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PEER-TO-PEER CLINICAL CONVERSATIONS |
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| Multimodal Artificial Intelligence Concordance Between Whole-Mount Specimens and Tissue Microarrays in Prostate Cancer Analysis |
Adam Dicker, MD, PhD, FASTRO
Alicia Morgans interviews Adam Dicker about the ArteraAI Prostate Test, an MMAI tool for prostate pathology analysis. Dr. Dicker shares findings from a study comparing whole-mount specimens to tissue microarrays across 100 patients with locally advanced disease. |
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| Comparing Traditional Risk Groups vs AI Analysis in Prostate Cancer Management - Journal Club |
| Matthew Cooperberg, MD, MPH, Jonathan Tward, MD, PhD, and Ashley Ross, MD, PhD |
| Matthew Cooperberg moderates a patient-oriented discussion with Jonathan Tward and Ashley Ross about a prostate cancer test that uses artificial intelligence to analyze digital pathology slides. |
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| AI-Derived Tumor Volume from Multiparametric MRI and Outcomes in Localized Prostate Cancer |
Martin King, MD, PhD, and David Yang, MD
Martin King and David Yang join Zachary Klaassen to discuss their study on AI-derived tumor volume assessment in prostate cancer MRI. The research demonstrates how their AI model successfully segments and measures prostate tumors from MRI images, showing strong prognostic value for cancer outcomes in both radiation and surgery patients. |
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| PRIMARY Score and Where it Plays a Role in Prostate Cancer Diagnosis "Presentation" |
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Matthew Cooperberg, MD, MPH, Jonathan Tward, MD, PhD, and Ashley Ross, MD, PhD
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| Matthew Cooperberg moderates a patient-oriented discussion with Jonathan Tward and Ashley Ross about a prostate cancer test that uses artificial intelligence to analyze digital pathology slides.
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| Prognostic Evaluation of an Artificial Intelligence (AI)-based Risk Score for Prostate Cancer on PSMA PET/CT |
| Kevin Leung, PhD |
| Kevin Leung presented an AI-based imaging risk score developed from automated whole-body tumor quantification on PSMA PET/CT scans to predict survival outcomes in prostate cancer patients. This risk score, integrated with clinical variables, accurately stratified patients by overall and progression-free survival at 1, 3, and 5 years, aligning well with observed outcomes across different treatment groups. The model shows promise for personalized prognosis and treatment planning in prostate cancer care. |
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| Prospective, Randomized Trial Comparing PSMA PET/CT and MRI-Guided Versus MRI Only-Guided Prostate Biopsy for Identification of Clinically Significant Prostate Cancer |
| Ida Sonni, MD |
| Ida Sonni presented a randomized phase II trial comparing combined 18F-DCFPyL PSMA PET/CT plus mpMRI-guided prostate biopsy versus mpMRI alone in men with PI-RADS 4 lesions. Preliminary results showed no statistically significant difference in clinically significant prostate cancer detection at the patient or lesion levels between the two approaches, although PSMA PET/CT demonstrated a higher positive predictive value at the lesion level. |
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| Impact of PSMA PET Staging on Initial Treatment in Newly Diagnosed Prostate Cancer: An Emulated Randomized Controlled Trial |
| Sean Miller, MD |
| Sean Miller presented an emulated randomized trial showing that PSMA PET staging in newly diagnosed unfavorable intermediate to very high-risk prostate cancer led to increased use of ADT and androgen receptor pathway inhibitors, with a trend toward more radiotherapy but less radical prostatectomy. Patients with PSMA-detected nodal or metastatic disease were more likely to receive systemic therapies and less likely to undergo surgery. Overall, PSMA PET staging prompted intensification of systemic treatment and reduction in local surgical interventions. |
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| Artificial Intelligence Applications in Prostate Cancer Diagnosis and Treatment |
| Victoria Bird, MD |
| Victoria Bird reviewed diverse AI applications in prostate cancer diagnosis and treatment, highlighting advanced AI algorithms like random forests and neural networks used in pathology, imaging, and therapy planning. AI platforms improve prostate cancer detection accuracy through digital pathology, ultrasound, and MRI segmentation, while also enhancing focal therapy precision and radiation dose adaptation. Despite promising developments, challenges remain in standardizing imaging inputs, expanding AI in epigenetics and environment data, and addressing regulatory and ethical concerns around autonomous robotic surgery. |
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| Precision Diagnostics in Prostate Cancer Treatment (PREDICT): A Phase 2 Multi-arm Biomarker-Based Study (Alliance A032102) |
| Rana McKay, MD |
| The ASCO GU 2025 PREDICT trial (Alliance A032102) is a phase II multi-arm biomarker-driven study investigating targeted therapies for metastatic castration-resistant prostate cancer (mCRPC) based on genomic alterations. Patients with actionable mutations such as RB1 loss or neuroendocrine signatures receive the EZH2 inhibitor valemetostat, while those with tumor suppressor gene losses and platinum sensitivity markers are treated with cabazitaxel plus carboplatin; others receive physician’s choice of approved therapies. |
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