Electron Density and Biologically Effective Dose (BED) Radiomics-Based Machine Learning Models to Predict Late Radiation-Induced Subcutaneous Fibrosis

Electron Density and Biologically Effective Dose (BED) Radiomics-Based Machine Learning Models to Predict Late Radiation-Induced Subcutaneous Fibrosis

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...

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Bibliographic Details
Main Authors: Michele Avanzo, Giovanni Pirrone, Lorenzo Vinante, Angela Caroli, Joseph Stancanello, Annalisa Drigo, Samuele Massarut, Mario Mileto, Martina Urbani, Marco Trovo, Issam el Naqa, Antonino De Paoli, Giovanna Sartor
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-04-01
Series:Frontiers in Oncology
Subjects:
radiomics
radiotherapy
machine learning
breast cancer
fibrosis
Online Access:https://www.frontiersin.org/article/10.3389/fonc.2020.00490/full

Internet

https://www.frontiersin.org/article/10.3389/fonc.2020.00490/full

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