{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T12:39:53Z","timestamp":1774528793028,"version":"3.50.1"},"reference-count":62,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,10,18]],"date-time":"2021-10-18T00:00:00Z","timestamp":1634515200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Although prostate cancer is one of the most common causes of mortality and morbidity in advancing-age males, early diagnosis improves prognosis and modifies the therapy of choice. The aim of this study was the evaluation of a combined radiomics and machine learning approach on a publicly available dataset in order to distinguish a clinically significant from a clinically non-significant prostate lesion. A total of 299 prostate lesions were included in the analysis. A univariate statistical analysis was performed to prove the goodness of the 60 extracted radiomic features in distinguishing prostate lesions. Then, a 10-fold cross-validation was used to train and test some models and the evaluation metrics were calculated; finally, a hold-out was performed and a wrapper feature selection was applied. The employed algorithms were Na\u00efve bayes, K nearest neighbour and some tree-based ones. The tree-based algorithms achieved the highest evaluation metrics, with accuracies over 80%, and area-under-the-curve receiver-operating characteristics below 0.80. Combined machine learning algorithms and radiomics based on clinical, routine, multiparametric, magnetic-resonance imaging were demonstrated to be a useful tool in prostate cancer stratification.<\/jats:p>","DOI":"10.3390\/jimaging7100215","type":"journal-article","created":{"date-parts":[[2021,10,19]],"date-time":"2021-10-19T22:52:47Z","timestamp":1634683967000},"page":"215","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["A Combined Radiomics and Machine Learning Approach to Distinguish Clinically Significant Prostate Lesions on a Publicly Available MRI Dataset"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9746-2265","authenticated-orcid":false,"given":"Leandro","family":"Donisi","sequence":"first","affiliation":[{"name":"Department of Advanced Biomedical Sciences, University of Naples \u201cFederico II\u201d, 80131 Naples, Italy"},{"name":"Bioengineering Unit, Institute of Care and Scientific Research Maugeri, 27100 Pavia, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8303-5900","authenticated-orcid":false,"given":"Giuseppe","family":"Cesarelli","sequence":"additional","affiliation":[{"name":"Bioengineering Unit, Institute of Care and Scientific Research Maugeri, 27100 Pavia, Italy"},{"name":"Department of Chemical, Materials and Production Engineering, University of Naples \u201cFederico II\u201d, 80125 Naples, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anna","family":"Castaldo","sequence":"additional","affiliation":[{"name":"Department of Advanced Biomedical Sciences, University of Naples \u201cFederico II\u201d, 80131 Naples, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5059-4615","authenticated-orcid":false,"given":"Davide Raffaele","family":"De Lucia","sequence":"additional","affiliation":[{"name":"Department of Advanced Biomedical Sciences, University of Naples \u201cFederico II\u201d, 80131 Naples, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francesca","family":"Nessuno","sequence":"additional","affiliation":[{"name":"Department of Advanced Biomedical Sciences, University of Naples \u201cFederico II\u201d, 80131 Naples, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gaia","family":"Spadarella","sequence":"additional","affiliation":[{"name":"Department of Advanced Biomedical Sciences, University of Naples \u201cFederico II\u201d, 80131 Naples, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7290-6432","authenticated-orcid":false,"given":"Carlo","family":"Ricciardi","sequence":"additional","affiliation":[{"name":"Bioengineering Unit, Institute of Care and Scientific Research Maugeri, 27100 Pavia, Italy"},{"name":"Department of Electrical Engineering and Information Technologies, University of Naples \u201cFederico II\u201d, 80125 Naples, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3322\/caac.21654","article-title":"Cancer statistics","volume":"71","author":"Siegel","year":"2021","journal-title":"CA Cancer J. 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