Assessing the robustness of a machine-learning model for early detection of pancreatic adenocarcinoma (PDA): evaluating resilience to variations in image acquisition and radiomics workflow using image perturbation methods
Author:
Funder
Funk-Zitiello Foundation
Centene Charitable Foundation
U.S. Department of Defense
National Cancer Institute
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00261-023-04127-1.pdf
Reference33 articles.
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3. Chhoda A, Vodusek Z, Wattamwar K, Mukherjee E, Gunderson C, Grimshaw A, et al. (2022) Late-Stage Pancreatic Cancer Detected During High-Risk Individual Surveillance: A Systematic Review and Meta-Analysis. Gastroenterology 162(3):786-98 https://doi.org/10.1053/j.gastro.2021.11.021.
4. Klatte DCF, Boekestijn B, Onnekink AM, Dekker FW, van der Geest LG, Wasser M, et al. (2023) Surveillance for Pancreatic Cancer in High-Risk Individuals Leads to Improved Outcomes: A Propensity Score-Matched Analysis. Gastroenterology 164(7):1223-31 e4 https://doi.org/10.1053/j.gastro.2023.02.032.
5. Overbeek KA, Goggins MG, Dbouk M, Levink IJM, Koopmann BDM, Chuidian M, et al. (2022) Timeline of Development of Pancreatic Cancer and Implications for Successful Early Detection in High-Risk Individuals. Gastroenterology 162(3):772-85 e4 https://doi.org/10.1053/j.gastro.2021.10.014.
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