Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Trissia Brown"'
Autor:
Justin D. Krogue, Shekoofeh Azizi, Fraser Tan, Isabelle Flament-Auvigne, Trissia Brown, Markus Plass, Robert Reihs, Heimo Müller, Kurt Zatloukal, Pema Richeson, Greg S. Corrado, Lily H. Peng, Craig H. Mermel, Yun Liu, Po-Hsuan Cameron Chen, Saurabh Gombar, Thomas Montine, Jeanne Shen, David F. Steiner, Ellery Wulczyn
Publikováno v:
Communications Medicine, Vol 3, Iss 1, Pp 1-9 (2023)
Abstract Background Presence of lymph node metastasis (LNM) influences prognosis and clinical decision-making in colorectal cancer. However, detection of LNM is variable and depends on a number of external factors. Deep learning has shown success in
Externí odkaz:
https://doaj.org/article/05e3699f48ec4fbbbf46c7ae3d19fa89
Autor:
Ronnachai Jaroensri, Ellery Wulczyn, Narayan Hegde, Trissia Brown, Isabelle Flament-Auvigne, Fraser Tan, Yuannan Cai, Kunal Nagpal, Emad A. Rakha, David J. Dabbs, Niels Olson, James H. Wren, Elaine E. Thompson, Erik Seetao, Carrie Robinson, Melissa Miao, Fabien Beckers, Greg S. Corrado, Lily H. Peng, Craig H. Mermel, Yun Liu, David F. Steiner, Po-Hsuan Cameron Chen
Publikováno v:
npj Breast Cancer, Vol 8, Iss 1, Pp 1-12 (2022)
Abstract Histologic grading of breast cancer involves review and scoring of three well-established morphologic features: mitotic count, nuclear pleomorphism, and tubule formation. Taken together, these features form the basis of the Nottingham Gradin
Externí odkaz:
https://doaj.org/article/e87e4501ec7945b0aef836b8987fc7e1
Autor:
Apaar Sadhwani, Huang-Wei Chang, Ali Behrooz, Trissia Brown, Isabelle Auvigne-Flament, Hardik Patel, Robert Findlater, Vanessa Velez, Fraser Tan, Kamilla Tekiela, Ellery Wulczyn, Eunhee S. Yi, Craig H. Mermel, Debra Hanks, Po-Hsuan Cameron Chen, Kimary Kulig, Cory Batenchuk, David F. Steiner, Peter Cimermancic
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
Abstract Both histologic subtypes and tumor mutation burden (TMB) represent important biomarkers in lung cancer, with implications for patient prognosis and treatment decisions. Typically, TMB is evaluated by comprehensive genomic profiling but this
Externí odkaz:
https://doaj.org/article/bb85166704e240079299ad3d736a4fbb
Autor:
Ellery Wulczyn, David F. Steiner, Melissa Moran, Markus Plass, Robert Reihs, Fraser Tan, Isabelle Flament-Auvigne, Trissia Brown, Peter Regitnig, Po-Hsuan Cameron Chen, Narayan Hegde, Apaar Sadhwani, Robert MacDonald, Benny Ayalew, Greg S. Corrado, Lily H. Peng, Daniel Tse, Heimo Müller, Zhaoyang Xu, Yun Liu, Martin C. Stumpe, Kurt Zatloukal, Craig H. Mermel
Publikováno v:
npj Digital Medicine, Vol 4, Iss 1, Pp 1-13 (2021)
Abstract Deriving interpretable prognostic features from deep-learning-based prognostic histopathology models remains a challenge. In this study, we developed a deep learning system (DLS) for predicting disease-specific survival for stage II and III
Externí odkaz:
https://doaj.org/article/da537cd907ad48cb854376055689af37
Autor:
Timo Kohlberger, Yun Liu, Melissa Moran, Po-Hsuan Cameron Chen, Trissia Brown, Jason D Hipp, Craig H Mermel, Martin C Stumpe
Publikováno v:
Journal of Pathology Informatics, Vol 10, Iss 1, Pp 39-39 (2019)
Background: Digital pathology enables remote access or consults and powerful image analysis algorithms. However, the slide digitization process can create artifacts such as out-of-focus (OOF). OOF is often only detected on careful review, potentially
Externí odkaz:
https://doaj.org/article/3eedd774764d45138c12e5cfaef91f3b
Autor:
Isabelle Auvigne-Flament, Craig H. Mermel, Peter Cimermancic, David F. Steiner, Ellery Wulczyn, Trissia Brown, Cory Batenchuk, Vanessa Velez, Apaar Sadhwani, Kamilla Tekiela, Kimary Kulig, Chang Huang-Wei, Ali Behrooz, Eunhee S. Yi, Robert Findlater, Debra Hanks, Hardik Patel, Po-Hsuan Cameron Chen, Fraser Tan
Publikováno v:
Scientific Reports
Scientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
Scientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
Both histologic subtypes and tumor mutation burden (TMB) represent important biomarkers in lung cancer, with implications for patient prognosis and treatment decisions. Typically, TMB is evaluated by comprehensive genomic profiling but this requires
Determining breast cancer biomarker status and associated morphological features using deep learning
Autor:
Lily Peng, Carrie L. Robinson, Emad A. Rakha, Paul Gamble, Peter Regitnig, Melissa Moran, Yun Liu, Hongwu Wang, Michael S. Toss, Greg S. Corrado, Niels Olson, Craig H. Mermel, Trissia Brown, James H. Wren, Po-Hsuan Cameron Chen, Fraser Tan, Isabelle Flament-Auvigne, David F. Steiner, Ronnachai Jaroensri, David J. Dabbs
Publikováno v:
Communications Medicine. 1
Breast cancer management depends on biomarkers including estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 (ER/PR/HER2). Though existing scoring systems are widely used and well-validated, they can involve costly
Autor:
Lily Peng, Yuannan Cai, Peter Regitnig, Robert Reihs, Farah Nader, Markus Plass, Heimo Müller, Matthew Symonds, Craig H. Mermel, Kurt Zatloukal, Melissa Moran, Trissia Brown, Andreas Holzinger, Martin C. Stumpe, Greg S. Corrado, Po-Hsuan Cameron Chen, Ellery Wulczyn, Kunal Nagpal, Mahul B. Amin, Isabelle Flament-Auvigne, Fraser Tan, David F. Steiner, Yun Liu
Publikováno v:
Communications Medicine. 1
Gleason grading of prostate cancer is an important prognostic factor, but suffers from poor reproducibility, particularly among non-subspecialist pathologists. Although artificial intelligence (A.I.) tools have demonstrated Gleason grading on-par wit
Autor:
Apaar Sadhwani, Robert C. MacDonald, Markus Plass, Ellery Wulczyn, Craig H. Mermel, Narayan Hegde, Heimo Müller, Trissia Brown, Benny Ayalew, Kurt Zatloukal, Isabelle Flament-Auvigne, Martin C. Stumpe, Daniel Tse, Zhaoyang Xu, Melissa Moran, Lily Peng, David F. Steiner, Robert Reihs, Fraser Tan, Yun Liu, Po-Hsuan Cameron Chen, Peter Regitnig, Greg S. Corrado
Publikováno v:
NPJ Digital Medicine
npj Digital Medicine, Vol 4, Iss 1, Pp 1-13 (2021)
npj Digital Medicine, Vol 4, Iss 1, Pp 1-13 (2021)
Deriving interpretable prognostic features from deep-learning-based prognostic histopathology models remains a challenge. In this study, we developed a deep learning system (DLS) for predicting disease-specific survival for stage II and III colorecta
Autor:
Martin C. Stumpe, Liron Yatziv, Pan-Pan Jiang, Benjamin D Wedin, Michael Terry, Kunal Nagpal, Fraser Tan, Greg S. Corrado, Adam Pearce, Isabelle Flament-Auvigne, Rory Sayres, Po-Hsuan Cameron Chen, David F. Steiner, Samantha Winter, Davis Foote, Yun Liu, Carrie J. Cai, Andrei Kapishnikov, Matthew Symonds, Craig H. Mermel, Trissia Brown
Publikováno v:
JAMA Network Open. 3:e2023267
Importance Expert-level artificial intelligence (AI) algorithms for prostate biopsy grading have recently been developed. However, the potential impact of integrating such algorithms into pathologist workflows remains largely unexplored. Objective To