Risk Stratification of Prostate Cancer Using the Combination of Histogram Analysis of Apparent Diffusion Coefficient...

The effectiveness of quantitative MRI and clinical information in the risk stratification of prostate cancer (PCa) patients was evaluated separately in previous research; however, the differentiation power of combining quantitative MRI and clinical information has yet to be investigated.

To investigate the power of combining histogram analysis of apparent diffusion coefficient (ADC) of tumor diffusion volume (tDv) with clinical information for the differentiation of low-grade (Gleason score [GS] ≤6) and high-grade (GS ≥7) PCa.

Retrospective.

Fifty-nine PCa patients who underwent preoperative diffusion-weighted imaging (DWI) (acquired with b = 0, 1000 mm² /s) and followed by radical prostatectomy within 6 months.

T₂ -weighted, DWI, and ADC images at 3.0T.

tDv defined with different ADC thresholds were analyzed for each patient and combined with age and prostate-specific antigen (PSA) level. Binary logistic regression with backward feature selection was applied to determine the best discrimination and corresponding combination of parameters.

Kolmogorov-Smirnov test; independent samples t-test; Mann-Whitney U-test; Spearman's rank correlation; receiver operating characteristic (ROC) analysis; binary logistical regression.

PSA and the 10^th percentile ADC value of tDv defined with different diffusion thresholds were significantly different between low-grade and high-grade PCa groups (P < 0.05 for all). Median ADC of tDv based on a threshold of 1.008 × 10^-3 mm² /s exhibited the best performance (AUC = 0.86, 95% confidence interval [CI]: 0.75-0.94), whereas binary logistic regression with backward feature selection achieved 97.20% accuracy with AUC = 0.978 (95% CI: 0.929-0.997).

The discriminatory power of a single histogram variable of ADC in tDv was not significantly superior to that of a single clinical parameter. The combination of histogram analysis of ADC of tDv and clinical information using logistic regression might significantly improve the risk stratification of PCa and achieve reasonably high accuracy.

4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:556-564.