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.

Loss of the NF1 tumor suppressor gene causes the autosomal dominant condition, neurofibromatosis type 1 (NF1). Children and adults with NF1 suffer from pathologies including benign and malignant tumors to cognitive deficits, seizures, growth abnormalities, and peripheral neuropathies. NF1 encodes neurofibromin, a Ras-GTPase activating protein, and NF1 mutations result in hyperactivated Ras signaling in patients. Existing NF1 mutant mice mimic individual aspects of NF1, but none comprehensively models the disease. We describe a potentially novel Yucatan miniswine model bearing a heterozygotic mutation in NF1 (exon 42 deletion) orthologous to a mutation found in NF1 patients. NF1 ^+/ex42del miniswine phenocopy the wide range of manifestations seen in NF1 patients, including café au lait spots, neurofibromas, axillary freckling, and neurological defects in learning and memory. Molecular analyses verified reduced neurofibromin expression in swine NF1 ^+/ex42del fibroblasts, as well as hyperactivation of Ras, as measured by increased expression of its downstream effectors, phosphorylated ERK1/2, SIAH, and the checkpoint regulators p53 and p21. Consistent with altered pain signaling in NF1, dysregulation of calcium and sodium channels was observed in dorsal root ganglia expressing mutant NF1 . Thus, these NF1 ^+/ex42del miniswine recapitulate the disease and provide a unique, much-needed tool to advance the study and treatment of NF1.

Lung cancer is the leading cause of cancer-related death, killing 1.37 million people in the world in 2008 alone.¹ While the overall 5-year survival rate of lung cancer is 15.9%, this statistic greatly improves with early diagnosis, up to 52% for the earliest stage of lung cancer.² The significant improvement in survival with earlier diagnosis has led to the implementation of screening for high-risk asymptomatic individuals, such as smokers and past smokers. Screening by computed tomography (CT) has been found to be an effective technique. The National Lung Screening Trial (NLST) found that using CT to screen for lung cancer reduced lung cancer mortality by 20% compared to screening using projection radiograph.³ However, 96.4% of the nodules marked as suspicious on CT were found to be benign upon further evaluation.⁴ These false positives result in unnecessary and invasive follow-up procedures and costs while incurring additional emotional stress for the patient.

Accurate phenotyping of patients with pulmonary hypertension (PH) is an integral part of informing disease classification, treatment, and prognosis. The impact of lung disease on PH outcomes and response to treatment remains a challenging area with limited progress. Imaging with computed tomography (CT) plays an important role in patients with suspected PH when assessing for parenchymal lung disease, however, current assessments are limited by their semi-qualitative nature. Quantitative chest-CT (QCT) allows numerical quantification of lung parenchymal disease beyond subjective visual assessment. This has facilitated advances in radiological assessment and clinical correlation of a range of lung diseases including emphysema, interstitial lung disease, and coronavirus disease 2019 (COVID-19). Artificial Intelligence approaches have the potential to facilitate rapid quantitative assessments. Benefits of cross-sectional imaging include ease and speed of scan acquisition, repeatability and the potential for novel insights beyond visual assessment alone. Potential clinical benefits include improved phenotyping and prediction of treatment response and survival. Artificial intelligence approaches also have the potential to aid more focused study of pulmonary arterial hypertension (PAH) therapies by identifying more homogeneous subgroups of patients with lung disease. This state-of-the-art review summarizes recent QCT developments and potential applications in patients with PH with a focus on lung disease.

Quantitative imaging features of intensity, texture, and shape were extracted from breast lesions and surrounding tissue in 287 mammograms (150 malignant, 137 benign). A feature set reduction method to remove highly intra-correlated features was devised using k-medoids clustering and k-fold cross validation. A novel feature selection method using information theory was introduced which builds a feature set for classification by determining a group of class-informative features with low set co-information. An artificial neural network was built from the selected feature set using 10-hidden layer nodes and the tanh activation function. The resulting computer-aided diagnosis tool achieved a training accuracy of 96.2%, sensitivity of 97.6%, specificity of 95.2%, and area-under-the-curve of 0.971 along with 97.1% sensitivity and 94.9% specificity a blinded validation set.