{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T15:52:04Z","timestamp":1786981924918,"version":"build-2736575974"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,11,25]],"date-time":"2021-11-25T00:00:00Z","timestamp":1637798400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,11,25]],"date-time":"2021-11-25T00:00:00Z","timestamp":1637798400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Radiol med"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s11547-021-01431-y","type":"journal-article","created":{"date-parts":[[2021,11,25]],"date-time":"2021-11-25T06:07:10Z","timestamp":1637820430000},"page":"83-89","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Automatic PI-RADS assignment by means of formal methods"],"prefix":"10.1007","volume":"127","author":[{"given":"Luca","family":"Brunese","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria Chiara","family":"Brunese","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mattia","family":"Carbone","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vincenzo","family":"Ciccone","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9425-1657","authenticated-orcid":false,"given":"Francesco","family":"Mercaldo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonella","family":"Santone","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,11,25]]},"reference":[{"issue":"3","key":"1431_CR1","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1055\/s-0032-1330270","volume":"185","author":"R Matthias","year":"2013","unstructured":"Matthias R, Blondin D, Schlemmer HP, Franiel T (2013) Pi-rads classification: structured reporting for mri of the prostate. Rofo 185(3):253\u2013261","journal-title":"Rofo"},{"issue":"1","key":"1431_CR2","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.eururo.2015.08.038","volume":"69","author":"JO Barentsz","year":"2016","unstructured":"Barentsz JO, Weinreb JC, Verma S, Thoeny HC, Tempany CM, Shtern F, Padhani AR, Margolis D, Macura KJ, Haider MA et al (2016) Synopsis of the pi-rads v2 guidelines for multiparametric prostate magnetic resonance imaging and recommendations for use. Eur Urol 69(1):41","journal-title":"Eur Urol"},{"issue":"2","key":"1431_CR3","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/s10334-016-0591-y","volume":"30","author":"R Fusco","year":"2017","unstructured":"Fusco R, Sansone M, Petrillo A (2017) A comparison of fitting algorithms for diffusion-weighted mri data analysis using an intravoxel incoherent motion model. Magn Reson Mater Phys, Biol Med 30(2):113\u2013120","journal-title":"Magn Reson Mater Phys, Biol Med"},{"issue":"6","key":"1431_CR4","doi-asserted-by":"publisher","first-page":"1112","DOI":"10.1016\/j.eururo.2014.10.033","volume":"67","author":"HJ Hamoen Esther","year":"2015","unstructured":"Hamoen Esther HJ, de\u00a0Rooij M, Witjes JA, Barentsz JO, Rovers MM (2015) Use of the prostate imaging reporting and data system (pi-rads) for prostate cancer detection with multiparametric magnetic resonance imaging: a diagnostic meta-analysis. Eur Urol 67(6):1112\u20131121","journal-title":"Eur Urol"},{"issue":"11","key":"1431_CR5","doi-asserted-by":"publisher","first-page":"1165","DOI":"10.1016\/j.crad.2015.06.093","volume":"70","author":"B Tristan","year":"2015","unstructured":"Tristan B, Baris T, Choyke Peter L (2015) Pi-rads version 2: what you need to know. Clin Radiol 70(11):1165\u20131176","journal-title":"Clin Radiol"},{"issue":"1","key":"1431_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13027-016-0113-6","volume":"12","author":"F Roberta","year":"2017","unstructured":"Roberta F, Mario S, Vincenza G, Venanzio SS, Antonella P (2017) A systematic review on multiparametric mr imaging in prostate cancer detection. Infect Agents Cancer 12(1):1\u201314","journal-title":"Infect Agents Cancer"},{"issue":"3","key":"1431_CR7","doi-asserted-by":"publisher","first-page":"035025","DOI":"10.1088\/2057-1976\/ab12bb","volume":"5","author":"M Sansone","year":"2019","unstructured":"Sansone M, Fusco R, Petrillo A (2019) D-optimal design of b-values for precise intra-voxel incoherent motion imaging. Biomed Phys Eng Express 5(3):035025","journal-title":"Biomed Phys Eng Express"},{"issue":"10","key":"1431_CR8","doi-asserted-by":"publisher","first-page":"2421","DOI":"10.3390\/cancers13102421","volume":"13","author":"F Roberta","year":"2021","unstructured":"Roberta F, Vincenza G, Mauro MR, Paolo V, Pasquale DRA, Claudio S, Maurizio DB, Antonella P, Mario S (2021) Blood oxygenation level dependent magnetic resonance imaging (mri), dynamic contrast enhanced mri and diffusion weighted mri for benign and malignant breast cancer discrimination: A preliminary experience. Cancers 13(10):2421","journal-title":"Cancers"},{"issue":"1","key":"1431_CR9","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1148\/radiol.16151133","volume":"280","author":"PS Yoon","year":"2016","unstructured":"Yoon PS, Chul JD, Taik OY, Hoon CN, Deuk CY, Ho RK, Joon HS, Kyunghwa H (2016) Prostate cancer: Pi-rads version 2 helps preoperatively predict clinically significant cancers. Radiology 280(1):108\u2013116","journal-title":"Radiology"},{"issue":"1","key":"1431_CR10","doi-asserted-by":"publisher","first-page":"70","DOI":"10.21037\/tau.2017.12.31","volume":"7","author":"G Schoots Ivo","year":"2018","unstructured":"Schoots Ivo G (2018) Mri in early prostate cancer detection: how to manage indeterminate or equivocal pi-rads 3 lesions? Transl Androl Urol 7(1):70","journal-title":"Transl Androl Urol"},{"key":"1431_CR11","doi-asserted-by":"crossref","unstructured":"Brunese L, Mercaldo F, Reginelli A, Santone A (2019) Neural networks for lung cancer detection through radiomic features. In: 2019 international joint conference on neural networks (IJCNN), pp 1\u201310. IEEE","DOI":"10.1109\/IJCNN.2019.8852169"},{"issue":"6","key":"1431_CR12","doi-asserted-by":"publisher","first-page":"1077","DOI":"10.1007\/s00259-010-1399-z","volume":"37","author":"H Till-Alexander","year":"2010","unstructured":"Till-Alexander H, Sherko K, Angela K, Hamami Monia E, Steffen H, Anton Q, Andreas B, Michael F, Thomas L, Gerald A et al (2010) Diagnostic value of diffusion-weighted magnetic resonance imaging (dwi) compared to fdg pet\/ct for whole-body breast cancer staging. Eur J Nucl Med Mol Imag 37(6):1077\u20131086","journal-title":"Eur J Nucl Med Mol Imag"},{"key":"1431_CR13","doi-asserted-by":"crossref","unstructured":"Iima M, Partridge SC, Le\u00a0Bihan D (2020) Six dwi questions you always wanted to know but were afraid to ask: clinical relevance for breast diffusion mri. European Radiology, Springer, pp 1\u201310","DOI":"10.1007\/s00330-019-06648-0"},{"issue":"9","key":"1431_CR14","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1016\/j.mri.2012.06.010","volume":"30","author":"G Kumar Virendra","year":"2012","unstructured":"Kumar Virendra G, Yuhua BS, Anders B, Eschrich Steven A, Schabath Matthew B, Kenneth F, Aerts Hugo JWL, Andre D, David F et al (2012) Radiomics: the process and the challenges. Magn Reson Imag 30(9):1234\u20131248","journal-title":"Magn Reson Imag"},{"key":"1431_CR15","doi-asserted-by":"crossref","unstructured":"Stirling C (1989) An introduction to modal and temporal logics for CCS. UK\/Japan Workshop on Concurrency, Springer, pp 1\u201320","DOI":"10.1007\/3-540-53932-8_41"},{"key":"1431_CR16","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.mri.2019.08.030","volume":"66","author":"L Brunese","year":"2020","unstructured":"Brunese L, Mercaldo F, Reginelli A, Santone A (2020) Formal methods for prostate cancer gleason score and treatment prediction using radiomic biomarkers. Magn Reson Imaging 66:165\u2013175","journal-title":"Magn Reson Imaging"},{"key":"1431_CR17","doi-asserted-by":"publisher","first-page":"186236","DOI":"10.1109\/ACCESS.2019.2961754","volume":"7","author":"L Brunese","year":"2019","unstructured":"Brunese L, Mercaldo F, Reginelli A, Santone A (2019) Prostate gleason score detection and cancer treatment through real-time formal verification. IEEE Access 7:186236\u2013186246","journal-title":"IEEE Access"},{"key":"1431_CR18","unstructured":"Milner R (1989) Communication and concurrency. PHI Series in computer science. Prentice Hall"},{"key":"1431_CR19","unstructured":"Cleaveland R, Li T, Sims S (2000) The concurrency workbench of the new century, version 1.2-user\u2019s manual"},{"key":"1431_CR20","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.neucom.2019.07.006","volume":"365","author":"R Leonardo","year":"2019","unstructured":"Leonardo R, Changhee H, Yudai N, Jin Z, Ryuichiro H, Carmelo M, Andrea T, Nobile Marco S, Claudio F, Daniela B et al (2019) Use-net: incorporating squeeze-and-excitation blocks into u-net for prostate zonal segmentation of multi-institutional mri datasets. Neurocomputing 365:31\u201343","journal-title":"Neurocomputing"},{"issue":"4","key":"1431_CR21","volume":"4","author":"C Tyler","year":"2017","unstructured":"Tyler C, Junjie Z, Sameer B, Alexander W, Haider MA, Khalvati F (2017) Fully automated segmentation of prostate whole gland and transition zone in diffusion-weighted mri using convolutional neural networks. J Med Imag 4(4):041307","journal-title":"J Med Imag"},{"issue":"1","key":"1431_CR22","doi-asserted-by":"publisher","first-page":"338","DOI":"10.3390\/app10010338","volume":"10","author":"P Lapa","year":"2020","unstructured":"Lapa P, Castelli M, Gon\u00e7alves I, Sala E, Rundo L (2020) A hybrid end-to-end approach integrating conditional random fields into cnns for prostate cancer detection on mri. Appl Sci 10(1):338","journal-title":"Appl Sci"},{"issue":"5","key":"1431_CR23","doi-asserted-by":"publisher","first-page":"1127","DOI":"10.1109\/TMI.2017.2789181","volume":"37","author":"Z Wang","year":"2018","unstructured":"Wang Z, Liu C, Cheng D, Wang L, Yang X, Cheng K-T (2018) Automated detection of clinically significant prostate cancer in mp-mri images based on an end-to-end deep neural network. IEEE Trans Med Imag 37(5):1127\u20131139","journal-title":"IEEE Trans Med Imag"},{"issue":"3","key":"1431_CR24","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1148\/radiol.2019190938","volume":"293","author":"S Patrick","year":"2019","unstructured":"Patrick S, Simon K, Philipp RJ, Manuel W, Philipp K, Sebastian B, Anselm KT, Albrecht S, Markus H, Heinz-Peter S et al (2019) Classification of cancer at prostate mri: deep learning versus clinical pi-rads assessment. Radiology 293(3):607\u2013617","journal-title":"Radiology"},{"issue":"10","key":"1431_CR25","doi-asserted-by":"publisher","first-page":"4082","DOI":"10.1007\/s00330-017-4800-5","volume":"27","author":"J Wang","year":"2017","unstructured":"Wang J, Chen-Jiang W, Bao M-L, Zhang J, Wang X-N, Zhang Y-D (2017) Machine learning-based analysis of mr radiomics can help to improve the diagnostic performance of pi-rads v2 in clinically relevant prostate cancer. Eur Radiol 27(10):4082\u20134090","journal-title":"Eur Radiol"},{"issue":"1","key":"1431_CR26","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1148\/radiol.2018173064","volume":"289","author":"B David","year":"2018","unstructured":"David B, Simon K, Manuel W, Patrick S, Philipp RJ, Michael G, Philipp K, Kaneschka Y, Bertram H, Nils G et al (2018) Radiomic machine learning for characterization of prostate lesions with mri: comparison to adc values. Radiology 289(1):128\u2013137","journal-title":"Radiology"},{"issue":"3","key":"1431_CR27","doi-asserted-by":"publisher","first-page":"875","DOI":"10.1002\/jmri.26243","volume":"49","author":"T Chen","year":"2019","unstructured":"Chen T, Li M, Gu Y, Zhang Y, Yang S, Wei C, Wu J, Li X, Zhao W, Shen J (2019) Prostate cancer differentiation and aggressiveness: assessment with a radiomic-based model vs. pi-rads v2. J Magn Reson Imag 49(3):875\u2013884","journal-title":"J Magn Reson Imag"}],"container-title":["La radiologia medica"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11547-021-01431-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11547-021-01431-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11547-021-01431-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T10:40:27Z","timestamp":1643280027000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11547-021-01431-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,25]]},"references-count":27,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["1431"],"URL":"https:\/\/doi.org\/10.1007\/s11547-021-01431-y","relation":{},"ISSN":["0033-8362","1826-6983"],"issn-type":[{"value":"0033-8362","type":"print"},{"value":"1826-6983","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,25]]},"assertion":[{"value":"28 July 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 November 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there is no conflict of interest. This research involves human participants. The informed consent was obtained from all participants to the study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}