Purpose: Computed tomography (CT) is an effective method for detecting and characterizing lung nodules in vivo. With the growing use of chest CT, the detection frequency of lung nodules is increasing. Noninvasive methods to distinguish malignant from benign nodules have the potential to decrease the clinical burden, risk, and cost involved in follow-up procedures on the large number of false-positive lesions detected. This study examined the benefit of including perinodular parenchymal features in machine learning (ML) tools for pulmonary nodule assessment. Methods: Lung nodule cases with pathology confirmed diagnosis (74 malignant, 289 benign) were used to extract quantitative imaging characteristics from computed tomography scans of the nodule and perinodular parenchyma tissue. A ML tool development pipeline was employed using k-medoids clustering and information theory to determine efficient predictor sets for different amounts of parenchyma inclusion and build an artificial neural network classifier. The resulting ML tool was validated using an independent cohort (50 malignant, 50 benign). Results: The inclusion of parenchymal imaging features improved the performance of the ML tool over exclusively nodular features (P < 0.01). The best performing ML tool included features derived from nodule diameter-based surrounding parenchyma tissue quartile bands. We demonstrate similar high-performance values on the independent validation cohort (AUC-ROC = 0.965). A comparison using the independent validation cohort with the Fleischner pulmonary nodule follow-up guidelines demonstrated a theoretical reduction in recommended follow-up imaging and procedures. Conclusions: Radiomic features extracted from the parenchyma surrounding lung nodules contain valid signals with spatial relevance for the task of lung cancer risk classification. Through standardization of feature extraction regions from the parenchyma, ML tool validation performance of 100% sensitivity and 96% specificity was achieved.

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Neurofibromatosis type 1 (NF1) is a rare, autosomal dominant disease with variable clinical presentations. Large animal models are useful to help dissect molecular mechanisms, determine relevant biomarkers, and develop effective therapeutics. Here, we studied a NF1 minipig model (NF1 +/ex42del ) for the first 12 months of life to evaluate phenotype development, track disease progression, and provide a comparison to human subjects. Through systematic evaluation, we have shown that compared to littermate controls, the NF1 model develops phenotypic characteristics of human NF1: [1] café-au-lait macules, [2] axillary/inguinal freckling, [3] shortened stature, [4] tibial bone curvature, and [5] neurofibroma. At 4 months, full body computed tomography imaging detected significantly smaller long bones in NF1 +/ex42del minipigs compared to controls, indicative of shorter stature. We found quantitative evidence of tibial bowing in a subpopulation of NF1 minipigs. By 8 months, an NF1 +/ex42del boar developed a large diffuse shoulder neurofibroma, visualized on magnetic resonance imaging, which subsequently grew in size and depth as the animal aged up to 20 months. the NF1 +/ex42del minipig model progressively demonstrates signature attributes that parallel clinical manifestations seen in humans and provides a viable tool for future translational NF1 research.Neurofibromatosis type 1 (NF1) is an autosomal dominant disorder with nearly 100% penetrance and an incidence of approximately 1 in every 3,000 births worldwide 1 . The clinical manifestations of the disorder are highly variable, even among individuals with the same mutation in NF1 1,2 . The most common phenotypic presentations of NF1 include café-au-lait macules (CALMs), axillary and inguinal freckling, optic pathway gliomas, pilocytic astrocytoma, and the presence of neurofibromas and plexiform neurofibromas (PNs) that have the potential to develop into malignant peripheral nerve sheath tumors (MPNSTs) 1-3 . Some individuals with NF1 may exhibit learning disabilities, increased pain, short stature, or macrocephaly at birth 3,4 . Although uncommon, they may also be born with or develop tibial bowing and sphenoid wing dysplasia 4 . Those affected by NF1 are also at increased risk of developing a number of different cancers including glioblastoma, breast cancer, and leukemia 1 . Because of the heterogeneity, impact on quality of life, and significant morbidity associated with NF1, there is a considerable need for research into its underlying pathology and noninvasive monitoring of its progression.Systematic study of NF1 in human subjects is a major challenge as the variability in disease presentation between individuals makes it difficult to create an effective longitudinal cohort. Medical imaging is used in the NF1 population to detect and monitor disease phenotypes including optic-pathway glioma 5-7 , plexiform neurofibroma and MPNST, spinal neurofibroma, tibial bowing and scoliosis. Retrospectively collected imaging studies have assessed prevalence and NF1 findings i...