NIMG-16. COMPARISON OF A STIR- AND T1-WEIGHTED-BASED RADIOMICS MODEL TO DIFFERENTIATE BETWEEN PLEXIFORM NEUROFIBROMAS AND MALIGNANT PERIPHERAL NERVE SHEATH TUMORS IN NEUROFIBROMATOSIS TYPE 1 (NF1)

BACKGROUND

Plexiform neurofibromas (PNF) and malignant peripheral nerve sheath tumors (MPNST) are best visualized on short TI inversion recovery (STIR) sequences on MRI. However, STIR sequences are not routinely acquired in the clinical setting. T1-weighted pre-contrast (T1W) sequences are more standardly obtained but provide insufficient contrast for tumor identification. We developed a radiomics model based on STIR and T1W sequences to differentiate between NF1-associated PNF and MPNST.

METHODS

Using a 3D quantitative imaging analysis software (3DQI), 68 MPNST and 79 PNF from 134 participants at nine centers were segmented on STIR sequences (if available) or T2 fat-saturated or T1-weighted fat-saturated post-contrast sequences. Tumor regions of interest were co-registered to T1W sequences. Standard pre-processing included N4 bias field correction, intensity normalization (mean 120 SI, SD 80 SI), and resampling (1 mm3 voxel resolution). 107 radiomic features were extracted using PyRadiomics. To classify tumors as PNF or MPNST, we applied the Boruta algorithm and correlation removal for selection of important features. A Random Forest model was built using the top five selected features. The data were divided into a training/validation and test set (7:3 ratio). Five-fold cross-validation was performed and repeated 100 times. Model performance was evaluated using AUC, sensitivity, specificity, accuracy, and 95% CI.

RESULTS

For the STIR-based model, AUC was 0.856 (95% CI 0.727-0.984), sensitivity 0.6, specificity 0.833, and accuracy 0.727 in the test set. For the T1W-based model, AUC was 0.867 (95% CI 0.743-0.990), sensitivity 0.8, specificity 0.79, and accuracy 0.794 in the test set.

CONCLUSIONS

Our radiomics models demonstrate high and comparable performance to distinguish between PNF and MPNST on STIR and T1W sequences. Our inclusion of multicenter MRIs enhances model generalizability. These models can potentially be integrated into the radiologic workflow to help clinicians in the early identification of MPNST or pre-malignant atypical neurofibromas on clinical MRIs.

NIMG-08. A MULTI-CENTER RADIOMICS-BASED MODEL TO DIFFERENTIATE BETWEEN NEUROFIBROMATOSIS TYPE 1-ASSOCIATED PLEXIFORM NEUROFIBROMAS AND MALIGNANT PERIPHERAL NERVE SHEATH TUMORS

BACKGROUND

Several MRI features are proposed to distinguish between plexiform neurofibromas (PNF) and malignant peripheral nerve sheath tumors (MPNST) in neurofibromatosis type 1 (NF1), including tumor size, margins, and degree of heterogeneity. However, most of these features are descriptive in nature, subject to intra-/interrater variability, and based on small single-institution studies. The goal of this study was to identify radiomic features that can differentiate between NF1-associated PNF and MPNST.

METHODS

31 MPNSTs and 24 PNFs from five centers were segmented on short TI inversion recovery sequences using a semi-automated segmentation software (3DQI). Standard pre-processing was performed, including N4 bias field correction, intensity normalization (using a mean of 120 SI and standard deviation of 80 SI), and resampling to 1 mm3 voxel resolution. 1688 radiomic features were extracted from the tumor region of interest using PyRadiomics, an open-source Python radiomics package. To classify tumors as PNF or MPNST, we implemented the Boruta algorithm and correlation removal for selection of important features. A Random Forest model was built using the top ten selected features. Five-fold cross-validation was performed and repeated 100 times. Model performance was evaluated using the area under the ROC curve (AUC), sensitivity, specificity, accuracy, and confidence intervals.

RESULTS

The top ten features included in the model were five intensity features, two shape features, and three texture features. The model demonstrated an AUC of 0.891 (95% CI 0.882-0.899), sensitivity of 0.744, specificity of 0.847, and accuracy of 0.802 (95% CI 0.792-0.813).

CONCLUSIONS

Our machine learning model demonstrated high performance in classifying tumors as either PNF or MPNST in NF1 individuals. Inclusion of additional tumors for model training and testing on an independent dataset are underway. Ultimately, our model may enable improved differentiation between PNF and MPNST compared to descriptive MRI features, permit early patient risk stratification, and improve patient outcomes.

INNV-04. A MULTI-INSTITUTIONAL CLINICAL AND MRI REPOSITORY OF NEUROFIBROMATOSIS TYPE 1-ASSOCIATED PERIPHERAL NERVE SHEATH TUMORS

BACKGROUND

Individuals with neurofibromatosis type 1 (NF1) frequently have peripheral nerve sheath tumors (PNST), including plexiform neurofibromas (PNF), atypical neurofibromas (ANF), and malignant peripheral nerve sheath tumors (MPNST). These tumors reflect a histologic spectrum from benign to malignant. Various clinical and MRI-based features are proposed as risk factors for MPNST development based on small single-institution studies. A major barrier to study these risk factors is collation and annotation of multi-center serial MRIs. To address this, we created a standardized database of clinical data and longitudinal MRIs from NF1-associated PNST from nine international NF1 referral centers.

METHODS

Clinical data from NF1 patients are collected in Research Electronic Data Capture databases housed at Massachusetts General Hospital and Washington University, including demographic information, genotype, disease course, treatment history, and survival. ANF and MPNST require histologic confirmation whereas a diagnosis of PNF can also be made based on clinical/radiographic stability. Longitudinal MRIs predating the histologic diagnosis are uploaded to a HIPAA-compliant cloud-based system.

RESULTS

Data from 200 patients (87 females, 113 males) with 217 tumors (75 PNF, 40 ANF, 102 MPNST) have been collected. 280 regional and 108 whole-body MRIs have been identified. Median age at the time of histologic diagnosis is 30 years (range 5-64). All tumors are histologically confirmed except for 6 PNF which remained stable over time. Median follow-up time is 32 months. Of 147 patients with available survival data, 32 (21.7%) have died from MPNST progression; estimated median overall survival is 20 months.

CONCLUSIONS

In this ongoing work, we have assembled one of the largest systematically annotated clinical and MRI repositories of NF1-associated PNST from pediatric and adult NF1 patients. The data will be accessible to outside researchers which will promote interdisciplinary and multi-center collaborations. Active efforts include the identification of radiomic MRI features to differentiate between PNF and MPNST.

Treatment With Hydrolyzed Diet Supplemented With Prebiotics and Glycosaminoglycans Alters Lipid Metabolism in Canine Inflammatory Bowel Disease

Canine inflammatory bowel disease (IBD) is a chronic, immunologically mediated intestinal disorder, resulting from the complex interaction of genetic, environmental and immune factors. Hydrolyzed diets are used in dogs with food-responsive diarrhea (FRD) to reduce adverse responses to immunostimulatory proteins. Prebiotics (PRBs) and glycosaminoglycans (GAGs) have previously been demonstrated to show anti-inflammatory activity in the intestinal mucosa. Notably, hydrolyzed diets combined with the administration of PRBs and GAGs offer a promising approach for the treatment of canine IBD. Our aim was to investigate the effects of hydrolyzed diet and GAG+PRB co-treatment on the serum metabolomic profile of IBD dogs. Dogs with IBD randomly received either hydrolyzed diet supplemented with GAGs and PRBs (treatment 1) or hydrolyzed diet alone (treatment 2) for 10 weeks. A targeted metabolomics approach using mass spectrometry was performed to quantify changes in the serum metabolome before and after treatment and between treatment 1 and 2. Principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), hierarchical cluster analysis (HCA) and univariate statistics were used to identify differences between the treatment groups. PCA, PLS-DA, and HCA showed a clear clustering of IBD dogs before and after hydrolyzed diet, indicating that the treatment impacted the serum metabolome. Univariate analysis revealed that most of the altered metabolites were involved in lipid metabolism. The most impacted lipid classes were components of cell membranes, including glycerophospholipids, sphingolipids, and di- and triglycerides. In addition, changes in serum metabolites after GAG+PRB co-treatment suggested a possible additional beneficial effect on the lipid metabolism in IBD dogs. In conclusion, the present study showed a significant increase in metabolites that protect gut cell membrane integrity in response to hydrolyzed diet alone or in combination with GAG+PRB co-treatment. Administration of such treatment over 70 days improved selected serum biomarkers of canine IBD, possibly indicating improved intestinal membrane integrity.

The Gut-Brain Axis in Neurodegenerative Diseases and Relevance of the Canine Model: A Review

Identifying appropriate animal models is critical in developing translatable in vitro and in vivo systems for therapeutic drug development and investigating disease pathophysiology. These animal models should have direct biological and translational relevance to the underlying disease they are supposed to mimic. Aging dogs not only naturally develop a cognitive decline in many aspects including learning and memory deficits, but they also exhibit human-like individual variability in the aging process. Neurodegenerative processes that can be observed in both human and canine brains include the progressive accumulation of β-amyloid (Aβ) found as diffuse plaques in the prefrontal cortex (PFC), including the gyrus proreus (i.e., medial orbital PFC), as well as the hippocampus and the cerebral vasculature. Tau pathology, a marker of neurodegeneration and dementia progression, was also found in canine hippocampal synapses. Various epidemiological data show that human patients with neurodegenerative diseases have concurrent intestinal lesions, and histopathological changes in the gastrointestinal (GI) tract occurs decades before neurodegenerative changes. Gut microbiome alterations have also been reported in many neurodegenerative diseases including Alzheimer's (AD) and Parkinson's diseases, as well as inflammatory central nervous system (CNS) diseases. Interestingly, the dog gut microbiome more closely resembles human gut microbiome in composition and functional overlap compared to rodent models. This article reviews the physiology of the gut-brain axis (GBA) and its involvement with neurodegenerative diseases in humans. Additionally, we outline the advantages and weaknesses of current in vitro and in vivo models and discuss future research directions investigating major human neurodegenerative diseases such as AD and Parkinson's diseases using dogs.