Researchers at Sylvester Comprehensive Cancer Center and the Desai Sethi Urology Institute are pioneering research to harness machine learning for the diagnosis and prognosis of prostate cancer, using magnetic resonance imaging (MRI).
The recent integration of open-source data with machine learning models, especially in the medical field, has opened new doors to studying disease progression and/or regression. The use of conventional MRI in prostate cancer research and treatment is effective for prognosis, diagnosis, active surveillance, and reducing the need for biopsy procedures in lower-risk patients.
“Recent advances in machine learning suggest there is promise in developing pipelines that could automate standardized and objective assessments from MRI images while reducing time, human capital, and other resource costs,” according to Himanshu Arora, Ph.D., assistant professor at Sylvester and the Desai Sethi Urology Institute at the Miller School of Medicine.
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But there are obstacles to effectively using machine learning in patient care, including the ability to effectively use machine learning approaches for specific cancers, the specificity of training data for a particular medical condition, etc. In this context, the most recent technologies, like generative adversarial networks (GANs), are being looked upon as a potential way to generate high-quality synthetic data that preserve the clinical variability of a condition and be applied to PET, CT, MRI, ultrasound, and X-ray imaging in the brain, abdomen, and chest. However, despite some success, GAN model usage remains minimal when depicting the heterogeneity of a disease such as prostate cancer.
The translational research team is focused on improvising the GAN tools, Dr. Arora said, to allow the output images to be applied and integrated with diagnostic and prognostic tools in prostate cancer. A pioneer in the use of GANs in machine learning for prostate cancer, Dr. Arora authored a study, “Generative Adversarial Networks Can Create High Quality Artificial Prostate Cancer Magnetic Resonance Images,” that was recently published in the Journal of Personalized Medicine as part of a special issue, “The Cutting Edge and Precision Medicine in Prostate Cancer. “
Timely Diagnosis of Prostate Cancer
Making headway in the use of machine learning in prostate cancer is especially important, given that it is the most common cancer among men and second most common cause of cancer-related death for men in the U.S.