Prostatic NeoplasmsMOPPNCT02943824
Machine-learning Optimization for Prostate Brachytherapy Planning
A study testing Machine Learning Planning and Radiation Therapist Planning in people with Prostatic Neoplasms, run by Sunnybrook Health Sciences Centre at 1 site in Canada.
Trial summary
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. Read more about this study
- Who is it for?
- Any age, men only
- Conditions the study is for
- Prostatic Neoplasms
- What is being tested?
- Machine Learning Planning and Radiation Therapist Planning
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 42 participants
- Where
- 1 siteCanada
- Study dates
- 24 Aug 2017to 4 Sep 2018
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- Men only
- No healthy volunteers
- Child, Adult, Older adult
Who may be able to join
- 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
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Machine-learning Optimization for Prostate Brachytherapy Planning (MOPP): a Randomized-controlled Trial Evaluating Dosimetric Outcomes
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.
Read the detailed description
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.
- Conditions
- Prostatic Neoplasms
- Being tested
- Machine Learning Planning, Radiation Therapist Planning
- Keywords
- brachytherapy, machine learning, treatment planning, Low-Dose-Rate brachytherapy
- Medical subject headings
- Prostatic Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Machine Learning PlanningOther
The intervention being tested is a novel approach to planning LDR treatment plans using a machine learning computer algorithm.
Radiation Therapist PlanningOther
The intervention being compared to the experimental arm is conventional manual planning by a human expert LDR brachytherapy planner.
| Study group | Role | What they receive |
|---|---|---|
| Machine Learning Planning | Experimental | 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. Receives: Other: Machine Learning Planning |
| Radiation Therapist Planning | Active comparator | 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. Receives: Other: Radiation Therapist Planning |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 1 month
Secondary outcomes (3)
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.
Measured: 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.
Measured: 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.
Measured: 1-5 min
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Oct 2016 (estimated)
- Study start24 Aug 2017
- Primary completion24 Aug 2018
- Study completion4 Sep 2018
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Prostatic NeoplasmsStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 42 participants (actual)
Sponsor and investigators
- Lead sponsor
- Sunnybrook Health Sciences Centre
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Ananth Ravi, PhD, Principal investigator, Toronto Sunnybrook Regional Cancer Centre
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT02943824
- Sponsor's protocol ID
- 1.1.1
- Acronym
- MOPP
- Status last verified
- Mar 2018
- First submitted
- 21 Oct 2016
- Last updated
- 6 Sep 2018
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 6 Sep 2018. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Machine-learning Optimization for Prostate Brachytherapy Planning (MOPP): a Randomized-controlled Trial Evaluating Dosimetric Outcomes
- ClinicalTrials.gov ID
- NCT02943824
- Acronym / sponsor ID
- MOPP / 1.1.1
- Conditions
- Prostatic Neoplasms
- Keywords
- brachytherapy, machine learning, treatment planning, Low-Dose-Rate brachytherapy
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 42 participants (actual)
- Lead sponsor
- Sunnybrook Health Sciences CentreUniversity, hospital or other
- Study start
- 24 Aug 2017 (actual)
- Primary completion
- 24 Aug 2018 (actual)
- Study completion
- 4 Sep 2018 (actual)
- First posted
- 25 Oct 2016 (estimated)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
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.
Study design
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Machine Learning Planning | Experimental | Other: Machine Learning PlanningPatients 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. |
| Radiation Therapist Planning | Active comparator | Other: Radiation Therapist PlanningPatients 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. |
Who may qualify
- Sex
- Male only
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (3 listed)
- 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
Key exclusion criteria (5 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Machine Learning PlanningThe intervention being tested is a novel approach to planning LDR treatment plans using a machine learning computer algorithm. | Other | Machine Learning Planning |
| Radiation Therapist PlanningThe intervention being compared to the experimental arm is conventional manual planning by a human expert LDR brachytherapy planner. | Other | Radiation Therapist Planning |
Primary outcome
post-operative prostate V100%
Time frame: 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.
3 secondary outcomes
Pre-operative planning time
Time frame: 1 min to 1 hour
Pre-operative dosimetry
Time frame: 1 min to 1 hour
Frequency & magnitude of plan modifications
Time frame: 1-5 min
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Sep 2018.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Ananth Ravi, PhD principal investigator
Toronto Sunnybrook Regional Cancer Centre
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Machine Learning Planning and Radiation Therapist Planning in people with Prostatic Neoplasms, run by Sunnybrook Health Sciences Centre at 1 site in Canada.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 6 Sep 2018. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Canada. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
post-operative prostate V100% (time frame: 1 month). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 6 Sep 2018.