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Multiparametric ultrasound and micro-ultrasound in prostate cancer: a comprehensive review.

ABSTRACT: Prostate cancer (PCa) is the most common non-cutaneous cancer diagnosed in males. Traditional tools for screening and diagnosis, such as prostate-specific antigen, digital rectal examination and conventional transrectal ultrasound (TRUS), present low accuracy for PCa detection. Multiparametric MRI has become a game changer in the PCa diagnosis pathway and MRI-targeted biopsies are currently recommended for males at risk of clinically significant PCa, even in biopsy-naïve patients. Recent advances in ultrasound have also emerged with the goal to provide a readily accessible and cost-effective tool for detection of PCa. These newer techniques include elastography and contrast-enhanced ultrasound, as well as improved B-mode and Doppler techniques. These modalities can be combined to define a novel ultrasound approach, multiparametric ultrasound. High frequency Micro-ultrasound has emerged as a promising imaging technology for PCa diagnosis. Initial results have shown high sensitivity of Micro-ultrasound in detecting PCa in addition to its potential in improving the accuracy of targeted biopsies, based on targeting under real-time visualization, rather than relying on cognitive/fusion software MRI-transrectal ultrasound-guided biopsy.

SUBMITTER: Dias AB

PROVIDER: S-EPMC8978255 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Multiparametric ultrasound and micro-ultrasound in prostate cancer: a comprehensive review.

Dias Adriano Basso AB O'Brien Ciara C Correas Jean-Michel JM Ghai Sangeet S

The British journal of radiology 20211109 1131

Prostate cancer (PCa) is the most common non-cutaneous cancer diagnosed in males. Traditional tools for screening and diagnosis, such as prostate-specific antigen, digital rectal examination and conventional transrectal ultrasound (TRUS), present low accuracy for PCa detection. Multiparametric MRI has become a game changer in the PCa diagnosis pathway and MRI-targeted biopsies are currently recommended for males at risk of clinically significant PCa, even in biopsy-naïve patients. Recent advance ...[more]

PMID: 34752132

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Project description:ObjectivesUltrasound (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.MethodsThrough 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.ResultsFinally, 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.ConclusionThe 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 statementThis 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.

Project description:ObjectivesThe aim of this study was to assess the potential of machine learning based on B-mode, shear-wave elastography (SWE), and dynamic contrast-enhanced ultrasound (DCE-US) radiomics for the localization of prostate cancer (PCa) lesions using transrectal ultrasound.MethodsThis study was approved by the institutional review board and comprised 50 men with biopsy-confirmed PCa that were referred for radical prostatectomy. Prior to surgery, patients received transrectal ultrasound (TRUS), SWE, and DCE-US for three imaging planes. The images were automatically segmented and registered. First, model-based features related to contrast perfusion and dispersion were extracted from the DCE-US videos. Subsequently, radiomics were retrieved from all modalities. Machine learning was applied through a random forest classification algorithm, using the co-registered histopathology from the radical prostatectomy specimens as a reference to draw benign and malignant regions of interest. To avoid overfitting, the performance of the multiparametric classifier was assessed through leave-one-patient-out cross-validation.ResultsThe multiparametric classifier reached a region-wise area under the receiver operating characteristics curve (ROC-AUC) of 0.75 and 0.90 for PCa and Gleason > 3 + 4 significant PCa, respectively, thereby outperforming the best-performing single parameter (i.e., contrast velocity) yielding ROC-AUCs of 0.69 and 0.76, respectively. Machine learning revealed that combinations between perfusion-, dispersion-, and elasticity-related features were favored.ConclusionsIn this paper, technical feasibility of multiparametric machine learning to improve upon single US modalities for the localization of PCa has been demonstrated. Extended datasets for training and testing may establish the clinical value of automatic multiparametric US classification in the early diagnosis of PCa.Key points• Combination of B-mode ultrasound, shear-wave elastography, and contrast ultrasound radiomics through machine learning is technically feasible. • Multiparametric ultrasound demonstrated a higher prostate cancer localization ability than single ultrasound modalities. • Computer-aided multiparametric ultrasound could help clinicians in biopsy targeting.

Project description:BackgroundThe diagnostic pathway for prostate cancer (PCa) is advancing towards an imaging-driven approach. Multiparametric magnetic resonance imaging, although increasingly used, has not shown sufficient accuracy to replace biopsy for now. The introduction of new ultrasound (US) modalities, such as quantitative contrast-enhanced US (CEUS) and shear wave elastography (SWE), shows promise but is not evidenced by sufficient high quality studies, especially for the combination of different US modalities. The primary objective of this study is to determine the individual and complementary diagnostic performance of greyscale US (GS), SWE, CEUS and their combination, multiparametric ultrasound (mpUS), for the detection and localization of PCa by comparison with corresponding histopathology.Methods/designIn this prospective clinical trial, US imaging consisting of GS, SWE and CEUS with quantitative mapping on 3 prostate imaging planes (base, mid and apex) will be performed in 50 patients with biopsy-proven PCa before planned radical prostatectomy using a clinical ultrasound scanner. All US imaging will be evaluated by US readers, scoring the four quadrants of each imaging plane for the likelihood of significant PCa based on a 1 to 5 Likert Scale. Following resection, PCa tumour foci will be identified, graded and attributed to the imaging-derived quadrants in each prostate plane for all prostatectomy specimens. Primary outcome measure will be the sensitivity, specificity, negative predictive value and positive predictive value of each US modality and mpUS to detect and localize significant PCa evaluated for different Likert Scale thresholds using receiver operating characteristics curve analyses.DiscussionIn the evaluation of new PCa imaging modalities, a structured comparison with gold standard radical prostatectomy specimens is essential as first step. This trial is the first to combine the most promising ultrasound modalities into mpUS. It complies with the IDEAL stage 2b recommendations and will be an important step towards the evaluation of mpUS as a possible option for accurate detection and localization of PCa.Trial registrationThe study protocol for multiparametric ultrasound was prospectively registered on Clinicaltrials.gov on 14 March 2017 with the registry name 'Multiparametric Ultrasound-Study for the Detection of Prostate Cancer' and trial registration number NCT03091231.

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Multiparametric ultrasound and micro-ultrasound in prostate cancer: a comprehensive review.

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Multiparametric ultrasound and micro-ultrasound in prostate cancer: a comprehensive review.

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