Predicting prostate cancer with advanced machine learning
Dr. LaViolette began his imaging research at MCW in brain cancer, inspired by his interested in comparing physical tissue to MRI scans to improve what can be extracted from the MRI data. His team was the first to build the software necessary to conduct this comparison. While doing so, he realized he could further analyze the data generated by applying machine learning.
Machine learning is a radically different approach to interfacing with a computer than traditional programming. Rather than discretely telling the computer every parameter of a problem with handwritten code, machine learning developers and scientists apply artificial intelligence to set up frameworks whereby computers teach themselves.
For Dr. LaViolette, a similar approach allows his lab to teach a computer how to predict the location and severity of prostate tumors from the data in MRI scans.
Patients whose cancerous prostates are being removed and who agree to participate have an MRI scan taken before surgery. Afterward, Dr. LaViolette uses dimensions calculated from the MRI scan to print a custom 3-D mold of each prostate. This mold allows the prostate to be sliced and processed into glass slides that precisely match the MRI scan. The tissue is then analyzed for tumor severity (grade), growth pattern and location. The annotated pathology and MRI data are sent to a machine learning program that compares the images to learn what features in the MRI are predictive of tumors of varying severity or growth pattern.
The long-term goal is to improve the staging and monitoring of patients who have prostate cancer but may not need surgery. It also may allow for more targeted biopsies and radiation therapy, which would improve the accuracy of diagnosis and reduce side effects from damage to healthy tissue.
"More and more, active surveillance and watchful waiting are being seen as reasonable approaches. Not every prostate cancer case is aggressive and needs immediate surgery or radiation. But we need more information to get better at monitoring patients and staging their treatments," says Dr. LaViolette.
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