(Monday, 7/15/2019) 1:15 PM - 1:45 PM
Room: Exhibit Hall | Forum 6
Purpose: The purpose of this work was to explore the accuracy of knowledge-based organ-at-risk (OAR) dose estimation for high-dose rate (HDR) brachytherapy treatment of cervical cancer. Using established external-beam knowledge-based dose-volume histogram (DVH) estimation methods, we sought to accurately predict bladder, rectum, and sigmoid D2cc for standard tandem-and-ovoid (T&O) treatments.
Methods: 32 (16:16 training:validation) locoregionally advanced cervical cancer patients treated with 136 (70:66) T&O CT-guided HDR brachytherapy fractions were analyzed. Single fraction prescription doses were 5.5-7Gy, with external beam+brachytherapy equivalent dose in 2Gy (EQD2) planning goals for high-risk clinical target volume (HRCTV D90>85Gy) and OARs (D2cc_bladder<90Gy; D2cc_rectum<75Gy; D2cc_sigmoid<75Gy). DVH estimation models were obtained by subdividing OARs into HRCTV boundary distance sub-volumes and computing a cohort-averaged differential DVH estimate from the training set sub-volumes. Full DVH estimation was performed on all cases in the training and validation sets by applying OAR sub-volume DVH models to each fraction’s OARs, with model performance quantified by ΔD2cc=D2cc_predicted-D2cc_actual (mean and standard deviation). ΔD2cc’s between training and validation sets were compared with an unpaired Student’s t-test (p>0.05 significance threshold).
Results: Observed D2cc [minimum, median, maximum] in the training:validation sets were D2cc_bladder(Gy)=[1.78, 4.41, 5.94 training; 2.29,4.54,6.37 validation], D2cc_rectum(Gy)=[1.35, 2.96, 5.28 training;1.12, 3.38, 5.69 validation] and D2cc_sigmoid(Gy)=[1.84, 3.90, 5.17 training; 1.77, 3.94, 6.58 validation]. Training set deviations were ΔD2cc_bladder=0.02±0.48Gy, ΔD2cc_rectum=-0.18±0.50Gy, and ΔD2cc_sigmoid=-0.06±0.48Gy. Within the validation set, model accuracy was statistically identical: ΔD2cc_bladder=0.04±0.62Gy (p=0.55), ΔD2cc_rectum=-0.17±0.54Gy (p=0.32), and ΔD2cc_sigmoid=-0.09±0.65Gy (p=0.60).
Conclusion: A simple boundary distance-driven knowledge-based DVH estimation exhibited promising results in predicting critical brachytherapy dose metrics. Incorporating geometric quantification beyond this single-variate formalism will likely increase accuracy.
Funding Support, Disclosures, and Conflict of Interest: KLM acknowledges funding support from AHRQ (R01 HS025440-01). KLM acknowledges research funding, travel support, and honoraria from Varian Medical Systems. SM, JM, and KLM acknowledge support from Padres Pedal the Cause.
Keywords
Brachytherapy, Treatment Planning, Quality Control
Taxonomy
TH- Brachytherapy: Dose optimization and planning
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