Customize Consent Preferences

We use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below.

The cookies that are categorized as "Necessary" are stored on your browser as they are essential for enabling the basic functionalities of the site. ... 

Always Active

Necessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data.

No cookies to display.

Functional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features.

No cookies to display.

Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc.

No cookies to display.

Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors.

No cookies to display.

Advertisement cookies are used to provide visitors with customized advertisements based on the pages you visited previously and to analyze the effectiveness of the ad campaigns.

No cookies to display.

Biopsy

AI Tool Enhances Detection of Metastatic Breast Cancer

UT Southwestern Medical Center researchers have developed an AI tool to improve the detection of metastatic breast cancer, reducing the need for invasive biopsies. The AI model, led by Dr. Basak Dogan, utilizes MRI and machine learning to identify axillary metastasis with high accuracy, potentially avoiding unnecessary surgical procedures and improving clinical outcomes.

PET/MRI Technology Shows Promise in Prostate Cancer Classification Study

A recent study published in The Journal of Nuclear Medicine highlights the potential of PET/MRI technology in accurately classifying prostate cancer patients, potentially avoiding unnecessary biopsies. The research focused on applying the PRIMARY scoring system to PET/MRI results, revealing that utilizing this system could help in avoiding over 80% of unnecessary biopsies while potentially missing only one in eight clinically significant prostate cancer cases. Dr. Hongqian Guo emphasized the value of 68Ga-PSMA PET/MRI in classifying PI-RADS 3 lesions, offering new insights into its clinical application and suggesting that patients could benefit from undergoing this imaging before considering prostate biopsies.