PO-0851: Artificial neural networks for toxicity prediction in RT: a method to validate their “intelligence”

Published on May 1, 2017in Radiotherapy and Oncology4.856
· DOI :10.1016/S0167-8140(17)31288-4
E. Massari1
Estimated H-index: 1
,
Tiziana Rancati27
Estimated H-index: 27
+ 8 AuthorsMauro Carrara16
Estimated H-index: 16
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2013
1 Author (Nong Ye)
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#1Michele AvanzoH-Index: 14
#2G. PirroneH-Index: 8
Last. G. SartorH-Index: 7
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Purpose: to predict the occurrence of late subcutaneous radiation induced fibrosis (RIF) after partial breast irradiation (PBI) for breast carcinoma by using machine learning (ML) models and radiomic features from 3D Biologically Effective Dose (3D-BED) and Relative Electron Density (3D-RED). Methods: 165 patients underwent external PBI following a hypo-fractionation protocol consisting of 40 Gy/10 fractions, 35 Gy/7 fractions, and 28 Gy/4 fractions, for 73, 60, and 32 patients, respectively. Ph...
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