AutoContour (RADAC V5)

K260509

Radformation, Inc. · cleared 2026-03-19 · product code QKB · Radiology

Premarket evidence — what FDA accepted

Device typesamd

source quote (p.7)

“None – software-only application.”

Algorithmmachine learning based contouring using deep-learning-based structure models

source quote (p.6)

“automatically contour various structures of interest for radiation therapy treatment planning using machine learning based contouring. The deep-learning-based structure models are trained using imaging datasets consisting of anatomical organs of the head and neck, thorax, abdomen, and pelvis for adult male and female patients”

Adaptive (vs locked)FDA source did not state this

PCCPNo

Cybersecurity addressedNo

Validation studies (2)

Standalone

sample size not stated

endpoints: Mean Dice Similarity Coefficient (DSC)

Retrospective clinical

sample size not stated

endpoints: Dice Similarity Coefficient (DSC); qualitative clinical appropriateness rating (1 to 5 scale)

Reported performance (3 observations)

diceas written: “Mean Dice Similarity Coefficient (DSC) for CT Structure models (Large)”0.91

source quote (p.19)

“For CT Structure models large, medium and small structures resulted in a mean DSC of 0.91+/-0.14, 0.86+/-0.13, and 0.75+/-0.20 respectively.”

diceas written: “Mean Dice Similarity Coefficient (DSC) for CT Structure models (Medium)”0.86

source quote (p.19)

“For CT Structure models large, medium and small structures resulted in a mean DSC of 0.91+/-0.14, 0.86+/-0.13, and 0.75+/-0.20 respectively.”

diceas written: “Mean Dice Similarity Coefficient (DSC) for CT Structure models (Small)”0.75

source quote (p.19)

“For CT Structure models large, medium and small structures resulted in a mean DSC of 0.91+/-0.14, 0.86+/-0.13, and 0.75+/-0.20 respectively.”

Each value carries its own analysis unit and task — never compare or pool across devices. Source: 510(k) summary PDF.

Predicate network

Postmarket — what happened after clearance

recalls in product code, 24mo

MAUDE reports in code, 12mo

—

vs code's own 3-yr baseline

drift signals on this device

Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device). Signals are descriptive observables with sources — never a judgment that the device is unsafe or drifting. Snapshot 2026-07-08.

Reimbursement — how devices like this got paid

Not yet tracked — no payment pathway indexed for this clearance (the reimbursement corpus is a growing seed set).