Abstract
Lung cancer, the most commonly diagnosed cancer worldwide, usually presents as solid pulmonary nodules (SPNs) on early diagnostic images. Classification of malignant disease at this early timepoint is critical for improving the success of surgical resection and increasing 5-year survival rates. 18F-fluorodeoxyglucose (18F-FDG) PET/CT has demonstrated value for SPNs diagnosis with high sensitivity to detect malignant SPNs, but lower specificity in diagnosing malignant SPNs in populations with endemic infectious lung disease. This study aimed to determine whether quantitative heterogeneity derived from various texture features on dual time FDG PET/CT images (DTPI) can differentiate between malignant and benign SPNs in patients from granuloma-endemic regions. Machine learning methods were employed to find optimal discrimination between malignant and benign nodules. Machine learning models trained by texture features on DTPI images achieved significant improvements over standard clinical metrics and visual interpretation for discriminating benign from malignant SPNs, especially by texture features on delayed FDG PET/CT images.
Highlights
Solitary pulmonary nodules (SPNs) are common clinical findings, often incidental, that may represent malignant disease in the lung
18F- fluorodeoxyglucose (18F-FDG) PET has been demonstrated its utility for Single Pulmonary Nodule (SPN) diagnosis with a high sensitivity to malignant SPNs detection; the application of FDG PET/CT is limited by its variable specificity estimates4. 18F-FDG, a PET tracer of glucose metabolism, has shown significant difference in uptake between malignant and benign lesions
In order to improve the specificity of FDG PET/CT, some authors have proposed dual time point imaging (DTPI), using retention index (RI) to help differentiate benign and malignant SPNs
Summary
Solitary pulmonary nodules (SPNs) are common clinical findings, often incidental, that may represent malignant disease in the lung. Using SUVmax above 2.5 as a diagnostic threshold for malignant SPNs has been reported[5], use of FDG PET/CT is less specific in diagnosing malignancy in populations with endemic infectious lung disease as compared with non-endemic regions. In order to improve the specificity of FDG PET/CT, some authors have proposed dual time point imaging (DTPI), using retention index (RI) to help differentiate benign and malignant SPNs. the results of DTPI studies www.nature.com/scientificreports/. Many factors, such as cellular proliferation, necrosis, blood flow and hypoxia, may contribute to intra-lesion heterogeneity[10] Measurements of this heterogeneity might help to distinguish benign from malignant pulmonary nodules. There are only a few studies looking at the diagnostic value of quantitative heterogeneity features in FDG PET/CT imaging[15, 16]. There are no studies evaluating the use of quantitative heterogeneity in DTPI PET/CT images for SPN differentiation
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