LOCATOR - Locally Optimised Contouring With AI Technology for Radiotherapy
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 444
- 试验地点
- 3
- 主要终点
- Assessment of differences in Contour Quality
研究概览
简要总结
LOCATOR is a multicentre phase II randomised clinical trial that is looking at the process of contouring in radiation treatment for breast cancer patients. This study looks at whether contouring aided by artificial intelligence (AI) is comparable in quality to that of contouring done completely manually by a radiation oncologist. We are also looking at whether AI assisted contouring saves radiation oncologists time when compared to fully manual contouring.
LOCATOR uses the LOCATOR software which is an in-house software developed locally and trained on local data.
详细描述
LOCATOR is a multicentre phase II non-inferiority randomised controlled trial looking at comparing AI assisted contours (with in-house LOCATOR software) against fully manual contouring in breast cancer patients. The primary endpoint is to show non inferiority in grade of AI assisted contouring when compared to fully manual contouring with a poor contour (score <= 2) as per the MD Anderson Contouring Grade Scale. Secondary endpoints include geometric assessments of contour accuracy, dosimetric differences based on contours, performance (geometric) when compared to commercially available tools as well as economic cost-benefit analysis if in-house AI contouring tools.
The study will randomise patients 3:1 to the intervention arm of LOCATOR assisted contours to manual contours. An initial AI contouring model for each tumor type will be trained on contours from 45 previous breast cases using a nnUNetv2 framework. The model will then be iteratively updated every 20-50 patients.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Treatment
- 盲法
- Double (Participant, Outcomes Assessor)
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •18 years and older who are planned for primary breast malignancy
- •ECOG performance 0-2
- •Ability to understand and willingness to sign a written informed consent document
- •The target volume must be able to be objectively reviewed by current published national or international clinical guidelines
排除标准
- •Patients under 18 years of age
- •Patients unable to understand consent documents
研究组 & 干预措施
AI assisted contouring
Patients in this arm will have their contours/segmentations generated by a combination of the LOCATOR (AI) software before manual edits and checks by a radiation oncologist.
干预措施: AI assisted contouring (Device)
Manual contouring
Patients in this arm will have standard of care which is fully manual contours/segmentations generated and checked by a radiation oncologist.
结局指标
主要结局
Assessment of differences in Contour Quality
时间窗: 18 months
To assess the contour quality of fully manual segmentation vs AI assisted segmentation. This assessment will be done using the MD Anderson Cancer Centre five-point likert scale used to validate autosegmentation models ranging from (Strongly disagree to Strongly Agree). The measure will be the proportion of unacceptable contours (as defined by MD Anderson autocontouring score \<= 2) between manual contouring and AI-assisted contouring.
次要结局
- Assessment of quality of AI assisted contours with and without manual edits(18 months)
- Time Savings(18 months)
- To assess the differences in acute clinician reported toxicity between patients treated with contours assisted by AI contouring versus manual contouring.(18 months)
- To assess the differences in late clinician reported toxicity between patients treated with contours assisted by AI contouring versus manual contouring.(5 years)
- To assess the differences in patient reported general acute quality of life outcomes between patients treated with contours assisted by AI contouring versus manual contouring.(18 months)
- To assess the differences in patient reported general late quality of life outcomes between patients treated with contours assisted by AI contouring versus manual contouring.(5 years)
- To assess the differences in patient reported breast specific acute quality of life outcomes between patients treated with contours assisted by AI contouring versus manual contouring.(18 months)
- To assess the differences in patient reported breast specific late quality of life outcomes between patients treated with contours assisted by AI contouring versus manual contouring.(5 years)
- Assessment of accuracy of AI assisted contours before and after manual edits using surface dice similarity coefficient (sDSC).(18 months)
- Assessment of accuracy of AI assisted contours before and after manual edits using dice similarity coefficient (DSC).(18 months)
- Assessment of accuracy of AI assisted contours before and after manual edits using added path length (APL)(18 months)
- Assessment of accuracy of AI assisted contours before and after manual edits using mean slice-wise Hausdorff distance (MSHD).(18 months)
- Assessment of dosimetric differences in plans optimised on AI assisted contours before and after manual edits.(18 months)
- Assessment of accuracy in contours with an initial and retrained AI model using surface dice similarity coefficient (sDSC).(18 months)
- Assessment of accuracy in contours with an initial and retrained AI model using dice similarity coefficient (DSC).(18 months)
- Assessment of accuracy in contours between different AI systems using surface dice similarity coefficient (sDSC).(18 months)
- Assessment of accuracy in contours between different AI systems using dice similarity coefficient (DSC).(18 months)
- Assessment of quality in contours between different AI systems(18 months)
- Assessment of patient perception and attitudes on AI use in their care(18 months)
- Economic Cost Benefit Analysis(18 months)
- Assessment of dosimetric differences between patient planned with AI-contours and those planned with manual contours.(18 months)