{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T02:48:38Z","timestamp":1782874118009,"version":"3.54.5"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:00:00Z","timestamp":1769731200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:00:00Z","timestamp":1769731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"crossref","award":["R01CA252311"],"award-info":[{"award-number":["R01CA252311"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Prostate cancer (PCa) diagnosis is increasingly guided by imaging, with ultrasound (US) emerging as a cost-effective and widely accessible modality. This study develops a deep learning-based classifier predicting the presence of clinically significant (cs)PCa using quantitative features extracted from 3D multiparametric (mp)US.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>A multicenter prospective cohort of 327 patients with suspicion of PCa underwent transrectal 3D mpUS scanning, including dynamic contrast-enhanced US and shear-wave elastography. Acquisitions were registered to 3D histology from radical prostatectomy, which served as the reference standard for the presence of csPCa. Voxels within lesions with International Society of Urological Pathology (ISUP) Grade Group \u2265\u20092 were considered malignant, and the rest were benign. A 3D deep learning classifier was trained on quantitative mpUS features to detect csPCa. The classifier was trained and internally evaluated on 250 patients and externally evaluated on 77 patients acquired later. Classifier performance was evaluated per voxel using the area under the receiver operating characteristic curve (ROC AUC).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Using quantitative mpUS features from 327 patients, the classifier achieved a ROC AUC of 0.87 (95% CI: 0.85\u20130.89) on the internal evaluation set, using 7-fold cross-validation. On the external evaluation cohort, the classifier achieved a ROC AUC of 0.88 (95% CI: 0.87\u20130.89).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed classifier accurately detects csPCa using quantitative features from 3D mpUS and generalizes well to the external dataset. These results support mpUS as a promising, cost-effective tool for csPCa diagnosis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Question:<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Can quantitative features extracted from 3D multiparametric ultrasound (mpUS) reliably detect clinically significant prostate cancer (csPCa), enabling more accessible and affordable diagnosis?<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings:<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Predicting csPCa using quantitative multiparametric ultrasound features achieved an area under the receiver operating characteristic curve of 0.87, increasing to 0.88 when externally evaluated<\/jats:italic>\n                      .\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical relevance:<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Our proposed deep learning-based classifier using quantitative 3D mpUS features accurately detects csPCa, as validated on the largest mpUS prostate dataset to date. This opens the door to ultrasound as an accurate, cost-effective method for csPCa detection.<\/jats:italic>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-026-12323-y","type":"journal-article","created":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:43:22Z","timestamp":1769733802000},"page":"4539-4550","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Development of a quantitative multiparametric ultrasound and deep learning classifier for the detection of prostate cancer"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2937-9796","authenticated-orcid":false,"given":"Florian","family":"Delberghe","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueting","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel L.","family":"van den Kroonenberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simona","family":"Turco","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wim","family":"Zwart","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giuseppe","family":"Valvano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Auke","family":"Jager","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arnoud W.","family":"Postema","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hessel","family":"Wijkstra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jorg R.","family":"Oddens","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Massimo","family":"Mischi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,30]]},"reference":[{"key":"12323_CR1","doi-asserted-by":"publisher","first-page":"1510","DOI":"10.21037\/tau.2019.09.20","volume":"9","author":"ME Noureldin","year":"2020","unstructured":"Noureldin ME, Connor MJ, Boxall N et al (2020) Current techniques of prostate biopsy: an update from past to present. Transl Androl Urol 9:1510","journal-title":"Transl Androl Urol"},{"key":"12323_CR2","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.juro.2007.03.017","volume":"178","author":"T Graif","year":"2007","unstructured":"Graif T, Loeb S, Roehl KA et al (2007) Under diagnosis and over diagnosis of prostate cancer. J Urol 178:88\u201392","journal-title":"J Urol"},{"key":"12323_CR3","doi-asserted-by":"publisher","first-page":"876","DOI":"10.1016\/j.eururo.2013.05.049","volume":"64","author":"S Loeb","year":"2013","unstructured":"Loeb S, Vellekoop A, Ahmed HU et al (2013) Systematic review of complications of prostate biopsy. Eur Urol 64:876\u2013892. https:\/\/doi.org\/10.1016\/j.eururo.2013.05.049","journal-title":"Eur Urol"},{"key":"12323_CR4","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.eururo.2020.09.042","volume":"79","author":"N Mottet","year":"2021","unstructured":"Mottet N, van den Bergh RCN, Briers E et al (2021) EAU-EANM-ESTRO-ESUR-SIOG Guidelines on Prostate Cancer\u20142020 Update. Part 1: screening, diagnosis, and local treatment with curative intent. Eur Urol 79:243\u2013262. https:\/\/doi.org\/10.1016\/j.eururo.2020.09.042","journal-title":"Eur Urol"},{"key":"12323_CR5","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/S1470-2045(18)30569-2","volume":"20","author":"O Rouvi\u00e8re","year":"2019","unstructured":"Rouvi\u00e8re O, Puech P, Renard-Penna R et al (2019) Use of prostate systematic and targeted biopsy on the basis of multiparametric MRI in biopsy-naive patients (MRI-FIRST): a prospective, multicentre, paired diagnostic study. Lancet Oncol 20:100\u2013109. https:\/\/doi.org\/10.1016\/S1470-2045(18)30569-2","journal-title":"Lancet Oncol"},{"key":"12323_CR6","doi-asserted-by":"publisher","first-page":"1767","DOI":"10.1016\/j.eururo.2018.05.029","volume":"378","author":"V Kasivisvanathan","year":"2018","unstructured":"Kasivisvanathan V, Rannikko ASAS, Borghi M et al (2018) MRI-targeted or standard biopsy for prostate-cancer diagnosis. N Engl J Med 378:1767\u20131777. https:\/\/doi.org\/10.1016\/j.eururo.2018.05.029","journal-title":"N Engl J Med"},{"key":"12323_CR7","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1148\/radiol.2020190646","volume":"296","author":"AC Westphalen","year":"2020","unstructured":"Westphalen AC, McCulloch CE, Anaokar JM et al (2020) Variability of the positive predictive value of PI-RADS for prostate MRI across 26 centers: experience of the Society of Abdominal Radiology prostate cancer disease-focused panel. Radiology 296:76\u201384","journal-title":"Radiology"},{"key":"12323_CR8","doi-asserted-by":"publisher","first-page":"936","DOI":"10.1016\/S1470-2045(23)00298-X","volume":"24","author":"K L\u00e5ng","year":"2023","unstructured":"L\u00e5ng K, Josefsson V, Larsson A-M et al (2023) Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study. Lancet Oncol 24:936\u2013944","journal-title":"Lancet Oncol"},{"key":"12323_CR9","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1038\/s41591-021-01620-2","volume":"28","author":"W Bulten","year":"2022","unstructured":"Bulten W, Kartasalo K, Chen P-HC et al (2022) Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge. Nat Med 28:154\u2013163","journal-title":"Nat Med"},{"key":"12323_CR10","doi-asserted-by":"publisher","first-page":"1687","DOI":"10.1056\/NEJMoa1917130","volume":"382","author":"D Milea","year":"2020","unstructured":"Milea D, Najjar RP, Jiang Z et al (2020) Artificial intelligence to detect papilledema from ocular fundus photographs. N Engl J Med 382:1687\u20131695","journal-title":"N Engl J Med"},{"key":"12323_CR11","doi-asserted-by":"publisher","first-page":"e2515672","DOI":"10.1001\/jamanetworkopen.2025.15672","volume":"8","author":"JJ Twilt","year":"2025","unstructured":"Twilt JJ, Saha A, Bosma JS et al (2025) AI-assisted vs unassisted identification of prostate cancer in magnetic resonance images. JAMA Netw Open 8:e2515672. https:\/\/doi.org\/10.1001\/jamanetworkopen.2025.15672","journal-title":"JAMA Netw Open"},{"key":"12323_CR12","doi-asserted-by":"publisher","first-page":"335","DOI":"10.3390\/jimaging11100335","volume":"11","author":"V-O Bolocan","year":"2025","unstructured":"Bolocan V-O, Mitoi A, Nicu-Canareica O et al (2025) Artificial intelligence in prostate MRI: current evidence and clinical translation challenges\u2014a narrative review. J Imaging 11:335. https:\/\/doi.org\/10.3390\/jimaging11100335","journal-title":"J Imaging"},{"key":"12323_CR13","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1016\/S1470-2045(24)00220-1","volume":"25","author":"A Saha","year":"2024","unstructured":"Saha A, Bosma JS, Twilt JJ et al (2024) Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study. Lancet Oncol 25:879\u2013887. https:\/\/doi.org\/10.1016\/S1470-2045(24)00220-1","journal-title":"Lancet Oncol"},{"key":"12323_CR14","doi-asserted-by":"publisher","unstructured":"Cornford P, van den Bergh RC, Briers E et al (2024) EAU-EANM-ESTRO-ESUR-ISUP-SIOG guidelines on prostate cancer\u20142024 update. Part I: screening, diagnosis, and local treatment with curative intent. Eur Urol 86:148\u2013163. https:\/\/doi.org\/10.1016\/j.eururo.2024.03.027","DOI":"10.1016\/j.eururo.2024.03.027"},{"key":"12323_CR15","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1016\/S1470-2045(22)00016-X","volume":"23","author":"ADR Grey","year":"2022","unstructured":"Grey ADR, Scott R, Shah B et al (2022) Multiparametric ultrasound versus multiparametric MRI to diagnose prostate cancer (CADMUS): a prospective, multicentre, paired-cohort, confirmatory study. Lancet Oncol 23:428\u2013438. https:\/\/doi.org\/10.1016\/S1470-2045(22)00016-X","journal-title":"Lancet Oncol"},{"key":"12323_CR16","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 et al (2016) PI-RADS prostate imaging\u2014Reporting and Data System: 2015, Version 2. Eur Urol 69:16\u201340. https:\/\/doi.org\/10.1016\/j.eururo.2015.08.052","journal-title":"Eur Urol"},{"key":"12323_CR17","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1007\/s00428-023-03589-4","volume":"483","author":"A Jager","year":"2023","unstructured":"Jager A, Postema AW, van der Linden H et al (2023) Reliability of whole mount radical prostatectomy histopathology as the ground truth for artificial intelligence assisted prostate imaging. Virchows Arch 483:197\u2013206. https:\/\/doi.org\/10.1007\/s00428-023-03589-4","journal-title":"Virchows Arch"},{"key":"12323_CR18","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.euros.2022.12.018","volume":"49","author":"A Jager","year":"2023","unstructured":"Jager A, Postema AW, Mischi M et al (2023) Clinical trial protocol: developing an image classification algorithm for prostate cancer diagnosis on three-dimensional multiparametric transrectal ultrasound. Eur Urol Open Sci 49:32\u201343. https:\/\/doi.org\/10.1016\/j.euros.2022.12.018","journal-title":"Eur Urol Open Sci"},{"key":"12323_CR19","doi-asserted-by":"publisher","DOI":"10.1186\/s12894-025-01736-4","volume":"25","author":"A Jager","year":"2025","unstructured":"Jager A, Zwart MJ, Postema AW et al (2025) Development and validation of a framework for registration of whole-mount radical prostatectomy histopathology with three-dimensional transrectal ultrasound. BMC Urol 25:73. https:\/\/doi.org\/10.1186\/s12894-025-01736-4","journal-title":"BMC Urol"},{"key":"12323_CR20","doi-asserted-by":"publisher","first-page":"e87","DOI":"10.1097\/PAS.0000000000001497","volume":"44","author":"GJ Van Leenders","year":"2020","unstructured":"Van Leenders GJ, Van Der Kwast TH, Grignon DJ et al (2020) The 2019 International Society of Urological Pathology (ISUP) consensus conference on grading of prostatic carcinoma. Am J Surg Pathol 44:e87\u2013e99","journal-title":"Am J Surg Pathol"},{"key":"12323_CR21","doi-asserted-by":"publisher","first-page":"035014","DOI":"10.1088\/2057-1976\/aa7073","volume":"3","author":"RR Wildeboer","year":"2017","unstructured":"Wildeboer RR, Schalk SG, Demi L et al (2017) Three-dimensional histopathological reconstruction as a reliable ground truth for prostate cancer studies. Biomed Phys Eng Express 3:035014. https:\/\/doi.org\/10.1088\/2057-1976\/aa7073","journal-title":"Biomed Phys Eng Express"},{"key":"12323_CR22","doi-asserted-by":"publisher","unstructured":"Du L, Scharf L (1990) Wiener filters for interpolation and extrapolation. In 1990 Conference Record Twenty-Fourth Asilomar Conference on Signals, Systems and Computers, IEEE. 1990. p. 418\u2013422. https:\/\/doi.org\/10.1109\/ACSSC.1990.523371","DOI":"10.1109\/ACSSC.1990.523371"},{"key":"12323_CR23","doi-asserted-by":"publisher","unstructured":"Mischi M (Massimo) (2004) Contrast echocardiography for cardiac quantifications. Technische Universiteit Eindhoven. https:\/\/doi.org\/10.6100\/IR579395","DOI":"10.6100\/IR579395"},{"key":"12323_CR24","doi-asserted-by":"publisher","first-page":"1493","DOI":"10.1109\/TMI.2011.2125981","volume":"30","author":"MPJ Kuenen","year":"2011","unstructured":"Kuenen MPJ, Mischi M, Wijkstra H (2011) Contrast-ultrasound diffusion imaging for localization of prostate cancer. IEEE Trans Med Imaging 30:1493\u20131502. https:\/\/doi.org\/10.1109\/TMI.2011.2125981","journal-title":"IEEE Trans Med Imaging"},{"key":"12323_CR25","doi-asserted-by":"publisher","first-page":"2593","DOI":"10.1109\/TMI.2018.2843396","volume":"37","author":"RR Wildeboer","year":"2018","unstructured":"Wildeboer RR, Van Sloun RJG, Schalk SG et al (2018) Convective-dispersion modeling in 3D contrast-ultrasound imaging for the localization of prostate cancer. IEEE Trans Med Imaging 37:2593\u20132602. https:\/\/doi.org\/10.1109\/TMI.2018.2843396","journal-title":"IEEE Trans Med Imaging"},{"key":"12323_CR26","doi-asserted-by":"publisher","first-page":"1631","DOI":"10.1016\/j.ultrasmedbio.2013.03.004","volume":"39","author":"MPJ Kuenen","year":"2013","unstructured":"Kuenen MPJ, Saidov TA, Wijkstra H, Mischi M (2013) Contrast-ultrasound dispersion imaging for prostate cancer localization by improved spatiotemporal similarity analysis. Ultrasound Med Biol 39:1631\u20131641. https:\/\/doi.org\/10.1016\/j.ultrasmedbio.2013.03.004","journal-title":"Ultrasound Med Biol"},{"key":"12323_CR27","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1109\/TUFFC.2012.2241","volume":"59","author":"M Mischi","year":"2012","unstructured":"Mischi M, Kuenen MPJ, Wijkstra H (2012) Angiogenesis imaging by spatiotemporal analysis of ultrasound contrast agent dispersion kinetics. IEEE Trans Ultrason Ferroelect Freq Contr 59:621\u2013629. https:\/\/doi.org\/10.1109\/TUFFC.2012.2241","journal-title":"IEEE Trans Ultrason Ferroelect Freq Contr"},{"key":"12323_CR28","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1109\/TBME.2016.2571624","volume":"64","author":"SG Schalk","year":"2017","unstructured":"Schalk SG, Demi L, Bouhouch N et al (2017) Contrast-enhanced ultrasound angiogenesis imaging by mutual information analysis for prostate cancer localization. IEEE Trans Biomed Eng 64:661\u2013670. https:\/\/doi.org\/10.1109\/TBME.2016.2571624","journal-title":"IEEE Trans Biomed Eng"},{"key":"12323_CR29","doi-asserted-by":"publisher","first-page":"826","DOI":"10.1109\/TMI.2016.2629851","volume":"36","author":"RJ van Sloun","year":"2017","unstructured":"van Sloun RJ, Demi L, Postema AW et al (2017) Entropy of ultrasound-contrast-agent velocity fields for angiogenesis imaging in prostate cancer. IEEE Trans Med Imaging 36:826\u2013837. https:\/\/doi.org\/10.1109\/TMI.2016.2629851","journal-title":"IEEE Trans Med Imaging"},{"key":"12323_CR30","doi-asserted-by":"publisher","first-page":"2887","DOI":"10.3390\/cancers14122887","volume":"14","author":"C Luthold","year":"2022","unstructured":"Luthold C, Hallal T, Labb\u00e9 DP, Bordeleau F (2022) The extracellular matrix stiffening: a trigger of prostate cancer progression and castration resistance? Cancers 14:2887","journal-title":"Cancers"},{"key":"12323_CR31","doi-asserted-by":"publisher","first-page":"672","DOI":"10.1109\/58.393110","volume":"42","author":"T Loupas","year":"1995","unstructured":"Loupas T, Powers J, Gill RW (1995) An axial velocity estimator for ultrasound blood flow imaging, based on a full evaluation of the Doppler equation by means of a two-dimensional autocorrelation approach. IEEE Trans Ultrason Ferroelectr Freq Control 42:672\u2013688","journal-title":"IEEE Trans Ultrason Ferroelectr Freq Control"},{"key":"12323_CR32","doi-asserted-by":"publisher","first-page":"1343","DOI":"10.1016\/j.ultrasmedbio.2013.12.026","volume":"40","author":"P Song","year":"2014","unstructured":"Song P, Manduca A, Zhao H et al (2014) Fast shear compounding using robust 2-D shear wave speed calculation and multi-directional filtering. Ultrasound Med Biol 40:1343\u20131355","journal-title":"Ultrasound Med Biol"},{"key":"12323_CR33","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1038\/s41391-024-00852-w","volume":"27","author":"FB Denijs","year":"2024","unstructured":"Denijs FB, Van Harten MJ, Meenderink JJL et al (2024) Risk calculators for the detection of prostate cancer: a systematic review. Prostate Cancer Prostatic Dis 27:544\u2013557. https:\/\/doi.org\/10.1038\/s41391-024-00852-w","journal-title":"Prostate Cancer Prostatic Dis"},{"key":"12323_CR34","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1038\/s41391-017-0024-7","volume":"21","author":"T Nordstr\u00f6m","year":"2018","unstructured":"Nordstr\u00f6m T, Akre O, Aly M et al (2018) Prostate-specific antigen (PSA) density in the diagnostic algorithm of prostate cancer. Prostate Cancer Prostatic Dis 21:57\u201363. https:\/\/doi.org\/10.1038\/s41391-017-0024-7","journal-title":"Prostate Cancer Prostatic Dis"},{"key":"12323_CR35","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.euros.2025.03.005","volume":"75","author":"DL van den Kroonenberg","year":"2025","unstructured":"van den Kroonenberg DL, Delberghe FT, Jager A et al (2025) Development and validation of an algorithm for segmentation of the prostate and its zones from three-dimensional transrectal multiparametric ultrasound images. Eur Urol Open Sci 75:48\u201354. https:\/\/doi.org\/10.1016\/j.euros.2025.03.005","journal-title":"Eur Urol Open Sci"},{"key":"12323_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102155","volume":"73","author":"A Saha","year":"2021","unstructured":"Saha A, Hosseinzadeh M, Huisman H (2021) End-to-end prostate cancer detection in bpMRI via 3D CNNs: Effects of attention mechanisms, clinical priori and decoupled false positive reduction. Med Image Anal 73: 102155. https:\/\/doi.org\/10.1016\/j.media.2021.102155","journal-title":"Med Image Anal"},{"key":"12323_CR37","doi-asserted-by":"publisher","first-page":"897","DOI":"10.1097\/00000478-198812000-00001","volume":"12","author":"JE McNeal","year":"1988","unstructured":"McNeal JE, Redwine EA, Freiha FS, Stamey TA (1988) Zonal distribution of prostatic adenocarcinoma: correlation with histologic pattern and direction of spread. Am J Surg Pathol 12:897","journal-title":"Am J Surg Pathol"},{"key":"12323_CR38","doi-asserted-by":"crossref","unstructured":"Pepe MS (2004) The statistical evaluation of medical tests for classification and prediction. Oxford University Press, Oxford","DOI":"10.1093\/oso\/9780198509844.001.0001"},{"key":"12323_CR39","doi-asserted-by":"publisher","first-page":"2368","DOI":"10.1118\/1.4918318","volume":"42","author":"JT Kwak","year":"2015","unstructured":"Kwak JT, Xu S, Wood BJ et al (2015) Automated prostate cancer detection using T2-weighted and high-b-value diffusion-weighted magnetic resonance imaging. Med Phys 42:2368\u20132378. https:\/\/doi.org\/10.1118\/1.4918318","journal-title":"Med Phys"},{"key":"12323_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2022.110494","volume":"156","author":"R Alfano","year":"2022","unstructured":"Alfano R, Bauman GS, Gomez JA et al (2022) Prostate cancer classification using radiomics and machine learning on mp-MRI validated using co-registered histology. Eur J Radiol 156:110494. https:\/\/doi.org\/10.1016\/j.ejrad.2022.110494","journal-title":"Eur J Radiol"},{"key":"12323_CR41","doi-asserted-by":"publisher","first-page":"e599","DOI":"10.1016\/j.clon.2021.07.016","volume":"33","author":"MA Haider","year":"2021","unstructured":"Haider MA, Brown J, Yao X et al (2021) Multiparametric magnetic resonance imaging in the diagnosis of clinically significant prostate cancer: an updated systematic review. Clin Oncol 33:e599\u2013e612. https:\/\/doi.org\/10.1016\/j.clon.2021.07.016","journal-title":"Clin Oncol"},{"key":"12323_CR42","doi-asserted-by":"publisher","unstructured":"Gayet MCW, Aa AAMAVD, Beerlage HP et al (2020) Cancer detection rates of systematic and targeted prostate biopsies after biparametric MRI. Prostate Cancer 2020. https:\/\/doi.org\/10.1155\/2020\/4626781","DOI":"10.1155\/2020\/4626781"},{"key":"12323_CR43","doi-asserted-by":"publisher","first-page":"e0118432","DOI":"10.1371\/journal.pone.0118432","volume":"10","author":"T Saito","year":"2015","unstructured":"Saito T, Rehmsmeier M (2015) The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS One 10:e0118432. https:\/\/doi.org\/10.1371\/journal.pone.0118432","journal-title":"PLoS One"},{"key":"12323_CR44","doi-asserted-by":"publisher","first-page":"806","DOI":"10.1007\/s00330-019-06436-w","volume":"30","author":"RR Wildeboer","year":"2020","unstructured":"Wildeboer RR, Mannaerts CK, van Sloun RJG et al (2020) Automated multiparametric localization of prostate cancer based on B-mode, shear-wave elastography, and contrast-enhanced ultrasound radiomics. Eur Radiol 30:806\u2013815. https:\/\/doi.org\/10.1007\/s00330-019-06436-w","journal-title":"Eur Radiol"},{"key":"12323_CR45","doi-asserted-by":"publisher","first-page":"1194","DOI":"10.1016\/j.ultrasmedbio.2024.04.007","volume":"50","author":"P Chen","year":"2024","unstructured":"Chen P, Turco S, Wang Y et al (2024) Can 3D multiparametric ultrasound imaging predict prostate biopsy outcome? Ultrasound Med Biol 50:1194\u20131202. https:\/\/doi.org\/10.1016\/j.ultrasmedbio.2024.04.007","journal-title":"Ultrasound Med Biol"},{"key":"12323_CR46","doi-asserted-by":"publisher","first-page":"2713","DOI":"10.1016\/j.ultrasmedbio.2019.05.017","volume":"45","author":"RR Wildeboer","year":"2019","unstructured":"Wildeboer RR, van Sloun RJG, Huang P et al (2019) 3-D Multi-parametric contrast-enhanced ultrasound for the prediction of prostate cancer. Ultrasound Med Biol 45:2713\u20132724. https:\/\/doi.org\/10.1016\/j.ultrasmedbio.2019.05.017","journal-title":"Ultrasound Med Biol"},{"key":"12323_CR47","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1016\/j.ultrasmedbio.2017.12.005","volume":"44","author":"SG Schalk","year":"2018","unstructured":"Schalk SG, Huang J, Li J et al (2018) 3-D quantitative dynamic contrast ultrasound for prostate cancer localization. Ultrasound Med Biol 44:807\u2013814. https:\/\/doi.org\/10.1016\/j.ultrasmedbio.2017.12.005","journal-title":"Ultrasound Med Biol"},{"key":"12323_CR48","doi-asserted-by":"publisher","unstructured":"Litjens G, Debats O, Barentsz J, Karssemeijer N, Huisman H (2017) SPIE-AAPM PROSTATEx Challenge Data (Version 2) [dataset]. The Cancer Imaging Archive. https:\/\/doi.org\/10.7937\/K9TCIA.2017.MURS5CL","DOI":"10.7937\/K9TCIA.2017.MURS5CL"},{"key":"12323_CR49","doi-asserted-by":"publisher","first-page":"806","DOI":"10.2214\/AJR.17.18056","volume":"209","author":"S Woo","year":"2017","unstructured":"Woo S, Suh CH, Kim SY et al (2017) Shear-wave elastography for detection of prostate cancer: a systematic review and diagnostic meta-analysis. Am J Roentgenol 209:806\u2013814. https:\/\/doi.org\/10.2214\/AJR.17.18056","journal-title":"Am J Roentgenol"},{"key":"12323_CR50","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-017-02187-0","volume":"7","author":"L Sang","year":"2017","unstructured":"Sang L, Wang XMX-M, Xu DYD-Y, Cai YFY-F (2017) Accuracy of shear wave elastography for the diagnosis of prostate cancer: a meta-analysis. Sci Rep 7:1949. https:\/\/doi.org\/10.1038\/s41598-017-02187-0","journal-title":"Sci Rep"},{"key":"12323_CR51","doi-asserted-by":"publisher","first-page":"2089","DOI":"10.1016\/j.ultrasmedbio.2025.07.025","volume":"51","author":"X Li","year":"2025","unstructured":"Li X, Delberghe F, Turco S et al (2025) Three-dimensional shear-wave viscoelastographic estimation by system identification for prostate cancer localization. Ultrasound Med Biol 51:2089\u20132098. https:\/\/doi.org\/10.1016\/j.ultrasmedbio.2025.07.025","journal-title":"Ultrasound Med Biol"},{"key":"12323_CR52","doi-asserted-by":"publisher","unstructured":"van den Kroonenberg DL, Delberghe F, Jager A, et al (2025) Evaluation of an artificial intelligence model based on multiparametric transrectal ultrasound for localizing clinically significant prostate cancer by simulation of targeted biopsies. Eur Radiol. https:\/\/doi.org\/10.1007\/s00330-025-12114-x","DOI":"10.1007\/s00330-025-12114-x"},{"key":"12323_CR53","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.euros.2024.06.012","volume":"66","author":"DL van den Kroonenberg","year":"2024","unstructured":"van den Kroonenberg DL, Jager A, Garrido-Utrilla A et al (2024) Clinical validation of multiparametric ultrasound for detecting clinically significant prostate cancer using computer-aided diagnosis: a direct comparison with the magnetic resonance imaging pathway. Eur Urol Open Sci 66:60\u201366. https:\/\/doi.org\/10.1016\/j.euros.2024.06.012","journal-title":"Eur Urol Open Sci"},{"key":"12323_CR54","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1097\/RLI.0000000000000543","volume":"54","author":"P Kapetas","year":"2019","unstructured":"Kapetas P, Clauser P, Woitek R et al (2019) Quantitative multiparametric breast ultrasound: application of contrast-enhanced ultrasound and elastography leads to an improved differentiation of benign and malignant lesions. Invest Radiol 54:257\u2013264. https:\/\/doi.org\/10.1097\/RLI.0000000000000543","journal-title":"Invest Radiol"},{"key":"12323_CR55","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1136\/postgradmedj-2018-136111","volume":"95","author":"I Grgurevic","year":"2019","unstructured":"Grgurevic I, Tjesic Drinkovic I, Pinzani M (2019) Multiparametric ultrasound in liver diseases: an overview for the practising clinician. Postgrad Med J 95:425\u2013432. https:\/\/doi.org\/10.1136\/postgradmedj-2018-136111","journal-title":"Postgrad Med J"},{"key":"12323_CR56","doi-asserted-by":"publisher","first-page":"4745","DOI":"10.3390\/cancers14194745","volume":"14","author":"M Brandenstein","year":"2022","unstructured":"Brandenstein M, Wiesinger I, K\u00fcnzel J et al (2022) Multiparametric sonographic imaging of thyroid lesions: chances of B-mode, elastography and CEUS in relation to preoperative histopathology. Cancers (Basel) 14:4745. https:\/\/doi.org\/10.3390\/cancers14194745","journal-title":"Cancers (Basel)"}],"container-title":["European Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-026-12323-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00330-026-12323-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-026-12323-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T12:26:02Z","timestamp":1779798362000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00330-026-12323-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,30]]},"references-count":56,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["12323"],"URL":"https:\/\/doi.org\/10.1007\/s00330-026-12323-y","relation":{},"ISSN":["1432-1084"],"issn-type":[{"value":"1432-1084","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,30]]},"assertion":[{"value":"2 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 December 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 December 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 January 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The scientific guarantor of this publication is Massimo Mischi.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Guarantor"}},{"value":"The authors of this manuscript declare relationships with the following companies: Angiogenesis Analytics, JADS Venture Campus, Sint Janssingel 88 5211DA, \u2018s-Hertogenbosch, The Netherlands.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"No complex statistical methods were necessary for this paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was obtained from all subjects (patients) in this study.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Institutional Review Board approval was obtained.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Some study subjects or cohorts have been previously reported in \"Development and Validation of an Algorithm for Segmentation of the Prostate and Its Zones from Three-Dimensional Transrectal Multiparametric Ultrasound Images\";\n                      \n                      .","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}