Benign causes of diffusion restriction foci in the peripheral zone of the prostate: diagnosis and differential diagnosis

A pictorial essay of PI-RADS pearls and pitfalls: toward less ambiguity and better practice

Prostate Imaging Reporting and Data System (PI-RADS) was designed to standardize the interpretation of multiparametric magnetic resonance imaging (MRI) of the prostate, aiding in assessing the probability of clinically significant prostate cancer. By providing a structured scoring system, it enables better risk stratification, guiding decisions regarding the need for biopsy and subsequent treatment options. In this article, we explore both the strengths and weaknesses of PI-RADS, offering insights into its updated diagnostic performance and clinical applications, while also addressing potential pitfalls using diverse, representative MRI cases.

Imaging Spectrum of Granulomatous Diseases of the Abdomen and Pelvis

A granuloma is a compact organization of mature macrophages that forms because of persistent antigenic stimulation. At the microscopic level, granulomas can undergo various morphologic changes, ranging from necrosis to fibrosis, which along with other specialized immune cells define the appearance of the granulomatous process. Accordingly, the imaging features of granulomatous diseases vary and can overlap with those of other diseases, such as malignancy, and lead to surgical excisions and biopsy. However, given the heterogeneity of granulomas as a disease group, it is often hard to make a diagnosis on the basis of the histopathologic features of granulomatous diseases alone owing to overlapping microscopic features. Instead, a multidisciplinary approach is often helpful. Radiologists need to be familiar with the salient clinical manifestations and imaging findings of granulomatous diseases to generate an appropriate differential diagnosis. ^©RSNA, 2021.

The Clinical and Pathologic Relevance of a Prostate MRI Diagnosis of "Prostatitis"

OBJECTIVE

To investigate the relationship between magnetic resonance imaging evidence of prostatitis with clinical symptomatology. Non-malignant abnormalities in peripheral zone are common in prostate multiparametric prostate magnetic resonance imaging (mpMRI). These findings are sometimes reported as "prostatitis" or "inflammation" and lead to patient anxiety and urologic referral.

METHODS

Retrospective review of patients undergoing prostate mpMRI (2016-2017) was performed. Two cohort groups based on the presence of "prostatitis" or "inflammation" in the radiology report were identified. Clinical characteristics included age, prostate specific antigen, biopsy/intervention history, true lower urinary tract symptoms (LUTS), pain, use of urologic medications, prostate volume, and PIRADS score. Pathologic finding of inflammation was recorded. Groups were compared using chi-square for dichotomous variables and t-tests for continuous variables.

RESULTS

One hundred and four patients were identified with "prostatitis/inflammation" and 273 without. Report of LUTS was high in both groups (58% and 62% for prostatitis and no prostatitis respectively, P= .49), though report of moderate/severe LUTS (physician description or IPSS of 8-19 and 20+) was more common in the no prostatitis group (7% vs 18%, P= .008). Use of urologic medication was similar between the 2 groups (31% and 37% for prostatitis and no prostatitis respectively, P = .23). Biopsy finding of inflammation was more common in the prostatitis group (57% vs 43% P = .027). Reports of pelvic pain, dysuria, or urinary findings of inflammation were uncommon in both groups.

CONCLUSION

While mpMRI findings of prostatitis may indicate NIH Category IV prostatitis, there is no evidence of correlation with categories I, II or III prostatitis nor with symptomatic LUTS, and patients should be reassured that further investigation is not warranted.

Prostate MRI: practical guidelines for interpreting and reporting according to PI-RADS version 2.1

The increasing precision of multiparametric magnetic resonance imaging of the prostate, together with greater experience and standardization in its interpretation, has given this technique an important role in the management of prostate cancer, the most prevalent non-cutaneous cancer in men. This article reviews the concepts in PI-RADS version 2.1 for estimating the probability and zonal location of significant tumors of the prostate, using a practical approach that includes current considerations about the prerequisites for carrying out the test and recommendations for interpreting the findings. It emphasizes benign findings that can lead to confusion and the criteria for evaluating the probability of local spread, which must be included in the structured report.

Prostatitis, the Great Mimicker of Prostate Cancer: Can We Differentiate Them Quantitatively With Multiparametric MRI?

OBJECTIVE. The purpose of this study was to investigate the diagnostic performance of semiquantitative and quantitative pharmacokinetic parameters and quantitative apparent diffusion coefficient (ADC) values obtained from prostate multiparametric MRI (mpMRI) to differentiate prostate cancer (PCa) and prostatitis objectively. MATERIALS AND METHODS. We conducted a retrospective review of patients with biopsy-proven PCa or prostatitis who underwent mpMRI study between January 2015 and February 2018. Mean ADC, forward volume transfer constant (K^trans), reverse volume transfer constant (k_ep), plasma volume fraction (V_p), extravascular extracellular space volume fraction (V_e), and time to peak (TTP) values were calculated for both lesions and contralateral normal prostate tissue. Signal intensity-time curves were analyzed. Lesion-to-normal prostate tissue ratios of pharmacokinetic parameters were also calculated. The diagnostic accuracy and cutoff points of all parameters were analyzed to differentiate PCa from prostatitis. RESULTS. A total of 138 patients (94 with PCa and 44 with prostatitis) were included in the study. Statistically, ADC, quantitative pharmacokinetic parameters (K^trans, k_ep, V_e, and V_p), their lesion-to-normal prostate tissue ratios, and TTP values successfully differentiated PCa and prostatitis. Surprisingly, we found that V_e values were significantly higher in prostatitis lesions. The combination of these parameters had 92.7% overall diagnostic accuracy. ADC, k_ep, and TTP made up the most successful combination for differential diagnosis. Analysis of the signal intensity-time curves showed mostly type 2 and type 3 enhancement curve patterns for patients with PCa. Type 3 curves were not seen in any prostatitis cases. CONCLUSION. Quantitative analysis of mpMRI differentiates PCa from prostatitis with high sensitivity and specificity, appears to have significant potential, and may improve diagnostic accuracy. In addition, evaluating these parameters does not cause any extra burden to the patients.

Apparent Diffusion Coefficient Distinguishes Malignancy in T1-Hyperintense Small Renal Masses

OBJECTIVE. Small renal masses (< 4 cm) can be difficult to accurately classify as benign or malignant, particularly when they appear T1 hyperintense on MRI. This intrinsic signal, potentially related to intralesional hemorrhage, may limit evaluation of signal intensity on DWI. The purpose of this study was to test whether apparent diffusion coefficient (ADC) measurements may distinguish malignancy. MATERIALS AND METHODS. This single-center retrospective study identified patients with a T1-hyperintense renal mass less than 4 cm on MRI. Malignant lesions were pathologically proven; a benign mass was established by a predefined hierarchy of pathologic proof, follow-up ultrasound, or follow-up imaging showing more than 5 years of stability. T1 hyperintensity, defined as a signal intensity equivalent to or greater than the adjacent renal cortex, was confirmed by a senior abdominal radiologist. Two additional abdominal radiologists independently measured ADC of the lesion, which was normalized to the ADC of the background ipsilateral kidney and represented as ADC_ratio. RESULTS. The final cohort included 58 benign and 37 malignant renal lesions in 95 patients. Interrater agreement for ADC measurements was almost perfect (κ = 0.836-0.934). ADC_ratio was significantly lower in malignant compared with benign lesions (0.65 ± 0.29 vs 1.03 ± 0.32; p < 0.001). Malignant lesions were significantly larger than benign lesions (2.66 ± 0.86 cm vs 1.50 ± 0.65 cm; p < 0.001); however, after controlling for lesion size, ADC_ratio remained a significant predictor of malignancy (p < 0.001). CONCLUSION. ADC_ratio was highly reproducible for T1-hyperintense small renal masses and was significantly lower in malignant compared with benign renal masses.

[Value of PI-RADS v2 scores combined with prostate specific antigen in diagnosis of peripheral zone prostate cancer: a logistic regression analysis]

OBJECTIVE

To assess the value of Prostate Imaging and Reporting and Data System: Version 2 (PI-RADS v2) combined with prostate specific antigen (PSA) in the diagnosis of peripheral zone (PZ) prostate cancer (PCa).

METHODS

The preoperative magnetic resonance imaging and PSA data were ananlyzed for 69 patients with pathologically confirmed PCa and 109 non-PCa patients. PI-RADS v2 scores (1-5) was used to evaluate the risk of PZ PCa. The total PSA (tPSA) level, free to total PSA ratio (f/t PSA), PSA density (PSAD), PZ-PSAD and PI-RADS v2 scores were compared between the PCa and non-PCa patients. Logistic regression models were established with parameters that differed significantly the two groups. The receiver opearting characteristics (ROC) curve was constructed based on the P values derived from the logical regression models and PI-RADS scores to assess the diagnostic efficiency.

RESULTS

PI-RADS v2 score, tPSA, f/t PSA, PSAD and PZ-PSAD differed significantly between the two groups (P<0.01). Four predictive multivariate models were established: Logit P=-6.825+1.024PI-RADS v2+ 0.223tPSA (A), Logit P=-4.354+1.586PI-RADS v2-12.7841f/tPSA (B), Logit P=-8.993+1.630PI-RADS v2+17.091PSAD (C), and Logit P=-9.434+1.596PI-RADS v2+10.494PZ-PSAD (D), whose area under the ROC curves was 0.908, 0.891, 0.944, and 0.961, respectively, all significantly greater than that of PI-RADS v2 score (P<0.05).

CONCLUSION

Compared with PI-RADS v2 score alone, the combination of PI-RADS v2 score and PSA in the logistic regression model can improve the diagnostic efficiency of PZ PCa and offers better confidence in the decision of biopsy in suspected cases.

[Value of PI-RADS v2 scores combined with prostate specific antigen in diagnosis of peripheral zone prostate cancer: a logistic regression analysis]

OBJECTIVE

To assess the value of Prostate Imaging and Reporting and Data System: Version 2 (PI-RADS v2) combined with prostate specific antigen (PSA) in the diagnosis of peripheral zone (PZ) prostate cancer (PCa).

METHODS

The preoperative magnetic resonance imaging and PSA data were ananlyzed for 69 patients with pathologically confirmed PCa and 109 non-PCa patients. PI-RADS v2 scores (1-5) was used to evaluate the risk of PZ PCa. The total PSA (tPSA) level, free to total PSA ratio (f/t PSA), PSA density (PSAD), PZ-PSAD and PI-RADS v2 scores were compared between the PCa and non-PCa patients. Logistic regression models were established with parameters that differed significantly the two groups. The receiver opearting characteristics (ROC) curve was constructed based on the P values derived from the logical regression models and PI-RADS scores to assess the diagnostic efficiency.

RESULTS

PI-RADS v2 score, tPSA, f/t PSA, PSAD and PZ-PSAD differed significantly between the two groups (P<0.01). Four predictive multivariate models were established: Logit P=-6.825+1.024PI-RADS v2+ 0.223tPSA (A), Logit P=-4.354+1.586PI-RADS v2-12.7841f/tPSA (B), Logit P=-8.993+1.630PI-RADS v2+17.091PSAD (C), and Logit P=-9.434+1.596PI-RADS v2+10.494PZ-PSAD (D), whose area under the ROC curves was 0.908, 0.891, 0.944, and 0.961, respectively, all significantly greater than that of PI-RADS v2 score (P<0.05).

CONCLUSION

Compared with PI-RADS v2 score alone, the combination of PI-RADS v2 score and PSA in the logistic regression model can improve the diagnostic efficiency of PZ PCa and offers better confidence in the decision of biopsy in suspected cases.