Analysis of Cross-Combinations of Feature Selection and Machine-Learning Classification Methods Based on [F-18]F-FDG PET/CT Radiomic Features for Metabolic Response Prediction of Metastatic Breast Cancer Lesions

We acknowledge support from the Spanish Government (RTI2018-095800-A-I00 and RTI2018-098868-B-I00), from Comunidad de Madrid (B2017/BMD-3888 PRONTO-CM), NIH R01-CA215700-5 grant, and University of Pisa (Direzione Area Medica).
Simple Summary Breast cancer is a leading cause of morbidity and mortality worldwide. The metastatic disease is largely responsible for cancer patient deaths, and its treatment implies usually different therapies. In this context, the prediction of response to treatment or depiction of treatment-resistant phenotypes is essential in clinical practice, especially in the new era of precision medicine. In this study, we used several combinations of feature selection methods and machine-learning classifiers to construct predictive models of the metabolic response to the treatment of metastatic breast cancer lesions. These models were based on clinical variables and radiomic features extracted from 2-deoxy-2-[F-18]fluoro-D-glucose positron emission tomography/computed tomography ([F-18]F-FDG PET/CT) images, obtained prior to the treatment. Our main goal was to know if this prediction was feasible and to identify those combinations with better predictive performance. We found that several combinations were successful to predict the metabolic response to treatment, of which the least absolute shrinkage and selection operator (Lasso) + support vector machines (SVM) had the best mean performance in terms of area under the curve, in both training and validation cohorts. Model performances depended largely on the selected combinations. Background: This study aimed to identify optimal combinations between feature selection methods and machine-learning classifiers for predicting the metabolic response of individual metastatic breast cancer lesions, based on clinical variables and radiomic features extracted from pretreatment [F-18]F-FDG PET/CT images. Methods: A total of 48 patients with confirmed metastatic breast cancer, who received different treatments, were included. All patients had an [F-18]F-FDG PET/CT scan before and after the treatment. From 228 metastatic lesions identified, 127 were categorized as responders (complete or partial metabolic response) and 101 as non-resp
Gobierno de España
Comunidad de Madrid
University of Pisa
NIH
Depto. de Estructura de la Materia, Física Térmica y Electrónica
Fac. de Ciencias Físicas
TRUE
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