Panel Discussion: PROSTATE IMAGING
Baris Turkbey, MD, Steven P. Rowe, MD, PhD, and Sandeep Arora, MBBS, FSAR presented “Panel...
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Talks by Baris Turkbey, MD
Novel Molecular Tracers for Prostate Imaging
Ismail Baris Turkbey, MD, Senior Clinician, Molecular Imaging Branch (MIB), National Cancer Institute (NCI), National Institutes for Health (NIH) discusses novel molecular tracers for prostate imaging. Dr. Turkbey begins by listing and describing various molecular tracers used with positron emission tomography (PET) imaging of the prostate, including tracers used historically, explaining what each targets (e.g. glucose, cell membrane synthesis). Dr. Turkbey summarizes past trials of various tracers that fell short of prostate-specific membrane antigen (PSMA) in identifying localized disease.
He calls the last few years “exciting” in terms of developments in molecular imaging and several PET tracers targeting PSMA. Dr. Turkbey outlines 68Ga-PSMA-11, 18F-DCFPYL, and 18F-rhPSMA-7.3 and illustrates their use with patient case examples whereby these PSMA tracers helped identify local disease and guide focal therapies. He lists the various tracers available and explains that each has advantages and disadvantages and it depends on the biology to be profiled. Dr. Turkbey reiterates that the strongest evidence for identifying and staging localized disease is PSMA PET CT scans which are now available.
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Panel Discussion Part Two: PROSTATE IMAGING
Baris Turkbey, MD, and Sadhna Verma, MD, FSAR
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Rise of the Machines: AI in Prostate MRI
Baris Turkbey, MD, explores the capabilities and training gaps of Artificial Intelligence in MRI prostate cancer detection. He begins by establishing the ubiquity of MRI technology in prostate cancer treatment across the globe and highlighting the shortcomings of previous and current guidelines on the use of MRI for disease detection.
Dr. Turkbey highlights the shortcomings of previous and current guidelines on the use of MRI for disease detection, noting that current data is too dependent on the experience of the practitioner. He then introduces the idea of using Artificial Intelligence (AI) to standardize prostate cancer detection.
Dr. Turkbey then presents examples of AI successfully detecting prostate cancer lesions on MRI imaging. However, he cautions that more rigorous training and supervision is still needed for AI imaging, as AI has a high false-positive rate.
Dr. Turkbey concludes by examining the impact of image quality on AI performance, indicating that more training is required before these models can be deployed as a diagnostic aid. He presents data that demonstrates that low-quality images significantly impact the cancer detection rate of AI models.
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