Although ovarian cancer is the deadliest type of cancer for women, only about 20% of cases are found at an early stage, as there are no real screening tests for them and few symptoms to prompt them.

Although ovarian cancer is the deadliest type of cancer for women, only about 20% of cases are found at an early stage, as there are no real screening tests for them and few symptoms to prompt them.

A newly devised tumor-specific fluorescent agent and imaging system guided surgeons in real time to remove additional tumors in ovarian cancer patients that were not visible without fluorescence ...

An MRI-based imaging technique developed at the University of Cambridge predicts the response of ovarian cancer tumors to treatment, and rapidly reveals how well treatment is working, in patient ...

Imaging Method Lets Surgeons Remove Small Ovarian Tumors May 2, 2019 Tagging tumors with carbon nanotubes that fluoresce under near-infrared light helps improve surgical survival rates.

Machine learning model builds on imaging methods to better detect ovarian lesions Peer-Reviewed Publication. Washington University in St. Louis ...

The appearance of ovarian lesions on ultrasound is an effective predictor of cancer risk that can help women avoid unnecessary surgery, according to a new study published in the journal Radiology.

March 22 (UPI) --Ovarian lesions seen on ultrasound can help predict cancer risk and help women avoid unnecessary surgery, a study published Tuesday by the journal Radiology found.The ultrasound ...

Ovarian cancer is the third-most-common gynaecological cancer in Germany and the seventh-most-common in people with ovaries across the world. In Germany, there were roughly 7,300 new cases in 2018 ...

Stromal tumors are a rare type of ovarian cancer that starts in the connective tissue cells that hold the ovary together and ...

Machine learning model builds on imaging methods to better detect ovarian lesions. ScienceDaily . Retrieved May 24, 2025 from www.sciencedaily.com / releases / 2022 / 11 / 221129134412.htm ...