New diagnostic tool Ovarian Cancer treatment

A new type of diagnostic system using sophisticated computer software capable of analyzing and comparing cancerous tissue against a vast databank of digital images of cancer samples could speed up treatment for ovarian cancer.
The system, being designed by Aïcha BenTaieb, a Simon Fraser University computing science PhD student, aims to automate the identification of ovarian carcinomas for a more reliable, faster and available diagnosis. What is known is that there are five main subtypes. Effective treatment depends on identifying the subtype as soon as possible. But current methods are subjective, time-consuming and prone to error. Using information collated via computers, BenTaieb believes she has found a better way to identify these subtypes. Each ovarian cancer subtype shows individual structural and cellular characteristics. For treatment to be effectively targeted, the subtype must first be identified.
Currently, pathologists analyze tissue samples using a microscope, digital scanner and computer software. However, identification can easily be impaired by technical factors such as lighting and the pathologist’s experience.
With BenTaieb’s method, an artificial intelligence feature is integrated into the software that helps the pathologist analyze the tissue sample. This feature is trained, through a large dataset of expert annotated slides, to automatically identify the characteristic visual patterns for each subtype of carcinoma.
“We’re looking at the whole image, in different regions, in a more efficient way, using computers to extract image-based features,” BenTaieb said.