Radiation Planning Assistant (RPA)
K222728University of Texas, MD Anderson Cancer Center · cleared 2023-05-17 · product code MUJ · Radiology
Premarket evidence — what FDA accepted
source quote (p.4)
“The Radiation Planning Assistant (RPA) is a web-based contouring and radiotherapy treatment planning software tool that incorporates the basic radiation planning functions from automated contouring, automated planning with dose optimization, and quality control checks.”
source quote (p.7)
“The RPA uses deep learning algorithms which Eclipse does not.”
Validation studies (5)
Retrospective clinical
n=50 patients · 5 site(s)
endpoints: Assess the safety of use of the RPA by comparing the number of patient plans that pass accepted dosimetric metrics when assessed on the RPA contour with the number that pass when assessed on the clinical contour.; Assess the effectiveness of use of the RPA by comparing the number of RPA plans that pass accepted dosimetric metrics (e.g., percentage volume of the PTV receiving 95% of the prescribed dose) when compared with clinical plans.; Assess the geometric effectiveness of the RPA targets using recall.; Assess the quality of body contouring generated by the RPA by comparing primary and secondary body contours generated by the RPA with manual body contours. Surface DSC (2mm) should be greater than 0.8 for 95% of the CT scans.; Assess the ability of the RPA to accurately identify the marked isocenter.
standards: IEC 62304 Medical device software - Software life cycle processes, IEC 62083 Requirements for the safety of radiotherapy treatment planning systems
Retrospective clinical
n=47 patients · 5 site(s)
endpoints: Assess the safety of use of the RPA by comparing the number of patient plans that pass accepted dosimetric metrics when assessed on the RPA contour with the number that pass when assessed on the clinical contour.; Assess the effectiveness of use of the RPA by comparing the number of RPA plans that pass accepted dosimetric metrics (e.g., percentage volume of the PTV receiving 95% of the prescribed dose) when compared with clinical plans.; Assess the geometric effectiveness of the RPA targets using recall.; Assess the quality of body contouring generated by the RPA by comparing primary and secondary body contours generated by the RPA with manual body contours. Surface DSC (2mm) should be greater than 0.8 for 95% of the CT scans.; Assess the ability of the RPA to accurately identify the marked isocenter.
standards: IEC 62304 Medical device software - Software life cycle processes, IEC 62083 Requirements for the safety of radiotherapy treatment planning systems
Retrospective clinical
n=46 patients · 5 site(s)
endpoints: Assess the safety of use of the RPA by comparing the number of patient plans that pass accepted dosimetric metrics when assessed on the RPA contour with the number that pass when assessed on the clinical contour.; Assess the effectiveness of use of the RPA by comparing the number of RPA plans that pass accepted dosimetric metrics (e.g., mean dose to the organ-at-risk) when compared with clinical plans.; Assess the quality of body contouring generated by the RPA by comparing primary and secondary body contours generated by the RPA with manual body contours. Surface DSC (2mm) should be greater than 0.8 for 95% of the CT scans.
standards: IEC 62304 Medical device software - Software life cycle processes, IEC 62083 Requirements for the safety of radiotherapy treatment planning systems
Retrospective clinical
n=86 patients · 5 site(s)
endpoints: Assess the safety of use of RPA normal structures for treatment planning by comparing the number of patient plans that passed accepted dosimetric metrics (e.g., mean dose to the parotid) when assessed on the RPA contour with the number that passed when assessed on the clinical contour.; Assess the effectiveness of use of RPA normal structures for treatment planning by comparing the number of RPA plans that pass accepted dosimetric metrics (e.g., mean dose to the parotid) when compared with clinical plans.; Assess the effectiveness of the RPA plan for target structures by comparing the number of RPA plans that pass accepted dosimetric metrics (e.g., percentage volume of the PTV receiving 95% of the prescribed dose) when compared with clinical plans.; Assess the geometric effectiveness of the RPA targets using recall.; Assess the quality of body contouring generated by the RPA by comparing body contours generated by the RPA with manual body contours. Surface DSC (2mm) should be greater than 0.8 for 95% of the CT scans.; Assess the ability of the RPA to accurately identify the marked isocenter.
standards: IEC 62304 Medical device software - Software life cycle processes, IEC 62083 Requirements for the safety of radiotherapy treatment planning systems
Retrospective clinical
n=46 patients · 5 site(s)
endpoints: Assess the safety of using the RPA plan for normal structures by comparing the number of patient plans that pass accepted dosimetric metrics when assessed on the RPA contour with the number that pass when assessed on the clinical contour.; Assess the effectiveness of the RPA plan for normal structures by comparing the number of RPA plans that pass accepted dosimetric metrics when compared with clinical plans.; Assess the effectiveness of the RPA plan for target structures by comparing the number of RPA plans that pass accepted dosimetric metrics (e.g., percentage volume of the brain receiving 95% of the prescribed dose) when compared with clinical plans.; Assess the quality of body contouring generated by the RPA by comparing primary and secondary body contours generated by the RPA with manual body contours. Surface DSC (2mm) should be greater than 0.8 for 95% of the CT scans.; Assess the ability of the RPA to accurately identify the marked isocenter.
standards: IEC 62304 Medical device software - Software life cycle processes, IEC 62083 Requirements for the safety of radiotherapy treatment planning systems
Reported performance (5 observations)
source quote (p.14)
“25th percentile for recall > 0.7”
source quote (p.14)
“Surface DSC > 0.8 for 95% of CT scans”
source quote (p.16)
“Surface DSC > 0.8 for 95% of CT scans”
source quote (p.18)
“25th percentile for recall > 0.7”
source quote (p.18)
“Surface DSC > 0.8 for >95% of CT scans”
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
- recall_reason_pattern
Software/algorithm-related recall in product code MUJ (Philips Medical Systems (Cleveland) Inc, initiated 2025-08-05): "Due to a software issue, there is a potential image error of the Region of Interest for expansion/contraction for HFP (Head First Prone), FFS (Feet First Supine) and FFP (Feet Firs" Recalling firm is another firm in the same product code.
first seen 2026-07-08 · recall res_event_number:97049
- recall_reason_pattern
Software/algorithm-related recall in product code MUJ (Philips Medical Systems (Cleveland) Inc, initiated 2025-07-17): "Due to software issue, Radiation Therapy Planning system may provide incorrect dataset calculations when performing the "Stopping Power Ratio" (SPR) ," Recalling firm is another firm in the same product code.
first seen 2026-07-08 · recall res_event_number:97309
Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device). 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).