ABSTRACT
PURPOSE
The purpose of this work was to develop a knowledge-based dose prediction system using
a convolution neural network (CNN) for cervical brachytherapy treatments with a tandem-and-ovoid
applicator.
METHODS
A 3D U-NET CNN was utilized to make voxel-wise dose predictions based on organ-at-risk
(OAR), high-risk clinical target volume (HRCTV), and possible source location geometry.
The model comprised 395 previously treated cases: training (273), validation (61),
test (61). To assess voxel prediction accuracy, we evaluated dose differences in all
cohorts across the dose range of 20−130% of prescription, mean (SD) and standard deviation
(, as well as isodose dice similarity coefficients for clinical and/or predicted dose
distributions. We examined discrete Dose-Volume Histogram (DVH) metrics utilized for
brachytherapy plan quality assessment (HRCTV D90%; bladder, rectum, and sigmoid D2cc)
with mean, standard deviation, and Pearson correlation coefficient further quantifying
model performance.
RESULTS
Ranges of voxel-wise dose difference accuracy ( for 20−130% dose interval in training (test) sets ranged from [-0.5% ± 2.0% to +2.0%
± 14.0%] ([-0.1% ± 4.0% to +4.0% ± 26.0%]) in all voxels, [-1.7% ± 5.1% to -3.5% ±
12.8%] ([-2.9% ± 4.8% to -2.6% ± 18.9%]) in HRCTV, [-0.02% ± 2.40% to +3.2% ± 12.0%]
([-2.5% ± 3.6% to +0.8% ± 12.7%]) in bladder, [-0.7% ± 2.4% to +15.5% ± 11.0%] ([-0.9%
± 3.2% to +27.8% ± 11.6%]) in rectum, and [-0.7% ± 2.3% to +10.7% ± 15.0%] ([-0.4%
± 3.0% to +18.4% ± 11.4%]) in sigmoid. Isodose dice similarity coefficients ranged
from [0.96,0.91] for training and [0.94,0.87] for test cohorts. Relative DVH metric
prediction in the training (test) set were HRCTV = -0.19 ± 0.55Gy (-0.09 ± 0.67 Gy), bladder = -0.06 ± 0.54Gy (-0.17 ± 0.67 Gy), rectum = -0.03 ± 0.36Gy (-0.04 ± 0.46 Gy), and sigmoid = -0.01 ± 0.34Gy (0.00 ± 0.44 Gy).
CONCLUSIONS
A 3D knowledge-based dose predictions provide voxel-level and DVH metric estimates
that could be used for treatment plan quality control and data-driven plan guidance.
Keywords
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Article Info
Publication History
Published online: May 10, 2022
Accepted:
March 12,
2022
Received in revised form:
February 28,
2022
Received:
November 5,
2021
Footnotes
Disclosures: KLM reports honoraria and personal fees from Varian Medical Systems, outside the submitted work. KLM holds two patents (Developing Predictive Dose-Volume Relationships for a Radiotherapy Treatment and Knowledge-based Prediction of Three-Dimensional Dose Distributions), licensed to Varian Medical Systems. AS reports personal fees from Courage Health, Inc., outside the submitted work. JM reports personal fees from AstraZeneca, grants from NRG Oncology, grants from GOG Foundation, personal fees from Varian Medical Systems, outside the submitted work.
Identification
Copyright
© 2022 American Brachytherapy Society. Published by Elsevier Inc. All rights reserved.
