Prostate Cancer Detection and Diagnosis: Role of Ultrasound with MRI Correlates

Liau, Joy; Goldberg, Dani; Arif-Tiwari, Hina

doi:10.1007/s40134-019-0318-8

Cited by 12 publications

(6 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… 71 – 73 When visible on ultrasound, cancers most often appear hypoechoic 74 because they reflect significantly less sound echoes than normal tissue. To supplement grayscale ultrasound, other new ultrasound-based imaging techniques such as shear-wave elastography, 75 – 77 color doppler ultrasound, 77 contrast-enhanced ultrasound, 78 micro-ultrasound, 79 and their combination have been proposed. These alternative ultrasound-based imaging modalities provide enhanced image resolution and better visualization of the prostate compared to grayscale ultrasound, and enable prostate cancer detection with better sensitivity compared to grayscale ultrasound.…”

Section: Ai Models For Prostate Cancer Detection and Characterizing C...mentioning

confidence: 99%

A review of artificial intelligence in prostate cancer detection on imaging

Bhattacharya

Khandwala

Vesal

et al. 2022

Therapeutic Advances in Urology

View full text Add to dashboard Cite

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.

show abstract

Section: Ai Models For Prostate Cancer Detection and Characterizing C...mentioning

confidence: 99%

A review of artificial intelligence in prostate cancer detection on imaging

Bhattacharya

Khandwala

Vesal

et al. 2022

Therapeutic Advances in Urology

View full text Add to dashboard Cite

show abstract

“…Zhang Cancer detection rate of TRE may also depend on Gleason scores as higher scores represent increased cell density and stiffness, thus the higher sensitivity of TRE. 16 Addition of TRE targeted biopsy to standard biopsy can increase prostate cancer detection rate. Schiffman et al also found high specificity (90.9%) of elastography targeted prostate biopsy.…”

Section: Resultsmentioning

confidence: 99%

Real Time Trans-Rectal Elastography of Prostate Correlation with Histopathology in a Suspected Case of Prostate Cancer

Basnet¹,

Suwal

Chataut

et al. 2021

Kathmandu Univ. Med. J.

View full text Add to dashboard Cite

Background Early detection of prostate cancer, the second most common cancer in men worldwide, is the key for its successful treatment. Commonly used clinical criteria and imaging tools for detection of prostate cancer are less sensitive. Objective This study was aimed to find role of real time transrectal elastography of prostate for detection of prostate cancer. Method Study was conducted in 66 patients with clinical suspicion of prostate cancer, who were sent for ultrasound guided prostate biopsy. Transrectal ultrasound with real time elastography was performed in all the patients prior to the biopsy and looked for hard areas within the prostate. Then six-core tru-cut biopsy were taken in six zones of prostate, including the hard areas detected in the elastography. The histopathology report were correlated with the elastography findings. Result Median prostate specific antigen of the patients was 11.5 ng/ml with interquartile range of 8 to 23.5 ng/ml. Digital rectal examination showed hard nodular findings in 35 patients. Transrectal ultrasound showed 81 hypoechoic lesions in 31 patients. Elastography showed 127 hard areas in 31 patients. Histopathology showed 90 positive biopsy cores in 23 patients. Cancer detection rate of elastography was 82.6%. At 95% confidence interval, patients with elastography detected hard lesions had 19.4 times more likelihood to have prostate cancer. Sensitivity of elastography was high as compared to digital rectal examination and transrectal ultrasound alone. Conclusion Transrectal elastography had high sensitivity over clinical tools and transrectal ultrasonography for detection of prostate cancer.

show abstract

“…Simple and Neural stimulation on normal and cancerous prostate tissue and the reaction of the tissues used to investigate the presence or absence of the mass. Since the prostate tissue is symmetrical and the characteristics of forming cancerous masses are similar to the prostate tissue, 26 to analyze the neural stimulation velocity and frequency on the tissue, neural stimulation applied to the prostate gland at 0, 5, and 10 Hz with different velocities. Once again, it was placed on the spherical zone of the prostate gland; and the conditions of the previous states repeated.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of the Differences between Normal and Cancerous Prostate Tissue Response to Simple and Vibro-Neural Stimulation

Zein

Ghomsheh

Jamshidian

2020

Int Clin Neurosci J

View full text Add to dashboard Cite

Background: Early detection of prostate cancer has significant benefits for its treatment and can increase the survival chance in patients. In recent years, new methods such as shear wave elastography and vibro-elastography, as well as artificial tactile sensing, have been used to detect a mass in the prostate tissue in-vivo and ex-vivo. This paper aims to investigate the difference between normal and malignant prostate tissue reaction to simple and vibro-neural stimulation for prostate tissue mass detection in order to determine neural stimulation intensity, velocity, and frequency to obtain the best result in detecting the type and location of the tumor. Methods: This study has utilized neural stimulation devices in normal and cancerous tissues. The stimulation velocity, probe location, and the frequency of neural stimulation considered as the independent variables. Results: The results show that for superficial masses, although dependent on the probe, the accuracy of detection at the low speed of 5mm/s is 50% higher than other conditions. On the other hand, in deep masses, with increasing mass depth, the accuracy of detection at the medium speed of 8mm/s is 30% higher than the low speed. Finally, the results showed that with increased stimulation frequency, the possibility of tumor detection, and its accuracy increases by 35%. Conclusion: By improving the accuracy of the neural stimulation device, it can apply to detect hard materials such as tumors and malignant tissues.

show abstract

Prostate Cancer Detection and Diagnosis: Role of Ultrasound with MRI Correlates

Cited by 12 publications

References 45 publications

A review of artificial intelligence in prostate cancer detection on imaging

A review of artificial intelligence in prostate cancer detection on imaging

Real Time Trans-Rectal Elastography of Prostate Correlation with Histopathology in a Suspected Case of Prostate Cancer

Evaluation of the Differences between Normal and Cancerous Prostate Tissue Response to Simple and Vibro-Neural Stimulation

Contact Info

Product

Resources

About