{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T15:53:15Z","timestamp":1776873195836,"version":"3.51.2"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T00:00:00Z","timestamp":1738108800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T00:00:00Z","timestamp":1738108800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"National Key Research and Development Program of China"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"DOI":"10.1038\/s41746-025-01470-z","type":"journal-article","created":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T01:16:14Z","timestamp":1738113374000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Whole slide image based deep learning refines prognosis and therapeutic response evaluation in lung adenocarcinoma"],"prefix":"10.1038","volume":"8","author":[{"given":"Tao","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jialiang","family":"Wen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinchen","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaqi","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiajun","family":"Deng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengmeng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Long","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunyan","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bentong","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minglei","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minjie","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junqi","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2841-1250","authenticated-orcid":false,"given":"Yunlang","family":"She","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifan","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Likun","family":"Hou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9489-5447","authenticated-orcid":false,"given":"Yanrui","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9981-3110","authenticated-orcid":false,"given":"Chang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,29]]},"reference":[{"key":"1470_CR1","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1200\/JCO.21.01589","volume":"40","author":"JE Chaft","year":"2022","unstructured":"Chaft, J. E., Shyr, Y., Sepesi, B. & Forde, P. M. Preoperative and postoperative systemic therapy for operable non-small-cell lung cancer. J. Clin. Oncol. 40, 546\u2013555 (2022).","journal-title":"J. Clin. Oncol."},{"key":"1470_CR2","doi-asserted-by":"publisher","first-page":"1130","DOI":"10.1016\/j.athoracsur.2014.11.052","volume":"99","author":"H Huang","year":"2015","unstructured":"Huang, H., Wang, T., Hu, B. & Pan, C. Visceral pleural invasion remains a size-independent prognostic factor in stage I non-small cell lung cancer. Ann. Thorac. Surg. 99, 1130\u20131139 (2015).","journal-title":"Ann. Thorac. Surg."},{"key":"1470_CR3","doi-asserted-by":"publisher","first-page":"624","DOI":"10.1093\/ejcts\/ezaa361","volume":"59","author":"Y Zhong","year":"2021","unstructured":"Zhong, Y. et al. Prognostic impact of tumour spread through air space in radiological subsolid and pure solid lung adenocarcinoma. Eur. J. Cardiothorac. Surg. 59, 624\u2013632 (2021).","journal-title":"Eur. J. Cardiothorac. Surg."},{"key":"1470_CR4","doi-asserted-by":"publisher","first-page":"785","DOI":"10.1016\/j.jtcvs.2018.02.108","volume":"156","author":"L Okiror","year":"2018","unstructured":"Okiror, L. et al. Prognostic factors including lymphovascular invasion on survival for resected non-small cell lung cancer. J. Thorac. Cardiovasc. Surg. 156, 785\u2013793 (2018).","journal-title":"J. Thorac. Cardiovasc. Surg."},{"key":"1470_CR5","doi-asserted-by":"publisher","first-page":"700","DOI":"10.1016\/j.jtho.2022.02.005","volume":"17","author":"R Fujikawa","year":"2022","unstructured":"Fujikawa, R. et al. Clinicopathologic and genotypic features of lung adenocarcinoma characterized by the International Association for the Study of Lung Cancer Grading System. J. Thorac. Oncol. 17, 700\u2013707 (2022).","journal-title":"J. Thorac. Oncol."},{"key":"1470_CR6","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/s41379-021-00994-5","volume":"35","author":"L Hou","year":"2022","unstructured":"Hou, L. et al. Prognostic and predictive value of the newly proposed grading system of invasive pulmonary adenocarcinoma in Chinese patients: a retrospective multicohort study. Mod. Pathol. 35, 749\u2013756 (2022).","journal-title":"Mod. Pathol."},{"key":"1470_CR7","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1016\/j.chest.2016.10.010","volume":"151","author":"FC Detterbeck","year":"2017","unstructured":"Detterbeck, F. C., Boffa, D. J., Kim, A. W. & Tanoue, L. T. The eighth edition lung cancer stage classification. Chest 151, 193\u2013203 (2017).","journal-title":"Chest"},{"key":"1470_CR8","doi-asserted-by":"publisher","unstructured":"Wulczyn, E. et al. Interpretable survival prediction for colorectal cancer using deep learning. NPJ Digit Med. 4. https:\/\/doi.org\/10.1038\/s41746-021-00427-2 (2021)","DOI":"10.1038\/s41746-021-00427-2"},{"key":"1470_CR9","doi-asserted-by":"publisher","unstructured":"Qaiser, T. et al. Usability of deep learning and H&E images predict disease outcome-emerging tool to optimize clinical trials. NPJ Precis. Oncol. 6. https:\/\/doi.org\/10.1038\/s41698-022-00275-7 (2022)","DOI":"10.1038\/s41698-022-00275-7"},{"key":"1470_CR10","doi-asserted-by":"publisher","first-page":"1519","DOI":"10.1038\/s41591-019-0583-3","volume":"25","author":"P Courtiol","year":"2019","unstructured":"Courtiol, P. et al. Deep learning-based classification of mesothelioma improves prediction of patient outcome. Nat. Med. 25, 1519\u20131525 (2019).","journal-title":"Nat. Med."},{"key":"1470_CR11","doi-asserted-by":"publisher","unstructured":"Ding, R. et al. Image analysis reveals molecularly distinct patterns of TILs in NSCLC associated with treatment outcome. NPJ Precis. Oncol. 6. https:\/\/doi.org\/10.1038\/s41698-022-00277-5 (2022)","DOI":"10.1038\/s41698-022-00277-5"},{"key":"1470_CR12","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1016\/j.annonc.2023.04.005","volume":"34","author":"M Rakaee","year":"2023","unstructured":"Rakaee, M. et al. Machine learning-based immune phenotypes correlate with STK11\/KEAP1 co-mutations and prognosis in resectable NSCLC: a sub-study of the TNM-I trial. Ann. Oncol. 34, 578\u2013588 (2023).","journal-title":"Ann. Oncol."},{"key":"1470_CR13","doi-asserted-by":"publisher","first-page":"951","DOI":"10.1136\/gutjnl-2020-320930","volume":"70","author":"J-Y Shi","year":"2021","unstructured":"Shi, J.-Y. et al. Exploring prognostic indicators in the pathological images of hepatocellular carcinoma based on deep learning. Gut 70, 951\u2013961 (2021).","journal-title":"Gut"},{"key":"1470_CR14","doi-asserted-by":"publisher","unstructured":"Lee, Y. et al. Derivation of prognostic contextual histopathological features from whole-slide images of tumours via graph deep learning. Nat. Biomed. Eng. https:\/\/doi.org\/10.1038\/s41551-022-00923-0 (2022)","DOI":"10.1038\/s41551-022-00923-0"},{"key":"1470_CR15","doi-asserted-by":"publisher","first-page":"e33","DOI":"10.1016\/S2589-7500(23)00208-X","volume":"6","author":"X Jiang","year":"2024","unstructured":"Jiang, X. et al. End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study. Lancet Digit Health 6, e33\u2013e43 (2024).","journal-title":"Lancet Digit Health"},{"key":"1470_CR16","doi-asserted-by":"publisher","first-page":"E2970","DOI":"10.1073\/pnas.1717139115","volume":"115","author":"P Mobadersany","year":"2018","unstructured":"Mobadersany, P. et al. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc. Natl Acad. Sci. USA 115, E2970\u2013e2979 (2018).","journal-title":"Proc. Natl Acad. Sci. USA"},{"key":"1470_CR17","doi-asserted-by":"publisher","first-page":"2000","DOI":"10.1002\/hep.31207","volume":"72","author":"C Saillard","year":"2020","unstructured":"Saillard, C. et al. Predicting survival after hepatocellular carcinoma resection using deep learning on histological slides. Hepatology 72, 2000\u20132013 (2020).","journal-title":"Hepatology"},{"key":"1470_CR18","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1038\/s41746-024-01003-0","volume":"7","author":"Y Zhang","year":"2024","unstructured":"Zhang, Y. et al. Histopathology images-based deep learning prediction of prognosis and therapeutic response in small cell lung cancer. NPJ Digit Med. 7, 15 (2024).","journal-title":"NPJ Digit Med."},{"key":"1470_CR19","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1038\/s41746-019-0112-2","volume":"2","author":"K Nagpal","year":"2019","unstructured":"Nagpal, K. et al. Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer. NPJ Digit Med. 2, 48 (2019).","journal-title":"NPJ Digit Med."},{"key":"1470_CR20","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1136\/bmj.311.7010.899","volume":"311","author":"W Alberti","year":"1995","unstructured":"Alberti, W. et al. Chemotherapy in non-small cell lung cancer: a meta-analysis using updated data on individual patients from 52 randomised clinical trials. Non-small Cell Lung Cancer Collaborative Group. BMJ 311, 899\u2013909 (1995).","journal-title":"BMJ"},{"key":"1470_CR21","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1056\/NEJMoa031644","volume":"350","author":"R Arriagada","year":"2004","unstructured":"Arriagada, R. et al. Cisplatin-based adjuvant chemotherapy in patients with completely resected non-small-cell lung cancer. N. Engl. J. Med. 350, 351\u2013360 (2004).","journal-title":"N. Engl. J. Med."},{"key":"1470_CR22","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1016\/S1470-2045(06)70804-X","volume":"7","author":"JY Douillard","year":"2006","unstructured":"Douillard, J. Y. et al. Adjuvant vinorelbine plus cisplatin versus observation in patients with completely resected stage IB-IIIA non-small-cell lung cancer (Adjuvant Navelbine International Trialist Association [ANITA]): a randomised controlled trial. Lancet Oncol. 7, 719\u2013727 (2006).","journal-title":"Lancet Oncol."},{"key":"1470_CR23","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.ejcts.2004.03.041","volume":"26","author":"D Waller","year":"2004","unstructured":"Waller, D. et al. Chemotherapy for patients with non-small cell lung cancer: the surgical setting of the Big Lung Trial. Eur. J. Cardiothorac. Surg. 26, 173\u2013182 (2004).","journal-title":"Eur. J. Cardiothorac. Surg."},{"key":"1470_CR24","doi-asserted-by":"publisher","first-page":"3552","DOI":"10.1200\/JCO.2007.13.9030","volume":"26","author":"JP Pignon","year":"2008","unstructured":"Pignon, J. P. et al. Lung adjuvant cisplatin evaluation: a pooled analysis by the LACE Collaborative Group. J. Clin. Oncol. 26, 3552\u20133559 (2008).","journal-title":"J. Clin. Oncol."},{"key":"1470_CR25","doi-asserted-by":"publisher","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","volume":"385","author":"GS Collins","year":"2024","unstructured":"Collins, G. S. et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 385, e078378 (2024).","journal-title":"BMJ"},{"key":"1470_CR26","doi-asserted-by":"publisher","first-page":"1599","DOI":"10.1016\/j.jtho.2020.06.001","volume":"15","author":"AL Moreira","year":"2020","unstructured":"Moreira, A. L. et al. A grading system for invasive pulmonary adenocarcinoma: a proposal from the International Association for the Study of Lung Cancer Pathology Committee. J. Thorac. Oncol. 15, 1599\u20131610 (2020).","journal-title":"J. Thorac. Oncol."},{"key":"1470_CR27","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1038\/s41551-020-00682-w","volume":"5","author":"MY Lu","year":"2021","unstructured":"Lu, M. Y. et al. Data-efficient and weakly supervised computational pathology on whole-slide images. Nat. Biomed. Eng. 5, 555\u2013570 (2021).","journal-title":"Nat. Biomed. Eng."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01470-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01470-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01470-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T01:16:17Z","timestamp":1738113377000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01470-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,29]]},"references-count":27,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1470"],"URL":"https:\/\/doi.org\/10.1038\/s41746-025-01470-z","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,29]]},"assertion":[{"value":"30 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2025","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","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"69"}}