The use of ADC histogram analysis in the diagnosis and determination of aggressiveness of peripheral zone prostate cancer.

Purpose: The purpose of this study was to determine the effectiveness of ADC histogram analysis in diagnosing and determining the aggressiveness of peripheral zone (PZ) prostate cancer, and to reveal the relationship between Gleason and PI-RADS scores.Material and method: 61 patients who underwent standard 12-core and cognitive prostate biopsy and multiparametric prostate magnetic resonance imaging before biopsy were included in the study. According to the pathology results, patients were classified as either having clinically significant cancer with malignancy ( = 35) or as clinically insignificant - benign ( = 26). The effectiveness of ADC histogram parameters to distinguish between benign and malignant lesions was investigated. Subsequently, 35 patients in the malignant group were grouped according to their Gleason scores, and the relationship between ADC histogram parameters and Gleason scores was examined.

Results: ADC max, standard deviation, entropy, voxel count, and volume were found to be significantly different between the benign and malignant groups ( < 0.05; < 0.05; < 0.01; < 0.01; < 0.01). According to the ROC curve: entropy (AUC = 0.75; 95% CI: 0.63-0.87), voxel count (AUC = 0.83; 95% CI: 0.73-0.93), and volume values (AUC = 0.83; 95% CI: 0.73-0.93) were statistically significant in the diagnosis of benign and malignant lesions in the prostate gland (area under the ROC curves). In the logistic regression analysis models (backward), it was found that an increase in volume increased the risk of malignant tumours by 1.75 times ( = 0.04; OR = 1.75; 95% CI: 1.00-3.04).

Conclusions: ADC histogram data contribute to the diagnosis of benign-malignant differentiation in PZ prostate lesions and predict the Gleason score in malignant lesions.