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

Abstract

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 to use quantitative PET imaging features and clinical parameters to construct a multi-objective machine learning predictive model. Materials/Methods: Seventy-five patients with stage IB2-IVA disease treated at our institution from 2009–2012 were analyzed. Models predicting locoregional and distant failure were generated using clinical parameters (age, race, stage, histology, tumor size, nodal status) and imaging features (12 textural, 9 intensity, 8 geometric features, 2 additional imaging features) from pre-treatment PET. Model features were selected based on a multi-objective evolutionary algorithm to maximize specificity given a fixed moderately high sensitivity using support vector machine learning methods. Model 1 used clinical parameters only (C), Model 2 used imaging features only (I), and Model 3 used clinical and imaging features (C+I). Sensitivity, specificity, area under a receiver-operating characteristic curve (AUC), and p-values were compared to assess ability to predict locoregional and distant failure. Results: C+I had the highest performance for both locoregional failure (AUC 0.84, p < 0.01; specificity: 0.86; sensitivity: 0.79) and distant failure (AUC 0.75, p < 0.01; specificity: 0.75; sensitivity: 0.75). Conclusions: Based on a moderately high fixed sensitivity and optimized for specificity, the model using both clinical parameters and imaging features (C+I) had the best performance in predicting both locoregional failure and distant failure.