{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T08:00:33Z","timestamp":1726041633566},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030299293"},{"type":"electronic","value":"9783030299309"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-29930-9_7","type":"book-chapter","created":{"date-parts":[[2019,8,22]],"date-time":"2019-08-22T15:53:35Z","timestamp":1566489215000},"page":"65-75","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["May Radiomic Data Predict Prostate Cancer Aggressiveness?"],"prefix":"10.1007","author":[{"given":"Danila","family":"Germanese","sequence":"first","affiliation":[]},{"given":"Sara","family":"Colantonio","sequence":"additional","affiliation":[]},{"given":"Claudia","family":"Caudai","sequence":"additional","affiliation":[]},{"given":"Maria Antonietta","family":"Pascali","sequence":"additional","affiliation":[]},{"given":"Andrea","family":"Barucci","sequence":"additional","affiliation":[]},{"given":"Nicola","family":"Zoppetti","sequence":"additional","affiliation":[]},{"given":"Simone","family":"Agostini","sequence":"additional","affiliation":[]},{"given":"Elena","family":"Bertelli","sequence":"additional","affiliation":[]},{"given":"Laura","family":"Mercatelli","sequence":"additional","affiliation":[]},{"given":"Vittorio","family":"Miele","sequence":"additional","affiliation":[]},{"given":"Roberto","family":"Carpi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,8,23]]},"reference":[{"key":"7_CR1","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","volume":"14","author":"P Lambin","year":"2017","unstructured":"Lambin, P., et al.: Radiomics: the bridge between medical imaging and personalized medicine. Nat. Rev. Clin. Oncol. 14, 749 (2017). \n                    https:\/\/doi.org\/10.1038\/nrclinonc.2017.141","journal-title":"Nat. Rev. Clin. Oncol."},{"issue":"2","key":"7_CR2","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1148\/radiol.2015151169","volume":"278","author":"RJ Gillies","year":"2016","unstructured":"Gillies, R.J., Kinahan, P.E., Hricak, H.: Radiomics: images are more than pictures, they are data. Radiology 278(2), 563\u2013577 (2016). \n                    https:\/\/doi.org\/10.1148\/radiol.2015151169","journal-title":"Radiology"},{"key":"7_CR3","doi-asserted-by":"publisher","first-page":"1544","DOI":"10.1080\/0284186X.2017.1351624","volume":"56","author":"RTHM Larue","year":"2017","unstructured":"Larue, R.T.H.M., et al.: Influence of gray level discretization on radiomic feature stability for different CT scanners, tube currents and slice thiknesses: a comprehensive phantom study. Acta Oncol. 56, 1544\u20131553 (2017)","journal-title":"Acta Oncol."},{"key":"7_CR4","doi-asserted-by":"publisher","unstructured":"Barucci, A., et al.: Exposing cancer\u2019s complexity using radiomics in clinical imaging. An investigation on the role of histogram analysis as imaging biomarker to unravel intra-tumour heterogeneity. In: 2018 IEEE Workshop on Complexity in Engineering (COMPENG), pp. 1\u20135 (2018). \n                    https:\/\/doi.org\/10.1109\/CompEng.2018.8536244","DOI":"10.1109\/CompEng.2018.8536244"},{"issue":"4","key":"7_CR5","doi-asserted-by":"publisher","first-page":"432","DOI":"10.21037\/tcr.2016.06.20","volume":"5","author":"R Stoyanova","year":"2016","unstructured":"Stoyanova, R., et al.: Prostate cancer radiomics and the promise of radiogenomics. Transl. Cancer Res. 5(4), 432\u2013447 (2016). \n                    https:\/\/doi.org\/10.21037\/tcr.2016.06.20","journal-title":"Transl. Cancer Res."},{"key":"7_CR6","doi-asserted-by":"publisher","first-page":"4006","DOI":"10.1038\/ncomms5006","volume":"5","author":"HJ Aerts","year":"2014","unstructured":"Aerts, H.J., Velazquez, E.R., Leijenaar, R.T., Parmar, C., Grossmann, P., Carvalho, S., et al.: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5, 4006 (2014). \n                    https:\/\/doi.org\/10.1038\/ncomms5006","journal-title":"Nat. Commun."},{"key":"7_CR7","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.ejmp.2017.05.071","volume":"38","author":"M Avanzo","year":"2017","unstructured":"Avanzo, M., Stancanello, J., El Naga, I.: Beyond imaging: the promise of radiomics. Physica Med. 38, 122\u2013139 (2017). \n                    https:\/\/doi.org\/10.1016\/j.ejmp.2017.05.071","journal-title":"Physica Med."},{"key":"7_CR8","doi-asserted-by":"publisher","first-page":"394","DOI":"10.3322\/caac.21492","volume":"68","author":"F Bray","year":"2018","unstructured":"Bray, F., et al.: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68, 394\u2013424 (2018)","journal-title":"CA Cancer J. Clin."},{"key":"7_CR9","doi-asserted-by":"publisher","first-page":"1636","DOI":"10.1111\/j.1464-410X.2011.10633.x","volume":"109","author":"HU Ahmed","year":"2012","unstructured":"Ahmed, H.U., et al.: Transatlantic consensus group on active surveillance and focal therapy for prostate cancer. BJU Int. 109, 1636\u20131647 (2012)","journal-title":"BJU Int."},{"key":"7_CR10","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1002\/1097-0215(20001220)90:6<326::AID-IJC3>3.0.CO;2-J","volume":"90","author":"CR King","year":"2000","unstructured":"King, C.R., Long, J.P.: Prostate biopsy grading errors: a sampling problem? Int. J. Cancer 90, 326\u2013330 (2000)","journal-title":"Int. J. Cancer"},{"key":"7_CR11","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1016\/j.eururo.2012.01.050","volume":"61","author":"JI Epstein","year":"2012","unstructured":"Epstein, J.I., Feng, Z., Trock, B.J., Pierorazio, P.M.: Upgrading and downgrading of prostate cancer from biopsy to radical prostatectomy: incidence and predictive factors using the modified Gleason grading system and factoring in tertiary grades. Eur. Urol. 61, 1019\u20131024 (2012)","journal-title":"Eur. Urol."},{"key":"7_CR12","doi-asserted-by":"publisher","first-page":"1964","DOI":"10.1016\/j.juro.2008.07.051","volume":"180","author":"RK Berglung","year":"2008","unstructured":"Berglung, R.K., et al.: Pathological upgrading and up staging with immediate repeat biopsy in patients elegible for active surveillance. J. Urol. 180, 1964\u20131967 (2008)","journal-title":"J. Urol."},{"key":"7_CR13","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1148\/radiol.13121454","volume":"267","author":"Y Peng","year":"2013","unstructured":"Peng, Y., et al.: Quantitative analysis of multiparametric prostate MR images: differentiation between prostate cancer and normal tissue and correlation with Gleason score-a computer-aided diagnosis development study. Radiology 267, 787\u2013796 (2013)","journal-title":"Radiology"},{"key":"7_CR14","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1002\/nbm.1777","volume":"25","author":"P Tiwari","year":"2012","unstructured":"Tiwari, P., Viswanath, S., Kurhanewicz, J., Sridhar, A., Madabhushi, A.: Multimodal wavelet embedding representation for data combination (MaWERiC): integrating magnetic resonance imaging and spectroscopy for prostate cancer detection. NMR Biomed. 25, 607\u2013619 (2012)","journal-title":"NMR Biomed."},{"key":"7_CR15","doi-asserted-by":"publisher","first-page":"1403","DOI":"10.1002\/jmri.23540","volume":"35","author":"M Moradi","year":"2012","unstructured":"Moradi, M., et al.: Multiparametric MRI maps for detection and grading of dominant prostate tumors. J. Magn. Reson. Imaging 35, 1403\u20131413 (2012)","journal-title":"J. Magn. Reson. Imaging"},{"key":"7_CR16","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1016\/j.ejmp.2018.04.310","volume":"56","author":"A. Barucci","year":"2018","unstructured":"Barucci, A., et al.: 301. Prostate cancer Radiomics using multiparametric MR imaging: an exploratory study. In: Proceedings of 10th Congress of the Associazione Italiana di Fisica Medica - AIFM. Physica Medica: Eur. J. Med. Phys. 56, 246. Elsevier (2018). \n                    https:\/\/doi.org\/10.1016\/j.ejmp.2018.04.310","journal-title":"Physica Medica"},{"key":"7_CR17","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1148\/radiol.2462070368","volume":"246","author":"Y Mazaheri","year":"2008","unstructured":"Mazaheri, Y., et al.: Prostate cancer: identification with combined diffusion weighted MR imaging and 3D 1H MR spectroscopic imaging-correlation with pathologic findings. Radiology 246, 480\u2013488 (2008)","journal-title":"Radiology"},{"key":"7_CR18","doi-asserted-by":"publisher","first-page":"2840","DOI":"10.1007\/s00330-015-3701-8","volume":"25","author":"A Wibmer","year":"2015","unstructured":"Wibmer, A., et al.: Haralick texture analysis of prostate MRI: Utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores. Eur. Radiol. 25, 2840\u20132850 (2015)","journal-title":"Eur. Radiol."},{"key":"7_CR19","doi-asserted-by":"publisher","first-page":"6265","DOI":"10.1073\/pnas.1505935112","volume":"112","author":"D Fehr","year":"2015","unstructured":"Fehr, D., et al.: Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images. Proc. Natl. Acad. Sci. USA 112, 6265\u20136273 (2015)","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"7_CR20","doi-asserted-by":"publisher","first-page":"875","DOI":"10.1002\/jmri.26243","volume":"49","author":"T Chen","year":"2019","unstructured":"Chen, T., et al.: Prostate cancer differentiation and aggressiveness: assessment with a radiomic-based model vs. PI-RADS v2. J. Magn. Reson. Imaging 49, 875\u2013884 (2019). \n                    https:\/\/doi.org\/10.1002\/jmri.26243","journal-title":"J. Magn. Reson. Imaging"},{"key":"7_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00330-016-4579-9","volume":"27","author":"HS Sidhu","year":"2017","unstructured":"Sidhu, H.S., et al.: Textural analysis of multiparametric MRI detects transition zone prostate cancer. Eur. Radiol. 27, 1\u201311 (2017)","journal-title":"Eur. Radiol."},{"key":"7_CR22","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1186\/s12880-015-0069-9","volume":"15","author":"F Khalvati","year":"2015","unstructured":"Khalvati, F., Wong, A., Haider, M.A.: Automated prostate cancer detection via comprehensive multi-parametric magnetic resonance imaging texture feature model. BMC Med. Imaging 15, 27 (2015)","journal-title":"BMC Med. Imaging"},{"key":"7_CR23","doi-asserted-by":"publisher","first-page":"2685","DOI":"10.1088\/0031-9155\/60\/7\/2685","volume":"60","author":"A Vignati","year":"2015","unstructured":"Vignati, A., et al.: Texture features on T2-weighted magnetic resonance imaging: new potential biomarkers for prostate cancer aggressiveness. Phys. Med. Biol. 60, 2685\u20132701 (2015)","journal-title":"Phys. Med. Biol."},{"key":"7_CR24","doi-asserted-by":"publisher","first-page":"3050","DOI":"10.1007\/s00330-016-4663-1","volume":"27","author":"G Nketiah","year":"2016","unstructured":"Nketiah, G., et al.: T2-weighted MRI-derived textural features reflect prostate cancer aggressiveness: preliminary results. Eur. Radiol. 27, 3050\u20133059 (2016)","journal-title":"Eur. Radiol."},{"key":"7_CR25","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.eururo.2015.08.052","volume":"69","author":"JC Weinreb","year":"2016","unstructured":"Weinreb, J.C., et al.: PI-RADS prostate imaging - reporting and data systems: 2015, version 2. Eur. Urol. 69, 16\u201340 (2016)","journal-title":"Eur. Urol."},{"key":"7_CR26","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1148\/radiol.10091343","volume":"255","author":"DL Langer","year":"2010","unstructured":"Langer, D.L., et al.: Prostate tissue composition and MR measurements: investigating the relationships between ADC, T2, K(trans), v(e), and corresponding histologic features. Radiology 255, 485\u2013494 (2010)","journal-title":"Radiology"},{"key":"7_CR27","doi-asserted-by":"publisher","first-page":"1382","DOI":"10.2214\/AJR.11.6861","volume":"197","author":"A Oto","year":"2011","unstructured":"Oto, A., et al.: Diffusion-weighted and dynamic contrast-enhanced MRI of prostate cancer: correlation of quantitative MR parameters with Gleason score and tumor angiogenesis. AJR Am. J. Roentgenol. 197, 1382\u20131390 (2011)","journal-title":"AJR Am. J. Roentgenol."},{"key":"7_CR28","doi-asserted-by":"publisher","first-page":"33","DOI":"10.5405\/jmbe.1183","volume":"33","author":"MB Nagarajan","year":"2013","unstructured":"Nagarajan, M.B., et al.: Classification of small lesions in breast MRI: evaluating the role of dynamically extracted texture features through feature selection. J. Med. Biol. Eng. 33, 33 (2013)","journal-title":"J. Med. Biol. Eng."},{"issue":"9","key":"7_CR29","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1016\/j.mri.2012.05.001","volume":"30","author":"A Fedorov","year":"2012","unstructured":"Fedorov, A., et al.: 3D slicer as an image computing platform for the quantitative imaging network. Magn. Reson. Imaging 30(9), 1323\u20131341 (2012)","journal-title":"Magn. Reson. Imaging"},{"issue":"21","key":"7_CR30","doi-asserted-by":"publisher","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","volume":"77","author":"JJM Griethuysen van","year":"2017","unstructured":"van Griethuysen, J.J.M., et al.: Computational radiomics system to decode the radiographic phenotype. Cancer Res. 77(21), e104\u2013e107 (2017)","journal-title":"Cancer Res."}],"container-title":["Communications in Computer and Information Science","Computer Analysis of Images and Patterns"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-29930-9_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,22]],"date-time":"2019-08-22T15:54:48Z","timestamp":1566489288000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-29930-9_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030299293","9783030299309"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-29930-9_7","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"23 August 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CAIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer Analysis of Images and Patterns","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Salerno","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"caip2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/caip2019.unisa.it\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"176","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"106","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"60% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.68","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.40","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}