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Radiomics from multisite MRI and clinical data to predict clinically significant prostate cancer
Örebro University, School of Medical Sciences. Department of Radiology and Medical Physics, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.ORCID iD: 0000-0002-8212-0211
Örebro University, School of Medical Sciences. Örebro University Hospital. Department of Urology.
Örebro University, School of Medical Sciences. School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden; Department of Medical Physics, Karlstad Central Hospital, Sweden.ORCID iD: 0000-0001-6389-7773
Örebro University, School of Medical Sciences. Örebro University Hospital. Department of Radiology and Medical Physics.ORCID iD: 0000-0002-1346-1450
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2024 (English)In: Acta Radiologica, ISSN 0284-1851, E-ISSN 1600-0455, Vol. 23, no 1, article id 103422Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: Magnetic resonance imaging (MRI) is useful in the diagnosis of clinically significant prostate cancer (csPCa). MRI-derived radiomics may support the diagnosis of csPCa. PURPOSE: To investigate whether adding radiomics from biparametric MRI to predictive models based on clinical and MRI parameters improves the prediction of csPCa in a multisite-multivendor setting.

MATERIAL AND METHODS: Clinical information (PSA, PSA density, prostate volume, and age), MRI reviews (PI-RADS 2.1), and radiomics (histogram and texture features) were retrieved from prospectively included patients examined at different radiology departments and with different MRI systems, followed by MRI-ultrasound fusion guided biopsies of lesions PI-RADS 3-5. Predictive logistic regression models of csPCa (Gleason score ≥7) for the peripheral (PZ) and transition zone (TZ), including clinical data and PI-RADS only, and combined with radiomics, were built and compared using receiver operating characteristic (ROC) curves.

RESULTS: In total, 456 lesions in 350 patients were analyzed. In PZ and TZ, PI-RADS 4-5 and PSA density, and age in PZ, were independent predictors of csPCa in models without radiomics. In models including radiomics, PI-RADS 4-5, PSA density, age, and ADC energy were independent predictors in PZ, and PI-RADS 5, PSA density and ADC mean in TZ. Comparison of areas under the ROC curve (AUC) for the models without radiomics (PZ: AUC = 0.82, TZ: AUC = 0.80) versus with radiomics (PZ: AUC = 0.82, TZ: AUC = 0.82) showed no significant differences (PZ: P = 0.366; TZ: P = 0.171).

CONCLUSION: PSA density and PI-RADS are potent predictors of csPCa. Radiomics do not add significant information to our multisite-multivendor dataset.

Place, publisher, year, edition, pages
Sage Publications, 2024. Vol. 23, no 1, article id 103422
Keywords [en]
PI-RADS, magnetic resonance imaging, multisite-multivendor, prostate cancer, radiomics
National Category
Urology and Nephrology Radiology, Nuclear Medicine and Medical Imaging Cancer and Oncology
Identifiers
URN: urn:nbn:se:oru:diva-110453DOI: 10.1177/02841851231216555ISI: 001127589000001PubMedID: 38115809Scopus ID: 2-s2.0-85180205504OAI: oai:DiVA.org:oru-110453DiVA, id: diva2:1821663
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Region Örebro CountyAvailable from: 2023-12-20 Created: 2023-12-20 Last updated: 2024-04-11Bibliographically approved

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Krauss, WolfgangJanusz, FreyHeydorn Lagerlöf, JakobLidén, MatsThunberg, Per

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Urology and NephrologyRadiology, Nuclear Medicine and Medical ImagingCancer and Oncology

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