Quantitative Imaging Features Predict Response of Immunotherapy in Non-Small Cell Lung Cancer Patients

[No Abstract Available

Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features.

Radiomics is to provide quantitative descriptors of normal and abnormal tissues during classification and prediction tasks in radiology and oncology. Quantitative Imaging Network members are developing radiomic "feature" sets to characterize tumors, in general, the size, shape, texture, intensity, margin, and other aspects of the imaging features of nodules and lesions. Efforts are ongoing for developing an ontology to describe radiomic features for lung nodules, with the main classes consisting of size, local and global shape descriptors, margin, intensity, and texture-based features, which are based on wavelets, Laplacian of Gaussians, Law's features, gray-level co-occurrence matrices, and run-length features. The purpose of this study is to investigate the sensitivity of quantitative descriptors of pulmonary nodules to segmentations and to illustrate comparisons across different feature types and features computed by different implementations of feature extraction algorithms. We calculated the concordance correlation coefficients of the features as a measure of their stability with the underlying segmentation; 68% of the 830 features in this study had a concordance CC of ≥0.75. Pairwise correlation coefficients between pairs of features were used to uncover associations between features, particularly as measured by different participants. A graphical model approach was used to enumerate the number of uncorrelated feature groups at given thresholds of correlation. At a threshold of 0.75 and 0.95, there were 75 and 246 subgroups, respectively, providing a measure for the features' redundancy

Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival Among Patients with Lung Adenocarcinoma

Lung cancer is the most common cause of cancer-related deaths in the USA. It can be detected and diagnosed using computed tomography images. For an automated classifier, identifying predictive features from medical images is a key concern. Deep feature extraction using pretrained convolutional neural networks (CNNs) has recently been successfully applied in some image domains. Here, we applied a pretrained CNN to extract deep features from 40 computed tomography images, with contrast, of non-small cell adenocarcinoma lung cancer, and combined deep features with traditional image features and trained classifiers to predict short-and long-term survivors. We experimented with several pretrained CNNs and several feature selection strategies. The best previously reported accuracy when using traditional quantitative features was 77.5% (area under the curve [AUC], 0.712), which was achieved by a decision tree classifier. The best reported accuracy from transfer learning and deep features was 77.5% (AUC, 0.713) using a decision tree classifier. When extracted deep neural network features were combined with traditional quantitative features, we obtained an accuracy of 90% (AUC, 0.935) with the 5 best post-rectified linear unit features extracted from a vgg-f pretrained CNN and the 5 best traditional features. The best results were achieved with the symmetric uncertainty feature ranking algorithm followed by a random forests classifier

Erratum: Semiâ automated pulmonary nodule interval segmentation using the NLST data

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/144604/1/mp12905_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/144604/2/mp12905.pd

Simulating microarrays using a parameterized model

Simulating a microarray includes defining a number of parameters. A microarray is generated according to the parameters using an imaging procedure. The microarray is compared to a known value, and the imaging procedure is evaluated in response to the comparison. A simulated microarray image can be generated based on parameters. The simulated microarray can be associated with known values. An imaging procedure is applied to the simulated microarray image to generate observed values. The known values (e.g., intensities) can be compared to the observed values to evaluate the imaging procedure.U

Simulating microarrays using a parameterized model

Simulating a microarray includes defining a number of parameters. A microarray is generated according to the parameters using an imaging procedure. The microarray is compared to a known value, and the imaging procedure is evaluated in response to the comparison. A simulated microarray image can be generated based on parameters. The simulated microarray can be associated with known values. An imaging procedure is applied to the simulated microarray image to generate observed values. The known values (e.g., intensities) can be compared to the observed values to evaluate the imaging procedure.U

Simulating microarrays using a parameterized model

Simulating a microarray includes defining a number of parameters. A microarray is generated according to the parameters using an imaging procedure. The microarray is compared to a known value, and the imaging procedure is evaluated in response to the comparison. A simulated microarray image can be generated based on parameters. The simulated microarray can be associated with known values. An imaging procedure is applied to the simulated microarray image to generate observed values. The known values (e.g., intensities) can be compared to the observed values to evaluate the imaging procedure.U