{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T09:20:09Z","timestamp":1772184009738,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Prostate cancer is one of the most prevalent cancers in the male population. Its diagnosis and classification rely on unspecific measures such as PSA levels and DRE, followed by biopsy, where an aggressiveness level is assigned in the form of Gleason Score. Efforts have been made in the past to use radiomics coupled with machine learning to predict prostate cancer aggressiveness from clinical images, showing promising results. Thus, the main goal of this work was to develop supervised machine learning models exploiting radiomic features extracted from bpMRI examinations, to predict biological aggressiveness; 288 classifiers were developed, corresponding to different combinations of pipeline aspects, namely, type of input data, sampling strategy, feature selection method and machine learning algorithm. On a cohort of 281 lesions from 183 patients, it was found that (1) radiomic features extracted from the lesion volume of interest were less stable to segmentation than the equivalent extraction from the whole gland volume of interest; and (2) radiomic features extracted from the whole gland volume of interest produced higher performance and less overfitted classifiers than radiomic features extracted from the lesions volumes of interest. This result suggests that the areas surrounding the tumour lesions offer relevant information regarding the Gleason Score that is ultimately attributed to that lesion.<\/jats:p>","DOI":"10.3390\/cancers13236065","type":"journal-article","created":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T02:40:02Z","timestamp":1638412802000},"page":"6065","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Prediction of Prostate Cancer Disease Aggressiveness Using Bi-Parametric Mri Radiomics"],"prefix":"10.3390","volume":"13","author":[{"given":"Ana","family":"Rodrigues","sequence":"first","affiliation":[{"name":"Champalimaud Foundation\u2014Centre for the Unknown, 1400-038 Lisbon, Portugal"},{"name":"Faculty of Sciences, University of Lisbon, 1649-004 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8174-2943","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Santinha","sequence":"additional","affiliation":[{"name":"Champalimaud Foundation\u2014Centre for the Unknown, 1400-038 Lisbon, Portugal"},{"name":"Instituto Superior T\u00e9cnico, University of Lisbon, 1649-004 Lisboa, Portugal"}]},{"given":"Bernardo","family":"Galv\u00e3o","sequence":"additional","affiliation":[{"name":"Faculty of Sciences and Technology, NOVA University, 2825-149 Caparica, Portugal"}]},{"given":"Celso","family":"Matos","sequence":"additional","affiliation":[{"name":"Champalimaud Foundation\u2014Centre for the Unknown, 1400-038 Lisbon, Portugal"}]},{"given":"Francisco M.","family":"Couto","sequence":"additional","affiliation":[{"name":"LASIGE, Faculty of Sciences, University of Lisbon, 1649-004 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3298-2072","authenticated-orcid":false,"given":"Nickolas","family":"Papanikolaou","sequence":"additional","affiliation":[{"name":"Champalimaud Foundation\u2014Centre for the Unknown, 1400-038 Lisbon, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,1]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization, International Agency for Research on Cancer, The Global Cancer Observatory (2021, March 01). 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