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PEER-TO-PEER CLINICAL CONVERSATIONS |
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| Predicting Response to Hormone Therapy in Prostate Cancer With a Post-Prostatectomy MMAI Model |
Todd M. Morgan, MD
Zach Klaassen discusses with Todd Morgan about innovative post-prostatectomy biomarkers. Dr. Morgan presents findings on a new AI model developed by ArteraAI for assessing biochemical recurrence in prostate cancer patients post-prostatectomy. |
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Novel 18-Gene Urine Test Improves Detection of Clinically Significant Prostate Cancer
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Jeffrey Tosoian, MD, MPH
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| Jeffrey Tosoian presents a new tool in prostate cancer diagnosis with the development and validation of an 18-gene urine test designed to identify clinically significant prostate cancer published in JAMA Oncology.
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MMAI Model Risk Stratification for NCCN Guidelines for Low, Intermediate, and High-Risk Prostate Cancer
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Daniel Spratt, MD
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| Zach Klaassen hosts a multidisciplinary roundtable discussing the NCCN Prostate Cancer Guidelines with a focus on the ArteraAI prostate test. Joined by Dr. Dan Spratt, Dr. Alicia Morgans, and Dr. Neal Shore, they explore the importance of risk stratification in prostate cancer, highlighting the superior predictive and prognostic capabilities of the ArteraAI test compared to traditional methods.
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| Disease Characterization: Are Biomarkers Better? |
| Brian Chapin, MD |
| Brian Chapin discusses the evolving role of biomarkers in improving disease characterization for prostate cancer. While current risk stratification relies on stage, histology, and biology, biomarkers such as Decipher and ArteraAI show promise for providing additional clinically actionable insights, particularly for patients with intermediate to high-risk disease. Dr. Chapin emphasizes the need for prospective trials and further validation before biomarkers can be widely adopted in clinical practice. |
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| Assessment of Prostate and Bladder Cancer Genomic Biomarkers Using Artificial Intelligence: a Systematic Review - Beyond the Abstract
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| Andrey Bazarkin, Andrey Morozov, Alexander Androsov et al.
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| A systematic review highlights the growing role of AI in prostate and bladder cancer genomics. AI's ability to quickly and accurately analyze vast amounts of genomic data is helping identify key genetic signatures and biomarkers, aiding in cancer detection, risk stratification, and predicting treatment responses. A
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Prospective Validation Study of a Novel Integrated Pathway Based on Clinical Features, Magnetic Resonance Imaging Biomarkers, and MicroRNAs for Early Detection of Prostate Cancer - Beyond the Abstract
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| Martina Pecoraro, Giuseppina Catanzaro, Federica Conte et al. |
| This study, conducted by Sapienza University’s STITCH Center, aimed to develop a novel diagnostic pathway for early prostate cancer detection by integrating clinical features, MRI biomarkers, and microRNAs. The research identified specific microRNAs, miR-302a-5p and miR-367-3p, that were differentially expressed in patients with clinically significant PCa. By incorporating these microRNAs into the diagnostic model, researchers found that the integrated pathway improved the accuracy of patient selection for biopsy, reducing overdiagnosis and overtreatment. |
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| Optimizing Detection of Clinically Significant Prostate Cancer Through Nomograms Incorporating MRI, Clinical Features, and Advanced Serum Biomarkers in Biopsy Naïve Men - Beyond the Abstract |
| Mohammad Siddiqui, MD, Eric Li, MD, & Ashley Ross, MD, PhD |
| This study sought to enhance the detection of clinically significant prostate cancer in biopsy-naïve men by developing nomograms that integrate clinical features, multiparametric MRI findings, and advanced serum biomarkers like the Prostate Health Index. The researchers used data from 1,494 biopsy-naïve men to create and validate these nomograms, achieving high accuracy with Areas Under the Curve ranging from 0.885 to 0.923. |
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| Artificial INtelligence to Support Informed DEcision-making (INSIDE) for Improved Literature Analysis in Oncology - Beyond the Abstract |
| Bob Schijvenaars |
| Defining optimal therapeutic sequencing strategies in prostate cancer (PC) is challenging and may be assisted by artificial intelligence (AI)-based tools for an analysis of the medical literature. To demonstrate that INSIDE PC can help clinicians query the literature on therapeutic sequencing in PC and to develop previously unestablished practices for evaluating the outputs of AI-based support platforms. |
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| Validation of a Digital Pathology-Based Multimodal Artificial Intelligence Model in Oligometastatic Castration-Sensitive Prostate Cancer, Including in Patients from the STOMP and ORIOLE Phase II Randomized Clinical Trials |
| Philip A. Sutera, MD |
| Philip Sutera presents a validation study of the ArteraAI multimodal artificial intelligence model in oligometastatic castration-sensitive prostate cancer. The study found that a high ArteraAI score was associated with poorer overall survival and higher PSA levels, Gleason scores, and metastatic disease presence. While the model did not predict metastasis-free survival in the STOMP and ORIOLE trials, it did identify patients who benefited more from metastasis-directed therapy. Further validation is needed to refine patient management strategies. |
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