Incorporating Robustness to Imaging Physics into Radiomic Feature Selection for Breast Cancer Risk Estimation

Incorporating Robustness to Imaging Physics into Radiomic Feature Selection for Breast Cancer Risk Estimation

Digital mammography has seen an explosion in the number of radiomic features used for risk-assessment modeling. However, having more features is not necessarily beneficial, as some features may be overly sensitive to imaging physics (contrast, noise, and image sharpness). To measure the effects of i...

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Auteurs principaux: Raymond J. Acciavatti, Eric A. Cohen, Omid Haji Maghsoudi, Aimilia Gastounioti, Lauren Pantalone, Meng-Kang Hsieh, Emily F. Conant, Christopher G. Scott, Stacey J. Winham, Karla Kerlikowske, Celine Vachon, Andrew D. A. Maidment, Despina Kontos
Format: article
Langue:EN
Publié: MDPI AG 2021
Sujets:
radiomics
digital mammography
robustness
feature selection
anthropomorphic phantom
case-control analysis
Neoplasms. Tumors. Oncology. Including cancer and carcinogens
RC254-282
Accès en ligne:https://doaj.org/article/e71709f14ea1497aa9a63b8d583d6da1
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https://doaj.org/article/e71709f14ea1497aa9a63b8d583d6da1

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