{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T16:24:45Z","timestamp":1743092685840,"version":"3.40.3"},"publisher-location":"Cham","reference-count":11,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031376481"},{"type":"electronic","value":"9783031376498"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T00:00:00Z","timestamp":1690243200000},"content-version":"vor","delay-in-days":205,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In recent years multiple deep-learning solutions have emerged that aim to assist radiologists in prostate cancer (PCa) diagnosis. Most of the studies however do not compare the diagnostic accuracy of the developed models to that of radiology specialists but simply report the model performance on the reference datasets. This makes it hard to infer the potential benefits and applicability of proposed methods in diagnostic workflows. In this paper, we investigate the effects of using pre-trained models in the differentiation of clinically significant PCa (csPCa) on mpMRI and report the results of conducted multi-reader multi-case pilot study involving human experts. The study aims to compare the performance of deep learning models with six radiologists varying in diagnostic experience. A subset of the ProstateX Challenge dataset counting 32 prostate lesions was used to evaluate the diagnostic accuracy of models and human raters using ROC analysis. Deep neural networks were found to achieve comparable performance to experienced readers in the diagnosis of csPCa. Results confirm the potential of deep neural networks in enhancing the cognitive abilities of radiologists in PCa assessment.<\/jats:p>","DOI":"10.1007\/978-3-031-37649-8_9","type":"book-chapter","created":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T04:02:08Z","timestamp":1690257728000},"page":"85-92","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Performance of\u00a0Deep CNN and\u00a0Radiologists in\u00a0Prostate Cancer Classification: A Comparative Pilot Study"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1752-2393","authenticated-orcid":false,"given":"Piotr","family":"Sobecki","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0753-7241","authenticated-orcid":false,"given":"Rafa\u0142","family":"J\u00f3\u017awiak","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7244-5637","authenticated-orcid":false,"given":"Ihor","family":"Mykhalevych","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,25]]},"reference":[{"issue":"4","key":"9_CR1","doi-asserted-by":"publisher","first-page":"044501","DOI":"10.1117\/1.JMI.5.4.044501","volume":"5","author":"SG Armato","year":"2018","unstructured":"Armato, S.G., et al.: PROSTATEx challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images. J. Med. Imaging 5(4), 044501 (2018)","journal-title":"J. Med. Imaging"},{"issue":"5","key":"9_CR2","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1016\/j.annonc.2020.02.009","volume":"31","author":"G Carioli","year":"2020","unstructured":"Carioli, G., et al.: European cancer mortality predictions for the year 2020 with a focus on prostate cancer. Ann. Oncol. 31(5), 650\u2013658 (2020)","journal-title":"Ann. Oncol."},{"key":"9_CR3","doi-asserted-by":"publisher","unstructured":"Litjens, G., Debats, O., Barentsz, J., Karssemeijer, N., Huisman, H.: SPIE-AAPM PROSTATEx challenge data (2017). https:\/\/doi.org\/10.7937\/K9TCIA.2017.MURS5CL, https:\/\/wiki.cancerimagingarchive.net\/x\/iIFpAQ","DOI":"10.7937\/K9TCIA.2017.MURS5CL"},{"issue":"27","key":"9_CR4","doi-asserted-by":"publisher","first-page":"4316","DOI":"10.1002\/sim.7433","volume":"36","author":"Q Liu","year":"2017","unstructured":"Liu, Q., Shepherd, B.E., Li, C., Harrell, F.E., Jr.: Modeling continuous response variables using ordinal regression. Stat. Med. 36(27), 4316\u20134335 (2017)","journal-title":"Stat. Med."},{"key":"9_CR5","first-page":"213","volume":"183","author":"JG MacKinnon","year":"2009","unstructured":"MacKinnon, J.G.: Bootstrap hypothesis testing. Handb. Comput. Econometrics 183, 213 (2009)","journal-title":"Handb. Comput. Econometrics"},{"key":"9_CR6","doi-asserted-by":"crossref","unstructured":"Mottet, N., et al.: EAU-EANM-ESTRO-ESUR-SIOG guidelines on prostate cancer-2020 update. part 1: screening, diagnosis, and local treatment with curative intent. Eur. Urol. 79(2), 243\u2013262 (2021)","DOI":"10.1016\/j.eururo.2020.09.042"},{"key":"9_CR7","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.11006","volume":"9","author":"P Sobecki","year":"2021","unstructured":"Sobecki, P., J\u00f3\u017awiak, R., Sklinda, K., Przelaskowski, A.: Effect of domain knowledge encoding in CNN model architecture-a prostate cancer study using mpMRI images. PeerJ 9, e11006 (2021)","journal-title":"PeerJ"},{"key":"9_CR8","doi-asserted-by":"crossref","unstructured":"Turkbey, B., et al.: Prostate imaging reporting and data system version 2.1: 2019 update of prostate imaging reporting and data system version 2. Eur. Urol. 76(3), 340\u2013351 (2019)","DOI":"10.1016\/j.eururo.2019.02.033"},{"issue":"6","key":"9_CR9","doi-asserted-by":"publisher","first-page":"959","DOI":"10.3390\/diagnostics11060959","volume":"11","author":"JJ Twilt","year":"2021","unstructured":"Twilt, J.J., van Leeuwen, K.G., Huisman, H.J., F\u00fctterer, J.J., de Rooij, M.: Artificial intelligence based algorithms for prostate cancer classification and detection on magnetic resonance imaging: a narrative review. Diagnostics 11(6), 959 (2021)","journal-title":"Diagnostics"},{"issue":"1","key":"9_CR10","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1148\/radiol.2020190646","volume":"296","author":"AC Westphalen","year":"2020","unstructured":"Westphalen, A.C., et al.: 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(1), 76 (2020)","journal-title":"Radiology"},{"issue":"3","key":"9_CR11","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1007\/s00345-019-02662-5","volume":"38","author":"L Witherspoon","year":"2020","unstructured":"Witherspoon, L., Breau, R.H., Lavall\u00e9e, L.T.: Evidence-based approach to active surveillance of prostate cancer. World J. Urol. 38(3), 555\u2013562 (2020)","journal-title":"World J. Urol."}],"container-title":["Lecture Notes in Networks and Systems","Digital Interaction and Machine Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-37649-8_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T04:10:43Z","timestamp":1690258243000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-37649-8_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031376481","9783031376498"],"references-count":11,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-37649-8_9","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"25 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIDI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Machine Intelligence and Digital Interaction Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"midi12022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/midi2022.opi.org.pl\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}