{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:26:28Z","timestamp":1767338788556,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,10,31]],"date-time":"2021-10-31T00:00:00Z","timestamp":1635638400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Life"],"abstract":"<jats:p>Prostate Cancer (PCa) is mostly asymptomatic at an early stage and often painless requiring active surveillance screening. Transrectal Ultrasound Guided Biopsy (TRUS) is the principal method to diagnose PCa following a histological examination by observing cell pattern irregularities and assigning the Gleason Score (GS) according to the recommended guidelines. This procedure presents sampling errors and, being invasive may cause complications to the patients. External Beam Radiotherapy Treatment (EBRT) is presented as curative option for localised and locally advanced disease, as a palliative option for metastatic low-volume disease or after prostatectomy for prostate bed and pelvic nodes salvage. In the EBRT worflow a Computed Tomography (CT) scan is performed as the basis for dose calculations and volume delineations. In this work, we evaluated the use of data-characterization algorithms (radiomics) from CT images for PCa aggressiveness assessment. The fundamental motivation relies on the wide availability of CT images and the need to provide tools to assess EBRT effectiveness. We used Pyradiomics and Local Image Features Extraction (LIFEx) to extract features and search for a radiomic signature within CT images. Finnaly, when applying Principal Component Analysis (PCA) to the features, we were able to show promising results.<\/jats:p>","DOI":"10.3390\/life11111164","type":"journal-article","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T22:22:24Z","timestamp":1635805344000},"page":"1164","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Prostate Cancer Aggressiveness Prediction Using CT Images"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7574-7630","authenticated-orcid":false,"given":"Bruno","family":"Mendes","sequence":"first","affiliation":[{"name":"Centro de Investiga\u00e7\u00e3o do Instituto Portugu\u00eas de Oncologia do Porto (CI-IPOP), Grupo de F\u00edsica M\u00e9dica, Radiobiologia e Protec\u00e7\u00e3o Radiol\u00f3gica, 4200-072 Porto, Portugal"},{"name":"Faculdade de Engenharia da Universidade do Porto (FEUP), 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2334-7280","authenticated-orcid":false,"given":"In\u00eas","family":"Domingues","sequence":"additional","affiliation":[{"name":"Centro de Investiga\u00e7\u00e3o do Instituto Portugu\u00eas de Oncologia do Porto (CI-IPOP), Grupo de F\u00edsica M\u00e9dica, Radiobiologia e Protec\u00e7\u00e3o Radiol\u00f3gica, 4200-072 Porto, Portugal"},{"name":"Instituto Superior de Engenharia de Coimbra (ISEC), 3030-199 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3786-2889","authenticated-orcid":false,"given":"Augusto","family":"Silva","sequence":"additional","affiliation":[{"name":"IEETA, Universidade de Aveiro (UA), 3810-193 Aveiro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2465-5143","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Santos","sequence":"additional","affiliation":[{"name":"Centro de Investiga\u00e7\u00e3o do Instituto Portugu\u00eas de Oncologia do Porto (CI-IPOP), Grupo de F\u00edsica M\u00e9dica, Radiobiologia e Protec\u00e7\u00e3o Radiol\u00f3gica, 4200-072 Porto, Portugal"},{"name":"Instituto de Ci\u00eancias Biom\u00e9dicas Abel Salazar (ICBAS), 4050-313 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. 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