.org\/10.32614\/RJ-2021-041 (2021).","journal-title":"R J."},{"issue":"3","key":"53520_CR21","first-page":"841","volume":"2","author":"I Hemant","year":"2008","unstructured":"Hemant, I., Udaya, B. K., Eugene, H. B. & Michael, S. L. Random survival forests. Ann. Appl. Stat. 2(3), 841\u2013860 (2008).","journal-title":"Ann. Appl. Stat."},{"key":"53520_CR22","doi-asserted-by":"publisher","DOI":"10.1111\/1754-9485.13426","author":"G Kothari","year":"2022","unstructured":"Kothari, G. Role of radiomics in predicting immunotherapy response. J. Med. Imaging Radiat. Oncol. https:\/\/doi.org\/10.1111\/1754-9485.13426 (2022).","journal-title":"J. Med. Imaging Radiat. Oncol."},{"issue":"2","key":"53520_CR23","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.acra.2019.04.016","volume":"27","author":"M Jiang","year":"2020","unstructured":"Jiang, M. et al. Assessing PD-L1 expression level by radiomic features from PET\/CT in nonsmall cell lung cancer patients: An initial result. Acad. Radiol. 27(2), 171\u2013179 (2020).","journal-title":"Acad. Radiol."},{"key":"53520_CR24","doi-asserted-by":"publisher","first-page":"12003","DOI":"10.2147\/OTT.S261068","volume":"13","author":"Q Wen","year":"2020","unstructured":"Wen, Q. et al. Pretreatment CT-based radiomics signature as a potential imaging biomarker for predicting the expression of PD-L1 and CD8+ TILs in ESCC. Onco. Targets. Ther. 13, 12003 (2020).","journal-title":"Onco. Targets. Ther."},{"issue":"2","key":"53520_CR25","doi-asserted-by":"publisher","DOI":"10.1136\/jitc-2020-001429","volume":"8","author":"R Sun","year":"2020","unstructured":"Sun, R. et al. Radiomics to predict outcomes and abscopal response of patients with cancer treated with immunotherapy combined with radiotherapy using a validated signature of CD8 cells. J Immunother Cancer. 8(2), e001429 (2020).","journal-title":"J Immunother Cancer."},{"key":"53520_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/s00259-021-05509-7","author":"Q Chen","year":"2021","unstructured":"Chen, Q. et al. Current status and quality of radiomic studies for predicting immunotherapy response and outcome in patients with non-small cell lung cancer: A systematic review and meta-analysis. Eur. J. Nucl. Med. Mol. Imaging. https:\/\/doi.org\/10.1007\/s00259-021-05509-7 (2021).","journal-title":"Eur. J. Nucl. Med. Mol. Imaging."},{"key":"53520_CR27","volume":"11","author":"Y Liu","year":"2021","unstructured":"Liu, Y. et al. Imaging biomarkers to predict and evaluate the effectiveness of immunotherapy in advanced non-small-cell lung cancer. Front. Oncol. 11, 773 (2021).","journal-title":"Front. Oncol."},{"issue":"9","key":"53520_CR28","doi-asserted-by":"publisher","first-page":"2151","DOI":"10.1158\/1078-0432.CCR-19-2942","volume":"26","author":"L Dercle","year":"2020","unstructured":"Dercle, L. et al. Identification of non-small cell lung cancer sensitive to systemic cancer therapies using radiomics. Clin Cancer Res. 26(9), 2151\u20132162 (2020).","journal-title":"Clin Cancer Res."},{"key":"53520_CR29","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.lungcan.2019.01.010","volume":"129","author":"I Tunali","year":"2019","unstructured":"Tunali, I. et al. Novel clinical and radiomic predictors of rapid disease progression phenotypes among lung cancer patients treated with immunotherapy: An early report. Lung Cancer 129, 75\u201379 (2019).","journal-title":"Lung Cancer"},{"key":"53520_CR30","doi-asserted-by":"publisher","DOI":"10.1136\/jitc-2020-001343","volume":"8","author":"P Vaidya","year":"2020","unstructured":"Vaidya, P. et al. Novel, non-invasive imaging approach to identify patients with advanced non-small cell lung cancer at risk of hyperprogressive disease with immune checkpoint blockade. J. Immunother. Cancer. 8, 2 (2020).","journal-title":"J. Immunother. Cancer."},{"issue":"1","key":"53520_CR31","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1158\/2326-6066.CIR-19-0476","volume":"8","author":"M Khorrami","year":"2020","unstructured":"Khorrami, M. et al. Changes in CT radiomic features associated with lymphocyte distribution predict overall survival and response to immunotherapy in non-small cell lung cancer. Cancer Immunol. Res. 8(1), 108\u2013119 (2020).","journal-title":"Cancer Immunol. Res."},{"issue":"2","key":"53520_CR32","first-page":"743","volume":"13","author":"Y Yang","year":"2021","unstructured":"Yang, Y. et al. A multi-omics-based serial deep learning approach to predict clinical outcomes of single-agent anti-PD-1\/PD-L1 immunotherapy in advanced stage non-small-cell lung cancer. Am J Transl Res. 13(2), 743 (2021).","journal-title":"Am J Transl Res."},{"issue":"5","key":"53520_CR33","doi-asserted-by":"publisher","first-page":"1168","DOI":"10.1007\/s00259-019-04625-9","volume":"47","author":"W Mu","year":"2020","unstructured":"Mu, W. et al. Radiomics of (18)F-FDG PET\/CT images predicts clinical benefit of advanced NSCLC patients to checkpoint blockade immunotherapy. Eur. J. Nucl. Med. Mol. Imaging 47(5), 1168\u20131182 (2020).","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"issue":"2","key":"53520_CR34","first-page":"1559","volume":"19","author":"V Nardone","year":"2020","unstructured":"Nardone, V. et al. Radiomics predicts survival of patients with advanced non-small cell lung cancer undergoing PD-1 blockade using Nivolumab. Oncol. Lett. 19(2), 1559\u20131566 (2020).","journal-title":"Oncol. Lett."},{"issue":"1","key":"53520_CR35","doi-asserted-by":"publisher","first-page":"11065","DOI":"10.1038\/s41598-023-38076-y","volume":"13","author":"S Yolchuyeva","year":"2023","unstructured":"Yolchuyeva, S. et al. Radiomics approaches to predict PD-L1 and PFS in advanced non-small cell lung patients treated with immunotherapy: a multi-institutional study. Sci Rep. 13(1), 11065 (2023).","journal-title":"Sci Rep."},{"issue":"11","key":"53520_CR36","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1002711","volume":"15","author":"A Hosny","year":"2018","unstructured":"Hosny, A. et al. Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study. PLoS Med. 15(11), e1002711 (2018).","journal-title":"PLoS Med."},{"issue":"1","key":"53520_CR37","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1186\/s40644-024-00779-4","volume":"24","author":"C-Y Liao","year":"2024","unstructured":"Liao, C.-Y. et al. Personalized prediction of immunotherapy response in lung cancer patients using advanced radiomics and deep learning. Cancer Imaging 24(1), 129 (2024).","journal-title":"Cancer Imaging"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-53520-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-53520-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-53520-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T17:15:36Z","timestamp":1785518136000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-53520-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,28]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["53520"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-53520-5","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,28]]},"assertion":[{"value":"22 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"WSMET receives research funding to the NKI-AVL from MSD, AZ and Regeneron. JWW declares the following disclosures: Accuray (SAB, consulting), Alpine Immune Science (SAB, consulting, equity), Boehringer Ingelheim (SAB, consulting), Checkmate Pharmaceuticals (SAB, consulting, equity), China Medical Tribune (SAB, consulting), Genentech (SAB, consulting, Research grant), GI Innovation (SAB, consulting), Kezar Life Sciences (consulting, SAB), Legion Healthcare Partners (SAB, consulting), Life Science Dynamic Limited (SAB, consulting), McKesson Corporation (SAB, consulting), Molecular Match (equity), Nanorobotix (SAB, consulting), OligoImmune (founder), Roche (SAB, consulting), Roche Molecular Systems (SAB, consulting), Nanobiotix (Research grant, Travel expenses, SAB), BMS (Research grant), Merck (Research grant), Varian (Research grant, Travel expenses, clinical sponsored research), Reflexion (Research grant, Travel expenses, Stock options, SAB), Hotspot Therapeutics (Research grant), Gilead (Research grant), Novocure (SAB, Research grant), Oncoresponse (SAB, Stock options), Astra Zeneca (consultant, Research grant), Bayer Healthcare (Research grant), Kiromic (Research grant), Alkermes (Research grant, SAB), Artidis (Research grant), Sciclone (Research grant), Takeda (Research grant), Nurix (Research grant). All other authors have declared no conflicts of interest.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"23362"}}