Opulus™ Lymphoma Precision

Roche Molecular Systems, Inc. • Radiology • 05/30/2025

510(k)IEC 62304 Class B

Device Purpose

The Opulus™ Lymphoma Precision device is a software-based system that employs a machine learning algorithm to automate the segmentation, visualization, and total metabolic tumor volume measurement of FDG-avid lymphoma lesions in whole-body FDG-PET/CT scans. Its purpose is to assist trained interpreting physicians in the analysis of these images, having demonstrated safety and effectiveness through performance evaluations confirming agreement with ground truth.

Summary

The Opulus™ Lymphoma Precision device, a Class II medical image management and processing system developed by Roche Molecular Systems, Inc., received a determination of substantial equivalence on May 30, 2025, allowing its marketing. This software device is designed to aid trained interpreting physicians in the analysis of whole-body FDG-PET/CT scans for patients with FDG-avid lymphomas, though a radiologist's final review remains essential for interpretation. The core of the Opulus™ system is a machine learning-based algorithm that automates the segmentation and visualization of suspected lesions, along with the measurement of their total metabolic tumor volume. Performance evaluations, conducted across diverse patient demographics and scanner types, confirmed the algorithm's agreement with established ground truth, thereby supporting its safety and effectiveness in providing automated quantitative assistance for lymphoma imaging.

AI Regulatory Concerns

Jev reads the full FDA letter and returns calibrated concern probabilities per regulatory category. These are AI estimates, not regulatory advice.

Keywords

Opulus™ Lymphoma PrecisionLymphomaFDG-PET/CTMetabolic tumor volumeMachine learningImage segmentationMedical deviceSubstantially equivalentClass II deviceClinical decision support

Device Metadata

Submission Number

K243863

Company

Roche Molecular Systems, Inc.

Panel

Radiology

Product Code

N/A

Data Type

N/A

Clinical Function

N/A

AI Function

N/A

AI Subclass

N/A

Relevant Clinical Questions

Q1. What are the specific clinical endpoints or metrics used in the performance evaluations to define 'agreement with established ground truth' for the Opulus device?; How does the machine learning algorithm specifically handle ambiguous lesions or imaging artifacts in FDG-PET/CT scans of lymphoma patients?; Are there any known limitations or specific lymphoma subtypes or patient characteristics for which the Opulus device's performance might be less reliable?; What is the inter-reader and intra-reader variability when using the Opulus system compared to manual segmentation and measurement methods?; How does the total metabolic tumor volume calculated by Opulus™ correlate with patient prognosis or treatment response in long-term follow-up studies?

Risk Classification

ESL Analysis

IEC 62304 ClassB

FDA Device ClassII

FDA Class II via 510(k) pathway; IEC 62304 Class B (software safety classification); Post-§524B but no clear network capability detected

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Cybersecurity Evidence

Based on AI-generated device summaries, not the full FDA submission. Items not detected here may still be present in the complete submission package.

Cyber Score5/100

SBOM Evidence⚠ Not in summary - likely in full submission per §524B

Cyber Risk Assessment⚠ Not in summary - likely in full submission per §524B

Postmarket Plan⚠ Not in summary - likely in full submission per §524B

§524B ApplicableYes

Supply Chain BOM Evidence

Based on the CycloneDX / OWASP ECMA-424 xBOM ecosystem. Detection is from the AI-generated summary.

SBOMSoftware Bill of Materials

- Not detected

ML-BOMMachine Learning Bill of Materials(machine learning, algorithm, ground truth)

✓ Detected

SaaSBOMSoftware-as-a-Service Bill of Materials

- Not detected

HBOMHardware Bill of Materials

- Not detected

CBOMCryptography Bill of Materials

- Not detected

VDR/VEXVulnerability Disclosure Report / Vulnerability Exploitability eXchange

- Not detected

OBOMOperations Bill of Materials

- Not detected

Analysis:

• Analysis is based on the AI-generated device summary, not the full FDA submission package. Click "Analyze Full FDA PDF" below to scan the actual submission document.

• Evidence of ML-BOM detected in AI-generated device summary.

• SBOM not mentioned in AI-generated device summary. Under §524B (effective Oct 2023), cyber devices must provide a SBOM.

• Cybersecurity risk assessment not referenced in AI-generated device summary. FDA cybersecurity guidance (2023) expects this for cyber devices.

• Postmarket cybersecurity monitoring plan not clearly documented in AI-generated device summary.

• Not detected in AI-generated device summary: SBOM, SaaSBOM, HBOM, CBOM, VDR/VEX, OBOM. These may be present in the full FDA submission.

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