Adding prostate-specific membrane antigen positron emission tomography (PSMA PET) to our prostate cancer armamentarium raises many questions

Diagnostic accuracy of multiparametric ultrasound in the diagnosis of prostate cancer: systematic review and meta-analysis

OBJECTIVES

Ultrasound (US) technology has recently made advances that have led to the development of modalities including elastography and contrast-enhanced ultrasound. The use of different US modalities in combination may increase the accuracy of PCa diagnosis. This study aims to assess the diagnostic accuracy of multiparametric ultrasound (mpUS) in the PCa diagnosis.

METHODS

Through September 2023, we searched through Cochrane CENTRAL, PubMed, Embase, Scopus, Web of Science, ClinicalTrial.gov, and Google Scholar for relevant studies. We used standard methods recommended for meta-analyses of diagnostic evaluation. We plot the SROC curve, which stands for summary receiver operating characteristic. To determine how confounding factors affected the results, meta-regression analysis was used.

RESULTS

Finally, 1004 patients from 8 studies that were included in this research were examined. The diagnostic odds ratio for PCa was 20 (95% confidence interval (CI), 8-49) and the pooled estimates of mpUS for diagnosis were as follows: sensitivity, 0.88 (95% CI, 0.81-0.93); specificity, 0.72 (95% CI, 0.59-0.83); positive predictive value, 0.75 (95% CI, 0.63-0.87); and negative predictive value, 0.82 (95% CI, 0.71-0.93). The area under the SROC curve was 0.89 (95% CI, 0.86-0.92). There was a significant heterogeneity among the studies (p < 0.01). According to meta-regression, both the sensitivity and specificity of mpUS in the diagnosis of clinically significant PCa (csPCa) were inferior to any PCa.

CONCLUSION

The diagnostic accuracy of mpUS in the diagnosis of PCa is moderate, but the accuracy in the diagnosis of csPCa is significantly lower than any PCa. More relevant research is needed in the future.

CRITICAL RELEVANCE STATEMENT

This study provides urologists and sonographers with useful data by summarizing the accuracy of multiparametric ultrasound in the detection of prostate cancer.

KEY POINTS

• Recent studies focused on the role of multiparametric ultrasound in the diagnosis of prostate cancer. • This meta-analysis revealed that multiparametric ultrasound has moderate diagnostic accuracy for prostate cancer. • The diagnostic accuracy of multiparametric ultrasound in the diagnosis of clinically significant prostate cancer is significantly lower than any prostate cancer.

Post-MRI transrectal micro-ultrasonography of transition zone PI-RADS > 2 lesions for biopsy guidance

OBJECTIVE

To prospectively determine the value of post-MRI micro-ultrasonography (microUS) in the diagnosis of transition zone (TZ) significant prostate cancer (sPCa).

PATIENTS AND METHODS

Eighty-four consecutive men (66 ± 6.3 years) with a mean PSA level of 10.2 ± 7.4 ng/mL and at least one TZ-PI-RADS > 2 lesion were included. All patients had MRI-directed microUS and biopsy. Sensitivity and specificity of post-MRI microUS to visualize PI-RADS > 2 TZ lesions, the cancer detection rate of TZ-sPCa, and tumor characteristics according to their visibility on microUS were evaluated. Interreader agreement for detecting microUS+ lesions was evaluated using Cohen's kappa test.

RESULTS

Of the 92 PI-RADS > 2 lesions, 71 (71/92; 77%) were visible on microUS and biopsy was performed without image fusion, which was required for the 21 invisible lesions (21/92; 22.8%). TZ-sPCa detection rate was 51.1% (47/92). Sensitivity and specificity of MRI-directed microUS were 83% (39/47; 95% CI: 69.2-92.4%) and 28.9% (13/45; 95% CI: 16.4-44.3%), on a per-lesion basis and 86.4% (38/45; 95% CI: 72.6-94.8%) and 27.5% (11/40; 95% CI: 14.6-43.9%) on a per-patient basis. Visible tumors on microUS exhibited a larger volume and a lower mean ADC value than non-visible tumors (15.8 ± 5.1 vs. 12.5 ± 3.6 mm and 0.82 ± 1.1 × 10³ vs. 0.9 ± 1.4 × 10^-3 mm²/s) (p = 0.02). Non-visible tumors showed a heterogeneous non-specific echotexture or were masked by the shadowing caused by corpora amylacea. Interreader agreement was almost perfect (kappa = 0.88; 95% CI: 0.79-0.95). The main limitation is the single-center feature of the study.

CONCLUSION

MRI-targeted transrectal microUS is effective to detect TZ-sPCa. TRUS-MRI image fusion helps overcome limitations due to TZ tissue heterogeneity.

KEY POINTS

microUS can visualize the majority of MRI-detected PI-RADS > 2 TZ lesions (sensitivity = 83%). Interreader agreement of MRI-directed microUS in the detection of TZ lesions appears excellent (kappa = 0.88). In 77% of PI-RADS > 2 TZ lesions, biopsy was performed under microUS visual control. MRI fusion system was only used to overcome limitations due to tissue heterogeneity of benign prostatic hyperplasia.

The challenge of prostate biopsy guidance in the era of mpMRI detected lesion: ultrasound-guided versus in-bore biopsy

The current recommendation in patients with a clinical suspicion for prostate cancer is to perform systematic biopsies extended with targeted biopsies, depending on mpMRI results. Following a positive mpMRI [i.e. Prostate Imaging Reporting and Data System (PI-RADS) ≥3], three targeted biopsy approaches can be performed: visual registration of the MRI images with real-time ultrasound imaging; software-assisted fusion of the MRI images and real-time ultrasound images, and in-bore biopsy within the MR scanner. This collaborative review discusses the advantages and disadvantages of each targeting approach and elaborates on future developments. Cancer detection rates seem to mostly depend on practitioner experience and selection criteria (biopsy naïve, previous negative biopsy, prostate-specific antigen (PSA) selection criteria, presence of a lesion on MRI), and to a lesser extent dependent on biopsy technique. There is no clear consensus on the optimal targeting approach. The choice of technique depends on local experience and availability of equipment, individual patient characteristics, and onsite cost-benefit analysis. Innovations in imaging techniques and software-based algorithms may lead to further improvements in this field.

A review of artificial intelligence in prostate cancer detection on imaging

A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care.

The Micro-ultrasound Guided Prostate Biopsy in Detection of Prostate Cancer: A Systematic Review and Meta-Analysis

BACKGROUND

To compare the detection rate of micro-ultrasound with multiparametric magnetic resonance imaging targeted biopsy (mpMRI-TB) for prostate cancer diagnosis.

METHODS

The studies on micro-ultrasound prostate biopsy for prostate cancer diagnosis were searched in PubMed, Cochrane library and EMBASE databases from inception to April.2021. we performed a systematic review and cumulative meta-analysis based on search results using Software Rev-Man 5.3.

RESULTS

A total of 11 studies involving 1081 patients were included. The Meta-analysis showed that no significant difference was found between micro-ultrasound and mpMRI-TB in the total detection of prostate cancer(OR:1.01, 95%CI:0.85~1.21, p=0.89), of Grading Groups(GG)=1(OR: 0.92, 95%CI:0.68~1.25, p=0.59),of GG≥2(OR:1.01, 95%CI:0.83~1.22, p=0.92), and of GG≥3(OR: 1.31, 95%CI:0.95~1.81, p=0.10).

CONCLUSIONS

Micro-ultrasound guided prostate biopsy provides comparable detection rates for prostate cancer diagnosis with the mpMRI-TB, which is expected to challenge mpMRI-TB in the diagnosis of prostate cancer.