{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:52:06Z","timestamp":1792788726837,"version":"4.5.0"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T00:00:00Z","timestamp":1742169600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T00:00:00Z","timestamp":1742169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100010270","name":"Capital Health Research and Development of Special Fund","doi-asserted-by":"publisher","award":["2020-2-40710"],"award-info":[{"award-number":["2020-2-40710"]}],"id":[{"id":"10.13039\/501100010270","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cancer Imaging"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>Accurate identification and evaluation of lymph nodes (LNs) in prostate cancer (PCa) patients is crucial for effective staging but can be time-consuming. We utilized a 3D V-Net model to improve the efficiency and accuracy of LN detection and segmentation.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>Utilizing pelvic diffusion-weighted imaging (DWI) scans, the 3D V-Net framework underwent training on a dataset comprising data from a hospital with 1,151 patients, encompassing 32,507 annotated LNs, following data augmentation procedures. Subsequently, external validation was conducted on data from 401 patients across three additional hospitals, encompassing 7,707 LNs. The segmentation performance was evaluated using the Dice similarity coefficient (DSC). The comparison between automated and manual segmentation regarding the short diameter and volume of LNs was conducted using Bland\u2013Altman plots and correlation analysis. The performance for suspicious metastatic LN detection (short diameter\u2009&gt;\u20098\u00a0mm) was evaluated using sensitivity, positive predictive value (PPV), and per-patient false-positive rate (FP\/vol) at the LN level and sensitivity, specificity, and PPV at the patient level.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>In the external validation test dataset, the model achieved a DSC of 0.77\u20130.82 for all, suspicious, and largest LNs. The model achieved a sensitivity, PPV, and FP\/vol of 60.1% (95% confidence interval (CI), 57.6-62.6%), 79.2% (95% CI, 76.6-81.5%), and 0.56 at the LN level, respectively. At the patient level, the model achieved a sensitivity, specificity, and PPV of 81.1% (95% CI, 76.5-85.0%), 75.6% (95% CI, 65.1-83.8%), and 93.2% (95% CI, 89.7-95.6%), respectively. The model achieved a strong correlation and good consistency between the short diameter and volume of the automatically segmented and manually annotated LNs.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>This 3D V-Net model can segment LNs effectively based on pelvic DWI images for PCa and holds great potential for facilitating N-staging in clinical practice.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s40644-025-00840-w","type":"journal-article","created":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T11:30:39Z","timestamp":1742211039000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Deep learning-based fully automated detection and segmentation of pelvic lymph nodes on diffusion-weighted images for prostate cancer: a multicenter study"],"prefix":"10.1186","volume":"25","author":[{"given":"Zhaonan","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengsheng","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tongtong","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ge","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huihui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8616-0782","authenticated-orcid":false,"given":"Xiaoying","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,17]]},"reference":[{"issue":"1","key":"840_CR1","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.juro.2010.03.039","volume":"184","author":"C von Bodman","year":"2010","unstructured":"von Bodman C, Godoy G, Chade DC, Cronin A, Tafe LJ, Fine SW, Laudone V, Scardino PT, Eastham JA. Predicting biochemical recurrence-free survival for patients with positive pelvic lymph nodes at radical prostatectomy. J Urol. 2010;184(1):143\u20138.","journal-title":"J Urol"},{"issue":"4","key":"840_CR2","doi-asserted-by":"publisher","first-page":"618","DOI":"10.1016\/j.eururo.2016.08.003","volume":"71","author":"N Mottet","year":"2017","unstructured":"Mottet N, Bellmunt J, Bolla M, Briers E, Cumberbatch MG, De Santis M, Fossati N, Gross T, Henry AM, Joniau S, et al. EAU-ESTRO-SIOG guidelines on prostate Cancer. Part 1: screening, diagnosis, and local treatment with curative intent. Eur Urol. 2017;71(4):618\u201329.","journal-title":"Eur Urol"},{"issue":"2","key":"840_CR3","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.eururo.2013.05.033","volume":"66","author":"G Gakis","year":"2014","unstructured":"Gakis G, Boorjian SA, Briganti A, Joniau S, Karazanashvili G, Karnes RJ, Mattei A, Shariat SF, Stenzl A, Wirth M, et al. The role of radical prostatectomy and lymph node dissection in lymph node-positive prostate cancer: a systematic review of the literature. Eur Urol. 2014;66(2):191\u20139.","journal-title":"Eur Urol"},{"issue":"6","key":"840_CR4","doi-asserted-by":"publisher","first-page":"972","DOI":"10.1111\/bju.14892","volume":"124","author":"Y Hou","year":"2019","unstructured":"Hou Y, Bao ML, Wu CJ, Zhang J, Zhang YD, Shi HB. A machine learning-assisted decision-support model to better identify patients with prostate cancer requiring an extended pelvic lymph node dissection. BJU Int. 2019;124(6):972\u201383.","journal-title":"BJU Int"},{"issue":"3","key":"840_CR5","doi-asserted-by":"publisher","first-page":"728","DOI":"10.1148\/radiol.2017161517","volume":"285","author":"HC Thoeny","year":"2017","unstructured":"Thoeny HC, Barbieri S, Froehlich JM, Turkbey B, Choyke PL. Functional and targeted lymph node imaging in prostate Cancer: current Status and Future challenges. Radiology. 2017;285(3):728\u201343.","journal-title":"Radiology"},{"issue":"3","key":"840_CR6","doi-asserted-by":"publisher","first-page":"W95","DOI":"10.2214\/AJR.17.18481","volume":"210","author":"S Woo","year":"2018","unstructured":"Woo S, Suh CH, Kim SY, Cho JY, Kim SH. The diagnostic performance of MRI for Detection of Lymph Node Metastasis in bladder and prostate Cancer: an updated systematic review and diagnostic Meta-analysis. AJR Am J Roentgenol. 2018;210(3):W95\u2013109.","journal-title":"AJR Am J Roentgenol"},{"issue":"1","key":"840_CR7","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.eururo.2015.08.052","volume":"69","author":"JC Weinreb","year":"2016","unstructured":"Weinreb JC, Barentsz JO, Choyke PL, Cornud F, Haider MA, Macura KJ, Margolis D, Schnall MD, Shtern F, Tempany CM, et al. PI-RADS prostate imaging - reporting and Data System: 2015, Version 2. Eur Urol. 2016;69(1):16\u201340.","journal-title":"Eur Urol"},{"issue":"4","key":"840_CR8","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1016\/j.crad.2007.05.022","volume":"63","author":"AM Hovels","year":"2008","unstructured":"Hovels AM, Heesakkers RA, Adang EM, Jager GJ, Strum S, Hoogeveen YL, Severens JL, Barentsz JO. The diagnostic accuracy of CT and MRI in the staging of pelvic lymph nodes in patients with prostate cancer: a meta-analysis. Clin Radiol. 2008;63(4):387\u201395.","journal-title":"Clin Radiol"},{"issue":"3","key":"840_CR9","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1016\/j.ejca.2012.09.034","volume":"49","author":"M Triantafyllou","year":"2013","unstructured":"Triantafyllou M, Studer UE, Birkhauser FD, Fleischmann A, Bains LJ, Petralia G, Christe A, Froehlich JM, Thoeny HC. Ultrasmall superparamagnetic particles of iron oxide allow for the detection of metastases in normal sized pelvic lymph nodes of patients with bladder and\/or prostate cancer. Eur J Cancer. 2013;49(3):616\u201324.","journal-title":"Eur J Cancer"},{"key":"840_CR10","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi S-A. V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV). IEEE Computer Society; 2016: 565\u2013571.","DOI":"10.1109\/3DV.2016.79"},{"issue":"2","key":"840_CR11","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1002\/jmri.27585","volume":"54","author":"R Cuocolo","year":"2021","unstructured":"Cuocolo R, Comelli A, Stefano A, Benfante V, Dahiya N, Stanzione A, Castaldo A, De Lucia DR, Yezzi A, Imbriaco M. Deep learning whole-gland and zonal prostate segmentation on a public MRI dataset. J Magn Reson Imaging. 2021;54(2):452\u20139.","journal-title":"J Magn Reson Imaging"},{"key":"840_CR12","doi-asserted-by":"crossref","unstructured":"Comelli A, Coronnello C, Dahiya N, Benfante V, Palmucci S, Basile A, Vancheri C, Russo G, Yezzi A, Stefano A. Lung segmentation on high-resolution computerized tomography images using deep learning: a preliminary step for Radiomics studies. J Imaging 2020, 6(11).","DOI":"10.3390\/jimaging6110125"},{"key":"840_CR13","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-assisted intervention\u2013 MICCAI 2015: 2015\/\/ 2015; Cham. Springer International Publishing; 2015. pp. 234\u201341.","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"1","key":"840_CR14","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1186\/s12880-021-00703-3","volume":"21","author":"X Liu","year":"2021","unstructured":"Liu X, Sun Z, Han C, Cui Y, Huang J, Wang X, Zhang X, Wang X. Development and validation of the 3D U-Net algorithm for segmentation of pelvic lymph nodes on diffusion-weighted images. BMC Med Imaging. 2021;21(1):170.","journal-title":"BMC Med Imaging"},{"issue":"6","key":"840_CR15","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren S, He K, Girshick R, Sun J. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell. 2017;39(6):1137\u201349.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"840_CR16","first-page":"424","volume-title":"Medical Image Computing and Computer-assisted intervention\u2013 MICCAI 2016: 2016\/\/ 2016","author":"\u00d6 \u00c7i\u00e7ek","year":"2016","unstructured":"\u00c7i\u00e7ek \u00d6, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O. 3D U-Net: learning dense volumetric segmentation from sparse annotation. Medical Image Computing and Computer-assisted intervention\u2013 MICCAI 2016: 2016\/\/ 2016. Cham: Springer International Publishing; 2016. pp. 424\u201332."},{"key":"840_CR17","doi-asserted-by":"publisher","first-page":"102780","DOI":"10.1016\/j.ebiom.2020.102780","volume":"56","author":"X Zhao","year":"2020","unstructured":"Zhao X, Xie P, Wang M, Li W, Pickhardt PJ, Xia W, Xiong F, Zhang R, Xie Y, Jian J, et al. Deep learning-based fully automated detection and segmentation of lymph nodes on multiparametric-mri for rectal cancer: a multicentre study. EBioMedicine. 2020;56:102780.","journal-title":"EBioMedicine"},{"issue":"1","key":"840_CR18","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1186\/s12880-022-00905-3","volume":"22","author":"X Liu","year":"2022","unstructured":"Liu X, Tian J, Wu J, Zhang Y, Wang X, Zhang X, Wang X. Utility of diffusion weighted imaging-based radiomics nomogram to predict pelvic lymph nodes metastasis in prostate cancer. BMC Med Imaging. 2022;22(1):190.","journal-title":"BMC Med Imaging"},{"issue":"9","key":"840_CR19","doi-asserted-by":"publisher","first-page":"3327","DOI":"10.1007\/s00261-022-03583-5","volume":"47","author":"X Liu","year":"2022","unstructured":"Liu X, Wang X, Zhang Y, Sun Z, Zhang X, Wang X. Preoperative prediction of pelvic lymph nodes metastasis in prostate cancer using an ADC-based radiomics model: comparison with clinical nomograms and PI-RADS assessment. Abdom Radiol (NY). 2022;47(9):3327\u201337.","journal-title":"Abdom Radiol (NY)"},{"issue":"8","key":"840_CR20","doi-asserted-by":"publisher","first-page":"5688","DOI":"10.1007\/s00330-022-08625-6","volume":"32","author":"H Zheng","year":"2022","unstructured":"Zheng H, Miao Q, Liu Y, Mirak SA, Hosseiny M, Scalzo F, Raman SS, Sung K. Multiparametric MRI-based radiomics model to predict pelvic lymph node invasion for patients with prostate cancer. Eur Radiol. 2022;32(8):5688\u201399.","journal-title":"Eur Radiol"},{"key":"840_CR21","doi-asserted-by":"crossref","unstructured":"Bourbonne V, Jaouen V, Nguyen TA, Tissot V, Doucet L, Hatt M, Visvikis D, Pradier O, Valeri A, Fournier G et al. Development of a Radiomic-Based Model Predicting Lymph Node Involvement in Prostate Cancer Patients. Cancers (Basel) 2021, 13(22).","DOI":"10.3390\/cancers13225672"}],"container-title":["Cancer Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40644-025-00840-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40644-025-00840-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40644-025-00840-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T11:30:43Z","timestamp":1742211043000},"score":1,"resource":{"primary":{"URL":"https:\/\/cancerimagingjournal.biomedcentral.com\/articles\/10.1186\/s40644-025-00840-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,17]]},"references-count":21,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["840"],"URL":"https:\/\/doi.org\/10.1186\/s40644-025-00840-w","relation":{},"ISSN":["1470-7330"],"issn-type":[{"value":"1470-7330","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,17]]},"assertion":[{"value":"22 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 March 2025","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 performed in accordance with the principles of the Declaration of Helsinki and was approved by the Committee for Medical Ethics, Peking University First Hospital (2021\u2013060). Informed consent was waived according to its retrospective design.","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":"Pengsheng Wu is from a medical technical corporation provided technical support for model development. The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"37"}}