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Value of MRI texture analysis for predicting high-grade prostate cancer by Hui Xiong; Xiaojing He; Dajing Guo is a Medicine article available to read on EtoBox.

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Purpose To explore the potential value of MRI texture analysis (TA) combined with prostate-related biomarkers to predict high-grade prostate cancer (HGPCa). Materials and methods Eighty-five patients who underwent MRI scanning, including T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) prior to trans-rectal ultrasound (TRUS)-guided core prostate biopsy, were retrospectively enrolled. TA parameters derived from T2WI and DWI, prostate-specific antigen (PSA), and free PSA (fPSA) were compared between the HGPCa and non-high-grade prostate cancer (NHGPCa) groups using independent Student's t-test and the Mann-Whitney U test. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the predictive value for HGPCa. Results Univariate analysis showed that PSA and entropy based on apparent diffusion coefficient (ADC) map differed significantly between the HGPCa and NHGPCa groups and showed higher diagnostic values for HGPCa (area under the curve (AUC) = 82.0% and 80.0%, respectively). Logistic regression and ROC curve analyses revealed that kurtosis, skewness and entropy derived from ADC maps had diagnostic power to predict HGPCa;

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Author

Hui Xiong; Xiaojing He; Dajing Guo

Publisher

Elsevier BV

Published

2021

Language

EN

Field

Medicine (Health Sciences)