Abstract
Purpose: To evaluate the value of texture analysis for the differential diagnosis of spinal metastases and to improve the diagnostic performance of 2-deoxy-2-[fluorine-18]fluoro-D-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) for spinal metastases.Methods: This retrospective analysis of patients who underwent PET/CT between December 2015 and January 2020 at Shanghai Tenth People's Hospital due to high FDG uptake lesions in the spine included 45 cases of spinal metastases and 44 cases of benign high FDG uptake lesions in the spine. The patients were randomly divided into a training group of 65 and a test group of 24. Seventy-two PET texture features were extracted from each lesion, and the Mann-Whitney U-test was used to screen the training set for texture parameters that differed between the two groups in the presence or absence of spinal metastases. Then, the diagnostic performance of the texture parameters was screened out by receiver operating characteristic (ROC) curve analysis. Texture parameters with higher area under the curve (AUC) values than maximum standardized uptake values (SUVmax) were selected to construct classification models using logistic regression, support vector machines, and decision trees. The probability output of the model with high classification accuracy in the training set was used to compare the diagnostic performance of the classification model and SUVmax using the ROC curve. For all patients with spinal metastases, survival analysis was performed using the Kaplan-Meier method and Cox regression.Results: There were 51 texture parameters that differed meaningfully between benign and malignant lesions, of which four had higher AUC than SUVmax. The texture parameters were input to build a classification model using logistic regression, support vector machine, and decision tree. The accuracy of classification was 87.5, 83.34, and 75%, respectively. The accuracy of the manual diagnosis was 84.27%. Single-factor survival analysis using the Kaplan-Meier method showed that intensity was correlated with patient survival.Conclusion: Partial texture features showed higher diagnostic value for spinal metastases than SUVmax. The machine learning part of the model combined with the texture parameters was more accurate than manual diagnosis. Therefore, texture analysis may be useful to assist in the diagnosis of spinal metastases.
Highlights
The spine is the third most common site of metastatic disease after the lungs and liver, with ∼60–70% of patients with systemic cancer developing spinal metastases [1]
We performed a single-factor survival analysis of the five texture parameters and survival by classifying the five texture parameters into low and high groups based on the median of the texture parameters as the criterion, and the results showed that patients in the low intensity group had shorter survival (P = 0.041) (Figure 3)
Our study demonstrates the significance of PET/Computed tomography (CT) texture analysis for the analysis of spinal positive FDG uptake lesions for the identification and diagnosis of spinal metastases
Summary
The spine is the third most common site of metastatic disease after the lungs and liver, with ∼60–70% of patients with systemic cancer developing spinal metastases [1]. And correct diagnosis of spinal metastases is helpful to guide clinical treatment, improve prognosis, and increase the survival rate. Computed tomography (CT) and magnetic resonance imaging (MRI) have their own advantages and disadvantages with regards to the diagnosis of spinal metastases. Two-deoxy-2-[fluorine18]fluoro-D-glucose (18F-FDG) imaging is more sensitive than conventional imaging examinations. It can facilitate early detection of the lesion by monitoring changes in glucose metabolism and can determine metastases of both the whole body skeletal system and soft tissues in a single examination. There is a certain degree of false-positive influence on the diagnosis, such as trauma-induced vertebral fractures, bone hyperplasia, and metabolic bone disease, which may interfere with the diagnosis
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