Application of deep learning on mammographies to discriminate between low and high-risk DCIS for patient participation in active surveillance trials

Application of deep learning on mammographies to discriminate between low and high-risk DCIS for patient participation in active surveillance trials

Abstract Background Ductal Carcinoma In Situ (DCIS) can progress to invasive breast cancer, but most DCIS lesions never will. Therefore, four clinical trials (COMET, LORIS, LORETTA, AND LORD) test whether active surveillance for women with low-risk Ductal carcinoma In Situ is safe (E. S. Hwang et al...

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Bibliographic Details
Main Authors: Sena Alaeikhanehshir, Madelon M. Voets, Frederieke H. van Duijnhoven, Esther H. lips, Emma J. Groen, Marja C. J. van Oirsouw, Shelley E. Hwang, Joseph Y. Lo, Jelle Wesseling, Ritse M. Mann, Jonas Teuwen, Grand Challenge PRECISION Consortium Steering Group
Format: Article
Language:English
Published: BMC 2024-04-01
Series:Cancer Imaging
Subjects:
DCIS
DCIS grade
Invasive breast cancer
Active surveillance
Artificial intelligence
Deep learning
Online Access:https://doi.org/10.1186/s40644-024-00691-x

Internet

https://doi.org/10.1186/s40644-024-00691-x

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