K-numberK241765
Device namePLANET Onco Dose (3.2)
ApplicantDosisoft SA
Product codeLLZ
Device classClass II
Decision dateMar 14, 2025
DecisionSubstantially Equivalent
Regulation892.2050
AI Summary extracted from FDA summary PDF · never regenerated
Intended use

PLANET Onco Dose (3.2) is standalone medical imaging software that manages, processes, and analyzes anatomical and functional images (CT, MRI, SPECT, PET, etc.) to assist in medical diagnosis, therapy response assessment, and internal dosimetry computation for radionuclide-based therapies. It is intended for retrospective dose determination only and is for use by qualified medical professionals in molecular imaging and medical oncology.

Technological characteristics

PLANET Onco Dose supports multi-modality imaging (CT, SPECT, PET), image registration (rigid and deformable), volume of interest management, import/export of contours in DICOM RT-Structure format and dose maps in DICOM RT-Dose format, and supports FDA-approved isotopes with beta and gamma emissions. Version 3.2 improved partial volume effect correction; the main difference from predicate Torch™ is the dose computation algorithm—PLANET uses voxel S Value dose kernel convolution and local energy deposition versus Torch's Monte Carlo method, with comparable performance demonstrated.

Test standards cited

Not stated in this summary.

Substantial equivalence argument

PLANET Onco Dose (3.2) is substantially equivalent because it shares the same intended use (retrospective absorbed dose estimation of FDA-approved radiopharmaceuticals), is designed for identical users (medical physicists/physicians), and provides equivalent core technological capabilities as predicates PLANET Onco Dose (3.1) and Torch™—including multi-modality image support, registration, and voxel-level dose computation. Although the dose algorithms differ (voxel S Value versus Monte Carlo), performance testing demonstrated consistency between methods. Differences in other functional features do not significantly affect safety or effectiveness.

Extracted by AI from the official FDA summary PDF →
Source

View the full FDA submission: accessdata.fda.gov

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