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Machine-learning Optimization for Prostate Brachytherapy Planning

NCT02943824 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 42

Last updated 2018-09-06

No results posted yet for this study

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.

Conditions

  • Prostatic Neoplasms

Interventions

OTHER

Machine Learning Planning

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

OTHER

Radiation Therapist Planning

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

Sponsors & Collaborators

  • Sunnybrook Health Sciences Centre

    lead OTHER

Principal Investigators

  • Ananth Ravi, PhD · Toronto Sunnybrook Regional Cancer Centre

Study Design

Allocation
RANDOMIZED
Purpose
OTHER
Masking
SINGLE
Model
PARALLEL

Eligibility

Sex
MALE
Healthy Volunteers
No

Timeline & Regulatory

Start
2017-08-24
Primary Completion
2018-08-24
Completion
2018-09-04

Countries

  • Canada

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT02943824 on ClinicalTrials.gov