⚠️ Educational illustration of the published Lung-RADS framework only. This is not a diagnostic tool and does not provide medical advice — real nodule assessment requires a radiologist reviewing the actual CT images.
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 1/2, 3, 4A or 4B and shows the real recommended follow-up interval for that category.
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.
Nodule-diameter slider, density/composition selector, follow-up-behaviour selector, and a growth-rate slider that appears when "Growing" is selected.
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?
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.
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.
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 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?