AutoContour (RADAC V5)

K260509

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

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
“As with AutoContour Model RADAC V4, the AutoContour Model RADAC V5 device is software that uses DICOM-compliant image data (CT or MR) as input to: (1) automatically contour various structures of interest for radiation therapy treatment planning using machine learning based contouring.”
Algorithmmachine learning based contouring; deep-learning-based structure models
source quote (p.6)
“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 (3)

Retrospective clinical

n=49 images

endpoints: Mean Dice Similarity Coefficient (DSC); qualitative clinical appropriateness

standards: NRG/RTOG/ESTRO

Retrospective clinical

n=58 images

endpoints: DSC values; qualitative clinical appropriateness

standards: NRG/RTOG

Retrospective clinical

n=50 images

endpoints: DSC values; qualitative clinical appropriateness

standards: NRG/RTOG

Reported performance (2 observations)

diceas written: “Mean DSC (CT Structures)”stated without valueCI 0.73+/-0.11, 0.84+/-0.09, and 0.93+/-0.05
source quote (p.27)
“All structures passed the minimum DSC criteria for small, medium and large structures with a mean DSC of 0.73+/-0.11, 0.84+/-0.09, and 0.93+/-0.05 respectively”
diceas written: “Mean DSC (MR Structures)”stated without valueCI 0.82+/-0.12 for medium models, and 0.72+/- 0.09 for small models
source quote (p.34)
“For MR Structure models, a mean training DSC of 0.82+/-0.12 for medium models, and 0.72+/- 0.09 for small models.”

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

0
recalls in product code, 24mo
0
MAUDE reports in code, 12mo
—
vs code's own 3-yr baseline
0
drift signals on this device

Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device); a recall is shown as device-attributed only when the recall record itself lists this clearance number. 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).

Applicable FDA guidance — what the submission is measured against

FDA guidance documents and guiding principles applicable to 510(k) AI/ML devices in the Radiology panel. A curated reference index, not legal or regulatory advice — each item states its own status, and a draft is never binding.

Applicability is derived from the device's FDA advisory panel and pathway — cross-cutting guidances apply to every AI/ML device; panel-specific ones are flagged. Titles, dates, and links verified against fda.gov as of July 2026.

Constat Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: constat.dev/precedent/device/K260509