Xiang-Ke Niu1, Zhi-Fan Chen1, Lin Chen2, Jun Li3, Tao Peng1, Xin Li4. 1. 1 Department of Radiology, Affiliated Hospital of Chengdu University, 82 2nd N Section of Second Ring Rd, Chengdu 610081, Sichuan, China. 2. 2 Department of Urology, Affiliated Hospital of Chengdu University, Chengdu, China. 3. 3 Department of General Surgery, Affiliated Hospital of Chengdu University, Chengdu, China. 4. 4 Life Science, Advanced Application Team, GE Healthcare, Beijing, China.
Abstract
OBJECTIVE: The purpose of this study was to investigate the performance of biparametric MRI texture analysis (TA) in detecting and evaluating high-grade prostate cancer in zone-specific regions. MATERIALS AND METHODS: A retrospective study included 184 consecutively registered biopsy-naive patients in whom prostate cancer was suspected who were undergoing multiparametric prostate MRI. MR images were scored and evaluated by two readers using the Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) and biparametric MRI TA in separate sessions. Interobserver agreement on PI-RADSv2 score and textural parameters of biparametric MRI was evaluated. The logistic regression model based on TA was built for different zones of the prostate. ROC analysis was used to compare the TA-based model with other parameters alone. The correlation of each parameter with Gleason score of high-grade prostate cancer was also assessed. RESULTS: Reader reliability ranged from moderate to good for PI-RADSv2 (Cohen κ = 0.525-0.616) and from good to excellent for textural metrics (intraclass correlation coefficient, 0.745-0.925). Diagnostic performance was significantly improved by use of the TA-based model (transition zone AUC, 0.87; peripheral zone AUC, 0.89) in comparison with PI-RADSv2 and other texture parameters alone. For the transition zone, entropy had moderate to good correlation with the Gleason score of high-grade prostate cancer (r = 0.562, p = 0.004). In the peripheral zone, entropy (r = 0.614, p = 0.003) and inertia (r = 0.663, p = 0.002) had moderate to good correlations with Gleason score. CONCLUSION: The results of this clinical study indicate that a TA-based model that includes biparametric MRI can be used for identifying high-grade prostate cancer and that specific parameters extracted from TA may be additional tools for assessing tumor aggressiveness.
OBJECTIVE: The purpose of this study was to investigate the performance of biparametric MRI texture analysis (TA) in detecting and evaluating high-grade prostate cancer in zone-specific regions. MATERIALS AND METHODS: A retrospective study included 184 consecutively registered biopsy-naive patients in whom prostate cancer was suspected who were undergoing multiparametric prostate MRI. MR images were scored and evaluated by two readers using the Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) and biparametric MRI TA in separate sessions. Interobserver agreement on PI-RADSv2 score and textural parameters of biparametric MRI was evaluated. The logistic regression model based on TA was built for different zones of the prostate. ROC analysis was used to compare the TA-based model with other parameters alone. The correlation of each parameter with Gleason score of high-grade prostate cancer was also assessed. RESULTS: Reader reliability ranged from moderate to good for PI-RADSv2 (Cohen κ = 0.525-0.616) and from good to excellent for textural metrics (intraclass correlation coefficient, 0.745-0.925). Diagnostic performance was significantly improved by use of the TA-based model (transition zone AUC, 0.87; peripheral zone AUC, 0.89) in comparison with PI-RADSv2 and other texture parameters alone. For the transition zone, entropy had moderate to good correlation with the Gleason score of high-grade prostate cancer (r = 0.562, p = 0.004). In the peripheral zone, entropy (r = 0.614, p = 0.003) and inertia (r = 0.663, p = 0.002) had moderate to good correlations with Gleason score. CONCLUSION: The results of this clinical study indicate that a TA-based model that includes biparametric MRI can be used for identifying high-grade prostate cancer and that specific parameters extracted from TA may be additional tools for assessing tumor aggressiveness.
Authors: He An; Yiang Wang; Esther M F Wong; Shanshan Lyu; Lujun Han; Jose A U Perucho; Peng Cao; Elaine Y P Lee Journal: Eur Radiol Date: 2021-01-06 Impact factor: 5.315