Electron Density and Biologically Effective Dose (BED) Radiomics-Based Machine Learning Models to Predict Late Radiation-Induced Subcutaneous Fibrosis-Reference-Cited by-同舟云学术

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

Published:2020-04-21 Issue: Volume:10 Page:
ISSN:2234-943X
Container-title:Frontiers in Oncology
language:
Short-container-title:Front. Oncol.

Author:

Avanzo Michele,Pirrone Giovanni,Vinante Lorenzo,Caroli Angela,Stancanello Joseph,Drigo Annalisa,Massarut Samuele,Mileto Mario,Urbani Martina,Trovo Marco,el Naqa Issam,De Paoli Antonino,Sartor Giovanna

Publisher

Frontiers Media SA

Subject

Cancer Research,Oncology

Reference45 articles.

1. Adverse effects to the skin and subcutaneous tissue;Avanzo,2019

2. Normal tissue complication probability modelling of tissue fibrosis following breast radiotherapy;Alexander;Phys Med Biol.,2007

3. Normal tissue complication probability (NTCP) parameters for breast fibrosis: pooled results from two randomised trials;Mukesh;Radiother Oncol.,2013

4. Complication probability model for subcutaneous fibrosis based on published data of partial and whole breast irradiation;Avanzo;Phys Med.,2012

5. Beyond imaging: the promise of radiomics;Avanzo;Phys Med.,2017

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