Home▸Bioinformatics▸Lung Nodule Lung-RADS Risk Classifier

🫁 Lung Nodule Lung-RADS Risk Classifier

Simulate how a CT-detected lung nodule is triaged using the real published Lung-RADS categories (1-4B) — adjust diameter, composition and growth to see the live category and recommended follow-up interval.

Bioinformatics2DModerate60 FPS

This simulator applies the real, published Lung-RADS (Lung CT Screening Reporting & Data System) assessment categories used by radiologists to triage pulmonary nodules found on low-dose CT lung cancer screening. It classifies a simulated nodule, defined by diameter, density/composition and follow-up growth behaviour, into Category 2, 3, 4A or 4B and shows the real recommended follow-up interval for that category.

🔬 What It Demonstrates

🎮 How to Use

Set the nodule diameter and composition type (solid, part-solid or ground-glass), then choose whether the nodule is stable or growing at a simulated follow-up scan. The classifier applies the published Lung-RADS v1.1 size and growth thresholds for each composition type to determine the category and its associated recommended CT interval, drawn live on the nodule diagram.

💡 Did You Know?

Did you know that the LIDC-IDRI dataset, annotated independently by four radiologists per case, was built specifically to capture how much even expert readers disagree on nodule borders and malignancy likelihood, real inter-reader variability that Lung-RADS was designed to reduce by standardizing how any radiologist reports a nodule?

⚙ Under the hood

Lung-RADSlung noduleLIDC-IDRICT screeningpulmonary nodulelung cancer screeningradiologynodule malignancy risk

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