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dc.contributor.authorSushentsev, Nikita
dc.contributor.authorRundo, Leonardo
dc.contributor.authorBlyuss, Oleg
dc.contributor.authorGnanapragasam, Vincent J
dc.contributor.authorSala, Evis
dc.contributor.authorBarrett, Tristan
dc.date.accessioned2021-07-26T14:30:01Z
dc.date.available2021-07-26T14:30:01Z
dc.date.issued2021-06-21
dc.identifier.citationSushentsev , N , Rundo , L , Blyuss , O , Gnanapragasam , V J , Sala , E & Barrett , T 2021 , ' MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance ' , Scientific Reports , vol. 11 , no. 1 , 12917 . https://doi.org/10.1038/s41598-021-92341-6
dc.identifier.issn2045-2322
dc.identifier.otherPURE: 25587500
dc.identifier.otherPURE UUID: a7b32c4c-1a06-4d01-83ba-56c83f03229d
dc.identifier.otherPubMed: 34155265
dc.identifier.otherPubMedCentral: PMC8217549
dc.identifier.otherScopus: 85108525334
dc.identifier.otherORCID: /0000-0002-0194-6389/work/97098414
dc.identifier.urihttp://hdl.handle.net/2299/24923
dc.description© The Author(s) 2021. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
dc.description.abstractNearly 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.en
dc.format.extent11
dc.language.isoeng
dc.relation.ispartofScientific Reports
dc.subjectGeneral
dc.titleMRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillanceen
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionUniversity of Hertfordshire
dc.description.statusPeer reviewed
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85108525334&partnerID=8YFLogxK
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.1038/s41598-021-92341-6
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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