Výsledky vyhledávání - "Jedidiah Mercer Monson"

Akademický článek

Intratumoral Administration of High-Concentration Nitric Oxide and Anti-mPD-1 Treatment Improves Tumor Regression Rates and Survival in CT26 Tumor-Bearing Mice

Autor: Hila Confino, Yogev Sela, Yana Epshtein, Lidor Malka, Matan Goldshtein, Selena Chaisson, Steve Lisi, Amir Avniel, Jedidiah Mercer Monson, Frederick M. Dirbas

Publikováno v: Cells, Vol 12, Iss 20, p 2439 (2023)

Background: Immune checkpoint inhibitors have transformed clinical oncology. However, their use is limited as response is observed in only ~20–50% of patients. Previously, we demonstrated that treating CT26 tumor-bearing mice with ultra-high-concen

Externí odkaz: https://doaj.org/article/07cb9dad2a0b4bbeae273d47cdda664d

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819 Intratumoral administration of high-concentration nitric oxide and anti PD-1 treatment leads to higher tumor regression rates and prolonged survival in CT26 tumor-bearing mice

Autor: Hila Confino, David Greenberg, Selena Chaisson, Jedidiah Mercer Monson, Steve Lisi, Amir Avniel, Ido Wolf, Yana Epshtein

Publikováno v: Regular and Young Investigator Award Abstracts.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a22d34ccc7246b72780907ec25ceb22f
https://doi.org/10.1136/jitc-2022-sitc2022.0819

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Patient-level data meta-analysis of a multi-modal artificial intelligence (MMAI) prognostic biomarker in high-risk prostate cancer: Results from six NRG/RTOG phase III randomized trials

Publikováno v: Journal of Clinical Oncology. 41:299-299

299 Background: Recently, an MMAI prognostic biomarker, ArteraAI Prostate, was trained and validated in localized prostate cancer to more accurately risk stratify patients for multiple endpoints compared to NCCN risk groups (Esteva et al., 2022). Pro

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::1eb7e0805a8c68eed76df8e6a33032cd
https://doi.org/10.1200/jco.2023.41.6_suppl.299

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Prostate cancer risk in African American men evaluated via digital histopathology multi-modal deep learning models developed on NRG Oncology phase III clinical trials

Publikováno v: Journal of Clinical Oncology. 40:108-108

108 Background: Artificial intelligence (AI) tools can display racial bias as a result of existing systemic health inequities and biased datasets. We have previously developed multi-modal AI (MMAI) prognostic models based on digital pathology images

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3f56917da6f6b512005a5c5c9f9df024
https://doi.org/10.1200/jco.2022.40.16_suppl.108

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Development and validation of a prognostic AI biomarker using multi-modal deep learning with digital histopathology in localized prostate cancer on NRG Oncology phase III clinical trials

Publikováno v: Journal of Clinical Oncology. 40:222-222

222 Background: Prognostication in localized prostate cancer is reliant on non-specific tools, an issue that leads to the over- and under-treatment of patients. Various tissue-based molecular biomarkers have attempted to fill this unmet need, but mos

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::5070e0c1c3a7d289679e394c9b56d4b4
https://doi.org/10.1200/jco.2022.40.6_suppl.222

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An AI-derived digital pathology-based biomarker to predict the benefit of androgen deprivation therapy in localized prostate cancer with validation in NRG/RTOG 9408

Autor: Daniel Eidelberg Spratt, Yilun Sun, Douwe Van der Wal, Shih-Cheng Huang, Osama Mohamad, Andrew J. Armstrong, Jonathan David Tward, Paul Nguyen, Emmalyn Chen, Sandy DeVries, Jedidiah Mercer Monson, Holly A Campbell, Michelle J. Ferguson, Jean-Paul Bahary, Phuoc T. Tran, Joseph P. Rodgers, Andre Esteva, Felix Y Feng

Publikováno v: Journal of Clinical Oncology. 40:223-223

223 Background: The current standard of care for men with intermediate- and high-risk localized prostate cancer treated with radiotherapy (RT) is the addition of androgen deprivation therapy (ADT). Presently, there are no validated predictive biomark

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::e903fadf88e5dcfb08ca730943d7eda0
https://doi.org/10.1200/jco.2022.40.6_suppl.223

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