Browsing by Subject "Stratification"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    IA-2A positivity increases risk of progression within and across established stages of type 1 diabetes
    (Springer, 2025) Sims, Emily K.; Cuthbertson, David; Ferrat, Lauric A.; Bosi, Emanuele; Evans‑Molina, Carmella; DiMeglio, Linda A.; Nathan, Brandon M.; Ismail, Heba M.; Jacobsen, Laura M.; Redondo, Maria J.; Oram, Richard A.; Sosenko, Jay M.; Pediatrics, School of Medicine
    Aims/hypothesis: Accurate understanding of type 1 diabetes risk is critical for optimisation of counselling, monitoring and interventions, yet even within established staging classifications, individual time to clinical disease varies. Previous work has associated IA-2A positivity with increased type 1 diabetes progression but a comprehensive assessment of the impact of screening for IA-2A positivity across the natural history of autoantibody positivity has not been performed. We asked whether IA-2A would consistently be associated with higher risk of progression within and across established stages of type 1 diabetes in a large natural history study. Methods: Genetic, autoantibody and metabolic data from adult and paediatric autoantibody-negative (n=192) and autoantibody-positive (n=4577) relatives of individuals with type 1 diabetes followed longitudinally in the Type 1 Diabetes TrialNet Pathway to Prevention Study were analysed. Cox regression was used to compare cumulative incidences of clinical diabetes by autoantibody profiles and disease stages. Results: Compared with IA-2A- individuals, IA-2A+ individuals had higher genetic risk scores and clinical progression risk within single-autoantibody-positive (5.3-fold increased 5 year risk), stage 1 (2.2-fold increased 5 year risk) and stage 2 (1.3-fold increased 5 year risk) type 1 diabetes categories. Individuals with single-autoantibody positivity for IA-2A showed increased metabolic dysfunction and diabetes progression compared with people who were autoantibody negative, those positive for another single autoantibody, and IA-2A- stage 1 individuals. Individuals at highest risk within the single-IA-2A+ category included children (HR 14.2 [95% CI 1.9, 103.1], p=0.009), individuals with IA-2A titres above the median (HR 3.5 [95% CI 1.9, 6.6], p<0.001), individuals with high genetic risk scores (HR 1.4 [95% CI 1.2,1.6], p<0.001) and individuals with HLA DR4-positive status (HR 3.7 [95% CI 1.6, 8.3], p=0.002). When considering all autoantibody-positive individuals, progression risk was similar for euglycaemic IA-2A+ individuals and dysglycaemic IA-2A- individuals. Conclusions/interpretation: IA-2A positivity is consistently associated with increased progression risk throughout the natural history of type 1 diabetes development. Individuals with single-autoantibody positivity for IA-2A have a greater risk of disease progression than those who meet stage 1 criteria but who are IA-2A-. Approaches to incorporate IA-2A+ status into monitoring strategies for autoantibody-positive individuals should be considered.
  • Thumbnail Image
    Item
    Prognostic stratification of glioblastoma patients by unsupervised clustering of morphology patterns on whole slide images furthering our disease understanding
    (Frontiers Media, 2024-05-20) Baheti, Bhakti; Innani, Shubham; Nasrallah, MacLean; Bakas, Spyridon; Pathology and Laboratory Medicine, School of Medicine
    Introduction: Glioblastoma (GBM) is a highly aggressive malignant tumor of the central nervous system that displays varying molecular and morphological profiles, leading to challenging prognostic assessments. Stratifying GBM patients according to overall survival (OS) from H&E-stained whole slide images (WSI) using advanced computational methods is challenging, but with direct clinical implications. Methods: This work is focusing on GBM (IDH-wildtype, CNS WHO Gr.4) cases, identified from the TCGA-GBM and TCGA-LGG collections after considering the 2021 WHO classification criteria. The proposed approach starts with patch extraction in each WSI, followed by comprehensive patch-level curation to discard artifactual content, i.e., glass reflections, pen markings, dust on the slide, and tissue tearing. Each patch is then computationally described as a feature vector defined by a pre-trained VGG16 convolutional neural network. Principal component analysis provides a feature representation of reduced dimensionality, further facilitating identification of distinct groups of morphology patterns, via unsupervised k-means clustering. Results: The optimal number of clusters, according to cluster reproducibility and separability, is automatically determined based on the rand index and silhouette coefficient, respectively. Our proposed approach achieved prognostic stratification accuracy of 83.33% on a multi-institutional independent unseen hold-out test set with sensitivity and specificity of 83.33%. Discussion: We hypothesize that the quantification of these clusters of morphology patterns, reflect the tumor's spatial heterogeneity and yield prognostic relevant information to distinguish between short and long survivors using a decision tree classifier. The interpretability analysis of the obtained results can contribute to furthering and quantifying our understanding of GBM and potentially improving our diagnostic and prognostic predictions.