Machine-learning Optimization for Prostate Brachytherapy Planning

Study Overview
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Brief Summary
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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.

Detailed Description
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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: If you decide to participate in this study your first visit will involve an ultrasound study of your prostate to map out the treatment area. After your initial visit for ultrasound imaging nothing further is required on your part for the purposes of the study. Your images and treatment information will then be used to create a brachytherapy treatment plan by both a human planner, and one by an ML program. Only one treatment plan from one of these groups (a process known as randomization) will be used, your treating physician will not know where your plan came from (a process known as blinding). Your physician will examine the plans, grade its acceptability, and make modifications to it if needed. This final plan will be used to deliver your brachytherapy. Follow-Up Visits: You will have a follow-up study approximately 1 month after your brachytherapy treatment. The purpose of this study is to gauge how well your brachytherapy was delivered. For the follow-up study you will have a CT scan to show the area that was treated (the prostate gland). No further action is required on your part. Length of Study Participation: Your participation in this study will after your follow-up visit, approximately 1 month after your brachytherapy treatment. A total of 42 patients will be enrolled in this study from the Odette Cancer Centre.

Official Title
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Machine-learning Optimization for Prostate Brachytherapy Planning (MOPP): a Randomized-controlled Trial Evaluating Dosimetric Outcomes

Conditions
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Prostatic Neoplasms

Intervention / Treatment
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* Other: Machine Learning Planning
* Other: Radiation Therapist Planning


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: 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 Exclusion Criteria: 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.
Sexes Eligible for Study
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Male

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Other
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Single


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Machine Learning Planning<br>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. | Other: Machine Learning Planning<br>* The intervention being tested is a novel approach to planning LDR treatment plans using a machine learning computer algorithm.<br>|
| Active Comparator: Radiation Therapist Planning<br>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. | Other: Radiation Therapist Planning<br>* The intervention being compared to the experimental arm is conventional manual planning by a human expert LDR brachytherapy planner.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| post-operative prostate V100% | 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. | 1 month | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Pre-operative planning time | During initial planning of brachytherapy the total planning time required for each case will be compared between ML and RT groups. | 1 min to 1 hour | 
| Pre-operative dosimetry | Along with planning time the final dosimetry of the preoperative plan will be compared between ML and RT groups. | 1 min to 1 hour | 
| Frequency & magnitude of plan modifications | During physician QA of both ML and RT plans the time, and magnitude of any plan modifications will be captured and compared between the two groups. | 1-5 min | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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brachytherapy, machine learning, treatment planning, Low-Dose-Rate brachytherapy