Prostate Cancer Aggressiveness Prediction Using CT Images

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 t...

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Autores principales:	Bruno Mendes, Inês Domingues, Augusto Silva, João Santos
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	prostate cancer radiomic features classification risk stratification computed tomography Science Q
Acceso en línea:	https://doaj.org/article/0bacbf016582467bb82f2c9b6db55dd7
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spelling	oai:doaj.org-article:0bacbf016582467bb82f2c9b6db55dd72021-11-25T18:10:49ZProstate Cancer Aggressiveness Prediction Using CT Images10.3390/life111111642075-1729https://doaj.org/article/0bacbf016582467bb82f2c9b6db55dd72021-10-01T00:00:00Zhttps://www.mdpi.com/2075-1729/11/11/1164https://doaj.org/toc/2075-1729Prostate 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.Bruno MendesInês DominguesAugusto SilvaJoão SantosMDPI AGarticleprostate cancerradiomic featuresclassificationrisk stratificationcomputed tomographyScienceQENLife, Vol 11, Iss 1164, p 1164 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	prostate cancer radiomic features classification risk stratification computed tomography Science Q
spellingShingle	prostate cancer radiomic features classification risk stratification computed tomography Science Q Bruno Mendes Inês Domingues Augusto Silva João Santos Prostate Cancer Aggressiveness Prediction Using CT Images
description	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.
format	article
author	Bruno Mendes Inês Domingues Augusto Silva João Santos
author_facet	Bruno Mendes Inês Domingues Augusto Silva João Santos
author_sort	Bruno Mendes
title	Prostate Cancer Aggressiveness Prediction Using CT Images
title_short	Prostate Cancer Aggressiveness Prediction Using CT Images
title_full	Prostate Cancer Aggressiveness Prediction Using CT Images
title_fullStr	Prostate Cancer Aggressiveness Prediction Using CT Images
title_full_unstemmed	Prostate Cancer Aggressiveness Prediction Using CT Images
title_sort	prostate cancer aggressiveness prediction using ct images
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0bacbf016582467bb82f2c9b6db55dd7
work_keys_str_mv	AT brunomendes prostatecanceraggressivenesspredictionusingctimages AT inesdomingues prostatecanceraggressivenesspredictionusingctimages AT augustosilva prostatecanceraggressivenesspredictionusingctimages AT joaosantos prostatecanceraggressivenesspredictionusingctimages
_version_	1718411481356173312

Prostate Cancer Aggressiveness Prediction Using CT Images

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