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Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review
Despite considerable research efforts, pancreatic cancer is associated with a dire prognosis and a 5-year survival rate of only 10%. Early symptoms of the disease are mostly nonspecific. The premise of improved survival through early detection is that more individuals will benefit from potentially c...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8041569/ https://www.ncbi.nlm.nih.gov/pubmed/33835956 http://dx.doi.org/10.1097/MPA.0000000000001762 |
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author | Kenner, Barbara Chari, Suresh T. Kelsen, David Klimstra, David S. Pandol, Stephen J. Rosenthal, Michael Rustgi, Anil K. Taylor, James A. Yala, Adam Abul-Husn, Noura Andersen, Dana K. Bernstein, David Brunak, Søren Canto, Marcia Irene Eldar, Yonina C. Fishman, Elliot K. Fleshman, Julie Go, Vay Liang W. Holt, Jane M. Field, Bruce Goldberg, Ann Hoos, William Iacobuzio-Donahue, Christine Li, Debiao Lidgard, Graham Maitra, Anirban Matrisian, Lynn M. Poblete, Sung Rothschild, Laura Sander, Chris Schwartz, Lawrence H. Shalit, Uri Srivastava, Sudhir Wolpin, Brian |
author_facet | Kenner, Barbara Chari, Suresh T. Kelsen, David Klimstra, David S. Pandol, Stephen J. Rosenthal, Michael Rustgi, Anil K. Taylor, James A. Yala, Adam Abul-Husn, Noura Andersen, Dana K. Bernstein, David Brunak, Søren Canto, Marcia Irene Eldar, Yonina C. Fishman, Elliot K. Fleshman, Julie Go, Vay Liang W. Holt, Jane M. Field, Bruce Goldberg, Ann Hoos, William Iacobuzio-Donahue, Christine Li, Debiao Lidgard, Graham Maitra, Anirban Matrisian, Lynn M. Poblete, Sung Rothschild, Laura Sander, Chris Schwartz, Lawrence H. Shalit, Uri Srivastava, Sudhir Wolpin, Brian |
author_sort | Kenner, Barbara |
collection | PubMed |
description | Despite considerable research efforts, pancreatic cancer is associated with a dire prognosis and a 5-year survival rate of only 10%. Early symptoms of the disease are mostly nonspecific. The premise of improved survival through early detection is that more individuals will benefit from potentially curative treatment. Artificial intelligence (AI) methodology has emerged as a successful tool for risk stratification and identification in general health care. In response to the maturity of AI, Kenner Family Research Fund conducted the 2020 AI and Early Detection of Pancreatic Cancer Virtual Summit (www.pdac-virtualsummit.org) in conjunction with the American Pancreatic Association, with a focus on the potential of AI to advance early detection efforts in this disease. This comprehensive presummit article was prepared based on information provided by each of the interdisciplinary participants on one of the 5 following topics: Progress, Problems, and Prospects for Early Detection; AI and Machine Learning; AI and Pancreatic Cancer—Current Efforts; Collaborative Opportunities; and Moving Forward—Reflections from Government, Industry, and Advocacy. The outcome from the robust Summit conversations, to be presented in a future white paper, indicate that significant progress must be the result of strategic collaboration among investigators and institutions from multidisciplinary backgrounds, supported by committed funders. |
format | Online Article Text |
id | pubmed-8041569 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-80415692021-04-19 Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review Kenner, Barbara Chari, Suresh T. Kelsen, David Klimstra, David S. Pandol, Stephen J. Rosenthal, Michael Rustgi, Anil K. Taylor, James A. Yala, Adam Abul-Husn, Noura Andersen, Dana K. Bernstein, David Brunak, Søren Canto, Marcia Irene Eldar, Yonina C. Fishman, Elliot K. Fleshman, Julie Go, Vay Liang W. Holt, Jane M. Field, Bruce Goldberg, Ann Hoos, William Iacobuzio-Donahue, Christine Li, Debiao Lidgard, Graham Maitra, Anirban Matrisian, Lynn M. Poblete, Sung Rothschild, Laura Sander, Chris Schwartz, Lawrence H. Shalit, Uri Srivastava, Sudhir Wolpin, Brian Pancreas Conference Report Despite considerable research efforts, pancreatic cancer is associated with a dire prognosis and a 5-year survival rate of only 10%. Early symptoms of the disease are mostly nonspecific. The premise of improved survival through early detection is that more individuals will benefit from potentially curative treatment. Artificial intelligence (AI) methodology has emerged as a successful tool for risk stratification and identification in general health care. In response to the maturity of AI, Kenner Family Research Fund conducted the 2020 AI and Early Detection of Pancreatic Cancer Virtual Summit (www.pdac-virtualsummit.org) in conjunction with the American Pancreatic Association, with a focus on the potential of AI to advance early detection efforts in this disease. This comprehensive presummit article was prepared based on information provided by each of the interdisciplinary participants on one of the 5 following topics: Progress, Problems, and Prospects for Early Detection; AI and Machine Learning; AI and Pancreatic Cancer—Current Efforts; Collaborative Opportunities; and Moving Forward—Reflections from Government, Industry, and Advocacy. The outcome from the robust Summit conversations, to be presented in a future white paper, indicate that significant progress must be the result of strategic collaboration among investigators and institutions from multidisciplinary backgrounds, supported by committed funders. Lippincott Williams & Wilkins 2021-03 2021-04-08 /pmc/articles/PMC8041569/ /pubmed/33835956 http://dx.doi.org/10.1097/MPA.0000000000001762 Text en Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. |
spellingShingle | Conference Report Kenner, Barbara Chari, Suresh T. Kelsen, David Klimstra, David S. Pandol, Stephen J. Rosenthal, Michael Rustgi, Anil K. Taylor, James A. Yala, Adam Abul-Husn, Noura Andersen, Dana K. Bernstein, David Brunak, Søren Canto, Marcia Irene Eldar, Yonina C. Fishman, Elliot K. Fleshman, Julie Go, Vay Liang W. Holt, Jane M. Field, Bruce Goldberg, Ann Hoos, William Iacobuzio-Donahue, Christine Li, Debiao Lidgard, Graham Maitra, Anirban Matrisian, Lynn M. Poblete, Sung Rothschild, Laura Sander, Chris Schwartz, Lawrence H. Shalit, Uri Srivastava, Sudhir Wolpin, Brian Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review |
title | Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review |
title_full | Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review |
title_fullStr | Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review |
title_full_unstemmed | Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review |
title_short | Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review |
title_sort | artificial intelligence and early detection of pancreatic cancer: 2020 summative review |
topic | Conference Report |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8041569/ https://www.ncbi.nlm.nih.gov/pubmed/33835956 http://dx.doi.org/10.1097/MPA.0000000000001762 |
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