Project Wonder - The art of science at the Medical College of Wisconsin

High Stakes Hide-and-Seek

Can artificial intelligence help surgeons and radiation oncologists find invisible brain tumors? Scientists at MCW and their peers have discovered the presence of cancerous tissue in some research volunteers that is undetectable by even the most sophisticated medical imaging techniques.

To better understand this problem, Drs. Peter LaViolette and Sam Bobholz worked with a team of collaborators in multiple MCW departments and two California medical schools to obtain and study 159 tissue samples from 65 deceased patients who had suffered from brain cancer and volunteered to participate to advance detection and treatment for future generations.

Drs. LaViolette, Bobholz and team then used these patients’ MRI scans to determine precise measurements and print unique 3-D molds to match each brain. This mold allows the brain to be sliced and processed into glass slides that accurately match the corresponding MRI scan. The team’s pathologists analyzed the tissue for tumor severity and location before sending annotated images to Drs. LaViolette and Bobholz. Drs. LaViolette and Bobholz then employed a machine learning program to read and compare the hundreds of pathologist-annotated slides and the MRI data.

Machine learning is a rapidly growing and evolving approach to analyzing and interpreting massive amounts of data. Unlike in traditional programming that requires scientists to provide a computer with every parameter of a problem using handwritten code, machine learning developers and scientists apply artificial intelligence to set up frameworks through which computers learn on their own. By comparing the many images provided by the scientists, the software learns what features in the MRI are predictive of the tumors found in the annotated slides.

Drs. LaViolette, Bobholz and team reported in a preprint of the upcoming manuscript that they have successfully identified previously invisible tumor in 72.5 percent of research subjects with their tumor probability maps. This demonstrates that a significant subset of brain cancer patients may one day benefit from clinical application of this mapping technique once further research has been conducted. By revealing these otherwise hidden boundaries of tumors, cancer progression may be able to be monitored with greater precision and future surgeries and targeted radiation treatments may become more effective, reducing the chance of recurrence.