Scientists excited by AI tool that grades severity of rare cancer

By recognising details invisible to the naked eye, AI was 82% accurate, compared with 44% for lab analysis.

Researchers from the Royal Marsden Hospital an Institute of Cancer Research say it could improve treatment and benefit thousands every year. They are also excited by its potential for spotting other cancers early.

AI is already showing huge promise for diagnosing breast cancers and reducing treatment times. omputers can be fed huge amounts of information and trained to identify the patterns in it to make predictions, solve problems and even learn from their own mistakes.

“We’re incredibly excited by the potential of this state-of-the-art technology,” said Professor Christina Messiou, consultant radiologist at The Royal Marsden NHS Foundation Trust and professor in imaging for personalised oncology at The Institute of Cancer Research, London. “It could lead to patients having better outcomes, through faster diagnosis and more effectively personalised treatment.”

Now that’s good news … :+1:

For those interested in the technological details of the research, full details are here:

A CT-based radiomics classification model for the prediction of histological type and tumour grade in retroperitoneal sarcoma (RADSARC-R): a retrospective multicohort analysis

… but it’s not an easy read … :face_with_raised_eyebrow:

The “tool”, I believe, is XNAT:

XNAT is an open source imaging informatics platform developed by the Neuroinformatics Research Group at Washington University. XNAT was originally developed at Washington University in the Buckner Lab, which is now located at Harvard University. It facilitates common management, productivity, and quality assurance tasks for imaging and associated data. Thanks to its extensibility, XNAT can be used to support a wide range of imaging-based projects.