AutoContour (Model RADAC V4)

K242729

Radformation, Inc. · cleared 2024-12-09 · product code QKB · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.6)
“As with AutoContour Model RADAC V3, the AutoContour Model RADAC V4 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.”
Algorithmdeep learning CNN
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, (2) allow the user to review and modify the resulting contours, and (3) generate DICOM-compliant structure set data the can be imported into a radiation therapy treatment planning system. (a) very similar CNN architecture was used to train these new CT models (a) very similar CNN architecture was used to train these new MR models”
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedNo

Validation studies (2)

Retrospective clinical

sample size not stated

endpoints: Dice Similarity Coefficient (DSC); External Reviewer Average Rating (1-5)

standards: NRG/RTOG guidelines

Retrospective clinical

sample size not stated

endpoints: Dice Similarity Coefficient (DSC); External Reviewer Average Rating (1-5)

standards: NRG/RTOG guidelines

Reported performance (6 observations)

diceas written: “Mean DSC for Large CT structures (external)”0.94CI +/-0.02
source quote (p.23)
“All structures passed the minimum DSC criteria for small, medium and large structures with a mean DSC of 0.76+/-0.09, 0.84+/-0.09, and 0.94+/-0.02 respectively”
diceas written: “Mean DSC for Medium CT structures (external)”0.84CI +/-0.09
source quote (p.23)
“All structures passed the minimum DSC criteria for small, medium and large structures with a mean DSC of 0.76+/-0.09, 0.84+/-0.09, and 0.94+/-0.02 respectively”
diceas written: “Mean DSC for Small CT structures (external)”0.76CI +/-0.09
source quote (p.23)
“All structures passed the minimum DSC criteria for small, medium and large structures with a mean DSC of 0.76+/-0.09, 0.84+/-0.09, and 0.94+/-0.02 respectively”
diceas written: “Mean DSC for Large MR structures (external)”0.8CI +/-0.09
source quote (p.29)
“All structures passed the minimum DSC criteria for small, medium, and large structures with a mean DSC of 0.61+/-0.14, 0.84+/-0.09, 0.80+/-.09 respectively”
diceas written: “Mean DSC for Medium MR structures (external)”0.84CI +/-0.09
source quote (p.29)
“All structures passed the minimum DSC criteria for small, medium, and large structures with a mean DSC of 0.61+/-0.14, 0.84+/-0.09, 0.80+/-.09 respectively”
diceas written: “Mean DSC for Small MR structures (external)”0.61CI +/-0.14
source quote (p.29)
“All structures passed the minimum DSC criteria for small, medium, and large structures with a mean DSC of 0.61+/-0.14, 0.84+/-0.09, 0.80+/-.09 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

0
recalls in product code, 24mo
0
MAUDE reports in code, 12mo
—
vs code's own 3-yr baseline
1
drift signals on this device
  • re_clearance

    The FDA AI/ML device list shows a newer 510(k) K260509 (decision 2026-03-19) from Radformation, Inc. for a matching device line ("AutoContour (RADAC V5)") — a new clearance for the same line is a change event.

    first seen 2026-07-08 · k_number:K260509

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/K242729