Abstract
The classification of pulmonary nodules using computed tomography (CT) and positron emission tomography (PET)/CT is often a hard task for physicians. To this end, in our previous study, we developed an automated classification method using PET/CT images. In actual clinical practice, in addition to images, patient information (e.g., laboratory test results) is available and may be useful for automated classification. Here, we developed a hybrid scheme for automated classification of pulmonary nodules using these images and patient information. We collected 36 conventional CT images and PET/CT images of patients who underwent lung biopsy following bronchoscopy. Patient information was also collected. For classification, 25 shape and functional features were first extracted from the images. Benign and malignant nodules were identified using machine learning algorithms along with the images’ features and 17 patient-information-related features. In the leave-one-out cross-validation of our hybrid scheme, 94.4% of malignant nodules were identified correctly, and 77.7% of benign nodules were diagnosed correctly. The hybrid scheme performed better than that of our previous method that used only image features. These results indicate that the proposed hybrid scheme may improve the accuracy of malignancy analysis.
Highlights
In this study, we focused on the automated analysis of the malignancy potential of the pulmonary nodules using positron emission tomography (PET)/Computed tomography (CT) images combined with patient-related information
We propose a hybrid scheme for the automated classification of pulmonary nodules using PET/CT images and patient information
When patient information was combined with CT images, an improvement of 44.5% was obtained, compared with the method that only uses CT images. These results indicate that information about the patient metabolism and inflammation might have been supplemented by blood-related information
Summary
Lung cancer is the leading cause of cancer-related deaths among men and women worldwide [1]. Lung cancer exhibits poor symptoms; screening is important for early detection. When lung nodules are detected during such screening, it becomes necessary to accurately classify the detected lesion as benign or malignant, for appropriate treatment. Computed tomography (CT) is widely used for lung cancer screening [2]. Screening with CT examinations has been reported to be effective for improving prognosis. If the suspicious lesions are found in CT images, positron emission tomography (PET)/CT examination is performed for detailed analysis. In PET examinations, fluorodeoxyglucose (FDG) accumulates more in cancer cells than in normal cells. Radiologists diagnose morphological abnormalities from CT images and determine functional abnormalities using the metabolism-related information in PET images.
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