LDCT image biomarkers that matter most for the deep learning classification of indeterminate pulmonary nodules.

Background: Continued improvement in deep learning methodologies has increased the rate at which deep neural networks are being evaluated for medical applications, including diagnosis of lung cancer. However, there has been limited exploration of the underlying radiological characteristics that the network relies on to identify lung cancer in computed tomography (CT) images.

Objective: In this study, we used a combination of image masking and saliency activation maps to systematically explore the contributions of both parenchymal and tumor regions in a CT image to the classification of indeterminate lung nodules.

Perinodular Parenchymal Features Improve Indeterminate Lung Nodule Classification.

Background: Radiomics, defined as quantitative features extracted from images, provide a non-invasive means of assessing malignant versus benign pulmonary nodules. In this study, we evaluate the consistency with which perinodular radiomics extracted from low-dose computed tomography images serve to identify malignant pulmonary nodules.

Materials And Methods: Using the National Lung Screening Trial (NLST), we selected individuals with pulmonary nodules between 4mm to 20mm in diameter.

Wavelet decomposition facilitates training on small datasets for medical image classification by deep learning.

The adoption of low-dose computed tomography (LDCT) as the standard of care for lung cancer screening results in decreased mortality rates in high-risk population while increasing false-positive rate. Convolutional neural networks provide an ideal opportunity to improve malignant nodule detection; however, due to the lack of large adjudicated medical datasets these networks suffer from poor generalizability and overfitting. Using computed tomography images of the thorax from the National Lung Screening Trial (NLST), we compared discrete wavelet transforms (DWTs) against convolutional layers found in a CNN in order to evaluate their ability to classify suspicious lung nodules as either malignant or benign.