PathoFusion

PathoFusion applies a bifocal convolutional neural network to identify pathomorphological features and map CD276 immunohistochemical reactivity in whole-slide glioblastoma sections for high-resolution morphological and immunohistochemical correlation.

Key Features:

• Bifocal Convolutional Neural Network (BCNN): Implements a BCNN architecture that captures both index (focal) and contextual information, mimicking pathologist focal-to-contextual assessment.
• Multi-scale image tiles: Processes image tiles of varying sizes extracted from digitized whole-slide images to capture features at different spatial scales.
• Adjacent-section H&E and CD276 processing: Analyzes paired hematoxylin and eosin (H&E) and CD276 immunohistochemical-stained adjacent tissue sections from glioblastoma cases.
• Neuropathologist-annotated training data: Trains on image tiles derived from digitized images annotated by a consultant neuropathologist.
• Patch-level pathomorphological recognition: Performs patch-level recognition of six typical pathomorphological features with reported AUCs of 0.985 ± 0.011.
• Immunoreactivity detection: Detects associated CD276 immunoreactivity with reported AUC of 0.988 ± 0.001.
• Spatial correlation with vasculature: Correlates CD276 immunoreactivity with abnormal tumor vasculature within glioblastoma tissue.
• Heatmap visualization: Produces heatmaps that visualize feature distributions and overlaps for spatial interpretation.
• Whole-slide qualitative and quantitative analysis: Enables high-resolution qualitative and quantitative morphological analyses across entire histological slides.

Scientific Applications:

• Glioblastoma tissue analysis: Correlates morphological features and CD276 immunoreactivity in glioblastoma H&E and IHC adjacent sections.
• Neuropathological feature identification: Identifies malignant neuropathological features at patch level for research on tumor morphology.
• Biomarker–vasculature association: Investigates associations between CD276 immunoreactivity and abnormal tumor vasculature.
• Spatial morphology quantification: Generates heatmaps for spatially resolved qualitative and quantitative analyses across whole-slide images.

Methodology:

Uses a bifocal convolutional neural network trained on consultant-neuropathologist-annotated image tiles from digitized adjacent H&E and CD276 sections, processes multi-scale tiles to produce patch-level predictions and heatmaps, and reports AUC-based performance metrics.