Measuring Heterogeneity in 18F-Fluorodeoxyglucose Positron Emission Tomography Images for Classifying Metastatic and Benign Bone Lesions in Patients with Cervical Cancer

Abstract

PurposeHeterogeneity assessment can be applied for medical imaging analysis. Here, we evaluated first-order and texture analysis (TA) metrics in 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) imaging for classification of metastatic and benign bone lesions in patients with cervical cancer.MethodsThe data of 18F-FDG PET studies performed on a specific PET/CT system from 2016 to 2018 in patients with cervical cancer were retrieved. The data of bone lesions extracted from studies over 2016–2017 and 2018 were used as training and validation datasets, respectively. Metastatic bone lesions were identified in each dataset, with an equal number of benign bone lesions selected. Cuboid volume of interest (VOI) consisting of 3 × 3 × 5 reconstructed voxels was applied for first-order metrics, and cubic VOI consisting of smaller voxels with trilinear interpolation of standardized uptake value (SUV) was adopted for TA metrics. First-order metrics included the maximum SUV (SUVmax) of lesions and the mean voxel SUV and its standard deviation (SUVsd), skewness, and kurtosis in VOI. In total, 4464 TA metrics based on 62 texture features were evaluated. Logistic regression was used for classification with area under the receiver operating characteristic curve (AUC) as the performance measure.ResultsFrom the training and validation datasets, 98 and 42 metastatic bone lesions were identified, respectively. SUVsd demonstrated higher performance than did SUVmax in both the training (AUC .798 vs .732, P = .001) and validation (AUC .786 vs .684, P < .001) datasets. Top-performing TA metrics demonstrated significantly higher performance in the training dataset, but not in the validation dataset.ConclusionA simple first-order measure of heterogeneity, SUVsd, was found to be superior to SUVmax for the classification of metastatic and benign bone lesions. Multiple hypothesis testing can result in false-positive findings in TA with multiple features and parameters; careful validation is required.