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Radiomics from multisite MRI and clinical data to predict clinically significant prostate cancer

Krauss, Wolfgang, 1973- (författare)
Örebro universitet,Institutionen för medicinska vetenskaper,Department of Radiology and Medical Physics, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
Janusz, Frey, 1975- (författare)
Örebro universitet,Institutionen för medicinska vetenskaper,Region Örebro län,Department of Urology
Heydorn Lagerlöf, Jakob, 1978- (författare)
Örebro universitet,Institutionen för medicinska vetenskaper,School of Medical Sciences, Faculty of Medicine and Health, Örebro University, Örebro, Sweden; Department of Medical Physics, Karlstad Central Hospital, Sweden
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Lidén, Mats, 1976- (författare)
Örebro universitet,Institutionen för medicinska vetenskaper,Region Örebro län,Department of Radiology and Medical Physics
Thunberg, Per, 1968- (författare)
Örebro universitet,Institutionen för medicinska vetenskaper,Department of Radiology and Medical Physics, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
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 (creator_code:org_t)
Sage Publications, 2024
2024
Engelska.
Ingår i: Acta Radiologica. - : Sage Publications. - 0284-1851 .- 1600-0455. ; 23:1
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • 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.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Urologi och njurmedicin (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Urology and Nephrology (hsv//eng)
MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Cancer and Oncology (hsv//eng)

Nyckelord

PI-RADS
magnetic resonance imaging
multisite-multivendor
prostate cancer
radiomics

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