“Timely diagnosis and assessment of prognosis are challenges for prostate cancer, and this results in many deaths and increases [risk of disease progression],” Dr. Arora said. “We cannot replace the human eye when it comes to medical decision-making. Still, the improvement in technologies could potentially assist radiation oncologists in making timely decisions.”
GAN provides a long-term impact on evolving the machine learning models by requiring less data and patient follow-ups for effective predictions. This is important for reducing health care costs and pain associated with repeat follow-ups.
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The rationale for using GAN is to use machine learning capabilities and generate digital images by learning from previous follow-ups (MRI images and clinical parameters) and understanding and predicting disease progression or regression patterns.
“Technically, the technology developed here is the first start to building more sophisticated models of ‘data augmentation’ where new digital images can be used in further analysis. This is an early phase of our study, but the outcomes are extremely promising,” Dr. Arora said.
High-Quality MRIs and Deep Learning
Dr. Arora and his colleagues conducted the study using prostate MRIs and digital pathology from various sources as training data to create a GAN model. Using deep learning, they trained the model to segment the prostate boundary on MRI and histology slices, which are microscopic structures of the tissues.
Scientists with varying degrees of experience assessed the resulting images compared to conventional MRI images of the prostate. The researchers demonstrated that the prostate cancer MRIs they generated using the model were high quality. Deep learning segmentation helped to remove images with high distortion, suggesting this GAN machine learning prostate cancer model has promising implications for complex prostate cancer patient imaging.
“Our group at Desai Sethi institute and Sylvester is leading this research by successfully developing the customized GAN to generate synthetic images of high enough quality to use in practice,” Dr. Arora said. “These images are being used to train the traditional machine learning models that could perform diagnosis and prognosis of prostate cancer by using in-house data from active surveillance trials and publicly available data from multiple resources.”
Dr. Arora and his team have presented their high-impact research on the use of GAN in prostate cancer imaging at major medical conferences, including the American Society of Human Genetics and American Urological Association annual meetings in 2022.
Dr. Arora’s co-authors on this study include Isaac R. L. Xu; Derek J. Van Booven; Sankalp Goberdhan; Adrian Breto; Joao Porto; Mohammad Alhusseini; Ahmad Algohary; Radka Stoyanova; Sanoj Punnen; and Anton Mahne.
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