A Phase II Randomized Study of the Use of Artificial Intelligence Generated Contours in Ultrasound-based Prostate HDR Brachytherapy
Phase: NA
Conditions: Prostate Cancer
Sponsor: Brigham and Women's Hospital

 
 
 
 
 
 
 
 
 
Page 1 
NCI Protocol #: N/A 
 
DF/HCC Protocol #: 25-116  
 
TITLE:  A Phase II Randomized Study of the Use of Artificial Intelligence Generated Contours 
in Ultrasound-based Prostate HDR Brachytherapy  
 
Coordinating Center: 
 
 
 
Brigham and Women’s Hospital 
75 Francis Street 
Boston, MA 02115 
 
Principal Investigator (PI): 
Martin King, MD PhD 
 
Brigham & Women’s Hospital  
 
Martin_King@dfci.harvard.edu 
 
 
 
Co-Investigator: 
Christian Guthier, PhD 
 
Brigham & Women’s Hospital  
Christian_Guthier@dfci.harvard.edu 
 
 
 
Other Investigators: 
Nataliya Moldovan, MD 
 
Brigham & Women’s Hospital  
 
Alicia Smart, MD 
 
Brigham & Women’s Hospital  
 
 
Statistician: 
Yu-Hui Chen, MS, MPH 
 
Dana-Farber Cancer Institute  
ychen@ds.dfci.harvard.edu 
  
 

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INDEX 
 
SCHEMA ................................................................................................................................................. 4 
1. 
Introduction ................................................................................................................................. 5 
1.1 
Study Disease ................................................................................................................. 5 
1.2 
Rationale ......................................................................................................................... 5 
2. 
OBJECTIVES ............................................................................................................................. 6 
2.1 
Study Design................................................................................................................... 6 
2.2 
Primary Objective .......................................................................................................... 6 
2.3 
Secondary Objectives .................................................................................................... 6 
3. 
PARTICIPANT SELECTION .................................................................................................. 6 
3.1 
Eligibility Criteria .......................................................................................................... 7 
3.2 
Exclusion Criteria........................................................................................................... 7 
3.3 
Inclusion of Women and Minorities ............................................................................. 7 
4. 
PRETREATMENT EVALUATIONS/MANAGEMENT ...................................................... 7 
5. 
REGISTRATION PROCEDURES ........................................................................................... 7 
5.1 
General Guidelines for DF/HCC Institutions .............................................................. 7 
5.2 
Registration Process for DF/HCC Institutions ............................................................ 8 
5.3 
General Guidelines for Other Investigative Sites........................................................ 8 
5.4 
Registration Process for Other Investigative Sites...................................................... 8 
6. 
TREATMENT plan .................................................................................................................... 8 
6.1 
Study contouring workflow........................................................................................... 8 
6.2 
New learner credentialing ............................................................................................. 9 
6.3 
Dose/Fractionation ......................................................................................................... 9 
6.4 
Target and Organs-at-risk (OAR) Volumes ..............................................................10 
6.5 
Contour Evaluation, Treatment Planning, and Treatment Delivery ........................10 
6.6 
Post-treatment TRUS ...................................................................................................10 
6.7 
Radiation Adverse Events ...........................................................................................10 
7. 
PARTICIPANT ASSESSMENTS ..........................................................................................10 
8. 
DATA COLLECTION .............................................................................................................11 
9. 
STATISTICAL CONSIDERATIONS ...................................................................................11 
9.1 
Study Design/Endpoints ..............................................................................................11 
9.2 
Primary Objective ........................................................................................................12 
9.3 
Sample Size, Accrual Rate and Study Duration ........................................................12 
9.4 
Stratification Factors ....................................................................................................13 
9.5 
Interim Monitoring Plan ..............................................................................................13 
9.6 
Analysis of Primary Endpoints ...................................................................................13 
9.7 
Analysis of Secondary Endpoints ...............................................................................14 

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9.8 
Reporting and Exclusions ............................................................................................14 
10. 
REGULATORY CONSIDERATIONS..................................................................................14 
10.1 
Data Safety Monitoring ...............................................................................................14 
11. 
PUBLICATION PLAN ............................................................................................................15 
REFERENCES ......................................................................................................................................16 
 
 
 

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SCHEMA 
 
 
 
 
 

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1. INTRODUCTION 
 
1.1 
Study Disease 
 
Prostate cancer is very common with about 1 in 8 men being diagnosed with prostate cancer 
during their lifetime 1. There are different treatment options for localized prostate cancer 
including surgery, external beam radiotherapy (EBRT), brachytherapy and hormonal systemic 
treatment or multimodality approaches 2. More recently, high dose rate (HDR) brachytherapy 
techniques have been developed in addition to the low dose rate (LDR) brachytherapy permanent 
radioactive seed implants for prostate cancer treatment. HDR brachytherapy has become a 
standard of care option as monotherapy or in combination with EBRT plus/minus androgen 
deprivation therapy as a boost 3 4. HDR brachytherapy has also been increasingly used to treat 
recurrent prostate cancer with local failures in the prostate after radiotherapy 5 6.  
 
1.2 
Rationale 
 
HDR brachytherapy in the treatment of prostate cancer is a procedure consisting of insertion of 
hollow needles into the prostate under transrectal ultrasound (TRUS) guidance with the patient in 
dorsal lithotomy position. An intraoperative radiation plan is created following acquisition of a 
planning image and delineation of the targets (prostate) and organs-at-risk (OARs including 
rectum, bladder, and urethra). The radiation plan is then delivered with an iridium-192 source 
using an afterloader machine. The needles are removed, and the patient is transferred to a 
recovery area followed by same day discharge.  
 
A key component of radiotherapy planning, including HDR brachytherapy planning, is 
identifying the volumes that will be treated and OARs that will be spared to deliver a safe 
radiation treatment through contouring of structures. Contouring for HDR prostate brachytherapy 
can be done on TRUS, CT or MRI images 7 8. TRUS is often preferred as it offers the 
convenience of the patient remaining in the same position throughout the entire procedure 
without a need to be moved for CT or MRI acquisition, which saves both time and resources. 
However, poor soft tissue contrast, prostate deformation and artifact on ultrasound can make 
prostate delineation on TRUS challenging especially with the needles in place for HDR 
treatment. The difficulty in contouring can be compounded for new learners developing 
brachytherapy expertise and working on obtaining proficiency in ultrasound-based prostate 
contouring.  
 
With the advent of deep-learning artificial algorithms, auto-contouring has been continually 
integrated into routine clinical use in EBRT planning. Deep learning algorithms are being 
developed for brachytherapy planning and can be a useful tool for learning brachytherapy 
contouring during the implant procedure. The standard U-net algorithm being investigated in this 
study was assessed retrospectively in a pilot cohort of 150 patients (110 training, 20 validation 
and 20 testing sets) to estimate the geometric accuracy of the algorithm 9. This was the first study 
to investigate auto-contouring with the needles in place in the prostate during HDR 
brachytherapy. The median Dice coefficient between AI and reference contours was 0.92 (IQR: 
0.90 – 0.94).  The current study aims to assess whether the same deep learning AI algorithm can 
improve the quality of prostate contours for new learners (resident or fellow/new attending) 

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performing prostate HDR brachytherapy.  
 
2. OBJECTIVES 
 
2.1 
Study Design 
 
This is a Phase II prospective study evaluating a deep learning AI algorithm for auto-contouring 
of the prostate during HDR prostate brachytherapy with the needles in place by new learners. 
The study will be conducted with a randomized design. Each patient will be assigned to a new 
learner and then randomized to manual versus AI-assisted contouring.  The randomization will 
be stratified by new learner type: resident versus fellow/new attending. The hypothesis is that 
AI-assisted learner contours will have improved Dice coefficients with respect to clinically 
approved contours compared with manual learner contours. All brachytherapy contours will 
undergo review by the treating radiation oncologist who is the experienced clinician for clinical 
approval prior to patient treatment. The experienced clinician will be blinded to the 
randomization. Clinicians will not be registered in OnCore. 
 
DF/HCC Office of Data Quality will perform randomization. If the new learner of a case is a 
resident, then the "resident" randomization tab will be used for the randomization. If the new 
learner of a case is a fellow/new attending, then the "fellow/new attending" tab should be used to 
determine which arm the case goes to. 
 
2.2 
Primary Objective 
 
The primary objective of the study is to evaluate the Dice coefficient [0: no match, 1: complete 
match] between the final clinically approved brachytherapy prostate contours versus the manual 
and AI-assisted contours provided by the new learner.  
 
2.3 
Secondary Objectives 
 
• To measure contouring time-to-complete needed to generate and edit manual vs AI-assisted 
contours for a new learner. 
• To evaluate the subjective impressions of AI or manual contours by the new learner 
based on survey responses.  
• To evaluate the subjective impressions of AI or manual contours by the experienced 
clinician based on survey responses.  
• To calculate Dice coefficients of expert clinician contours between TRUS images with 
implanted needles and TRUS images after removal of bottom needle rows. 
 
3. PARTICIPANT SELECTION 
 

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3.1 
Eligibility Criteria  
 
1. 18 years of age and older 
2. Deemed suitable candidates for whole gland HDR prostate brachytherapy under general 
anesthesia as a monotherapy, boost or salvage treatment.  
 
3.2 
Exclusion Criteria 
 
1. Prior permanent seed LDR brachytherapy implant   
2. Prior transurethral resection of the prostate (TURP) 
3. Presence or insertion of a rectal spacer 
4. Focal HDR brachytherapy treatment ie. not whole prostate     
 
3.3 
Inclusion of Women and Minorities 
 
All patients with a prostate of all races and ethnic groups are eligible for this trial.  
 
4. PRETREATMENT EVALUATIONS/MANAGEMENT 
 
• Within 90 days prior to registration 
o Informed consent 
o Diagnosis and staging information 
o Oncologic history 
 
5. REGISTRATION PROCEDURES 
 
5.1 
General Guidelines for DF/HCC Institutions 
 
Institutions will register eligible participants in the Clinical Trials Management System (CTMS) 
OnCore. Registrations must occur prior to the initiation of any protocol-specific therapy or 
intervention. Any participant not registered to the protocol before protocol-specific therapy or 
intervention begins will be considered ineligible and registration will be denied. 
 
An investigator will confirm eligibility criteria and a member of the study team will complete the 
protocol-specific eligibility checklist. 
 
The eligibility checklist(s) and all pages of the consent form(s) will be emailed to ODQ 
at ODQ@dfci.harvard.edu. The ODQ will (a) review the eligibility checklist, (b) register the 
participant on the protocol, and (c) randomize the participant. 
 
Randomization can only occur during ODQ business hours (8:30am - 5pm Eastern Time, 
Monday through Friday excluding holidays). 
 
An email confirmation of the registration and/or randomization will be sent to the study 
coordinator(s) from the registering site, treating investigator and registering person immediately 
following the registration and/or randomization. 

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Following registration, participants may begin protocol-specific therapy and/or intervention. 
Issues that would cause treatment delays should be discussed with the Principal Investigator (PI) 
of the registering site. If the subject does not receive protocol therapy following registration, the 
subject must be taken off study in the CTMS (OnCore) with an appropriate date and reason 
entered. 
 
5.2 
Registration Process for DF/HCC Institutions 
 
Applicable DF/HCC Policy REGIST-101 must be followed.   
 
5.3 
General Guidelines for Other Investigative Sites 
 
N/A 
 
5.4 
Registration Process for Other Investigative Sites 
 
N/A 
 
6. TREATMENT PLAN 
 
6.1 
Study contouring workflow 
 
As mentioned above, the goal of this study is to evaluate whether artificial-intelligence can 
improve the quality of implanted prostate contours for new learners by comparing the dice 
coefficient of manual versus AI-assisted learner prostate contours to the final clinically approved 
prostate contours. The diagram below describes the workflow for the study.  
 
 

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6.2 
New learner credentialing 
 
All new learners will undergo a credentialing process prior to participation on the study. New 
learners must be a radiation oncology resident, fellow, or a new attending who has performed 
less than 20 ultrasound-based prostate HDR brachytherapy cases independently.  
 
The credentialing procedure will take place over a 60 minute period under the direction of the 
principal investigator. First, the principal investigator will review a power-point presentation 
showing relevant anatomy of the prostate on ultrasound images with needles in-place. The 
principal investigator will then ask the new learner to provide contours of the prostate for 3 cases 
(case 1, case 2, and case 3). For each case, the principal investigator will then show the actual 
clinical contours utilized for treatment planning, and point out discrepancies between the new 
learner and clinical contours.  
 
6.3 
Dose/Fractionation  
 
High dose rate brachytherapy will be delivered to the clinical target volumes with prescription 
doses at the experienced clinician  discretion based on the clinical scenario. Participation in the 
trial does not alter the standard of care treatment.  
 

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6.4 
Target and Organs-at-risk (OAR) Volumes 
 
All contouring of prostate will be done on TRUS images which is the usual HDR brachytherapy 
workflow.  
 
• Prostate_Learner_Image1 = prostate contour prior to needle insertion on TRUS images 
#1 
• Prostate_Learner_Image2 = prostate contour generated by the new learner with or 
without AI assistance on TRUS images #2 with needles 
• Prostate_Clinician_Image2 = prostate contour reviewed and edited by experienced 
clinician 
• CTV = clinical target volumes contoured by experienced clinician (prostate +/- seminal 
vesicle target) 
• Prostate_Clinician_Image3 = prostate contoured by experienced clinician on TRUS 
images #3 following HDR treatment delivery with the bottom half needle rows removed 
to better visualize the anterior border of the prostate  
 
6.5 
Contour Evaluation, Treatment Planning, and Treatment Delivery  
 
The experienced clinician will review and edit all contours for final approval and clinical use for 
HDR prostate radiotherapy planning and treatment. Treatment planning will be done in the usual 
fashion by the brachytherapy medical physicist using Oncentra prostate treatment planning 
software. The experienced clinician will review and approve the HDR brachytherapy treatment 
plan per institutional metrics and constraints. The HDR brachytherapy plan will be delivered to 
the prostate using an iridium-192 afterloader per standard operating procedure. 
 
6.6 
Post-treatment TRUS  
 
Following treatment plan delivery, the bottom half of the interstitial prostate needles will be 
removed. TRUS images #3 will be obtained with better visualization of the anterior border of the 
prostate. These TRUS images represent the “ground truth” in terms of the location and outline of 
the prostate.  
 
The interstitial implant will then be completely removed, and the patient will be transferred to 
recovery area.  
 
6.7 
Radiation Adverse Events 
 
It is not anticipated that auto-contouring of the prostate would increase the risk of side effects of 
prostate HDR brachytherapy. The procedure may take longer overall, such as to obtain the post-
brachytherapy TRUS, increasing the length of general anesthesia. This is not expected to pose an 
increased risk to participants beyond the usual total procedure time variations which occur in the 
real-world setting.  
 
7. PARTICIPANT ASSESSMENTS 
 

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Patients with prostate cancer will be assessed in clinic for suitability for HDR prostate 
brachytherapy as a monotherapy, boost or salvage treatment per investigator discretion. No 
additional assessments are required for participation in this study.  
 
8. DATA COLLECTION 
 
Study Evaluation 
Patient 
screening  
 
Within 90 days 
prior to 
registration 
 
New learner 
prostate 
contouring 
training 
session1 
HDR 
Brachytherapy 
REQUIRED PATIENT 
ASSESSMENTS 
 
 
 
Informed consent  
X 
 
 
Diagnosis and staging information  
X 
 
 
Oncologic history 
X 
 
 
BRACHYTHERAPY 
 
 
 
New learner baseline contours 
 
X 
 
Prostate_Learner_Image1 
 
 
X 
Prostate_Learner_Image2 
 
 
X 
Prostate_Clinician_Image2 
 
 
X 
Prostate_Clinician_Image3 
 
 
X 
Time to generate and edit contours 
 
 
X 
STUDY SURVEYS 
 
 
 
New learner baseline experience 
survey 
 
X 
 
New learner contouring survey 
 
 
X 
Experienced clinician survey 
 
 
X 
1The training session will only be performed one time prior to the first case in which the new 
learner participates.  
 
 
9. STATISTICAL CONSIDERATIONS 
 
9.1 
Study Design/Endpoints 
 
The primary objective of this study is to evaluate whether AI-assisted contours perform better 
than manual contours as measured by the Dice coefficient between the final contours used for 
treatment delivery versus the contours, either manual or AI-assisted, provided by the new 
learners. Patients will be accrued in one stage. Each patient will be assigned to a new learner and 
then randomized to manual versus AI-assisted contouring.  The randomization will be stratified 
by new learner type: resident versus fellow/new attending. 
 
The experienced clinician will be blinded to the randomization of the contouring method by the 

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new learner. The prostate contour generated by the new learner will then undergo review by the 
experienced clinician. The dice coefficient between the new learner prostate contour and the 
experienced clinician contour will be calculated. The experienced clinician will approve the 
prostate brachytherapy contours to be used clinically for HDR prostate radiotherapy planning 
and treatment. 
 
9.2 
Primary Objective 
 
The primary objective of this study is to evaluate whether the addition of artificial intelligence 
can improve the quality of implanted prostate contours as measured by the Dice coefficient 
between the final contours used for treatment delivery versus the contours, either manual or AI-
assisted, provided by the new learners. The Dice coefficient ranges between 0 and 1, where 0 
indicates no match, and 1indicates complete match.  
 
9.3 
Sample Size, Accrual Rate and Study Duration 
 
The sample size of this study will be 36 patients.  
 
Based on data from previous published work with the AI algorithm for HDR brachytherapy 
prostate contouring with the needles in place, we can assume a mean Dice coefficient of 0.87 for 
manual new learner contours (without AI) and a mean Dice coefficient of 0.92 for AI-assisted 
new learner contours ꝰ. Using a standard deviation of 0.05, a one-sided alpha of 0.1, and a power 
of 0.9, a total of 27 patients will be needed for this study using a t-test. As each new learner will 
generate prostate contours for multiple patients in this study, the correlations among patients 
contoured by the same new learner need to be taken into account. Assuming an intra-cluster 
correlation of 0.15 and an average of 3 patients to be contoured by the same new learner in a 
given arm, the sample size will be inflated by 30% (36 analyzable patients) in order to maintain 
sufficient power.  The maximum number of cases that a new learner can contour would be 10.  
 
 
There are approximately 75 prostate brachytherapy patients that undergo HDR treatment of the 
whole gland in the Department of Radiation Oncology at Brigham and Women’s Hospital. If 
about half agree to participate, we expect the study to take about 12 months to accrue.  
 

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This study does not include follow-up after HDR brachytherapy treatment delivery and 
termination of accrual.  
 
 
Accrual Targets 
Ethnic Category 
Sex/Gender 
Females 
 
Males 
 
Total 
Hispanic or Latino 
0 
+ 
3 
= 
3 
Not Hispanic or Latino 
0 
+ 
33 
= 
33 
Ethnic Category: Total of all subjects 
0 
(A1) + 
36 
(B1) = 
36 
(C1) 
Racial Category 
 
American Indian or Alaskan Native 
0 
+ 
0 
= 
0 
Asian 
0 
+ 
3 
= 
3 
Black or African American 
0 
+ 
3 
= 
3 
Native Hawaiian or other Pacific 
Islander 
0 
+ 
0 
= 
0 
White 
0 
+ 
30 
= 
30 
Racial Category: Total of all subjects 
0 
(A2) + 
36 
(B2) = 
36 
(C2) 
 
(A1 = A2) 
 
(B1 = B2) 
 
(C1 = C2) 
 
Note that this study is limited to patients with a prostate. There may be some patients who 
identify as female and were assigned male at birth with a prostate eligible for this study not 
explicitly stated in the accrual targets.   
 
9.4 
Stratification Factors 
 
We will introduce a stratification factor by training category of new learner: 1. resident versus 2. 
fellow/new attending.  
 
9.5 
Interim Monitoring Plan 
 
This is not a therapeutic trial and the standard U-net algorithm generating a prostate auto-contour 
during HDR prostate brachytherapy will be used by the new learner. All brachytherapy contours 
will undergo review by the experienced clinician for clinical approval prior to patient treatment 
in the usual fashion of an academic teaching center. As such, there will not be planned interim 
safety monitoring. 
 
9.6 
Analysis of Primary Endpoints 
 
The participant cohort are patients undergoing whole gland prostate HDR brachytherapy for 
monotherapy, boost or salvage radiation at the Brigham and Women’s Hospital/Dana-Farber 
Cancer Institute Department of Radiation Oncology.  
 
The primary endpoint of the study is the Dice coefficient [0: no match, 1: complete match] 

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between the final clinically approved prostate contours versus the manual or AI-assisted contours 
provided by the new learner. The primary objective is to evaluate whether the addition of 
artificial intelligence can improve the quality of implanted prostate contours as measured by 
Dice coefficient. As multiple patients will be contoured by the same new learner on this study, 
the Dice coefficient will be compared between the two groups using a mixed-effect model with 
new learner as a random effect to account for the potential variability of contouring among the 
new learners. 
 
9.7 
Analysis of Secondary Endpoints 
 
• Descriptive statistics will be used to characterize measurement of contouring time-to-
complete needed to generate and edit manual vs AI-assisted contours for a new learner. 
The contouring time will be compared between the two groups using the t-test. 
• Likert scale data from the new learner and experienced clinician surveys will be reported 
using mean and standard deviation and compared between the two arms using a t-test 10.   
• The percentage of correct predictions by the experienced clinician of whether the prostate 
was contoured with or without AI-assistance will be reported along with 90% exact 
binomial confidence interval. 
• The prostate contour quality score of the new learner’s prostate contour presented to the 
experienced clinician during radiotherapy plan development with and without AI-
assistance, described using descriptive statistics and compared using the chi-squared test.  
 
 
9.8 
Reporting and Exclusions 
 
9.8.1 
Evaluation of Toxicity 
 
N/A- this is not a radiotherapy treatment trial and no additional toxicity from the 
inclusion of AI algorithm during prostate contouring by the new learner is expected. The 
HDR prostate radiotherapy delivered will be as per standard of care.  
 
9.8.2 
Evaluation of the Primary Efficacy Endpoint 
 
All patients will be analyzed for all primary and secondary endpoints listed above based 
on the intent-to-treat principle. 
 
10. REGULATORY CONSIDERATIONS 
 
10.1 
Data Safety Monitoring 
 
The DF/HCC Data and Safety Monitoring Board (DSMB) will review and monitor study 
progress, toxicity, safety and other data from this study. The Board is chaired by a medical 
oncologist from outside of DF/HCC and its membership composed of internal and external 
institutional representation. Information that raises any questions about participant safety or 
protocol performance will be addressed by the Sponsor-Investigator, statistician and study team. 
Should any major concerns arise, the DSMB will offer recommendations regarding whether or 

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not to suspend the study. 
  
The DSMB will meet twice a year to review accrual, toxicity, response and reporting 
information. Information to be provided to the DSMB may include: participant accrual; 
treatment regimen information; all adverse events and serious adverse events reported across all 
sites by category; summary of any deaths on study; audit results; and a summary provided by the 
study team. Other information (e.g. scans, laboratory values) will be provided upon request. 
  
 
11. PUBLICATION PLAN 
 
The results of this study are anticipated to be published in a peer-review journal and made public 
within 24 months of reaching the end of the study. The end of the study is the time point at 
which the last data items are to be reported. The initial release may be an abstract that meets the 
requirements of the International Committee of Medical Journal Editors. A full report of the 
outcomes will be made public no later than three (3) years after the end of the study.  
 
 

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REFERENCES 
  
1 American Cancer Society (2025). Key Statistics for Prostate Cancer. Retrieved 
https://www.cancer.org/cancer/types/prostate-cancer/about/key-statistics.html 
  
2 National Comprehensive Cancer Network. (2025). Prostate cancer (version 1.2025). Retrieved 
from https://www.nccn.org/professionals/physician_gls/pdf/prostate.pdf 
 
3 Morton, G., McGuffin, M., Chung, H. T., Tseng, C.-L., Helou, J., Ravi, A., Cheung, P., 
Szumacher, E., Liu, S., Chu, W., Zhang, L., Mamedov, A., & Loblaw, A. (2020). Prostate high 
dose-rate brachytherapy as monotherapy for low and intermediate risk prostate cancer: Efficacy 
results from a randomized phase II clinical trial of one fraction of 19 Gy or two fractions of 13.5 
Gy. Radiotherapy and Oncology, 146, 90–96. https://doi.org/10.1016/j.radonc.2020.02.009 
 
4 Crook, J., Marbán, M., & Batchelar, D. (2020). HDR Prostate Brachytherapy. Seminars in 
Radiation Oncology, 30(1), 49–60. https://doi.org/10.1016/j.semradonc.2019.08.003 
 
5 Chen, C. P., Weinberg, V., Shinohara, K., Roach, M., Nash, M., Gottschalk, A., Chang, A. J., 
& Hsu, I.-C. (2013). Salvage HDR Brachytherapy for Recurrent Prostate Cancer After Previous 
Definitive Radiation Therapy: 5-Year Outcomes. International Journal of Radiation 
Oncology*Biology*Physics, 86(2), 324–329. https://doi.org/10.1016/j.ijrobp.2013.01.027 
 
6 Valle, L. F., Lehrer, E. J., Markovic, D., Elashoff, D., Levin-Epstein, R., Karnes, R. J., Reiter, 
R. E., Rettig, M., Calais, J., Nickols, N. G., Dess, R. T., Spratt, D. E., Steinberg, M. L., Nguyen, 
P. L., Davis, B. J., Zaorsky, N. G., & Kishan, A. U. (2021). A Systematic Review and Meta-
analysis of Local Salvage Therapies After Radiotherapy for Prostate Cancer (MASTER). 
European Urology, 80(3), 280–292. https://doi.org/10.1016/j.eururo.2020.11.010 
 
7 Lavoie-Gagnon, H., Martin, A.-G., Poulin, E., Archambault, L., Pilote, L., Foster, W., 
Vigneault, E., Carignan, D., & Lacroix, F. (2022). Advantages of TRUS-based delineation for 
high-dose-rate prostate brachytherapy planning. Journal of Contemporary Brachytherapy, 14(1), 
1–6. https://doi.org/10.5114/jcb.2022.113544 
 
8 Podgorsak, A. R., Venkatesulu, B. P., Abuhamad, M., Harkenrider, M. M., Solanki, A. A., 
Roeske, J. C., & Kang, H. (2023). Dosimetric and workflow impact of synthetic-MRI use in 
prostate high-dose-rate brachytherapy. Brachytherapy, 22(5), 686–696. 
https://doi.org/10.1016/j.brachy.2023.05.005 
 
9 King, M. T., Kehayias, C. E., Chaunzwa, T., Rosen, D. B., Mahal, A. R., Wallburn, T. D., 
Milligan, M. G., Dyer, M. A., Nguyen, P. L., Orio, P. F., Harris, T. C., Buzurovic, I., & Guthier, 
C. V. (2023). Observer preference of artificial intelligence‐generated versus clinical prostate 
contours for ultrasound‐based high dose rate brachytherapy. Medical Physics, 50(10), 5935–
5943. https://doi.org/10.1002/mp.16716 
 
10 Sullivan, G. M., & Artino, A. R. (2013). Analyzing and Interpreting Data From Likert-Type 
  

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Scales. Journal of Graduate Medical Education, 5(4), 541–542. https://doi.org/10.4300/JGME-
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