Machine-learning Optimization for Prostate Brachytherapy Planning (MOPP): a Randomized-controlled Trial Evaluating Dosimetric Outcomes
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 42
- 试验地点
- 1
- 主要终点
- post-operative prostate V100%
研究概览
简要总结
The proposed, mono-institutional, randomized-controlled trial aims to determine whether the dosimetric outcomes following prostate Low-Dose-Rate (LDR) brachytherapy, planned using a novel machine learning (ML-LDR) algorithm, are equivalent to manual treatment planning techniques. Forty-two patients with low-to-intermediate-risk prostate cancer will be planned using ML-LDR and expert manual treatment planning over the course of the 12-month study. Expert radiation oncology (RO) physicians will then evaluate and modify blinded, randomized plans prior to implantation in patients. Planning time, pre-operative dosimetry, and plan modifications will be assessed before treatment, and post-operative dosimetry will be evaluated 1-month following the implant, respectively.
详细描述
Study Outline:
Traditionally treatment planning for prostate Low-Dose-Rate (LDR) brachytherapy has relied on manual planning by an expert treatment planner. This process involves the planner selecting the location of 80-110 small, radioactive seeds within the prostate; the goal of this process is to maximize the amount of radiation delivered to the cancer while minimizing radiation to healthy tissues, all while making sure the seeds are implantable by the physician. Although this process is effective it is time-consuming (taking anywhere from 30 minutes to several hours to plan).
Machine learning (ML), a form of statistical computation that relies on historical training information to adapt and predict novel solutions, has significant potential for improving the efficiency and uniformity of prostate LDR brachytherapy. The ability of this algorithm to mimic several features demonstrated by expert treatment plans has been difficult to perform using conventional computer algorithms and is a significant advantage. It is expected that by implementing an ML program in the planning workflow for prostate LDR brachytherapy it is possible to significantly decrease the planning time, while improving the uniformity of plan outcomes, and maintaining comparable quality to human planners.
This study will evaluate whether a computer program based on machine learning (ML) can be used to maintain plan quality in prostate LDR brachytherapy that is not inferior to manual planning by a human expert. In addition, it is expected that planning time may decrease to only a few minutes using ML planning.
What Will Happen:
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Other
- 盲法
- Single (Outcomes Assessor)
入排标准
- 性别
- Male
- 接受健康志愿者
- 否
入选标准
- •Diagnosed low- or intermediate-risk prostate cancer patients opting for I-125 LDR brachytherapy at the Sunnybrook Odette Cancer Centre.
- •Prostate volume on TRUS < 60 cc.
- •Ability to give informed consent to participate in the study
排除标准
- •Locally advanced or metastatic disease.
- •Prior Trans Urethral Resection of the Prostate (TURP).
- •International Prostate Symptom Score (IPSS) > 18
- •Patients receiving salvage or boost treatments after primary external radiation or brachytherapy.
- •Patients on study protocols with prescription doses other than 145 Gy.
研究组 & 干预措施
Machine Learning Planning
Patients will be pre-operatively planned using a machine-learning computer program. An expert radiation oncologist will evaluate the plan prior to implantation. The prescription dose is 145 Gy for monotherapy LDR brachytherapy.
干预措施: Machine Learning Planning (Other)
Radiation Therapist Planning
Patients will be pre-operatively planned manually by an expert radiation therapist (> 60 cases planned). An expert radiation oncologist will evaluate the plan prior to implantation.The prescription dose is 145 Gy for monotherapy LDR brachytherapy.
干预措施: Radiation Therapist Planning (Other)
结局指标
主要结局
post-operative prostate V100%
时间窗: 1 month
After receiving treatment patients are discharged. Over the coming month prostate edema decreases. Approximately 1 month following treatment patients have a CT scan and the plan dosimetry is re-computed from actual radioactive seed positions. One of the key dosimetry metrics used to assess the quality of the outcomes is the prostate V100%. This metric will be compared between ML and RT groups.
次要结局
- Pre-operative dosimetry(1 min to 1 hour)
- Pre-operative planning time(1 min to 1 hour)
- Frequency & magnitude of plan modifications(1-5 min)