MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance
| Autor: | Vincent J. Gnanapragasam, Oleg Blyuss, Evis Sala, Tristan Barrett, Nikita Sushentsev, Leonardo Rundo |
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| Přispěvatelé: | Rundo, Leonardo [0000-0003-3341-5483], Gnanapragasam, Vincent [0000-0003-4722-4207], Sala, Evis [0000-0002-5518-9360], Barrett, Tristan [0000-0002-1180-1474], Apollo - University of Cambridge Repository |
| Rok vydání: | 2021 |
| Předmět: |
Oncology
Male 030218 nuclear medicine & medical imaging Workflow Prostate cancer 0302 clinical medicine Radiomics Prostate Image Processing Computer-Assisted 692/4025/1752 Multidisciplinary article Disease Management Middle Aged Prognosis Magnetic Resonance Imaging medicine.anatomical_structure 030220 oncology & carcinogenesis Critical Pathways Disease Progression Medicine medicine.medical_specialty Science Clinical Decision-Making MEDLINE Urological cancer Cancer imaging 03 medical and health sciences Text mining Internal medicine 631/67/2321 medicine Humans Baseline (configuration management) Watchful Waiting Aged Neoplasm Staging Retrospective Studies business.industry Prostatic Neoplasms medicine.disease ROC Curve business 631/67/589 Biomarkers |
| Zdroj: | Scientific Reports, Vol 11, Iss 1, Pp 1-11 (2021) Scientific Reports |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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