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...

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Background Immunotherapies, such as programmed death 1/programmed death ligand 1 (PD-1/PD-L1) antibodies have been shown to improve overall and progression-free survival (PFS) in patients with locally advanced or metastatic non-small cell lung cancer (NSCLC). However, not all patients derive a meaningful clinical benefit. Additionally, patients receiving anti-PD-1/PD-L1 therapy can experience immune-related adverse events (irAEs). Clinically significant irAEs may require temporary pause or discontinuation of treatment. Having a tool to identify patients who may not benefit and/or are at risk for developing severe irAEs from immunotherapy will aid in an informed decision-making process for the patients and their physicians. Methods Computed tomography (CT) scans and clinical data were retrospectively collected for this study to develop three prediction models using (I) radiomic features, (II) clinical features, and (III) radiomic and clinical features combined. Each subject had 6 clinical features and 849 radiomic features extracted. Selected features were run through an artificial neural network (NN) trained on 70% of the cohort, maintaining the case and control ratio. The NN was assessed by calculating the area-under-the-receiver-operating-characteristic curve (AUC-ROC), area-under-the-precision-recall curve (AUC-PR), sensitivity, and specificity. Results A cohort of 132 subjects, of which 43 (33%) had a PFS ≤90 days and 89 (67%) of which had a PFS >90 days was used to develop the prediction models. The radiomic model was able to predict progression-free survival with a training AUC-ROC of 87% and testing AUC-ROC, sensitivity, and specificity of 83%, 75%, and 81%, respectively. In this cohort, the clinical and radiomic combined features did add a slight increase in the specificity (85%) but with a decrease in sensitivity (75%) and AUC-ROC (81%). Conclusions Whole lung segmentation and feature extraction can identify those that would see a benefit from anti-PD-1/PD-L1 therapy.

Late-infantile neuronal ceroid lipofuscinosis type 2 (CLN2) disease (Batten disease) is a rare pediatric disease, with symptom development leading to clinical diagnosis. Early diagnosis and effective tracking of disease progression are required for treatment. We hypothesize that brain volumetry is valuable in identifying CLN2 disease at an early stage and tracking disease progression in a genetically modified miniswine model. CLN2R208X/R208X miniswine and wild type controls were evaluated at 12- and 17-months of age, correlating to early and late stages of disease progression. Magnetic resonance imaging (MRI) T1- and T2-weighted data were acquired. Total intercranial, gray matter, cerebrospinal fluid, white matter, caudate, putamen, and ventricle volumes were calculated and expressed as proportions of the intracranial volume. The brain regions were compared between timepoints and cohorts using Gardner-Altman plots, mean differences, and confidence intervals. At an early stage of disease, the total intracranial volume (− 9.06 cm3), gray matter (− 4.37% 95 CI − 7.41; − 1.83), caudate (− 0.16%, 95 CI − 0.24; − 0.08) and putamen (− 0.11% 95 CI − 0.23; − 0.02) were all notably smaller in CLN2R208X/R208X miniswines versus WT, while cerebrospinal fluid was larger (+ 3.42%, 95 CI 2.54; 6.18). As the disease progressed to a later stage, the difference between the gray matter (− 8.27%, 95 CI − 10.1; − 5.56) and cerebrospinal fluid (+ 6.88%, 95 CI 4.31; 8.51) continued to become more pronounced, while others remained stable. MRI brain volumetry in this miniswine model of CLN2 disease is sensitive to early disease detection and longitudinal change monitoring, providing a valuable tool for pre-clinical treatment development and evaluation.