A knowledge-based organ dose prediction tool for brachytherapy treatment planning of patients with cervical cancer

Abstract

Purpose: The purpose of this study is to explore knowledge-based organ-at-risk dose estimation for intracavitary brachytherapy planning for cervical cancer. Using established external-beam knowledge-based dose-volume histogram (DVH) estimation methods, we sought to predict bladder, rectum, and sigmoid D_2cc for tandem and ovoid treatments.

Methods and materials: A total of 136 patients with loco-regionally advanced cervical cancer treated with 456 (356:100 training:validation ratio) CT-based tandem and ovoid brachytherapy fractions were analyzed. Single fraction prescription doses were 5.5-8 Gy with dose criteria for the high-risk clinical target volume, bladder, rectum, and sigmoid. DVH estimations were obtained by subdividing training set organs-at-risk into high-risk clinical target volume boundary distance subvolumes and computing cohort-averaged differential DVHs. Full DVH estimation was then performed on the training and validation sets. Model performance was quantified by ΔD_2cc = D_2cc(actual)-D_2cc(predicted) (mean and standard deviation). ΔD_2cc between training and validation sets were compared with a Student's t test (p < 0.01 significant). Categorical variables (physician, fraction-number, total fractions, and case complexity) that might explain model variance were examined using an analysis of variance test (Bonferroni-corrected p < 0.01 threshold).

Results: Training set deviations were bladder ΔD_2cc = -0.04 ± 0.61 Gy, rectum ΔD_2cc = 0.02 ± 0.57 Gy, and sigmoid ΔD_2cc = -0.05 ± 0.52 Gy. Model predictions on validation set did not statistically differ: bladder ΔD_2cc = -0.02 ± 0.46 Gy (p = 0.80), rectum ΔD_2cc = -0.007 ± 0.47 Gy (p = 0.53), and sigmoid ΔD_2cc = -0.07 ± 0.47 Gy (p = 0.70). The only significant categorical variable was the attending physician for bladder and rectum ΔD_2cc. CONCLUSION: A simple boundary distance-driven knowledge-based DVH estimation exhibited promising results in predicting critical brachytherapy dose metrics. Future work will examine the utility of these predictions for quality control and automated brachytherapy planning.

Keywords: Cervical cancer; Dose predictions; Knowledge-based planning; Machine learning; Quality control; Treatment planning.