Machine-learning Optimization for Prostate Brachytherapy Planning

NCT ID: NCT02943824

Last Updated: 2018-09-06

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 42 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2017-08-24
• Study Completion Date: 2018-09-04

Brief 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. 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.

Conditions

Prostatic Neoplasms

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: OTHER
• Blinding Strategy: SINGLE

Study Groups

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.

Group Type: EXPERIMENTAL

Machine Learning Planning

Intervention Type: OTHER

The intervention being tested is a novel approach to planning LDR treatment plans using a machine learning computer algorithm.

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.

Group Type: ACTIVE_COMPARATOR

Radiation Therapist Planning

Intervention Type: OTHER

The intervention being compared to the experimental arm is conventional manual planning by a human expert LDR brachytherapy planner.

Interventions

Machine Learning Planning

The intervention being tested is a novel approach to planning LDR treatment plans using a machine learning computer algorithm.

Intervention Type: OTHER

Radiation Therapist Planning

The intervention being compared to the experimental arm is conventional manual planning by a human expert LDR brachytherapy planner.

Intervention Type: OTHER

Eligibility Criteria

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.

• Eligible Sex: MALE
• Accepts Healthy Volunteers: No

Sponsors

Sunnybrook Health Sciences Centre

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Ananth Ravi, PhD

Role: PRINCIPAL_INVESTIGATOR

Toronto Sunnybrook Regional Cancer Centre

Locations

Sunnybrook Odette Cancer Centre

Toronto, Ontario, Canada

Countries

Canada

Other Identifiers

1.1.1

Identifier Type: -

Identifier Source: org_study_id