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PEER-TO-PEER CLINICAL CONVERSATIONS
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Beyond Genomics: AI Informing Decision Making in Prostate Cancer
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Ashley Ross, MD, Ph.D.
Alicia Morgans and Ashley Ross discuss the current clinical landscape of how risk is defined in localized prostate cancer highlighting a novel artificial intelligence (AI) derived digital pathology-based biomarker test for prostate cancer, Artera.
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Beyond the NCCN Localized Prostate Cancer Risk Category: The MMAI Prognostic Risk Stratification Model
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Jonathan Tward, MD, Ph.D., FASTRO
Jonathan Tward joins Alicia Morgans to discuss advancing prognostic capabilities in prostate cancer risk stratification using multimodal deep learning with digital histopathology within the context of a series of NRG Oncology phase III trials.
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| The State of the Art in Digital Pathology and AI: Progress in Prostate Cancer
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| Osama Mohamad, MD, Ph.D.
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| Osama Mohamad discusses the state of the art in digital pathology and artificial intelligence in prostate cancer. Dr. Mohamad notes that traditional histopathology includes slide preparation microscopic analysis slide storage, whereas digital histopathology includes slide preparation and whole slide imaging analysis slide storage.
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| Artificial Intelligence and Prostate Cancer: Risk Stratification After Primary Therapy, ADT Treatment Intensification, and Evaluation of Metastatic Disease
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| Zachary Klaassen, MD, MSc and Rashid K. Sayyid, MD, MSc
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| Artificial intelligence continues to transform the field of medicine, including the management of prostate cancer. In this Center of Excellence article, Zachary Klaassen and Rashid Sayyid discuss the contemporary literature evaluating artificial intelligence for risk stratification after primary therapy, ADT treatment intensification, and evaluation of metastatic disease.
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| Prostate Cancer Risk Stratification in NRG Oncology Phase III Randomized Trials Using Multi-Modal Deep Learning with Digital Histopathology
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| Jonathan Tward, MD, Ph.D., FASTRO
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| AI-derived prognostic biomarkers provide personalized risk estimates, that when grouped allows more streamlined communication. ArteraAI MMAI prognostic tool identifies 6-fold more low-risk patients than NCCN. These patients might consider omitting ADT with RT (NNT >25). Prognostic biomarkers help with shared decision making to avoid futile treatment intensification in patients who will derive minimal absolute benefit from treatment.
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| Best of the Journals: Prostate Cancer - Radiation Oncology
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| Angela Jia, MD, Ph.D.
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| Angela Jia presents from the point of view of a radiation oncologist. She emphasized that there have been important advances in understanding treatment approaches for patients with both newly diagnosed and recurrent disease, including considerations regarding radiotherapy fractionation, the use of concurrent androgen deprivation therapy, and biomarkers for prognostication.
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| Artificial Intelligence-Enabled Automated Identification of Key Steps in Robotic-Assisted Radical Prostatectomy
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| Abhinav Khanna, MD
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| Abhinav Khanna presents the results of the study investigating the use of artificial intelligence (AI) for the purposes of annotating surgical videos, specifically in robotic-assisted radical prostatectomy (RARP). Dr. Khanna and colleagues sought to develop an algorithm for the automated identification of key surgical steps during RARP.
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| Detection of Clinically Significant Prostate Cancer on MRI: A Comparison of an Artificial Intelligence Model versus Radiologists
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| Simon J. C. Soerensen, MD
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| In this study, an artificial intelligence model was trained and tested on a large and diverse multi-institutional cohort of MRIs. The model matched the performance of radiologist experts. In future work, Dr. Soerensen and colleagues envision comparing model performance against radiologists at different skill levels, comparing radiologist + artificial intelligence vs radiologist performance alone, and implement the model prospectively.
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| Bridging the Experience Gap in Prostate Multiparametric Magnetic Resonance Imaging Using Artificial Intelligence: A Prospective Multi-Reader Comparison Study on Inter-Reader Agreement in PI-RADS v2.1 - Beyond the Abstract
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| Ali Forookhi, Ludovica Laschena, Martina Pecoraro, Antonella Borrelli, Michele Massaro, Ailin Dehghanpour, Stefano Cipollari, Carlo Catalano, Valeria Panebianco
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Artificial Intelligence (AI) has the potential to improve the detection of prostate cancer (PCa) by standardizing imaging results and enhancing the interpretation of prostate Magnetic Resonance Imaging (MRI). Radiologists' expertise remains vital in accurately diagnosing and interpreting imaging results.
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