{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T04:20:09Z","timestamp":1772166009914,"version":"3.50.1"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T00:00:00Z","timestamp":1713744000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T00:00:00Z","timestamp":1713744000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"National Natural Science Foundation of China Youth Incubation Project of Shuguang Hospital affiliated Shanghai University of Traditional Chinese Medicine","award":["SGKJLC-202016"],"award-info":[{"award-number":["SGKJLC-202016"]}]},{"name":"Shanghai Municipal Health and Health Commission","award":["202040254"],"award-info":[{"award-number":["202040254"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>This study aims to build a machine learning (ML) model to predict the recurrence probability for postoperative non-lactating mastitis (NLM) by Random Forest (RF) and XGBoost algorithms. It can provide the ability to identify the risk of NLM recurrence and guidance in clinical treatment plan.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study was conducted on inpatients who were admitted to the Mammary Department of Shuguang Hospital affiliated to Shanghai University of Traditional Chinese Medicine between July 2019 to December 2021. Inpatient data follow-up has been completed until December 2022. Ten features were selected in this study to build the ML model: age, body mass index (BMI), number of abortions, presence of inverted nipples, extent of breast mass, white blood cell count (WBC), neutrophil to lymphocyte ratio (NLR), albumin-globulin ratio (AGR) and triglyceride (TG) and presence of intraoperative discharge. We used two ML approaches (RF and XGBoost) to build models and predict the NLM recurrence risk of female patients. Totally 258 patients were randomly divided into a training set and a test set according to a 75%-25% proportion. The model performance was evaluated based on Accuracy, Precision, Recall, F1-score and AUC. The Shapley Additive Explanations (SHAP) method was used to interpret the model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>There were 48 (18.6%) NLM patients who experienced recurrence during the follow-up period. Ten features were selected in this study to build the ML model. For the RF model, BMI is the most important influence factor and for the XGBoost model is intraoperative discharge. The results of tenfold cross-validation suggest that both the RF model and the XGBoost model have good predictive performance, but the XGBoost model has a better performance than the RF model in our study. The trends of SHAP values of all features in our models are consistent with the trends of these features\u2019 clinical presentation. The inclusion of these ten features in the model is necessary to build practical prediction models for recurrence.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The results of tenfold cross-validation and SHAP values suggest that the models have predictive ability. The trend of SHAP value provides auxiliary validation in our models and makes it have more clinical significance.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12911-024-02499-y","type":"journal-article","created":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T12:02:32Z","timestamp":1713787352000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Prediction models for postoperative recurrence of non-lactating mastitis based on machine learning"],"prefix":"10.1186","volume":"24","author":[{"given":"Jiaye","family":"Sun","sequence":"first","affiliation":[]},{"given":"Shijun","family":"Shao","sequence":"additional","affiliation":[]},{"given":"Hua","family":"Wan","sequence":"additional","affiliation":[]},{"given":"Xueqing","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Jiamei","family":"Feng","sequence":"additional","affiliation":[]},{"given":"Qingqian","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Wenchao","family":"Qu","sequence":"additional","affiliation":[]},{"given":"Lu","family":"Xie","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,22]]},"reference":[{"key":"2499_CR1","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20120657","author":"H Tan","year":"2013","unstructured":"Tan H, Li R, Peng W, Liu H, Gu Y, Shen X. Radiological and clinical features of adult non-puerperal mastitis. Br J Radiol. 2013. https:\/\/doi.org\/10.1259\/bjr.20120657","journal-title":"Br J Radiol"},{"key":"2499_CR2","first-page":"28","volume-title":"Non-lactating Mastitis","author":"J Feng","year":"2022","unstructured":"Feng J, Shao S, Qu W. Epidemiology of non-lactating mastitis. In: Wan H, Lu D, editors. Non-lactating Mastitis. Shanghai: Shanghai Scientific Technical; 2022. p. 28."},{"key":"2499_CR3","doi-asserted-by":"publisher","DOI":"10.3390\/nu14224816","author":"L Shi","year":"2022","unstructured":"Shi L, Wu J, Hu Y, Zhang X, Li Z, Xi P, Wei J, Ding Q. Biomedical indicators of patients with non-puerperal mastitis: a retrospective study. Nutrients. 2022. https:\/\/doi.org\/10.3390\/nu14224816","journal-title":"Nutrients"},{"key":"2499_CR4","doi-asserted-by":"publisher","DOI":"10.1186\/1472-6947-12-143","author":"D Ferreira","year":"2012","unstructured":"Ferreira D, Oliveira A, Freitas A. Applying data mining techniques to improve diagnosis in neonatal jaundice. BMC Med Inf Decis Mak. 2012. https:\/\/doi.org\/10.1186\/1472-6947-12-143","journal-title":"BMC Med Inf Decis Mak"},{"key":"2499_CR5","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyt.2015.00021","author":"MD Sacchet","year":"2015","unstructured":"Sacchet MD, Prasad G, Foland-Ross LC, Thompson PM, Gotlib IH. Support vector machine classification of major depressive disorder using diffusion-weighted neuroimaging and graph theory. Front Psychiatry. 2015. https:\/\/doi.org\/10.3389\/fpsyt.2015.00021","journal-title":"Front Psychiatry"},{"key":"2499_CR6","doi-asserted-by":"publisher","DOI":"10.1147\/rd.33.0210","author":"AL Samuel","year":"1959","unstructured":"Samuel AL. Some studies in machine learning using the game of Checkers. IBM J Res Dev. 1959. https:\/\/doi.org\/10.1147\/rd.33.0210","journal-title":"IBM J Res Dev"},{"key":"2499_CR7","doi-asserted-by":"publisher","unstructured":"Buchanan BGA (Very) Brief History of Artificial Intelligence, editor. AI Magazine. 2005. https:\/\/doi.org\/10.1609\/aimag.v26i4.1848","DOI":"10.1609\/aimag.v26i4.1848"},{"key":"2499_CR8","doi-asserted-by":"publisher","unstructured":"Chen T, Guestrin C, XGBoost:. A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD \u201816). 2016. https:\/\/doi.org\/10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"key":"2499_CR9","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-023-02166-8","author":"X Zhao","year":"2023","unstructured":"Zhao X, Jiang C. The prediction of distant metastasis risk for male breast cancer patients based on an interpretable machine learning model. BMC Med Inf Decis Mak. 2023. https:\/\/doi.org\/10.1186\/s12911-023-02166-8","journal-title":"BMC Med Inf Decis Mak"},{"key":"2499_CR10","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-022-02087-y","author":"AA Sorayaie","year":"2022","unstructured":"Sorayaie AA, Babaei RS, Naemi A, Bagherzadeh MJ, Pirnejad H, Bagherzadeh MM, Wiil UK. Application of machine learning techniques for predicting survival in ovarian cancer. BMC Med Inf Decis Mak. 2022. https:\/\/doi.org\/10.1186\/s12911-022-02087-y","journal-title":"BMC Med Inf Decis Mak"},{"key":"2499_CR11","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0138-9","author":"SM Lundberg","year":"2020","unstructured":"Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, Katz R, Himmelfarb J, Bansal N, Lee S. From local explanations to global understanding with explainable AI for trees. Nat Mach Intell. 2020. https:\/\/doi.org\/10.1038\/s42256-019-0138-9","journal-title":"Nat Mach Intell"},{"key":"2499_CR12","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-018-0304-0","author":"SM Lundberg","year":"2018","unstructured":"Lundberg SM, Nair B, Vavilala MS, Horibe M, Eisses MJ, Adams T, Liston DE, Low DK, Newman SF, Kim J, Lee S. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat Biomed Eng. 2018. https:\/\/doi.org\/10.1038\/s41551-018-0304-0","journal-title":"Nat Biomed Eng"},{"issue":"9","key":"2499_CR13","first-page":"730","volume":"25","author":"M Zheng","year":"2022","unstructured":"Zheng M, Dong C, Qi G, Shao X. Analysis of factors affecting postoperative recurrence of non-lactating granulomatous lobular mastitis. Chin J Curr Adv Gen Surg. 2022;25(9):730\u20133.","journal-title":"Chin J Curr Adv Gen Surg"},{"key":"2499_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.jamcollsurg.2010.04.007","author":"V Gollapalli","year":"2010","unstructured":"Gollapalli V, Liao J, Dudakovic A, Sugg SL, Scott-Conner CE, Weigel RJ. Risk factors for development and recurrence of primary breast abscesses. J Am Coll Surg. 2010. https:\/\/doi.org\/10.1016\/j.jamcollsurg.2010.04.007","journal-title":"J Am Coll Surg"},{"issue":"16","key":"2499_CR15","first-page":"16","volume":"12","author":"X Liang","year":"2020","unstructured":"Liang X, Liu Z, Huang H, Wu R, Liu x, Yang X, Zhong Y. Observation on treating non-puerperal mastitis in acute stage with Wuwei Xiaodu Yin and an analysis of the related factors of recurrence. Clin J Chin Med. 2020;12(16):16\u20139.","journal-title":"Clin J Chin Med"},{"issue":"2","key":"2499_CR16","first-page":"181","volume":"34","author":"S Zhong","year":"2021","unstructured":"Zhong S, Wan H, Tao Y, Feng J, Qu W. Correlation between mammary intraductal lipoid secretions and clinical features of non-puerperal mastitis. Chin J Clin Res. 2021;34(2):181\u20135.","journal-title":"Chin J Clin Res"},{"key":"2499_CR17","volume-title":"Clinical retrospective analysis and etiological exploration of 593 cases of acne mastoid carbuncle [D]","author":"FF Chen","year":"2015","unstructured":"Chen FF. Clinical retrospective analysis and etiological exploration of 593 cases of acne mastoid carbuncle [D]. Shanghai: Shanghai University of Traditional Chinese Medicine; 2015."},{"key":"2499_CR18","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.50","author":"J Huang","year":"2005","unstructured":"Huang J, Ling C. Using AUC and accuracy in evaluating learning algorithms. IEEE Trans Knowl Data Eng. 2005. https:\/\/doi.org\/10.1109\/TKDE.2005.50","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2499_CR19","unstructured":"Lundberg SM, Lee SA, Unified Approach to Interpreting Model Predictions. The 31th International Conference on Neural Information Processing Systems (NIPS\u201917). 2017. Curran Associates Inc., Red Hook, NY, USA, 4768\u20134777."},{"issue":"8","key":"2499_CR20","first-page":"144","volume":"17","author":"Y Ren","year":"2020","unstructured":"Ren Y, Xu J, Yang H, Zhang J. High risk factors for short-term recurrence of idiopathic granulomatous mastitis. CHINA Med HERALD. 2020;17(8):144\u20137.","journal-title":"CHINA Med HERALD"},{"key":"2499_CR21","doi-asserted-by":"publisher","DOI":"10.20960\/nh.04746","author":"C Wei","year":"2023","unstructured":"Wei C, Wang X, Zeng J, Zhang G. Body mass index and risk of inflammatory breast disease: a Mendelian randomization study. Nutr Hosp. 2023. https:\/\/doi.org\/10.20960\/nh.04746","journal-title":"Nutr Hosp"},{"key":"2499_CR22","doi-asserted-by":"publisher","DOI":"10.1186\/s12944-023-01887-z","author":"X Chen","year":"2023","unstructured":"Chen X, Shao S, Wu X, Feng J, Qu W, Gao Q, Sun J, Wan H. LC\/MS-based untargeted lipidomics reveals lipid signatures of nonpuerperal mastitis. Lipids Health Dis. 2023. https:\/\/doi.org\/10.1186\/s12944-023-01887-z","journal-title":"Lipids Health Dis"},{"key":"2499_CR23","doi-asserted-by":"publisher","DOI":"10.23750\/abm.v91i2.8592","author":"E Onalan","year":"2020","unstructured":"Onalan E, D\u00f6nder E. Neutrophil and platelet to lymphocyte ratio in patients with hypothyroid Hashimoto\u2019s thyroiditis. Acta Biomed. 2020. https:\/\/doi.org\/10.23750\/abm.v91i2.8592","journal-title":"Acta Biomed"},{"key":"2499_CR24","doi-asserted-by":"publisher","DOI":"10.3109\/14397595.2015.1091136","author":"B Qin","year":"2016","unstructured":"Qin B, Ma N, Tang Q, Wei T, Yang M, Fu H, Hu Z, Liang Y, Yang Z, Zhong R. Neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR) were useful markers in assessment of inflammatory response and disease activity in SLE patients. Mod Rheumatol. 2016. https:\/\/doi.org\/10.3109\/14397595.2015.1091136","journal-title":"Mod Rheumatol"},{"key":"2499_CR25","doi-asserted-by":"publisher","DOI":"10.1111\/eci.13037","author":"GL Erre","year":"2019","unstructured":"Erre GL, Paliogiannis P, Castagna F, Mangoni AA, Carru C, Passiu G, Zinellu A. Meta-analysis of neutrophil-to-lymphocyte and platelet-to-lymphocyte ratio in rheumatoid arthritis. Eur J Clin Invest. 2019. https:\/\/doi.org\/10.1111\/eci.13037","journal-title":"Eur J Clin Invest"},{"issue":"7","key":"2499_CR26","first-page":"1300","volume":"20","author":"Y Furuncuo\u011flu","year":"2016","unstructured":"Furuncuo\u011flu Y, Tulgar S, Dogan AN, Cakar S, Tulgar YK, Cakiroglu B. How obesity affects the neutrophil\/lymphocyte and platelet\/lymphocyte ratio, systemic immune-inflammatory index and platelet indices: a retrospective study. Eur Rev Med Pharmacol Sci. 2016;20(7):1300\u20136.","journal-title":"Eur Rev Med Pharmacol Sci"},{"key":"2499_CR27","doi-asserted-by":"publisher","DOI":"10.12659\/MSM.912495","author":"AN Seringec","year":"2019","unstructured":"Seringec AN, Yildirim CG, Gogebakan H, Acipayam C. The C-reactive protein\/albumin ratio and complete blood count parameters as indicators of disease activity in patients with Takayasu arteritis. Med Sci Monit. 2019. https:\/\/doi.org\/10.12659\/MSM.912495","journal-title":"Med Sci Monit"},{"key":"2499_CR28","doi-asserted-by":"publisher","DOI":"10.1186\/s12885-020-07700-9","author":"JY Kim","year":"2020","unstructured":"Kim JY, Jung EJ, Kim JM, Lee HS, Kwag SJ, Park JH, Park T, Jeong SH, Jeong CY, Ju YT. Dynamic changes of neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio predicts breast cancer prognosis. BMC Cancer. 2020. https:\/\/doi.org\/10.1186\/s12885-020-07700-9","journal-title":"BMC Cancer"},{"key":"2499_CR29","doi-asserted-by":"publisher","DOI":"10.1002\/ijc.32640","author":"J Kang","year":"2019","unstructured":"Kang J, Chang Y, Ahn J, Oh S, Koo DH, Lee YG, Shin H, Ryu S. Neutrophil-to-lymphocyte ratio and risk of lung cancer mortality in a low-risk population: a cohort study. Int J Cancer. 2019. https:\/\/doi.org\/10.1002\/ijc.32640","journal-title":"Int J Cancer"},{"issue":"9","key":"2499_CR30","first-page":"1736","volume":"28","author":"S Shao","year":"2022","unstructured":"Shao S, Feng J, Wan H. Current status of diagnosis and treatment of cystic neutrophilic granulomatous mastitis. Med Recapitulate. 2022;28(9):1736\u201340.","journal-title":"Med Recapitulate"},{"key":"2499_CR31","doi-asserted-by":"publisher","DOI":"10.2147\/JIR.S377804","author":"AB Ciftci","year":"2022","unstructured":"Ciftci AB, B\u00fck \u00d6F, Yemez K, Polat S, Yaz\u0131c\u0131o\u011flu \u0130M. Risk factors and the role of the albumin-to-globulin ratio in predicting recurrence among patients with idiopathic granulomatous mastitis. J Inflamm Res. 2022. https:\/\/doi.org\/10.2147\/JIR.S377804","journal-title":"J Inflamm Res"},{"key":"2499_CR32","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14163848","author":"S Rabinovici-Cohen","year":"2022","unstructured":"Rabinovici-Cohen S, Fern\u00e1ndez XM, Grandal Rejo B, Hexter E, Cubelos OH, Pajula J, P\u00f6l\u00f6nen H, Reyal F, Rosen-Zvi M. Multimodal prediction of five-year breast cancer recurrence in women who receive neoadjuvant chemotherapy. Cancers (Basel). 2022. https:\/\/doi.org\/10.3390\/cancers14163848","journal-title":"Cancers (Basel)"},{"key":"2499_CR33","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-92774-z","author":"J Yang","year":"2022","unstructured":"Yang J, Ju J, Guo L, Ji B, Shi, Yang Z, Gao S, Yuan X, Tian G, Liang Y, Yuan P. Prediction of HER2-positive breast cancer recurrence and metastasis risk from histopathological images and clinical information via multimodal deep learning. Comput Struct Biotechnol J. 2022. https:\/\/doi.org\/10.1038\/s41598-021-92774-z","journal-title":"Comput Struct Biotechnol J"},{"key":"2499_CR34","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac448","author":"Y Yao","year":"2022","unstructured":"Yao Y, Lv Ya, Tong L, Liang Y, Xi S, Ji B, Zhang G, Li L, Tian G, Tang M, Hu X, Li S, Yang J. ICSDA: a multi-modal deep learning model to predict breast cancer recurrence and metastasis risk by integrating pathological, clinical and gene expression data. Brief Bioinform. 2022. https:\/\/doi.org\/10.1093\/bib\/bbac448","journal-title":"Brief Bioinform"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-024-02499-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-024-02499-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-024-02499-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T12:02:52Z","timestamp":1713787372000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-024-02499-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,22]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["2499"],"URL":"https:\/\/doi.org\/10.1186\/s12911-024-02499-y","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-3433008\/v1","asserted-by":"object"}]},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,22]]},"assertion":[{"value":"11 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 April 2024","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 followed the principles of the Declaration of Helsinki. The ethics committee of Shuguang Hospital affiliated to Shanghai University of TCM approved this study (2019-746-101). This was a retrospective study and all patients signed an informed consent form agreeing to the use of case data for scientific research and no biological specimens were used in this study.","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":"106"}}