Home▸Bioinformatics▸Histopathology Metastasis Detection & pN Staging Simulator

🧫 Histopathology Metastasis Detection & pN Staging Simulator

Simulate how whole-slide-image AI trained on datasets like CAMELYON flags tumor-positive patches in a lymph-node section, then watch the real AJCC pN staging band (ITC, micrometastasis, macrometastasis) update live as you change tumor spread and pattern.

Bioinformatics2DModerate60 FPS

This simulates how a whole-slide-image AI model, of the kind benchmarked on the CAMELYON lymph-node metastasis detection challenge (Bejnordi et al.), scans a grid of tissue patches from a lymph-node section and flags which patches look tumor-positive, then classifies the largest contiguous deposit into the real AJCC pN staging bands used in breast-cancer pathology reports.

🔬 What It Demonstrates

🎮 How to Use

Move the tumor-positive fraction slider to set how much of the simulated field is flagged tumor-positive, and switch the pattern between "scattered" (isolated positive patches, illustrating isolated tumor cells) and "clustered" (a single contiguous growing deposit, illustrating a micro- or macrometastasis). Use the resample button to regenerate a fresh random layout at the same settings, and watch the pN category badge update live against the real AJCC size thresholds.

💡 Did You Know?

Did you know that the CAMELYON16 challenge found that the best-performing AI algorithms could match or approach the accuracy of expert pathologists at detecting lymph-node metastases in whole-slide images, and that combining an algorithm's output with a pathologist's review outperformed either working alone? A single lymph node cross-section can span thousands of high-power microscope fields, which is exactly the exhaustive-search problem this kind of AI-assisted screening is designed to help with.

⚙ Under the hood

CAMELYONhistopathologylymph node metastasispN stagingAJCCwhole slide imagingdigital pathologybreast cancer stagingmicrometastasisisolated tumor cells

2D · HTML5 Canvas 2D · 60 FPS target · runs fully client-side, no install