{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T21:53:27Z","timestamp":1781819607442,"version":"3.54.5"},"reference-count":41,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T00:00:00Z","timestamp":1776988800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003968","name":"Iran National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003968","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Array"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.array.2026.100860","type":"journal-article","created":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T06:20:34Z","timestamp":1777098034000},"page":"100860","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Non-invasive prediction of breast cancer biomarkers from blood inflammatory indices using a hybrid adaptive grey wolf optimizer-based machine learning framework"],"prefix":"10.1016","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3119-3349","authenticated-orcid":false,"given":"Razieh","family":"Sheikhpour","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shokouh","family":"Taghipour Zahir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fatemeh","family":"Pourhosseini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.array.2026.100860_bib1","first-page":"209","article-title":"Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J Clin"},{"issue":"11","key":"10.1016\/j.array.2026.100860_bib2","doi-asserted-by":"crossref","DOI":"10.1016\/j.jksus.2024.103551","article-title":"Merging from traditional to potential novel breast cancer biomarkers","volume":"36","author":"Alismail","year":"2024","journal-title":"J King Saud Univ Sci"},{"issue":"8","key":"10.1016\/j.array.2026.100860_bib3","doi-asserted-by":"crossref","DOI":"10.5812\/ijcm.9492","article-title":"The differences of age, tumor grade, and Her2 amplification in Estrogen and progesterone receptor status in patients with breast cancer","volume":"11","author":"Sheikhpour","year":"2018","journal-title":"International Journal of Cancer Management"},{"issue":"8","key":"10.1016\/j.array.2026.100860_bib4","doi-asserted-by":"crossref","first-page":"1194","DOI":"10.1093\/annonc\/mdz173","article-title":"Early breast cancer: ESMO clinical practice guidelines for diagnosis, treatment and follow-up","volume":"30","author":"Cardoso","year":"2019","journal-title":"Ann Oncol"},{"issue":"4","key":"10.1016\/j.array.2026.100860_bib5","first-page":"93","article-title":"Relation between Estrogen and progesterone receptor status with p53, Ki67 and Her-2 markers in patients with breast cancer","volume":"8","author":"Sheikhpour","year":"2016","journal-title":"Iranian Journal of Blood and Cancer"},{"key":"10.1016\/j.array.2026.100860_bib6","doi-asserted-by":"crossref","first-page":"227","DOI":"10.3389\/fmed.2017.00227","article-title":"Tumor heterogeneity in breast cancer","volume":"4","author":"Turashvili","year":"2017","journal-title":"Front Med"},{"issue":"9","key":"10.1016\/j.array.2026.100860_bib7","doi-asserted-by":"crossref","first-page":"1555","DOI":"10.3390\/diagnostics11091555","article-title":"Breast cancer heterogeneity","volume":"11","author":"Fumagalli","year":"2021","journal-title":"Diagnostics"},{"issue":"1","key":"10.1016\/j.array.2026.100860_bib8","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1186\/s13058-024-01860-3","article-title":"Promoter profiles in plasma CfDNA exhibits a potential utility of predicting the efficacy of neoadjuvant chemotherapy in breast cancer patients","volume":"26","author":"Yang","year":"2024","journal-title":"Breast Cancer Res"},{"key":"10.1016\/j.array.2026.100860_bib9","unstructured":"Sheikhpour R. Immunohistochemical assessment of ER and PR status in tissue tumors of breast cancer patients and their relation with proliferation and tumor grade."},{"issue":"4","key":"10.1016\/j.array.2026.100860_bib10","doi-asserted-by":"crossref","DOI":"10.2144\/fsoa-2021-0074","article-title":"An overview of artificial intelligence in oncology","volume":"8","author":"Farina","year":"2022","journal-title":"Future Sci OA"},{"issue":"5","key":"10.1016\/j.array.2026.100860_bib11","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1111\/cas.14377","article-title":"Artificial intelligence in oncology","volume":"111","author":"Shimizu","year":"2020","journal-title":"Cancer Sci"},{"key":"10.1016\/j.array.2026.100860_bib12","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.csbj.2014.11.005","article-title":"Machine learning applications in cancer prognosis and prediction","volume":"13","author":"Kourou","year":"2015","journal-title":"Comput Struct Biotechnol J"},{"issue":"2","key":"10.1016\/j.array.2026.100860_bib13","doi-asserted-by":"crossref","first-page":"61","DOI":"10.3390\/jpm11020061","article-title":"Breast cancer type classification using machine learning","volume":"11","author":"Wu","year":"2021","journal-title":"J Personalized Med"},{"issue":"7894","key":"10.1016\/j.array.2026.100860_bib14","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1038\/s41586-021-04278-5","article-title":"Multi-omic machine learning predictor of breast cancer therapy response","volume":"601","author":"Sammut","year":"2022","journal-title":"Nature"},{"issue":"7","key":"10.1016\/j.array.2026.100860_bib15","doi-asserted-by":"crossref","first-page":"4875","DOI":"10.1007\/s11831-021-09556-z","article-title":"A systematic review of applications of machine learning in cancer prediction and diagnosis","volume":"28","author":"Sharma","year":"2021","journal-title":"Arch Comput Methods Eng"},{"key":"10.1016\/j.array.2026.100860_bib16","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.2147\/JMDH.S410301","article-title":"Machine learning and AI in cancer prognosis, prediction, and treatment selection: a critical approach","author":"Zhang","year":"2023","journal-title":"J Multidiscip Healthc"},{"key":"10.1016\/j.array.2026.100860_bib17","first-page":"1","article-title":"Correlation of peripheral neutrophil-to-lymphocyte ratio (NLR) with Ki-67 and other clinicopathological parameters of breast carcinoma","volume":"14","author":"Mishra","year":"2025","journal-title":"Indian Journal of Surgical Oncology"},{"issue":"1","key":"10.1016\/j.array.2026.100860_bib18","doi-asserted-by":"crossref","first-page":"41","DOI":"10.3892\/ol.2024.14787","article-title":"Diagnostic role of the neutrophil-to-lymphocyte ratio and the platelet-to-lymphocyte ratio in breast cancer: a systematic review and meta-analysis","volume":"29","author":"Yang","year":"2024","journal-title":"Oncol Lett"},{"issue":"8","key":"10.1016\/j.array.2026.100860_bib19","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1007\/s12094-017-1630-5","article-title":"Association of neutrophil\/lymphocyte ratio and platelet\/lymphocyte ratio with ER and PR in breast cancer patients and their changes after neoadjuvant chemotherapy","volume":"19","author":"Xu","year":"2017","journal-title":"Clin Transl Oncol"},{"issue":"11","key":"10.1016\/j.array.2026.100860_bib20","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0207224","article-title":"Platelet-to-lymphocyte ratio as a predictive factor of complete pathologic response to neoadjuvant chemotherapy in breast cancer","volume":"13","author":"Cuello-L\u00f3pez","year":"2018","journal-title":"PLoS One"},{"issue":"1","key":"10.1016\/j.array.2026.100860_bib21","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s13058-016-0794-1","article-title":"Prognostic role of neutrophil-to-lymphocyte ratio in breast cancer: a systematic review and meta-analysis","volume":"19","author":"Ethier","year":"2017","journal-title":"Breast Cancer Res"},{"issue":"7","key":"10.1016\/j.array.2026.100860_bib22","doi-asserted-by":"crossref","first-page":"1204","DOI":"10.1158\/1055-9965.EPI-14-0146","article-title":"Prognostic role of platelet to lymphocyte ratio in solid tumors: a systematic review and meta-analysis","volume":"23","author":"Templeton","year":"2014","journal-title":"Cancer Epidemiol Biomarkers Prev"},{"issue":"20","key":"10.1016\/j.array.2026.100860_bib23","doi-asserted-by":"crossref","DOI":"10.1097\/MD.0000000000033811","article-title":"Association of neutrophil-to-lymphocyte ratio with clinical, pathological, radiological, laboratory features and disease outcomes of invasive breast cancer patients: a retrospective observational cohort study","volume":"102","author":"Jadoon","year":"2023","journal-title":"Medicine"},{"key":"10.1016\/j.array.2026.100860_bib24","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","article-title":"Grey wolf optimizer","volume":"69","author":"Mirjalili","year":"2014","journal-title":"Adv Eng Software"},{"issue":"10","key":"10.1016\/j.array.2026.100860_bib25","doi-asserted-by":"crossref","first-page":"7711","DOI":"10.1007\/s00521-021-06885-9","article-title":"Adaptive grey wolf optimizer","volume":"34","author":"Meidani","year":"2022","journal-title":"Neural Comput Appl"},{"issue":"5","key":"10.1016\/j.array.2026.100860_bib26","article-title":"Adaptive mechanism-based grey wolf optimizer for feature selection in high-dimensional classification","volume":"20","author":"Li","year":"2025","journal-title":"PLoS One"},{"issue":"3","key":"10.1016\/j.array.2026.100860_bib27","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1016\/j.acra.2023.10.010","article-title":"MRI-based radiomics methods for predicting Ki-67 expression in breast cancer: a systematic review and meta-analysis","volume":"31","author":"Tabnak","year":"2024","journal-title":"Acad Radiol"},{"issue":"5","key":"10.1016\/j.array.2026.100860_bib28","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0303669","article-title":"Diagnostic performance of ultrasound-based artificial intelligence for predicting key molecular markers in breast cancer: a systematic review and meta-analysis","volume":"19","author":"Fu","year":"2024","journal-title":"PLoS One"},{"key":"10.1016\/j.array.2026.100860_bib29","first-page":"1","article-title":"A comprehensive examination of machine learning and deep learning approaches for breast cancer detection, classification, segmentation, augmentation, and feature selection","author":"Majidpour","year":"2025","journal-title":"Arch Comput Methods Eng"},{"key":"10.1016\/j.array.2026.100860_bib30","first-page":"1","article-title":"Applications of GAN models in breast cancer detection: a comprehensive review","volume":"7","author":"Majidpour","year":"2025","journal-title":"Arch Comput Methods Eng"},{"issue":"1","key":"10.1016\/j.array.2026.100860_bib31","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.clbc.2023.09.014","article-title":"HER2GAN: overcome the scarcity of HER2 breast cancer dataset based on transfer learning and GAN model","volume":"24","author":"Mirimoghaddam","year":"2024","journal-title":"Clin Breast Cancer"},{"issue":"6","key":"10.1016\/j.array.2026.100860_bib32","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1016\/j.cell.2010.01.025","article-title":"Immunity, inflammation, and cancer","volume":"140","author":"Grivennikov","year":"2010","journal-title":"Cell"},{"issue":"1","key":"10.1016\/j.array.2026.100860_bib33","article-title":"Association between the neutrophil-to-lymphocyte ratio and cancer in adults from NHANES 2005\u20132018: a cross-sectional study","volume":"14","author":"Li","year":"2024","journal-title":"Sci Rep"},{"issue":"2","key":"10.1016\/j.array.2026.100860_bib34","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J Mach Learn Res"},{"issue":"14","key":"10.1016\/j.array.2026.100860_bib35","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1007\/s11227-025-07768-9","article-title":"Multi-objective feature selection of radiomics and deep learning features for breast cancer subtype detection","volume":"81","author":"Majidpour","year":"2025","journal-title":"J Supercomput"},{"key":"10.1016\/j.array.2026.100860_bib36","first-page":"228","article-title":"Neutrophil-lymphocyte ratio in different stages of breast cancer","volume":"16","author":"Elyasinia","year":"2017","journal-title":"Acta Med Iran"},{"issue":"12","key":"10.1016\/j.array.2026.100860_bib37","doi-asserted-by":"crossref","first-page":"1346","DOI":"10.1200\/JCO.19.02309","article-title":"Estrogen and progesterone receptor testing in breast cancer: ASCO\/CAP guideline update","volume":"38","author":"Allison","year":"2020","journal-title":"J Clin Oncol"},{"issue":"1","key":"10.1016\/j.array.2026.100860_bib38","article-title":"Impact of the 2018 ASCO\/CAP guidelines on HER2 fluorescence in situ hybridization interpretation in invasive breast cancers with immunohistochemically equivocal results","volume":"9","author":"Wang","year":"2019","journal-title":"Sci Rep"},{"issue":"2","key":"10.1016\/j.array.2026.100860_bib39","doi-asserted-by":"crossref","DOI":"10.3747\/co.25.3888","article-title":"Neutrophil\u2013lymphocyte ratio predicts response to chemotherapy in triple-negative breast cancer","volume":"25","author":"Chae","year":"2018","journal-title":"Curr Oncol"},{"issue":"3","key":"10.1016\/j.array.2026.100860_bib40","doi-asserted-by":"crossref","first-page":"425","DOI":"10.4048\/jbc.2019.22.e41","article-title":"The role of neutrophil-lymphocyte ratio and platelet-lymphocyte ratio in predicting neoadjuvant chemotherapy response in breast cancer","volume":"22","author":"Kim","year":"2019","journal-title":"Journal of Breast Cancer"},{"key":"10.1016\/j.array.2026.100860_bib41","doi-asserted-by":"crossref","DOI":"10.1038\/npjbcancer.2016.12","article-title":"Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA\/TCIA data set","volume":"2","author":"Li","year":"2016","journal-title":"NPJ Breast Cancer"}],"container-title":["Array"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2590005626001839?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2590005626001839?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T21:07:10Z","timestamp":1781816830000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2590005626001839"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":41,"alternative-id":["S2590005626001839"],"URL":"https:\/\/doi.org\/10.1016\/j.array.2026.100860","relation":{},"ISSN":["2590-0056"],"issn-type":[{"value":"2590-0056","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Non-invasive prediction of breast cancer biomarkers from blood inflammatory indices using a hybrid adaptive grey wolf optimizer-based machine learning framework","name":"articletitle","label":"Article Title"},{"value":"Array","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.array.2026.100860","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"100860"}}