Dr. Keith Dreyer, PhD

Nov 27, 2016
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True-positive PSMA-PET with positive MRI in 74-year-old male with prostate cancer (Gleason score of 4 + 5; left anterior and posterior apex positive cores). (A) Left posterior peripheral apical to midprostate hyposignal visible on transaxial MRI. (B) No definite CT correlate evident (white arrow). (C) Focal high PSMA expression detectable in left posterior mid to base with SUVmax of 12.4 on transaxial fused delayed pelvic PET/CT (yellow arrow). (D) Focal high PSMA expression found in left midgland to base of prostate on maximum-intensity-projection image (yellow arrow).
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PET/CT outperforms MRI after focal therapy for prostate cancer
By Will Morton
No accurate noninvasive method has been validated to evaluate treatment response after focal therapy.
August 20, 2026
True-positive PSMA-PET with positive MRI in 74-year-old male with prostate cancer (Gleason score of 4 + 5; left anterior and posterior apex positive cores). (A) Left posterior peripheral apical to midprostate hyposignal visible on transaxial MRI. (B) No definite CT correlate evident (white arrow). (C) Focal high PSMA expression detectable in left posterior mid to base with SUVmax of 12.4 on transaxial fused delayed pelvic PET/CT (yellow arrow). (D) Focal high PSMA expression found in left midgland to base of prostate on maximum-intensity-projection image (yellow arrow).
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NBPAS-certified physicians can now represent themselves as board certified in Florida physician advertising.
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The 10-year overall survival (OS) and cancer-specific survival (CSS) rates were 68.5% and 75.5%.
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The framework uses a DL model trained on low- and high-quality MR images and helped improve radiologist accuracy.
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Low-quality axial T2-weighted MRI scans with gradient-weighted class activation mapping. The low-quality MRI scans (top row) show blurred structures and artifacts making zonal and capsule boundaries indistinguishable. On the same scans with gradient-weighted class activation map overlays (bottom row), red indicates regions that were most heavily weighted by the deep learning model toward the low-quality prediction.
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Artificial intelligence (AI) assistance facilitates correct fracture identification in cases initially missed by human readers. A representative case is shown using anteroposterior radiographs in a 29-year-old male patient with wrist trauma (suspected nondisplaced transverse scaphoid fracture). The reader’s initial unaided assessment (top) resulted in a missed fracture, whereas with AI assistance (bottom), the fracture was correctly identified. The green box represents the fracture location as predicted by the AI tool, the blue box represents the reference standard annotation, and the red dots represent individual reader annotations.
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The petition seeks clarity on the regulation of AI vision language models used for diagnostic medical image analysis.
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