Related Experiment Video For apparent diffusion coefficient
Updated: Jul 8, 2026
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014
The role of apparent diffusion coefficient values in diagnosing prostate cancer for patients with equivocal PI-RADS 3
Changming Wang1, Qifei Dong1, Lei Yuan2
1Department of Urology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Purpose:
To evaluate the diagnostic performance of apparent diffusion coefficient (ADC) values for the detection of clinically significant prostate cancer (csPCa) in patients with equivocal Prostate Imaging-Reporting and Data System (PI-RADS) 3 lesions.
Materials And Methods:
In this multicenter retrospective study, data from 460 eligible patients meeting predefined inclusion criteria were analyzed. Following the establishment of a standardized region of interest delineation protocol, ADC measurements were obtained for all PI-RADS 3 lesions. Univariate and multivariate logistic regression analyses were performed to identify independent predictors. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curves with calculation of the area under the curve (AUC). The multivariate model's discriminative ability was assessed through ROC analysis, while calibration was evaluated using calibration plots. Clinical utility was quantified via decision curve analysis. A risk stratification system was subsequently developed to optimize clinical decision-making.
Results:
For the 460 patients with PI-RADS 3 lesions, 108 (23.5%) were diagnosed with any grade prostate cancer, and 62 (13.5%) were diagnosed with csPCa. The results of the multivariate analysis indicated that prostate volume (OR = 0.957, 95% CI: 0.931-0.984, P = 0.002), minimum ADC (ADCmin) (OR = 0.009, 95% CI: <0.001-0.381, P = 0.014), and lesions in the peripheral zone (OR = 6.269, 95% CI: 2.332-16.850, P < 0.001) were independent predictors of csPCa. Among the ADC parameters, ADCmin demonstrated superior diagnostic accuracy with an AUC of 0.773 (95% CI: 0.717-0.823) for csPCa. The multivariate prediction model incorporating prostate volume, ADCmin and lesion location showed good discrimination and satisfactory calibration in validation cohorts. Applying the threshold prostate volume <50 mL or ADCmin <0.65 × 10 -3 mm 2 /s as the diagnostic criteria of csPCa achieved very high sensitivity (93.5%) and negative predictive value (98.3%).
Conclusions:
Among the ADC parameters, ADCmin exhibits the highest diagnostic accuracy for identifying csPCa in patients presenting with PI-RADS 3 lesions. Furthermore, we developed a prediction model and a risk stratification system to aid in clinical decision-making regarding prostate biopsy.
