Quantitative PET Imaging and Clinical Parameters as Predictive Factors for Patients With Cervical Carcinoma: Implications of a Prediction Model Generated Using Multi-Objective Support Vector Machine Learning.

Zhiguo Zhou,Genevieve Maquilan,Kimberly Thomas,Jason W Wachsmann,Jing Wang,Michael R. Folkert,Kevin Albuquerque

Quantitative PET Imaging and Clinical Parameters as Predictive Factors for Patients With Cervical Carcinoma: Implications of a Prediction Model Generated Using Multi-Objective Support Vector Machine Learning.

2020

Zhiguo Zhou
Genevieve Maquilan
Kimberly Thomas
Jason W Wachsmann
Jing Wang
Michael R. Folkert
Kevin Albuquerque

Purpose:Quantitative features from pre-treatment positron emission tomography (PET) have been used to predict treatment outcomes for patients with cervical carcinoma. The purpose of this study is t...

Keywords:

Radiomics
Radiology
cervical carcinoma
multi objective model
Positron emission tomography
pet imaging
Support vector machine
treatment outcome
Medicine

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