Evaluation of Prostatic Lesions by Transrectal Ultrasound, Color Doppler, and the Histopathological Correlation

Predictive Value of Transrectal Ultrasonic Doppler and Elastographic Features in Prostate Cancer Detection in Lagos University Teaching Hospital

Introduction

This study aimed at determining the predictive value (PV) of transrectal ultrasonic Doppler and elastographic features in prostate cancer (PCa) detection among patients in Lagos University Teaching Hospital.

Materials and Methods

This prospective study involved patients that underwent evaluation for PCa. Participants had digital rectal examination (DRE), prostate-specific antigen (PSA) assay, and transrectal ultrasound-guided prostate biopsy using colour Doppler (CD) and elastography. All cores were sent for histopathology. Data were analysed using Statistical Package for the Social Sciences Version 22.0. CD and elastography PV in PCa detection and their relationships to the Gleason score (GS) were analysed (P < 0.05).

Results

Seventy men (aged between 45 and 87 years) were enrolled. Forty-three (61.4%) patients had PCa with a mean age of 69.37 ± 8.22years. The sensitivity, specificity, positive PV (PPV), negative PV (NPV) and accuracy of CD were 8.50%, 97.44%, 64.10%, 66.42% and 66.31%, respectively. The sensitivity, specificity, PPV, NPV and accuracy of elastography were 84.21%, 94.59%, 88.89%, 92.11% and 91.07%, respectively.

Conclusion

There is a significant association between decreased elasticity (elastography) and PCa detection but a weak association between increased vascularity (CD) and PCa detection. A positive correlation exists between extent of prostatic stiffness and GS.

Self-Supervised Learning With Limited Labeled Data for Prostate Cancer Detection in High-Frequency Ultrasound

Deep learning-based analysis of high-frequency, high-resolution micro-ultrasound data shows great promise for prostate cancer (PCa) detection. Previous approaches to analysis of ultrasound data largely follow a supervised learning (SL) paradigm. Ground truth labels for ultrasound images used for training deep networks often include coarse annotations generated from the histopathological analysis of tissue samples obtained via biopsy. This creates inherent limitations on the availability and quality of labeled data, posing major challenges to the success of SL methods. However, unlabeled prostate ultrasound data are more abundant. In this work, we successfully apply self-supervised representation learning to micro-ultrasound data. Using ultrasound data from 1028 biopsy cores of 391 subjects obtained in two clinical centers, we demonstrate that feature representations learned with this method can be used to classify cancer from noncancer tissue, obtaining an AUROC score of 91% on an independent test set. To the best of our knowledge, this is the first successful end-to-end self-SL (SSL) approach for PCa detection using ultrasound data. Our method outperforms baseline SL approaches, generalizes well between different data centers, and scales well in performance as more unlabeled data are added, making it a promising approach for future research using large volumes of unlabeled data. Our code is publicly available at https://www.github.com/MahdiGilany/SSL_micro_ultrasound.

Three-dimensional ultrasound integrating nomogram and the blood flow image for prostate cancer diagnosis and biopsy: A retrospective study

Backgrounds

Prostate cancer (PCa) is the second most common male cancer in the world and based on its high prevalence and overwhelming effect on patients, more precise diagnostic and therapeutic methods are essential research topics. As such, this study aims to evaluate the value of three-dimensional transrectal ultrasound (3D-TRUS) in the detection, diagnosis and biopsy of PCa, and to provide a basis for clinical practice of PCa.

Methods

Retrospective analysis and comparison of a total of 401 male patients who underwent prostate TRUS in our hospital from 2019 to 2020 were conducted, with all patients having prostate biopsy. Nomogram was used to estimate the probability of different ultrasound signs in diagnosing prostate cancer. The ROC curve was used to estimate the screening and diagnosis rates of 3D-TRUS, MRI and TRUS for prostate cancer.

Results

A total of 401 patients were randomly divided into two groups according to different methods of prostate ultrasonography, namely the TRUS group (251 patients) and the 3D-TRUS group (150 patients). Of these cases, 111 patients in 3D-TRUS group underwent MRI scan. The nomogram further determined the value of 3D-TRUS for prostate cancer. The ROC AUC of prostate cancer detected by TRUS, MRI and 3D-TRUS was 0.5580, 0.6216 and 0.6267 respectively. Biopsy complications were lower in 3D-TRUS group than TRUS group, which was statistically significant (P&lt;0.005).

Conclusions

The accuracy of 3D-TRUS was higher in diagnosis and biopsy of prostate cancer. Meanwhile, the positive rate of biopsy could be improved under direct visualization of 3D-TRUS, and the complications could be decreased markedly. Therefore, 3D-TRUS was of high clinical value in diagnosis and biopsy of prostate cancer.

Impact of a novel ultrasound microvascular imaging and elastography on prostate cancer classification

Background

This study was aimed to compare the sensitivity of transrectal ultrasound (TRUS) guided systematic biopsy (TRUS-SB), superb microvascular imaging guided targeted biopsy (SMI-TB), and elastography ultrasound guided targeted biopsy (EU-TB) in detecting prostate cancer (PCa).

Methods

One hundred and eighty-four patients with suspicious PCa were randomly divided into two groups, SMI-TB group (n=92) and EU-TB group (n=92). All the patients received a 2-core SMI-TB or a 2-core EU-TB before receiving a 12-core TRU-SB depending on the group. The 2-core SMI-TB was conducted at the most increased vascularity areas rated by a radiologist on a semi-quantitative criterion. The 2-core EU-TB was performed at the dark blue areas that identified as suspicious areas. The PCa detection rate was compared among TRUS-SB, SMI-TB, and EU-TB in the total population and in two groups.

Results

PCa was detected in 65 out of 184 patients. The SMI-TB group patients received 1,272 cores in total with a positive rate of 23.81% (40/168) for SMI-TB cores and 4.17% (46/1,104) for TRUS-SB cores. The EU-TB group patients received a total of 1,262 cores with a positive rate of 31.01% (49/158) for EU-TB cores and 5.34% (59/1,104) for TRUS-SB cores. Targeted cores (27.30%, 89/326) achieved significantly higher sensitivity in PCa detection than systematic cores (4.76%, 105/2,208) (SMI-TB vs. TRUS-SB: OR 7.188, P<0.001; EU-TB vs. TRUS-SB: OR 7.962, P<0.001).

Conclusions

Superb microvascular imaging and elastography ultrasound guided targeted biopsy may result in higher prostate cancer detection rate as adjunct to gray-scale ultrasound guided systematic biopsy. However, targeted biopsy alone is not sufficient to detect PCa, compared with systematic biopsy.

Prostate Cancer Vascularity: Superb Microvascular Imaging Ultrasonography with Histopathology Correlation

Background

The aim of this study was to evaluate the association between prostate cancer (PCa) vascularity detected by superb microvascular imaging (SMI) and Gleason score in biopsy specimens.

Material/Methods

A total of 119 patients with suspected PCa before biopsy underwent gray-scale ultrasound (US), color Doppler ultrasound (CDUS), and SMI imaging between June 2018 and March 2019. Vascularity quantity was assessed by SMI and compared with that of CDUS. The vessel parameter was also compared with the Gleason score. The sensitivity of PCa was compared between transrectal ultrasound guided systematic biopsy (SB) and SMI-guided targeted biopsy (SMI-guided TB).

Results

Pathology confirmed 74 of 119 patients had PCa. The microvascular quantity of PCa patients was significantly higher than that of non-malignant patients. SMI detected blood vessels in 97.3% (72/74) in the malignant group, while CDUS identified blood flow signals in 90.5% (67/74) of the PCa group. SMI visualized enriched microvascular in PCa of Gleason 8 (54.5%) and Gleason 9 (92.3%). There was a positive correlation between microvascular quantity detected by SMI and Gleason score, with a correlation coefficient of 0.373 (P<0.001). SMI-guided TB cores were significantly more likely than SB cores to detect PCa (OR=12.83, P<0.001).

Conclusions

SMI could be promising as a useful imaging technique in the detection and characterization of PCa. There was a positive correlation between microvascular quantity detected by SMI and Gleason score.