Abstract
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; when the three texture
parameters were combined, the area under the ROC curve reached the maximum (AUC = 84.6%;
95% confidence interval (CI): 0.758, 0.935; P = 0.000).
Conclusion
TA parameters derived from ADC may be a valuable tool in predicting HGPCa. The combination
of specific textural parameters extracted from ADC map may be additional tools to
predict HGPCa.
Keywords
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References
- Incidence of complications other than urinary incontinence or erectiledysfunctionafter radical prostatectomy or radiotherapy for prostate cancer: a population-based cohort study.Lancet Oncol. 2014; 15: 223-231
- Mortality results from a randomized prostate-cancer screening trial.N Engl J Med. 2009; 360: 1310-1319
- A multi-institutional evaluation of active surveillance for low risk prostate cancer.J Urol. 2013; 189: S19-S25
- Systematic review of complications of prostate biopsy.Eur Urol. 2013; 64: 876-892
- Prostate imaging reporting and data system version 2.1: 2019 update of prostate imaging reporting and data system version 2.Eur Urol. 2019; 76: 340-351
- Diagnostic value of biparametric magnetic resonance imaging (MRI) as an adjunct to prostate specific antigen (PSA)-based detection of prostate cancer in men without prior biopsies.BJU Int. 2015; 115: 381-388
- Biparametric 3T magnetic resonance imaging for prostatic cancer detection in a biopsy-naïve patient population: a further improvement of PI-RADSv2?.Eur J Radiol. 2016; 85: 2269-2274
- Correlation between apparent diffusion coefficient value on diffusion-weighted MR imaging and Gleason score in prostatecancer.Diagn Interv Imaging. 2017; 98: 63-71
- Apparent diffusion coeffcient value is a strong predictor of unsuspected aggressiveness of prostate cancer before radical prostatectomy.World J Urol. 2016; 34: 1389-1395
- Validation of quantitative analysis of multiparametric prostate MR images for prostate cancer detection and aggressiveness assessment: a cross-imager study.Radiology. 2014; 271: 461-471
- Prostate cancer aggressiveness: assessment with whole-lesion histogram analysis of the apparent diffusion coefficient.Radiology. 2014; 271: 143-152
- Multi-parametric (mp) MRI of prostatic ductal adenocarcinoma.J Magn Reson Imaging. 2015; 41: 1639-1645
- Textural analysis of multiparametric MRI detects transition zone prostate cancer.Eur Radiol. 2017; 27: 2348-2358
- T2-weighted MRI-derived textural features reflect prostate cancer aggressiveness: preliminary results.Eur Radiol. 2017; 27: 3050-3059
- Haralick textural features on T2 -weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer.J Magn Reson Imaging. 2017; 45: 103-117
- Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores.Eur Radiol. 2015; 25: 2840-2850
- Differentiation of prostatecancer lesions with high and with low Gleason score by diffusion-weighted MRI.Eur Radiol. 2017; 27: 1547-1555
- Developing a model for forecasting Gleason score ≥7 in potential prostate cancer patients to reduce unnecessary prostate biopsies.Int Urol Nephrol. 2016; 48: 535-540
- ISUP grading committee. Et al. the 2005 International Society of Urological Pathology (ISUP) consensus conference on Gleason grading of prostatic carcinoma.Am J Surg Pathol. 2005; 29: 1228-1242
- MR textural analysis on T2 FLAIR images for the prediction of true oligodendroglioma by the 2016 WHO genetic classification.J Magn Reson Imaging. 2018; 48: 74-83
- Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?.Insights Imaging. 2012; 3: 573-589
- The histogram analysis of diffusion-weighted intravoxel incoherent motion (IVIM) imaging for differentiating the Gleason grade of prostate cancer.Eur Radiol. 2015; 25: 994-1004
- Multiparametric MRI of prostate cancer: an update on state-of-the-art techniques and their performance in detecting and localizing prostate cancer.J Magn Reson Imaging. 2013; 37: 1035-1054
- Computer-aided diagnosis of prostate cancer in the peripheral zone using multiparametric MRI.Phys Med Biol. 2012; 57: 3833-3851
- Central gland and peripheral zone prostate tumors have significantly different quantitative imaging signatures on 3 tesla endorectal,in vivo T2-weighted MR imagery.J Magn Res Imaging. 2012; 36: 213-224
- Texture features on T2-weighted magnetic resonance imaging: new potential biomarkers for prostate cancer aggressiveness.Phys Med Biol. 2015; 60: 2685-2701
- The contemporary concept of significant versus insignificant prostate cancer.Eur Urol. 2011; 60: 291-303
- Limiting overdiagnosis of low-risk prostate cancer through an evaluation of the predictive value of transrectal and power Doppler ultrasonography.J Ultrasound. 2016; 19: 275-280
- Developing a new PI-RADS v2-based nomogram for forecasting high-grade prostate cancer.Clin Radiol. 2017; 72: 458-464
- Texture analysis software: integration with a radiological workstation.Stud Health Technol Inform. 2012; 180: 1030-1034
- Breast cancer heterogeneity: MR imaging texture analysis and survival outcomes.Radiology. 2017; 282: 665-675
- Value of texture analysis based on enhanced MRI for predicting an early therapeutic response to transcatheter arterial chemoembolisation combined with high-intensity focused ultrasound treatment in hepatocellular carcinoma.Clin Radiol. 2018; 73: 758.e9-758.e18
- Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images.Proc Natl Acad Sci U S A. 2015; 112: E6265-E6273
- Clinical application of biparametric MRI texture analysis for detection and evaluation of high-grade prostate cancer in zone-specific regions.AJR. 2018; 210: W1-W8
Article info
Publication history
Published online: October 23, 2020
Accepted:
October 14,
2020
Received in revised form:
September 7,
2020
Received:
February 28,
2020
Identification
Copyright
© 2020 Published by Elsevier Inc.
