Distributed knowledge-based platform for radiotherapy plan quality control
Distributed knowledge-based platform for radiotherapy plan quality control
批准号:
10369642
负责人:
James D Murphy
金额:
$37.46万
依托单位国家:
美国
项目类别:
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-06-11 至 2024-03-31
中文摘要
摘要
英文摘要
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.
.
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DOI:
10.1002/acm2.13399
发表时间:
2021-10
期刊:
Journal of applied clinical medical physics
影响因子:
2.1
作者:
[Moazzezi M, Rose B, Kisling K, Moore KL, Ray X]
通讯作者:
Ray X
Evaluating the sensitivity of Halcyon's automatic transit image acquisition for treatment error detection: A phantom study using static IMRT.
评估 Halcyon 自动传输图像采集对治疗错误检测的敏感性:使用静态 IMRT 的模型研究。
DOI:
10.1002/acm2.12749
发表时间:
2019
期刊:
Journal of applied clinical medical physics
影响因子:
2.1
作者:
[Ray,Xenia, Bojechko,Casey, Moore,KevinL]
通讯作者:
Moore,KevinL
A practical method to quantify knowledge-based DVH prediction accuracy and uncertainty with reference cohorts.
一种实用方法,可以用参考人群量化基于知识的DVH预测准确性和不确定性。
DOI:
10.1002/acm2.13199
发表时间:
2021-03
期刊:
Journal of applied clinical medical physics
影响因子:
2.1
作者:
[Covele BM, Carroll CJ, Moore KL]
通讯作者:
Moore KL
Knowledge-based dose prediction models to inform gynecologic brachytherapy needle supplementation for locally advanced cervical cancer.
基于知识的剂量预测模型,为局部晚期宫颈癌的妇科近距离放射治疗针补充提供信息。
DOI:
10.1016/j.brachy.2021.07.001
发表时间:
2021
期刊:
Brachytherapy
影响因子:
1.9
作者:
[Kallis,Karoline, Mayadev,Jyoti, Kisling,Kelly, Brown,Derek, Scanderbeg,Daniel, Ray,Xenia, Cortes,Katherina, Simon,Aaron, Yashar,CatherynM, Einck,JohnP, Mell,LorenK, Moore,KevinL, Meyers,SandraM]
通讯作者:
Meyers,SandraM
Evaluation of dose differences between intracavitary applicators for cervical brachytherapy using knowledge-based models.
使用基于知识的模型评估用于颈部近距离放射治疗的腔内施源器之间的剂量差异。
DOI:
10.1016/j.brachy.2021.08.010
发表时间:
2021
期刊:
Brachytherapy
影响因子:
1.9
作者:
[Kallis,Karoline, Mayadev,Jyoti, Covele,Brent, Brown,Derek, Scanderbeg,Daniel, Simon,Aaron, Frisbie-Firsching,Helena, Yashar,CatherynM, Einck,JohnP, Mell,LorenK, Moore,KevinL, Meyers,SandraM]
通讯作者:
Meyers,SandraM
TALENT Shared Resources Core
-
批准号:10762272
-
项目类别:
-
资助金额:$4.63万
-
财政年份:2023
-
负责人:James D Murphy
-
依托单位:
Research Education Core
-
批准号:10762277
-
项目类别:
-
资助金额:$12.22万
-
财政年份:2023
-
负责人:James D Murphy
-
依托单位:
Quantification and Characterization of Opioid Prescription, Overdose, and Fatalities in People with Cancer: A Massive Nationwide, Multi-Cohort Study
-
批准号:10195671
-
项目类别:
-
资助金额:$7.89万
-
财政年份:2021
-
负责人:James D Murphy
-
依托单位:
Quantification and Characterization of Opioid Prescription, Overdose, and Fatalities in People with Cancer: A Massive Nationwide, Multi-Cohort Study
-
批准号:10372162
-
项目类别:
-
资助金额:$7.9万
-
财政年份:2021
-
负责人:James D Murphy
-
依托单位:
Research Education Core
-
批准号:9768369
-
项目类别:
-
资助金额:$7.49万
-
财政年份:2019
-
负责人:James D Murphy
-
依托单位:
Interactive Contouring Atlas for Radiation Oncology
-
批准号:9182722
-
项目类别:
-
资助金额:$10.0万
-
财政年份:2016
-
负责人:James D Murphy
-
依托单位:
海外基金