{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T16:36:18Z","timestamp":1790354178030,"version":"4.1.0"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T00:00:00Z","timestamp":1679443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>As a common breast cancer-related complaint, pathological nipple discharge (PND) detected by ductoscopy is often missed diagnosed. Deep learning techniques have enabled great advances in clinical imaging but are rarely applied in breast cancer with PND. This study aimed to design and validate an Intelligent Ductoscopy for Breast Cancer Diagnostic System (IDBCS) for breast cancer diagnosis by analyzing real-time imaging data acquired by ductoscopy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and methods<\/jats:title><jats:p>The present multicenter, case-control trial was carried out in 6 hospitals in China. Images for consecutive patients, aged \u226518 years, with no previous ductoscopy, were obtained from the involved hospitals. All individuals with PND confirmed from breast lesions by ductoscopy were eligible. Images from Beijing Chao-Yang Hospital were randomly assigned (8:2) to the training (IDBCS development) and internal validation (performance evaluation of the IDBCS) datasets. Diagnostic performance was further assessed with internal and prospective validation datasets from Beijing Chao-Yang Hospital; further external validation was carried out with datasets from 5 primary care hospitals. Diagnostic accuracies, sensitivities, specificities, and positive and negative predictive values for IDBCS and endoscopists (expert, competent, or trainee) in the detection of malignant lesions were obtained by the Clopper-Pearson method.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Totally 11305 ductoscopy images in 1072 patients were utilized for developing and testing the IDBCS. Area under the curves (AUCs) in breast cancer detection were 0\u00b7975 (95%CI 0\u00b7899-0\u00b7998) and 0\u00b7954 (95%CI 0\u00b7925-0\u00b7975) in the internal validation and prospective datasets, respectively, and ranged between 0\u00b7922 (95%CI 0\u00b7866-0\u00b7960) and 0\u00b7965 (95%CI 0\u00b7892-0\u00b7994) in the 5 external validation datasets. The IDBCS had superior diagnostic accuracy compared with expert (0.912 [95%CI 0.839-0.959] vs 0.726 [0.672-0.775]; p&amp;lt;0.001), competent (0.699 [95%CI 0.645-0.750], p&amp;lt;0.001), and trainee (0.703 [95%CI 0.648-0.753], p&amp;lt;0.001) endoscopists.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>IDBCS outperforms clinical oncologists, achieving high accuracy in diagnosing breast cancer with PND. The novel system could help endoscopists improve their diagnostic efficacy in breast cancer diagnosis.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fonc.2023.1103145","type":"journal-article","created":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T06:13:33Z","timestamp":1679465613000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep learning for real-time detection of breast cancer presenting pathological nipple discharge by ductoscopy"],"prefix":"10.3389","volume":"13","author":[{"given":"Feng","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuang","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhihao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haifeng","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenhai","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuewen","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunzhao","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengxin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangqian","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"He","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanshuang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianxiu","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongchuan","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,3,22]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"394","DOI":"10.3322\/caac.21492","article-title":"Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"68","author":"Bray","year":"2018","journal-title":"CA: Cancer J Clin"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1002\/cam4.3793","article-title":"Diagnostic value of sialyl-tn immunocytochemistry in breast cancer presenting with pathological nipple discharge","volume":"10","author":"Xu","year":"2021","journal-title":"Cancer Med"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1016\/j.clbc.2020.05.015","article-title":"Network meta-analysis for the diagnostic approach to pathologic nipple discharge","volume":"20","author":"Filipe","year":"2020","journal-title":"Clin Breast Cancer"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1016\/j.amjsurg.2007.07.001","article-title":"Gradient index ductoscopy and intraductal biopsy of intraductal breast lesions","volume":"194","author":"H\u00fcnerbein","year":"2007","journal-title":"Am J Surg"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1016\/j.crad.2019.02.006","article-title":"Artificial intelligence in breast imaging","volume":"74","author":"Le","year":"2019","journal-title":"Clin Radiol"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.105014","article-title":"A deep learning approach using effective preprocessing techniques to detect COVID-19 from chest CT-scan and X-ray images","volume":"139","author":"Ahamed","year":"2021","journal-title":"Comput Biol Med"},{"key":"B7","doi-asserted-by":"publisher","first-page":"200075","DOI":"10.1016\/j.iswa.2022.200075","article-title":"Automatic driver distraction detection using deep convolutional neural networks","volume":"14","author":"Hossain","year":"2022","journal-title":"Intelligent Syst Applications"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(19)30637-0","article-title":"Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: A multicentre, case-control, diagnostic study","volume":"20","author":"Luo","year":"2019","journal-title":"Lancet Oncol"},{"key":"B9","doi-asserted-by":"publisher","first-page":"2961","DOI":"10.1038\/s41467-020-16777-6","article-title":"Diagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer","volume":"11","author":"Zhou","year":"2020","journal-title":"Nat Commun"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1111\/jgh.15190","article-title":"Application of artificial intelligence using a convolutional neural network for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging","volume":"36","author":"Ueyama","year":"2021","journal-title":"J Gastroenterol Hepatol"},{"key":"B11","doi-asserted-by":"publisher","first-page":"806","DOI":"10.1016\/j.gie.2018.11.011","article-title":"Application of convolutional neural network in the diagnosis of the invasion depth of gastric cancer based on conventional endoscopy","volume":"89","author":"Zhu","year":"2019","journal-title":"Gastrointest Endosc"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1038\/s41551-018-0301-3","article-title":"Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy","volume":"2","author":"Wang","year":"2018","journal-title":"Nat Biomed Eng"},{"key":"B13","doi-asserted-by":"publisher","first-page":"103146","DOI":"10.1016\/j.ebiom.2020.103146","article-title":"Development and validation of a real-time artificial intelligence-assisted system for detecting early gastric cancer: A multicentre retrospective diagnostic study","volume":"62","author":"Tang","year":"2020","journal-title":"EBioMedicine"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1111\/1754-9485.13261","article-title":"A review of medical image data augmentation techniques for deep learning applications","volume":"65","author":"Chlap","year":"2021","journal-title":"J Med Imaging Radiat Oncol"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc IEEE"},{"key":"B16","first-page":"1","article-title":"Very deep convolutional networks for Large-scale image recognition","author":"Simonyan","year":"2015"},{"key":"B17","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR.2016.308","article-title":"Rethinking the inception architecture for computer vision","author":"Szegedy","year":"2016"},{"key":"B18","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR.2016.90","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"B19","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR.2017.243","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern recognition Lett"},{"key":"B21","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1214\/ss\/1032280214","article-title":"Bootstrap confidence intervals","volume":"11","author":"DiCiccio","year":"1996","journal-title":"Stat Sci"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.1016\/0169-2607(95)01703-8","article-title":"MedCalc: A new computer program for medical statistics","volume":"48","author":"Schoonjans","year":"1995","journal-title":"Comput Methods Programs Biomed"},{"key":"B23","doi-asserted-by":"crossref","DOI":"10.1109\/CVPRW50498.2020.00020","article-title":"Score-CAM: Score-weighted visual explanations for convolutional neural networks","author":"Wang","year":"2020"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.1016\/0028-2243(93)90205-Q","article-title":"Nipple discharge and abnormal galactogram. results of a long-term study (1964-1990)","volume":"50","author":"Paterok","year":"1993","journal-title":"Eur J obstetrics gynecology Reprod Biol"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-021-07790-4","article-title":"Diagnostic accuracy of breast MRI for patients with suspicious nipple discharge and negative mammography and ultrasound: a prospective study","volume":"31","author":"Boisserie-Lacroix","year":"2021","journal-title":"Eur Radiol"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1007\/s10549-019-05321-w","article-title":"Can we see what is invisible? the role of MRI in the evaluation and management of patients with pathological nipple discharge","volume":"178","author":"Zacharioudakis","year":"2019","journal-title":"Breast Cancer Res Treat"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.1177\/106689690301100217","article-title":"Intraductal aspiration cytology and galactography for nipple discharge","volume":"88","author":"Baitchev","year":"2003","journal-title":"Int Surg"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1002\/bjs.10125","article-title":"Systematic review and meta-analysis of the diagnostic accuracy of ductoscopy in patients with pathological nipple discharge","volume":"103","author":"Waaijer","year":"2016","journal-title":"Br J Surg"},{"key":"B29","doi-asserted-by":"publisher","first-page":"m689","DOI":"10.1136\/bmj.m689","article-title":"Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies","volume":"368","author":"Nagendran","year":"2020","journal-title":"BMJ (Clinical Res ed)"},{"key":"B30","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1186\/s40880-018-0325-9","article-title":"Development and validation of an endoscopic images-based deep learning model for detection with nasopharyngeal malignancies","volume":"38","author":"Li","year":"2018","journal-title":"Cancer Commun (London England)"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.21873\/anticanres.14179","article-title":"Correlation of ductoscopic and histopathological findings and their relevance as predictors for malignancy: A German multicenter study","volume":"40","author":"Ohlinger","year":"2020","journal-title":"Anticancer Res"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.4274\/balkanmedj.2016.0184","article-title":"Diagnosis of nipple discharge: Value of magnetic resonance imaging and ultrasonography in comparison with ductoscopy","volume":"34","author":"Yilmaz","year":"2017","journal-title":"Balkan Med J"},{"key":"B33","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1186\/s12885-017-3288-3","article-title":"Diagnostic value of endoscopic appearance during ductoscopy in patients with pathological nipple discharge","volume":"17","author":"Han","year":"2017","journal-title":"BMC Cancer"},{"key":"B34","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1007\/s10549-011-1787-5","article-title":"Bloody nipple discharge is a predictor of breast cancer risk: A meta-analysis","volume":"132","author":"Chen","year":"2012","journal-title":"Breast Cancer Res Treat"}],"container-title":["Frontiers in Oncology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2023.1103145\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T06:13:36Z","timestamp":1679465616000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2023.1103145\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,22]]},"references-count":34,"alternative-id":["10.3389\/fonc.2023.1103145"],"URL":"https:\/\/doi.org\/10.3389\/fonc.2023.1103145","relation":{},"ISSN":["2234-943X"],"issn-type":[{"value":"2234-943X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,22]]},"article-number":"1103145"}}