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dc.contributor.authorSushentsev, Nikita
dc.contributor.authorRundo, Leonardo
dc.contributor.authorBlyuss, Oleg
dc.contributor.authorNazarenko, Tatiana
dc.contributor.authorSuvorov, Aleksandr
dc.contributor.authorGnanapragasam, Vincent J
dc.contributor.authorSala, Evis
dc.contributor.authorBarrett, Tristan
dc.date.accessioned2022-01-04T11:58:44Z
dc.date.available2022-01-04T11:58:44Z
dc.date.issued2022-01
dc.date.submitted2021-03-30
dc.identifier.issn0938-7994
dc.identifier.others00330-021-08151-x
dc.identifier.other8151
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/331848
dc.descriptionFunder: National Institute of Health Research Cambridge Biomedical Research Centre
dc.descriptionFunder: Engineering and Physical Sciences Research Council Imaging Centre in Cambridge and Manchester
dc.descriptionFunder: Cambridge Experimental Cancer Medicine Centre
dc.descriptionFunder: Gates Cambridge Trust; doi: http://dx.doi.org/10.13039/501100005370
dc.description.abstractOBJECTIVES: To compare the performance of the PRECISE scoring system against several MRI-derived delta-radiomics models for predicting histopathological prostate cancer (PCa) progression in patients on active surveillance (AS). METHODS: The study included AS patients with biopsy-proven PCa with a minimum follow-up of 2 years and at least one repeat targeted biopsy. Histopathological progression was defined as grade group progression from diagnostic biopsy. The control group included patients with both radiologically and histopathologically stable disease. PRECISE scores were applied prospectively by four uro-radiologists with 5-16 years' experience. T2WI- and ADC-derived delta-radiomics features were computed using baseline and latest available MRI scans, with the predictive modelling performed using the parenclitic networks (PN), least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests (RF) algorithms. Standard measures of discrimination and areas under the ROC curve (AUCs) were calculated, with AUCs compared using DeLong's test. RESULTS: The study included 64 patients (27 progressors and 37 non-progressors) with a median follow-up of 46 months. PRECISE scores had the highest specificity (94.7%) and positive predictive value (90.9%), whilst RF had the highest sensitivity (92.6%) and negative predictive value (92.6%) for predicting disease progression. The AUC for PRECISE (84.4%) was non-significantly higher than AUCs of 81.5%, 78.0%, and 80.9% for PN, LASSO regression, and RF, respectively (p = 0.64, 0.43, and 0.57, respectively). No significant differences were observed between AUCs of the three delta-radiomics models (p-value range 0.34-0.77). CONCLUSIONS: PRECISE and delta-radiomics models achieved comparably good performance for predicting PCa progression in AS patients. KEY POINTS: • The observed high specificity and PPV of PRECISE are complemented by the high sensitivity and NPV of delta-radiomics, suggesting a possible synergy between the two image assessment approaches. • The comparable performance of delta-radiomics to PRECISE scores applied by expert readers highlights the prospective use of the former as an objective and standardisable quantitative tool for MRI-guided AS follow-up. • The marginally superior performance of parenclitic networks compared to conventional machine learning algorithms warrants its further use in radiomics research.
dc.description.sponsorshipThe authors acknowledge support from National Institute of Health Research Cambridge Biomedical Research Centre, Cancer Research UK (Cambridge Imaging Centre grant number C197/A16465), the Engineering and Physical Sciences Research Council Imaging Centre in Cambridge and Manchester, and the Cambridge Experimental Cancer Medicine Centre. T. Nazarenko is supported by a Medical Research Council grant (MR/R02524X/1). A. Suvorov is supported by the Ministry of Science and Higher Education of the Russian Federation within the programme developing World-Class Research Centres "Digital Biodesign and Personalized Healthcare" (075-15-2020-926).
dc.languageen
dc.publisherSpringer Science and Business Media LLC
dc.subjectUrogenital
dc.subjectProstate cancer
dc.subjectMagnetic resonance imaging
dc.subjectActive surveillance
dc.subjectPRECISE
dc.subjectMachine learning
dc.titleComparative performance of MRI-derived PRECISE scores and delta-radiomics models for the prediction of prostate cancer progression in patients on active surveillance.
dc.typeArticle
dc.date.updated2022-01-04T11:58:44Z
prism.endingPage689
prism.issueIdentifier1
prism.publicationNameEur Radiol
prism.startingPage680
prism.volume32
dc.identifier.doi10.17863/CAM.79298
dcterms.dateAccepted2021-06-13
rioxxterms.versionofrecord10.1007/s00330-021-08151-x
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidSushentsev, Nikita [0000-0003-4500-9714]
dc.identifier.eissn1432-1084
pubs.funder-project-idCancer Research UK (C197/A16465)
pubs.funder-project-idMedical Research Council (MR/R02524X/1)
pubs.funder-project-idMinistry of Science and Higher Education of the Russian Federation (075-15-2020-926)
cam.issuedOnline2021-07-13


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