MIRA Clinical Learning Environment (MIRACLE): Lung
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 1,000
- 试验地点
- 1
- 主要终点
- Radiation oncologists use predictions provided from the model to support their clinical decision-making.
研究概览
简要总结
The goal of this quality improvement (QI) study is to develop automated clinical pipelines to implement machine learning models in the care pathway of lung cancer patients. The main questions it aims to answer are:
- Can model-prompted risk classifications be incorporated into clinician workflows to enable informed clinical decision-making?
- What are clinicians' perceptions of the information from model outputs, and do they change their decision about data already available to them as a result of the model-prompted risk classification (i.e., to re-review or further assess patients identified by the models as being higher risk)?
Participating radiation oncologists will receive the risk prediction from the model and be asked to complete a survey to give feedback on how they used the prediction in their decision-making.
详细描述
Novel data science and imaging-based methods to personalize care are being identified retrospectively and explored at many centers. Unfortunately, most of these methods require significant manual intervention to apply to any given patient situation and are difficult to deploy in a timely fashion to affect patient treatment decisions. Clinical implementation of data science research will require automated pipelines that are tied into the entire treatment pathway in ways that facilitate real-time data analysis and enable translational research.
The current process for clinical/translational researchers within Princess Margaret Hospital (PM)/University Health Network (UHN) to analyze imaging data involves extensive manual curation consisting of interactions with electronic databases and analysis tools to: identify patients with imaging data; collect that data; delineate targets of interest manually (minutes-to-hours per patient); analyze targets based on manually-selected images; and then correlate the analyzed images with clinical information sources (e.g. outcomes or correlative data). Thus, projects with large patient numbers often encounter insurmountable obstacles that limit research productivity.
MIRA (an in-house developed programming toolkit) solves a common problem for all researchers at PM/UHN studying diagnostic, radiotherapy treatment planning, and/or on-treatment imaging by providing a consistent automated analysis environment for these data. MIRA also enhances ethics approved studies with direct linkage to real-time clinical data including diagnostic imaging via collaboration with the Joint Department of Medical Imaging, radiation oncology treatment planning information, and daily radiation oncology on-treatment imaging. The MIRA Clinical Learning Environment (MIRACLE) quality improvement project intends to use the MIRA platform to develop automated clinical pipelines to address three specific study aims:
To identify lung cancer patients with undiagnosed underlying inflammatory lung disease (ILD) from pre-treatment diagnostic images
To estimate individual patients' tumor growth-rate between diagnostic and treatment planning images (specific growth-rate, SGR)
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Diagnosed with lung cancer stage I-IV and planned for treatment with radiotherapy at Princess Margaret hospital. The three aims of this project have specific inclusion criteria as follows.
- •Aim 1 ILD: All lung cancer patients receiving RT.
- •Aim 2 SGR: Node negative lung cancer patients receiving stereotactic body RT.
- •Aim 3 CBCT: Node positive lung cancer patients receiving standard RT.
排除标准
- •No exclusion criteria
结局指标
主要结局
Radiation oncologists use predictions provided from the model to support their clinical decision-making.
时间窗: January 2022 - December 2023
Clinicians will indicate in the survey their perceptions of accuracy and usefulness of the predictions and whether they have incorporated the predictions into their decision-making.
Rates of true positive diagnosis of ILD increase with high/low patient risk predictions being made available to clinicians.
时间窗: January 2022 - December 2023
An expert review of the cases and chart review will be correlated with survey responses to determine whether the rate of true positive cases were impacted by the implementation of the MIRACLE pathways.
Previously difficult-to-assess information are made available during the clinical workflow as an easily accessible information source available to clinicians
时间窗: January 2022 - December 2023
Clinicians will provide feedback on the communication of the predictions, the integration into their clinical workflow and timeliness of receiving the predictions in order to incorporate into their decision-making.
次要结局
- Additional expertise is focused on patients identified as being higher risk for ILD, SGR > 0.04, or possible pneumonitis.(January 2022 - December 2023)
研究者
Andrew Hope
Clinician Investigator
University Health Network, Toronto