MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance

Abstract Nearly half of patients with prostate cancer (PCa) harbour low- or intermediate-risk disease considered suitable for active surveillance (AS). However, up to 44% of patients discontinue AS within the first five years, highlighting the unmet clinical need for robust baseline risk-stratificat...

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Autores principales:	Nikita Sushentsev, Leonardo Rundo, Oleg Blyuss, Vincent J. Gnanapragasam, Evis Sala, Tristan Barrett
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2360cbf0331a41b6971d9ab39fa8b035
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spelling	oai:doaj.org-article:2360cbf0331a41b6971d9ab39fa8b0352021-12-02T18:02:44ZMRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance10.1038/s41598-021-92341-62045-2322https://doaj.org/article/2360cbf0331a41b6971d9ab39fa8b0352021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92341-6https://doaj.org/toc/2045-2322Abstract Nearly half of patients with prostate cancer (PCa) harbour low- or intermediate-risk disease considered suitable for active surveillance (AS). However, up to 44% of patients discontinue AS within the first five years, highlighting the unmet clinical need for robust baseline risk-stratification tools that enable timely and accurate prediction of tumour progression. In this proof-of-concept study, we sought to investigate the added value of MRI-derived radiomic features to standard-of-care clinical parameters for improving baseline prediction of PCa progression in AS patients. Tumour T2-weighted imaging (T2WI) and apparent diffusion coefficient radiomic features were extracted, with rigorous calibration and pre-processing methods applied to select the most robust features for predictive modelling. Following leave-one-out cross-validation, the addition of T2WI-derived radiomic features to clinical variables alone improved the area under the ROC curve for predicting progression from 0.61 (95% confidence interval [CI] 0.481–0.743) to 0.75 (95% CI 0.64–0.86). These exploratory findings demonstrate the potential benefit of MRI-derived radiomics to add incremental benefit to clinical data only models in the baseline prediction of PCa progression on AS, paving the way for future multicentre studies validating the proposed model and evaluating its impact on clinical outcomes.Nikita SushentsevLeonardo RundoOleg BlyussVincent J. GnanapragasamEvis SalaTristan BarrettNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Nikita Sushentsev Leonardo Rundo Oleg Blyuss Vincent J. Gnanapragasam Evis Sala Tristan Barrett MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance
description	Abstract Nearly half of patients with prostate cancer (PCa) harbour low- or intermediate-risk disease considered suitable for active surveillance (AS). However, up to 44% of patients discontinue AS within the first five years, highlighting the unmet clinical need for robust baseline risk-stratification tools that enable timely and accurate prediction of tumour progression. In this proof-of-concept study, we sought to investigate the added value of MRI-derived radiomic features to standard-of-care clinical parameters for improving baseline prediction of PCa progression in AS patients. Tumour T2-weighted imaging (T2WI) and apparent diffusion coefficient radiomic features were extracted, with rigorous calibration and pre-processing methods applied to select the most robust features for predictive modelling. Following leave-one-out cross-validation, the addition of T2WI-derived radiomic features to clinical variables alone improved the area under the ROC curve for predicting progression from 0.61 (95% confidence interval [CI] 0.481–0.743) to 0.75 (95% CI 0.64–0.86). These exploratory findings demonstrate the potential benefit of MRI-derived radiomics to add incremental benefit to clinical data only models in the baseline prediction of PCa progression on AS, paving the way for future multicentre studies validating the proposed model and evaluating its impact on clinical outcomes.
format	article
author	Nikita Sushentsev Leonardo Rundo Oleg Blyuss Vincent J. Gnanapragasam Evis Sala Tristan Barrett
author_facet	Nikita Sushentsev Leonardo Rundo Oleg Blyuss Vincent J. Gnanapragasam Evis Sala Tristan Barrett
author_sort	Nikita Sushentsev
title	MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance
title_short	MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance
title_full	MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance
title_fullStr	MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance
title_full_unstemmed	MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance
title_sort	mri-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2360cbf0331a41b6971d9ab39fa8b035
work_keys_str_mv	AT nikitasushentsev mriderivedradiomicsmodelforbaselinepredictionofprostatecancerprogressiononactivesurveillance AT leonardorundo mriderivedradiomicsmodelforbaselinepredictionofprostatecancerprogressiononactivesurveillance AT olegblyuss mriderivedradiomicsmodelforbaselinepredictionofprostatecancerprogressiononactivesurveillance AT vincentjgnanapragasam mriderivedradiomicsmodelforbaselinepredictionofprostatecancerprogressiononactivesurveillance AT evissala mriderivedradiomicsmodelforbaselinepredictionofprostatecancerprogressiononactivesurveillance AT tristanbarrett mriderivedradiomicsmodelforbaselinepredictionofprostatecancerprogressiononactivesurveillance
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MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance

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