# Distributed knowledge-based platform for radiotherapy plan quality control

> **NIH AHRQ R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2020 · $374,612

## Abstract

ABSTRACT
 Many recent studies focused on radiotherapy treatment plan quality have begun to quantify what
clinicians have long understood: even “optimized” radiotherapy is no guarantee of a truly optimal
treatment plan for every patient. Plan quality deficiencies have been shown to put a significant
proportion of patients who should have been at low risk of radiation-induced complications at much
higher risk for poor outcome. Available research clearly demonstrates a link between radiation provider
volume and survival, which emphasizes the importance of quality radiation delivery. Radiation providers
in rural or community practices by nature see a wide variety of cases, with lower provider volume for
each individual disease site. Through no fault of their own, physician and non-physician practitioners at
these rural and community centers could be inadvertently and systematically delivering low quality
radiotherapy to their patients simply due to the fact that no platform currently exists that could
benchmark their practice against a distributed, externally-validated plan quality control system. Our
research team has developed, tested, and clinically-implemented an important tool to combat
radiotherapy plan quality deficiencies known as knowledge-based planning (KBP). Knowledge-based
planning relies on the use of statistical learning techniques that analyzed a plurality of prior treatments
to discover patient-specific anatomical features can be precisely correlated to high quality radiation dose
delivery. Unfortunately, the clinical use of KBP has been limited to a handful of high-volume academic
centers and, without some external mechanism to increase utilization, its use is not likely to expand
significantly to rural and community centers because of the lack of any billing code associated with its
use. To provide just such an external mechanism, we intend to build ORBITeR (On-line Real-time
Benchmarking Informatics Technology for Radiotherapy), a freely available, on-line knowledge-based
radiotherapy plan quality control system. ORBITeR will allow clinicians to obtain automatic and
immediate feedback on the quality of any individual treatment plan prior to treatment. We will develop a
KBP-driven plan analysis system on a HIPAA-compliant web-based platform designed to give users real-
time radiotherapy plan quality feedback. To provide real-time feedback to clinical users, we will develop
reporting modules on the ORBITeR system that provide patient-specific feedback on the quality of the
intended treatment plan using already-validated head-and-neck, brain, prostate, cervix, lung, pancreas,
and liver cancer knowledge-based models. We then will disseminate and evaluate the effectiveness of the
ORBITeR plan quality resource among the greater radiation oncology community. Finally, we will
develop a quality analytics system to conduct widespread plan quality and patterns of care study across
submitting sites on the ORBITeR system.
.

## Key facts

- **NIH application ID:** 9900745
- **Project number:** 5R01HS025440-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Kevin Lawrence Moore
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2020
- **Award amount:** $374,612
- **Award type:** 5
- **Project period:** 2018-06-11 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9900745

## Citation

> US National Institutes of Health, RePORTER application 9900745, Distributed knowledge-based platform for radiotherapy plan quality control (5R01HS025440-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9900745. Licensed CC0.

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