Opulus™ Lymphoma Precision

K243863

Roche Molecular Systems, Inc. · cleared 2025-05-30 · product code QIH · Radiology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
“Opulus™ Lymphoma Precision is a software device that uses a machine learning-based algorithm to automate segmentation and visualization of lesions along with automation of measurement of total metabolic tumor volume within whole-body FDG-PET/CT scans of patients with FDG-avid lymphomas.”
Algorithmmachine learning-based algorithm
source quote (p.4)
“Opulus™ Lymphoma Precision is a software device that uses a machine learning-based algorithm to automate segmentation and visualization of lesions along with automation of measurement of total metabolic tumor volume within whole-body FDG-PET/CT scans of patients with FDG-avid lymphomas.”
Adaptive (vs locked)No
source quote (p.6)
“The user does not have the ability to modify the device output.”
PCCPNo
Cybersecurity addressedNo

Validation studies (1)

Retrospective clinical

n=182 patients

endpoints: agreement of TMTV quantitative estimates between aTMTV and manual (mTMTV); accuracy in lesion segmentation by comparing aTMTV-generated contours and ground truth

standards: IEC 62304:2006/AC:2015 - Medical device software – Software life cycle processes, FDA Guidance document, “Content of Premarket Submissions for Device Software Functions” June 14, 2023.

Reported performance (3 observations)

ppvas written: “mean difference between Opulus™ Lymphoma Precision algorithm and the ground truth for TMTV values < 2.5 cm”-0.2CI 95% CI, cm: -0.50, 0.10
source quote (p.9)
“The mean difference between Opulus™ Lymphoma Precision algorithm and the ground truth was -0.20 cm (95% CI, cm: -0.50, 0.10) for TMTV values < 2.5 cm”
ppvas written: “mean difference between Opulus™ Lymphoma Precision algorithm and the ground truth for TMTV values ≥ 2.5 cm”-0.23CI 95% CI, cm: -0.38, -0.09
source quote (p.9)
“and -0.23 cm (95% CI, cm: -0.38, -0.09) for TMTV values ≥ 2.5 cm.”
diceas written: “mean DSC score”0.7CI 95% CI, 0.66, 0.73
source quote (p.9)
“The mean DSC score was 0.70 (95% CI, 0.66, 0.73).”

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
3
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/K243863