{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T13:55:10Z","timestamp":1775224510624,"version":"3.50.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T00:00:00Z","timestamp":1772150400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T00:00:00Z","timestamp":1775174400000},"content-version":"vor","delay-in-days":35,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100016984","name":"Xijing University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100016984","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"DOI":"10.1186\/s12911-026-03398-0","type":"journal-article","created":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T11:31:43Z","timestamp":1772191903000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and validation of an interpretable machine learning model for postoperative radiotherapy decision-making in ypN0 breast cancer after neoadjuvant chemotherapy: a real-world study"],"prefix":"10.1186","volume":"26","author":[{"given":"Yunjiao","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Duan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Niuniu","family":"Hou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xue","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Panpan","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chutuo","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhe","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Ling","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,27]]},"reference":[{"issue":"1","key":"3398_CR1","first-page":"12","volume":"74","author":"JEMALA SIEGEL R L, GIAQUINTO A N","year":"2024","unstructured":"Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024 [J]. CA Cancer J Clin. 2024;74(1):12\u201349.","journal-title":"CA Cancer J Clin"},{"issue":"1","key":"3398_CR2","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1038\/s41392-024-02108-4","volume":"10","author":"X XIONG","year":"2025","unstructured":"Xiong X, Zheng LW, Ding Y, et al. Breast cancer: pathogenesis and treatments [J]. Signal Transduct Target Ther. 2025;10(1):49.","journal-title":"Signal Transduct Target Ther"},{"issue":"1","key":"3398_CR3","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1158\/2159-8290.CD-22-0475","volume":"13","author":"SS ONKAR","year":"2023","unstructured":"Onkar SS, Carleton NM, Lucas PC, et al. The great immune escape: Understanding the divergent immune response in breast cancer subtypes [J]. Cancer Discov. 2023;13(1):23\u201340.","journal-title":"Cancer Discov"},{"key":"3398_CR4","doi-asserted-by":"publisher","first-page":"2423","DOI":"10.2147\/DDDT.S253961","volume":"14","author":"H WANG","year":"2020","unstructured":"Wang H, Mao X. Evaluation of the efficacy of neoadjuvant chemotherapy for breast cancer [J]. Drug Des Devel Ther. 2020;14:2423\u201333.","journal-title":"Drug Des Devel Ther"},{"key":"3398_CR5","unstructured":"Kunkler IH. Does postmastectomy radiotherapy in \u2018intermediate-risk\u2019 breast cancer impact overall survival? 10-year results of the BIG 2\u201304 MRC randomized trial; proceedings of the San Antonio Breast Cancer Symposium (SABCS), San Antonio, TX, USA, F December 10\u201313, 2024, 2024 [C]."},{"issue":"11","key":"3398_CR6","doi-asserted-by":"publisher","first-page":"1940","DOI":"10.1016\/j.jtho.2016.06.018","volume":"11","author":"C BILLIET","year":"2016","unstructured":"Billiet C, Peeters S, Decaluw\u00e9 H, et al. Outcome after PORT in ypN2 or R1\/R2 versus no PORT in ypN0 stage III-N2 NSCLC after induction chemotherapy and resection [J]. J Thorac Oncol. 2016;11(11):1940\u201353.","journal-title":"J Thorac Oncol"},{"issue":"3","key":"3398_CR7","doi-asserted-by":"publisher","first-page":"848","DOI":"10.1016\/j.athoracsur.2018.04.064","volume":"106","author":"WS BRANDT","year":"2018","unstructured":"Brandt WS, Yan W. Postoperative radiotherapy for surgically resected ypN2 Non-Small cell lung cancer [J]. Ann Thorac Surg. 2018;106(3):848\u201355.","journal-title":"Ann Thorac Surg"},{"key":"3398_CR8","doi-asserted-by":"crossref","unstructured":"Kim JH, Byun SJ, Kim M, et al. Treatment outcomes after postoperative radiotherapy in triple-negative breast cancer: multi-institutional retrospective study (KROG 17\u2009\u2013\u200905) [J]. J Pers Med. 2024;14(9).","DOI":"10.3390\/jpm14090941"},{"issue":"3","key":"3398_CR9","doi-asserted-by":"publisher","first-page":"2023","DOI":"10.1245\/s10434-024-16625-7","volume":"32","author":"A LAWS","year":"2025","unstructured":"Laws A, Leonard S, Vincuilla J, et al. Risk of surgical overtreatment in cN1 breast cancer patients who become ypN0 after neoadjuvant chemotherapy: SLNB versus TAD [J]. Ann Surg Oncol. 2025;32(3):2023\u20138.","journal-title":"Ann Surg Oncol"},{"issue":"2","key":"3398_CR10","doi-asserted-by":"publisher","first-page":"592","DOI":"10.4143\/crt.2022.998","volume":"55","author":"KIMD KIM J H","year":"2023","unstructured":"Kim JH, Kim D, Kim IA, et al. Impact of postmastectomy radiation therapy on breast cancer patients according to pathologic nodal status after modern neoadjuvant chemotherapy [J]. Cancer Res Treat. 2023;55(2):592\u2013602.","journal-title":"Cancer Res Treat"},{"issue":"2","key":"3398_CR11","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1016\/j.ijrobp.2019.10.039","volume":"106","author":"Y FAYANJU O M, REN","year":"2020","unstructured":"Fayanju OM, Ren Y, Suneja G, et al. Nodal response to neoadjuvant chemotherapy predicts receipt of radiation therapy after breast cancer diagnosis [J]. Int J Radiat Oncol Biol Phys. 2020;106(2):377\u201389.","journal-title":"Int J Radiat Oncol Biol Phys"},{"issue":"4","key":"3398_CR12","doi-asserted-by":"publisher","first-page":"1030","DOI":"10.1016\/j.ijrobp.2020.06.028","volume":"108","author":"Z HUANG","year":"2020","unstructured":"Huang Z, Zhu L, Huang XB, et al. Postmastectomy radiation therapy based on pathologic nodal status in clinical Node-Positive stage II to III breast cancer treated with neoadjuvant chemotherapy [J]. Int J Radiat Oncol Biol Phys. 2020;108(4):1030\u20139.","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"3398_CR13","doi-asserted-by":"publisher","first-page":"103858","DOI":"10.1016\/j.breast.2024.103858","volume":"79","author":"Y ZHANG","year":"2025","unstructured":"Zhang Y, An W, Wang C, et al. Novel models based on machine learning to predict the prognosis of metaplastic breast cancer [J]. Breast. 2025;79:103858.","journal-title":"Breast"},{"key":"3398_CR14","doi-asserted-by":"crossref","unstructured":"Das SC, Tasnim W, Rana HK, et al. Comprehensive bioinformatics and machine learning analyses for breast cancer staging using TCGA dataset [J]. Brief Bioinform. 2024;26(1).","DOI":"10.1093\/bib\/bbae628"},{"key":"3398_CR15","doi-asserted-by":"publisher","first-page":"106073","DOI":"10.1016\/j.compbiomed.2022.106073","volume":"149","author":"M DIN N M U, DAR R A, RASOOL","year":"2022","unstructured":"Din NMU, Dar RA, Rasool M, et al. Breast cancer detection using deep learning: datasets, methods, and challenges ahead [J]. Comput Biol Med. 2022;149:106073.","journal-title":"Comput Biol Med"},{"issue":"1","key":"3398_CR16","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1186\/s12911-023-02377-z","volume":"23","author":"D ZUO","year":"2023","unstructured":"Zuo D, Yang L, Jin Y, et al. Machine learning-based models for the prediction of breast cancer recurrence risk [J]. BMC Med Inf Decis Mak. 2023;23(1):276.","journal-title":"BMC Med Inf Decis Mak"},{"key":"3398_CR17","doi-asserted-by":"publisher","first-page":"204209862513217","DOI":"10.1177\/20420986251321704","volume":"16","author":"SK NIAZI","year":"2025","unstructured":"Niazi SK, Mariam Z. Artificial intelligence in drug development: reshaping the therapeutic landscape [J]. Ther Adv Drug Saf. 2025;16:20420986251321704.","journal-title":"Ther Adv Drug Saf"},{"issue":"46","key":"3398_CR18","doi-asserted-by":"publisher","first-page":"17690","DOI":"10.1021\/acs.est.3c00653","volume":"57","author":"X JIA","year":"2023","unstructured":"Jia X, Wang T. Advancing computational toxicology by interpretable machine learning [J]. Environ Sci Technol. 2023;57(46):17690\u2013706.","journal-title":"Environ Sci Technol"},{"issue":"12","key":"3398_CR19","doi-asserted-by":"publisher","first-page":"102919","DOI":"10.1016\/j.dsx.2023.102919","volume":"17","author":"MS ISLAM M M, RAHMAN M J, RABBY","year":"2023","unstructured":"Islam MM, Rahman MJ, Rabby MS, et al. Predicting the risk of diabetic retinopathy using explainable machine learning algorithms [J]. Diabetes Metab Syndr. 2023;17(12):102919.","journal-title":"Diabetes Metab Syndr"},{"key":"3398_CR20","doi-asserted-by":"crossref","unstructured":"Gul S, Ayturan K, Hardala\u00e7 F. PyCaret for predicting type 2 diabetes: a phenotype- and gender-based approach with the nurses\u2019 health study and the health professionals\u2019 follow-up study datasets [J]. J Pers Med. 2024;14(8).","DOI":"10.3390\/jpm14080804"},{"key":"3398_CR21","doi-asserted-by":"publisher","first-page":"1367340","DOI":"10.3389\/fimmu.2024.1367340","volume":"15","author":"CAOS HU","year":"2024","unstructured":"Cao S, Hu Y. Creating machine learning models that interpretably link systemic inflammatory index, sex steroid hormones, and dietary antioxidants to identify gout using the SHAP (SHapley additive exPlanations) method [J]. Front Immunol. 2024;15:1367340.","journal-title":"Front Immunol"},{"key":"3398_CR22","doi-asserted-by":"publisher","first-page":"103470","DOI":"10.1016\/j.redox.2024.103470","volume":"79","author":"X QI","year":"2025","unstructured":"Qi X, Wang S, Fang C, et al. Machine learning and SHAP value interpretation for predicting comorbidity of cardiovascular disease and cancer with dietary antioxidants [J]. Redox Biol. 2025;79:103470.","journal-title":"Redox Biol"},{"issue":"1","key":"3398_CR23","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1093\/ehjdh\/ztae080","volume":"6","author":"T LIU","year":"2025","unstructured":"Liu T, Krentz A, Lu L, et al. Machine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis [J]. Eur Heart J Digit Health. 2025;6(1):7\u201322.","journal-title":"Eur Heart J Digit Health"},{"key":"3398_CR24","doi-asserted-by":"publisher","first-page":"1420328","DOI":"10.3389\/fonc.2024.1420328","volume":"14","author":"S JAVANMARD Z, ZAREAN SHAHRAKI","year":"2024","unstructured":"Javanmard Z, Zarean Shahraki S, Safari K, et al. Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis [J]. Front Oncol. 2024;14:1420328.","journal-title":"Front Oncol"},{"key":"3398_CR25","doi-asserted-by":"crossref","unstructured":"Bleaney CW, Abdelaal H, Reardon M, et al. Clinical biomarkers of tumour radiosensitivity and predicting benefit from radiotherapy: a systematic review [J]. Cancers (Basel). 2024;16(10).","DOI":"10.3390\/cancers16101942"},{"issue":"1","key":"3398_CR26","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1038\/s41392-020-0150-x","volume":"5","author":"HUANG R X","year":"2020","unstructured":"Huang RX, Zhou, PK. DNA damage response signaling pathways and targets for radiotherapy sensitization in cancer [J]. Signal Transduct Target Ther. 2020;5(1):60.","journal-title":"Signal Transduct Target Ther"},{"issue":"1","key":"3398_CR27","doi-asserted-by":"publisher","first-page":"4948","DOI":"10.1038\/s41467-022-32645-x","volume":"13","author":"Y ZHANG","year":"2022","unstructured":"Zhang Y, Sriramaneni RN, Clark PA, et al. Multifunctional nanoparticle potentiates the in situ vaccination effect of radiation therapy and enhances response to immune checkpoint Blockade [J]. Nat Commun. 2022;13(1):4948.","journal-title":"Nat Commun"},{"issue":"1","key":"3398_CR28","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1186\/s12874-024-02444-7","volume":"24","author":"A STROBEL","year":"2024","unstructured":"Strobel A, Wienke A, Gummert J, et al. Built-in selection or confounder bias? Dynamic landmarking in matched propensity score analyses [J]. BMC Med Res Methodol. 2024;24(1):316.","journal-title":"BMC Med Res Methodol"},{"issue":"1","key":"3398_CR29","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1186\/s12885-025-13838-1","volume":"25","author":"G CAI","year":"2025","unstructured":"Cai G, Zhang S, Gao S, et al. What is the impact of perineural invasion on the prognosis of cervical cancer: a systematic review and meta-analysis [J]. BMC Cancer. 2025;25(1):491.","journal-title":"BMC Cancer"},{"key":"3398_CR30","doi-asserted-by":"publisher","first-page":"1572396","DOI":"10.3389\/fonc.2025.1572396","volume":"15","author":"M WANG","year":"2025","unstructured":"Wang M, Pu N, Bo X, et al. Significance and mechanisms of perineural invasion in malignant tumors [J]. Front Oncol. 2025;15:1572396.","journal-title":"Front Oncol"},{"issue":"4","key":"3398_CR31","doi-asserted-by":"publisher","first-page":"270","DOI":"10.4048\/jbc.2024.0084","volume":"27","author":"M LONG","year":"2024","unstructured":"Long M, Li C, Mao K, et al. Effect of interval between neoadjuvant chemotherapy and surgery on oncological outcomes in poor responders with locally advanced breast cancer [J]. J Breast Cancer. 2024;27(4):270\u201380.","journal-title":"J Breast Cancer"},{"issue":"1","key":"3398_CR32","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1186\/s12911-025-03018-3","volume":"25","author":"ONAH E","year":"2025","unstructured":"Onah E, Eze UJ, Abdul Raheem AS, et al. Optimizing unsupervised feature engineering and classification pipelines for differentiated thyroid cancer recurrence prediction [J]. BMC Med Inf Decis Mak. 2025;25(1):182.","journal-title":"BMC Med Inf Decis Mak"},{"issue":"10","key":"3398_CR33","doi-asserted-by":"publisher","first-page":"1744","DOI":"10.1016\/j.ultrasmedbio.2025.06.016","volume":"51","author":"Y CHEN","year":"2025","unstructured":"Chen Y, Sun Z. Diabetic tibial neuropathy prediction: improving interpretability of various Machine-Learning models based on Multimodal-Ultrasound features using SHAP methodology [J]. Ultrasound Med Biol. 2025;51(10):1744\u201353.","journal-title":"Ultrasound Med Biol"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-026-03398-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-026-03398-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-026-03398-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T13:12:34Z","timestamp":1775221954000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12911-026-03398-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,27]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["3398"],"URL":"https:\/\/doi.org\/10.1186\/s12911-026-03398-0","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,27]]},"assertion":[{"value":"9 December 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was conducted in accordance with the ethical principles of the Declaration of Helsinki and was approved by the Institutional Review Board of the First Affiliated Hospital of Air Force Medical University (No.: KY20232266-F-1). Due to the retrospective nature of the study and the use of de-identified data, the requirement for informed consent was waived by the ethics committee.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"108"}}