Abstract
ObjectiveLung cancer usually presents as a solitary pulmonary nodule (SPN) on diagnostic imaging during the early stages of the disease. Since the early diagnosis of lung cancer is very important for treatment, the accurate diagnosis of SPNs has much importance. The aim of this study was to evaluate the discriminant power of dual time point imaging (DTPI) PET/CT in the differentiation of malignant and benign FDG-avid solitary pulmonary nodules by using neighborhood gray-tone difference matrix (NGTDM) texture features.MethodsRetrospective analysis was carried out on 116 patients with SPNs (35 benign and 81 malignant) who had DTPI 18F-FDG PET/CT between January 2005 and May 2015. Both PET and CT images were acquired at 1 h and 3 h after injection. The SUVmax and NGTDM texture features (coarseness, contrast, and busyness) of each nodule were calculated on dual time point images. Patients were randomly divided into training and validation datasets. Receiver operating characteristic (ROC) curve analysis was performed on all texture features in the training dataset to calculate the optimal threshold for differentiating malignant SPNs from benign SPNs. For all the lesions in the testing dataset, two visual interpretation scores were determined by two nuclear medicine physicians based on the PET/CT images with and without reference to the texture features.ResultsIn the training dataset, the AUCs of delayed busyness, delayed coarseness, early busyness, and early SUVmax were 0.87, 0.85, 0.75 and 0.75, respectively. In the validation dataset, the AUCs of visual interpretations with and without texture features were 0.89 and 0.80, respectively.ConclusionCompared to SUVmax or visual interpretation, NGTDM texture features derived from DTPI PET/CT images can be used as good predictors of SPN malignancy. Improvement in discriminating benign from malignant nodules using SUVmax and visual interpretation can be achieved by adding busyness extracted from delayed PET/CT images.
Highlights
A solitary pulmonary nodule (SPN) is defined radiologically as an intraparenchymal lung lesion of less than 3 cm in diameter, with no associated atelectasis or adenopathy [1]
The Pearson correlation test (Table 5 and Additional file 1) showed that: except early contrast, texture features were significantly correlated with visual interpretation
This study demonstrated that quantitative neighborhood gray-tone difference matrix (NGTDM) texture features derived from dual time point PET/CT images were good predictors for diagnosing malignant SPNs in patients from granuloma-endemic regions
Summary
A solitary pulmonary nodule (SPN) is defined radiologically as an intraparenchymal lung lesion of less than 3 cm in diameter, with no associated atelectasis or adenopathy [1]. The causes of SPNs range from malignancy, such as primary lung cancer or metastatic cancer sites, to inflammation and other benign diseases. Previous studies have shown that SPNs are detected in almost 70% of subjects receiving low-dose CT-based lung cancer screenings [2], whereas another study found that 53% of detected SPNs were characterized as malignant nodules [3]. Lung cancer usually presents as an SPN on diagnostic imaging during early stages of the disease [1]. Since the early diagnosis of lung cancer is very important for treatment, as it would allow surgical resection to increase survival rates, the accurate diagnosis of SPNs has even more importance
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