{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T05:13:56Z","timestamp":1791609236219,"version":"4.3.4"},"reference-count":70,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T00:00:00Z","timestamp":1785283200000},"content-version":"vor","delay-in-days":44,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52272281"],"award-info":[{"award-number":["52272281"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62406191"],"award-info":[{"award-number":["62406191"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Commun"],"DOI":"10.1038\/s41467-026-74474-2","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T20:24:22Z","timestamp":1781555062000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multimodal multitask deep learning for grading management system in non-small cell lung cancer"],"prefix":"10.1038","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4299-590X","authenticated-orcid":false,"given":"Xinyue","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0727-5655","authenticated-orcid":false,"given":"Fang","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3806-5160","authenticated-orcid":false,"given":"Jiawei","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huoqiang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaokai","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7021-4002","authenticated-orcid":false,"given":"Haoyue","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lishu","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kandi","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maotao","family":"Weng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6968-1586","authenticated-orcid":false,"given":"Siqiong","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8347-0830","authenticated-orcid":false,"given":"Hui","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2878-8595","authenticated-orcid":false,"given":"Yayi","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,15]]},"reference":[{"key":"74474_CR1","doi-asserted-by":"publisher","first-page":"1311","DOI":"10.1093\/annonc\/mdz141","volume":"30","author":"K Hastings","year":"2019","unstructured":"Hastings, K. et al. EGFR mutation subtypes and response to immune checkpoint blockade treatment in non-small-cell lung cancer. Ann. Oncol.: Off. J. Eur. Soc. Med. Oncol. 30, 1311\u20131320 (2019).","journal-title":"Ann. Oncol.: Off. J. Eur. Soc. Med. Oncol."},{"key":"74474_CR2","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.jconrel.2012.04.014","volume":"164","author":"TA Denison","year":"2012","unstructured":"Denison, T. A. & Bae, Y. H. Tumor heterogeneity and its implication for drug delivery. J. Control Release 164, 187\u2013191 (2012).","journal-title":"J. Control Release"},{"key":"74474_CR3","doi-asserted-by":"publisher","first-page":"2310","DOI":"10.1007\/s00259-017-3802-5","volume":"44","author":"S Rossi","year":"2017","unstructured":"Rossi, S. et al. Clinical characteristics of patient selection and imaging predictors of outcome in solid tumors treated with checkpoint-inhibitors. Eur. J. Nucl. Med. Mol. imaging 44, 2310\u20132325 (2017).","journal-title":"Eur. J. Nucl. Med. Mol. imaging"},{"key":"74474_CR4","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1200\/JCO.18.00149","volume":"37","author":"M Reck","year":"2019","unstructured":"Reck, M. et al. Updated Analysis of KEYNOTE-024: pembrolizumab versus platinum-based chemotherapy for advanced non-small-cell lung cancer with PD-L1 tumor proportion score of 50% or greater. J. Clin. Oncol. 37, 537\u2013546 (2019).","journal-title":"J. Clin. Oncol."},{"key":"74474_CR5","doi-asserted-by":"publisher","first-page":"1823","DOI":"10.1056\/NEJMoa1606774","volume":"375","author":"M Reck","year":"2016","unstructured":"Reck, M. et al. Pembrolizumab versus Chemotherapy for PD-L1-positive non-small-cell lung cancer. N. Engl. J. Med. 375, 1823\u20131833 (2016).","journal-title":"N. Engl. J. Med."},{"key":"74474_CR6","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.1016\/j.jtho.2020.03.028","volume":"15","author":"R Jotte","year":"2020","unstructured":"Jotte, R. et al. Atezolizumab in combination with carboplatin and nab-paclitaxel in advanced squamous NSCLC (IMpower131): results from a randomized phase III trial. J. Thorac. Oncol. 15, 1351\u20131360 (2020).","journal-title":"J. Thorac. Oncol."},{"key":"74474_CR7","doi-asserted-by":"publisher","first-page":"2288","DOI":"10.1056\/NEJMoa1716948","volume":"378","author":"MA Socinski","year":"2018","unstructured":"Socinski, M. A. et al. Atezolizumab for first-line treatment of metastatic nonsquamous NSCLC. N. Engl. J. Med 378, 2288\u20132301 (2018).","journal-title":"N. Engl. J. Med"},{"key":"74474_CR8","doi-asserted-by":"publisher","first-page":"1505","DOI":"10.1200\/JCO.19.03136","volume":"38","author":"S Gadgeel","year":"2020","unstructured":"Gadgeel, S. et al. Updated analysis from KEYNOTE-189: pembrolizumab or placebo plus pemetrexed and platinum for previously untreated metastatic nonsquamous non-small-cell lung cancer. J. Clin. Oncol. 38, 1505\u20131517 (2020).","journal-title":"J. Clin. Oncol."},{"key":"74474_CR9","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/S0140-6736(16)32517-X","volume":"389","author":"A Rittmeyer","year":"2017","unstructured":"Rittmeyer, A. et al. Atezolizumab versus docetaxel in patients with previously treated non-small-cell lung cancer (OAK): a phase 3, open-label, multicentre randomised controlled trial. Lancet (Lond., Engl.) 389, 255\u2013265 (2017).","journal-title":"Lancet (Lond., Engl.)"},{"key":"74474_CR10","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1111\/imm.12464","volume":"145","author":"R Appelberg","year":"2015","unstructured":"Appelberg, R. et al. The Warburg effect in mycobacterial granulomas is dependent on the recruitment and activation of macrophages by interferon-\u03b3. Immunology 145, 498\u2013507 (2015).","journal-title":"Immunology"},{"key":"74474_CR11","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1038\/s41591-022-02139-w","volume":"29","author":"GE Cacciamani","year":"2023","unstructured":"Cacciamani, G. E. et al. PRISMA AI reporting guidelines for systematic reviews and meta-analyses on AI in healthcare. Nat. Med 29, 14\u201315 (2023).","journal-title":"Nat. Med"},{"key":"74474_CR12","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2020.580809","volume":"10","author":"C Gao","year":"2020","unstructured":"Gao, C. et al. The growth trend predictions in pulmonary ground glass nodules based on radiomic CT features. Front. Oncol. 10, 580809 (2020).","journal-title":"Front. Oncol."},{"key":"74474_CR13","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1148\/radiol.13132195","volume":"270","author":"MD Kuo","year":"2014","unstructured":"Kuo, M. D. & Jamshidi, N. Behind the numbers: decoding molecular phenotypes with radiogenomics-guiding principles and technical considerations. Radiology 270, 320\u2013325 (2014).","journal-title":"Radiology"},{"key":"74474_CR14","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1007\/s00259-017-3837-7","volume":"45","author":"M Kirienko","year":"2018","unstructured":"Kirienko, M. et al. Prediction of disease-free survival by the PET\/CT radiomic signature in non-small cell lung cancer patients undergoing surgery. Eur. J. Nucl. Med. Mol. imaging 45, 207\u2013217 (2018).","journal-title":"Eur. J. Nucl. Med. Mol. imaging"},{"key":"74474_CR15","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, 1180\u20131191 (2018).","journal-title":"Lancet Oncol."},{"key":"74474_CR16","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 (Amst., Neth.) 129, 75\u201379 (2019).","journal-title":"Lung cancer (Amst., Neth.)"},{"key":"74474_CR17","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1038\/s41591-022-02134-1","volume":"29","author":"S Foersch","year":"2023","unstructured":"Foersch, S. et al. Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer. Nat. Med 29, 430\u2013439 (2023).","journal-title":"Nat. Med"},{"key":"74474_CR18","doi-asserted-by":"publisher","first-page":"3033","DOI":"10.1038\/s41591-023-02640-w","volume":"29","author":"K Cao","year":"2023","unstructured":"Cao, K. et al. Large-scale pancreatic cancer detection via non-contrast CT and deep learning. Nat. Med 29, 3033\u20133043 (2023).","journal-title":"Nat. Med"},{"key":"74474_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejro.2024.100561","volume":"12","author":"S Yaghoobpoor","year":"2024","unstructured":"Yaghoobpoor, S. et al. Machine learning approaches in the prediction of positive axillary lymph nodes post neoadjuvant chemotherapy using MRI, CT, or ultrasound: a systematic review. Eur. J. Radio. Open 12, 100561 (2024).","journal-title":"Eur. J. Radio. Open"},{"key":"74474_CR20","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-024-01241-4","volume":"24","author":"V Kumar","year":"2024","unstructured":"Kumar, V. et al. Unified deep learning models for enhanced lung cancer prediction with ResNet-50-101 and EfficientNet-B3 using DICOM images. BMC Med Imaging 24, 63 (2024).","journal-title":"BMC Med Imaging"},{"key":"74474_CR21","doi-asserted-by":"publisher","DOI":"10.1186\/s40644-024-00707-6","volume":"24","author":"Z Ma","year":"2024","unstructured":"Ma, Z. et al. Preoperative CT-based radiomic prognostic index to predict the benefit of postoperative radiotherapy in patients with non-small cell lung cancer: a multicenter study. Cancer Imaging 24, 61 (2024).","journal-title":"Cancer Imaging"},{"key":"74474_CR22","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-42964-8","volume":"13","author":"F Kinoshita","year":"2023","unstructured":"Kinoshita, F. et al. Development of artificial intelligence prognostic model for surgically resected non-small cell lung cancer. Sci. Rep. 13, 15683 (2023).","journal-title":"Sci. Rep."},{"key":"74474_CR23","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-88188-w","volume":"15","author":"T Meeradevi","year":"2025","unstructured":"Meeradevi, T., Sasikala, S., Murali, L., Manikandan, N. & Ramaswamy, K. Lung cancer detection with machine learning classifiers with multi-attribute decision-making system and deep learning model. Sci. Rep. 15, 8565 (2025).","journal-title":"Sci. Rep."},{"key":"74474_CR24","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-58345-8","volume":"14","author":"SP Maurya","year":"2024","unstructured":"Maurya, S. P., Sisodia, P. S., Mishra, R. & singh, D. P. Performance of machine learning algorithms for lung cancer prediction: a comparative approach. Sci. Rep. 14, 18562 (2024).","journal-title":"Sci. Rep."},{"key":"74474_CR25","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.lungcan.2017.08.005","volume":"112","author":"R Brody","year":"2017","unstructured":"Brody, R. et al. PD-L1 expression in advanced NSCLC: insights into risk stratification and treatment selection from a systematic literature review. Lung cancer (Amst., Neth.) 112, 200\u2013215 (2017).","journal-title":"Lung cancer (Amst., Neth.)"},{"key":"74474_CR26","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1016\/j.cmet.2022.01.007","volume":"34","author":"NN Pavlova","year":"2022","unstructured":"Pavlova, N. N., Zhu, J. & Thompson, C. B. The hallmarks of cancer metabolism: still emerging. Cell Metab. 34, 355\u2013377 (2022).","journal-title":"Cell Metab."},{"key":"74474_CR27","doi-asserted-by":"publisher","first-page":"998","DOI":"10.1093\/annonc\/mdz108","volume":"30","author":"S Trebeschi","year":"2019","unstructured":"Trebeschi, S. et al. Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers. Ann. Oncol.: Off. J. Eur. Soc. Med. Oncol. 30, 998\u20131004 (2019).","journal-title":"Ann. Oncol.: Off. J. Eur. Soc. Med. Oncol."},{"key":"74474_CR28","doi-asserted-by":"publisher","first-page":"3922","DOI":"10.1158\/0008-5472.CAN-17-0122","volume":"77","author":"E Rios Velazquez","year":"2017","unstructured":"Rios Velazquez, E. et al. Somatic mutations drive distinct imaging phenotypes in lung cancer. Cancer Res. 77, 3922\u20133930 (2017).","journal-title":"Cancer Res."},{"key":"74474_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3348\/kjr.2013.14.1.1","volume":"14","author":"SH Moon","year":"2013","unstructured":"Moon, S. H., Hyun, S. H. & Choi, J. Y. Prognostic significance of volume-based PET parameters in cancer patients. Korean J. Radio. 14, 1\u201312 (2013).","journal-title":"Korean J. Radio."},{"key":"74474_CR30","doi-asserted-by":"publisher","first-page":"947","DOI":"10.1148\/radiol.2016152234","volume":"281","author":"Y Huang","year":"2016","unstructured":"Huang, Y. et al. Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung Cancer. Radiology 281, 947\u2013957 (2016).","journal-title":"Radiology"},{"key":"74474_CR31","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1200\/JCO.2014.56.6661","volume":"33","author":"W Liang","year":"2015","unstructured":"Liang, W. et al. Development and validation of a nomogram for predicting survival in patients with resected non-small-cell lung cancer. J. Clin. Oncol. 33, 861\u2013869 (2015).","journal-title":"J. Clin. Oncol."},{"key":"74474_CR32","doi-asserted-by":"publisher","first-page":"e404","DOI":"10.1016\/S2589-7500(23)00082-1","volume":"5","author":"MB Saad","year":"2023","unstructured":"Saad, M. B. et al. Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study. Lancet Digit Health 5, e404\u2013e420 (2023).","journal-title":"Lancet Digit Health"},{"key":"74474_CR33","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-47512-0","volume":"15","author":"SJ Sujit","year":"2024","unstructured":"Sujit, S. J. et al. Enhancing NSCLC recurrence prediction with PET\/CT habitat imaging, ctDNA, and integrative radiogenomics-blood insights. Nat. Commun. 15, 3152 (2024).","journal-title":"Nat. Commun."},{"key":"74474_CR34","doi-asserted-by":"publisher","first-page":"1470","DOI":"10.1109\/LGRS.2019.2944599","volume":"17","author":"D Hong","year":"2020","unstructured":"Hong, D., Chanussot, J., Yokoya, N., Kang, J. & Zhu, X. X. Learning-Shared Cross-Modality Representation Using Multispectral-LiDAR and Hyperspectral Data. IEEE Geosci. Remote Sens. Lett. 17, 1470\u20131474 (2020).","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"74474_CR35","doi-asserted-by":"publisher","first-page":"ooae087","DOI":"10.1093\/jamiaopen\/ooae087","volume":"7","author":"S Kumar","year":"2024","unstructured":"Kumar, S., Yu, S. C., Michelson, A., Kannampallil, T. & Payne, P. R. O. HiMAL: multimodal hierarchical multi-task Auxiliary Learning framework for predicting Alzheimer\u2019s disease progression. JAMIA Open 7, ooae087 (2024).","journal-title":"JAMIA Open"},{"key":"74474_CR36","doi-asserted-by":"crossref","unstructured":"Malakouti, S. & Hauskrecht, M. Hierarchical Adaptive Multi-task Learning Framework for Patient Diagnoses and Diagnostic Category Classification. Proceedings (IEEE Int Conf Bioinformatics Biomed ) 2019 (2019).","DOI":"10.1109\/BIBM47256.2019.8983298"},{"key":"74474_CR37","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-025-57181-2","volume":"16","author":"G Arango-Argoty","year":"2025","unstructured":"Arango-Argoty, G. et al. Pretrained transformers applied to clinical studies improve predictions of treatment efficacy and associated biomarkers. Nat. Commun. 16, 2101 (2025).","journal-title":"Nat. Commun."},{"key":"74474_CR38","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1109\/TEVC.2023.3272663","volume":"28","author":"Y Zhang","year":"2024","unstructured":"Zhang, Y. et al. ESSR: evolving sparse sharing representation for multitask learning. IEEE Trans. Evolut. Comput. 28, 748\u2013762 (2024).","journal-title":"IEEE Trans. Evolut. Comput."},{"key":"74474_CR39","doi-asserted-by":"publisher","DOI":"10.1038\/s41698-024-00690-y","volume":"8","author":"M Meng","year":"2024","unstructured":"Meng, M. et al. Adaptive segmentation-to-survival learning for survival prediction from multi-modality medical images. npj Precis. Oncol. 8, 232 (2024).","journal-title":"npj Precis. Oncol."},{"key":"74474_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108635","volume":"177","author":"Y Li","year":"2024","unstructured":"Li, Y. et al. A review of deep learning-based information fusion techniques for multimodal medical image classification. Comput Biol. Med 177, 108635 (2024).","journal-title":"Comput Biol. Med"},{"key":"74474_CR41","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.636529","volume":"11","author":"S Cilla","year":"2021","unstructured":"Cilla, S. et al. Personalized Treatment planning automation in prostate cancer radiation oncology: a comprehensive dosimetric study. Front. Oncol. 11, 636529 (2021).","journal-title":"Front. Oncol."},{"key":"74474_CR42","doi-asserted-by":"crossref","unstructured":"Roychowdhury, S. in 2020 25th International Conference on Pattern Recognition (ICPR) 4581-4588 (2021).","DOI":"10.1109\/ICPR48806.2021.9412620"},{"key":"74474_CR43","doi-asserted-by":"publisher","first-page":"2078","DOI":"10.1056\/NEJMoa1801005","volume":"378","author":"L Gandhi","year":"2018","unstructured":"Gandhi, L. et al. Pembrolizumab plus chemotherapy in metastatic non-small-cell lung cancer. N. Engl. J. Med 378, 2078\u20132092 (2018).","journal-title":"N. Engl. J. Med"},{"key":"74474_CR44","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1038\/s41571-021-00473-5","volume":"18","author":"DB Doroshow","year":"2021","unstructured":"Doroshow, D. B. et al. PD-L1 as a biomarker of response to immune-checkpoint inhibitors. Nat. Rev. Clin. Oncol. 18, 345\u2013362 (2021).","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"74474_CR45","doi-asserted-by":"publisher","first-page":"2112","DOI":"10.1007\/s00259-019-04398-1","volume":"46","author":"D Ta\u00efeb","year":"2019","unstructured":"Ta\u00efeb, D. et al. European association of nuclear medicine practice guideline\/society of nuclear medicine and molecular imaging procedure standard 2019 for radionuclide imaging of phaeochromocytoma and paraganglioma. Eur. J. Nucl. Med. Mol. imaging 46, 2112\u20132137 (2019).","journal-title":"Eur. J. Nucl. Med. Mol. imaging"},{"key":"74474_CR46","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, e104\u2013e107 (2017).","journal-title":"Cancer Res."},{"key":"74474_CR47","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1002\/ana.26370","volume":"92","author":"G Bose","year":"2022","unstructured":"Bose, G. et al. Early predictors of clinical and mri outcomes using least absolute shrinkage and selection operator (LASSO) in multiple sclerosis. Ann. Neurol. 92, 87\u201396 (2022).","journal-title":"Ann. Neurol."},{"key":"74474_CR48","doi-asserted-by":"publisher","first-page":"3493","DOI":"10.1118\/1.3611983","volume":"38","author":"D Mason","year":"2011","unstructured":"Mason, D. SU-E-T-33: Pydicom: An Open Source DICOM Library. Med. Phys. 38, 3493\u20133493 (2011).","journal-title":"Med. Phys."},{"key":"74474_CR49","unstructured":"Rebuffi, S.-A. et al. in Proceedings of the 35th International Conference on Neural Information Processing Systems Article 2291 (Curran Associates Inc., 2021)."},{"key":"74474_CR50","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-31509-z","volume":"8","author":"RB Ger","year":"2018","unstructured":"Ger, R. B. et al. Comprehensive investigation on controlling for CT imaging variabilities in radiomics studies. Sci. Rep. 8, 13047 (2018).","journal-title":"Sci. Rep."},{"key":"74474_CR51","doi-asserted-by":"publisher","first-page":"5349","DOI":"10.1109\/TNNLS.2020.2966319","volume":"31","author":"F He","year":"2020","unstructured":"He, F., Liu, T. & Tao, D. Why ResNet works? Residuals generalize. IEEE Trans. Neural Netw. Learn Syst. 31, 5349\u20135362 (2020).","journal-title":"IEEE Trans. Neural Netw. Learn Syst."},{"key":"74474_CR52","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1007\/978-3-319-67558-9_28","volume":"2017","author":"CH Sudre","year":"2017","unstructured":"Sudre, C. H., Li, W., Vercauteren, T., Ourselin, S. & Jorge Cardoso, M. Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. Deep Learn Med Image Anal. Multimodal Learn Clin. Decis. Support (2017) 2017, 240\u2013248 (2017).","journal-title":"Deep Learn Med Image Anal. Multimodal Learn Clin. Decis. Support (2017)"},{"key":"74474_CR53","doi-asserted-by":"crossref","unstructured":"Setiawan, A. W. in 2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM) 97-102 (2020).","DOI":"10.1109\/CENIM51130.2020.9297970"},{"key":"74474_CR54","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1007\/s10278-021-00484-9","volume":"34","author":"Z Jiang","year":"2021","unstructured":"Jiang, Z. et al. CT-based hand-crafted radiomic signatures can predict PD-L1 expression levels in non-small cell lung cancer: a two-center study. J. Digit Imaging 34, 1073\u20131085 (2021).","journal-title":"J. Digit Imaging"},{"key":"74474_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2023.103200","volume":"117","author":"Y Hu","year":"2024","unstructured":"Hu, Y. et al. Ct-based subregional radiomics using hand-crafted and deep learning features for prediction of therapeutic response to anti-PD1 therapy in NSCLC. Phys. Med 117, 103200 (2024).","journal-title":"Phys. Med"},{"key":"74474_CR56","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1053\/j.semnuclmed.2022.04.007","volume":"52","author":"RJ Hicks","year":"2022","unstructured":"Hicks, R. J. The Value of the Standardized Uptake Value (SUV) and Metabolic Tumor Volume (MTV) in Lung Cancer. Semin. Nucl. Med. 52, 734\u2013744 (2022).","journal-title":"Semin. Nucl. Med."},{"key":"74474_CR57","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1016\/j.jclinepi.2003.11.002","volume":"57","author":"CM Tammemagi","year":"2004","unstructured":"Tammemagi, C. M., Neslund-Dudas, C., Simoff, M. & Kvale, P. In lung cancer patients, age, race-ethnicity, gender and smoking predict adverse comorbidity, which in turn predicts treatment and survival. J. Clin. Epidemiol. 57, 597\u2013609 (2004).","journal-title":"J. Clin. Epidemiol."},{"key":"74474_CR58","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20220772","volume":"96","author":"M Nakajo","year":"2023","unstructured":"Nakajo, M. et al. The usefulness of machine-learning-based evaluation of clinical and pretreatment (18)F-FDG-PET\/CT radiomic features for predicting prognosis in patients with laryngeal cancer. Br. J. Radio. 96, 20220772 (2023).","journal-title":"Br. J. Radio."},{"key":"74474_CR59","unstructured":"Lundberg, S. M. & Lee, S.-I. in Proceedings of the 31st International Conference on Neural Information Processing Systems 4768\u20134777 (Curran Associates Inc., Long Beach, California, USA; 2017)."},{"key":"74474_CR60","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-31384-3","volume":"13","author":"H Chen","year":"2022","unstructured":"Chen, H., Lundberg, S. M. & Lee, S.-I. Explaining a series of models by propagating Shapley values. Nat. Commun. 13, 4512 (2022).","journal-title":"Nat. Commun."},{"key":"74474_CR61","doi-asserted-by":"crossref","unstructured":"Huang, P.-H. Residual permutation tests for feature importance in machine learning. Br. J. Math. Stat. Psychol. 00, 1\u201325 (2025).","DOI":"10.31234\/osf.io\/ajxrb_v1"},{"key":"74474_CR62","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","volume":"2","author":"SM Lundberg","year":"2020","unstructured":"Lundberg, S. M. et al. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2, 56\u201367 (2020).","journal-title":"Nat. Mach. Intell."},{"key":"74474_CR63","doi-asserted-by":"publisher","unstructured":"Dai, F. Multimodal_Lung_Cancer_Dataset-CT-Mask-Clinical (Revision 5cedd76). Hugging Face. https:\/\/doi.org\/10.57967\/hf\/7942 (2026)","DOI":"10.57967\/hf\/7942"},{"key":"74474_CR64","doi-asserted-by":"publisher","unstructured":"Dai, F. MM-DLS: Multimodal deep learning survival model. Zenodo. https:\/\/doi.org\/10.5281\/zenodo.18859486 (2026)","DOI":"10.5281\/zenodo.18859486"},{"key":"74474_CR65","doi-asserted-by":"crossref","unstructured":"Tas, M. O. & Yavuz, H. S. Enhancing lung cancer survival prediction: 3D CNN analysis of CT images using novel GTV1-SliceNum feature and PEN-BCE loss function. Diagnostics (Basel) 14, 1309 (2024).","DOI":"10.3390\/diagnostics14121309"},{"key":"74474_CR66","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2022.104127","volume":"82","author":"B Huang","year":"2022","unstructured":"Huang, B. et al. Prediction of lung malignancy progression and survival with machine learning based on pre-treatment FDG-PET\/CT. EBioMedicine 82, 104127 (2022).","journal-title":"EBioMedicine"},{"key":"74474_CR67","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1109\/TCBB.2018.2806438","volume":"16","author":"D Sun","year":"2019","unstructured":"Sun, D., Wang, M. & Li, A. A Multimodal Deep Neural Network for Human Breast Cancer Prognosis Prediction by Integrating Multi-Dimensional Data. IEEE\/ACM Trans. Comput Biol. Bioinform 16, 841\u2013850 (2019).","journal-title":"IEEE\/ACM Trans. Comput Biol. Bioinform"},{"key":"74474_CR68","doi-asserted-by":"crossref","unstructured":"Amini, M. et al. Multi-level multi-modality (PET and CT) fusion radiomics: prognostic modeling for non-small cell lung carcinoma. Phys. Med. Biol. 66, 205017(2021).","DOI":"10.1088\/1361-6560\/ac287d"},{"key":"74474_CR69","doi-asserted-by":"publisher","first-page":"5407","DOI":"10.21037\/jtd.2019.11.53","volume":"11","author":"J Zhang","year":"2019","unstructured":"Zhang, J. et al. A nomogram to predict overall survival of patients with early stage non-small cell lung cancer. J. Thorac. Dis. 11, 5407\u20135416 (2019).","journal-title":"J. Thorac. Dis."},{"key":"74474_CR70","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms5006","volume":"5","author":"HJ Aerts","year":"2014","unstructured":"Aerts, H. J. et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5, 4006 (2014).","journal-title":"Nat. Commun."}],"container-title":["Nature Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41467-026-74474-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41467-026-74474-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41467-026-74474-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T18:18:51Z","timestamp":1785521931000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41467-026-74474-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,15]]},"references-count":70,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["74474"],"URL":"https:\/\/doi.org\/10.1038\/s41467-026-74474-2","relation":{},"ISSN":["2041-1723"],"issn-type":[{"value":"2041-1723","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,15]]},"assertion":[{"value":"27 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Ethics"}}],"article-number":"7579"}}