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

• Published in: Brachytherapy Vol. 19; no. 5; pp. 624 - 634
• Main Authors: Yusufaly, Tahir I., Kallis, Karoline, Simon, Aaron, Mayadev, Jyoti, Yashar, Catheryn M., Einck, John P., Mell, Loren K., Brown, Derek, Scanderbeg, Daniel, Hild, Sebastian J., Covele, Brent, Moore, Kevin L., Meyers, Sandra M.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.09.2020
• Subjects: Adult; Brachytherapy - methods; Cervical cancer; Colon, Sigmoid; Dose predictions; Female; Humans; Knowledge-based planning; Machine learning; Organs at Risk; Quality control; Radiotherapy Dosage; Radiotherapy Planning, Computer-Assisted - methods; Rectum; Tomography, X-Ray Computed - methods; Treatment planning; Urinary Bladder; Uterine Cervical Neoplasms - radiotherapy; Knowledge-based planning; Dose predictions; Treatment planning; Cervical cancer; Machine learning; Quality control
• ISSN: 1538-4721; 1873-1449
• DOI: 10.1016/j.brachy.2020.04.008

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 D2cc for tandem and ovoid treatments. 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 ΔD2cc = D2cc(actual)-D2cc(predicted) (mean and standard deviation). ΔD2cc 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). Training set deviations were bladder ΔD2cc = −0.04 ± 0.61 Gy, rectum ΔD2cc = 0.02 ± 0.57 Gy, and sigmoid ΔD2cc = −0.05 ± 0.52 Gy. Model predictions on validation set did not statistically differ: bladder ΔD2cc = −0.02 ± 0.46 Gy (p = 0.80), rectum ΔD2cc = −0.007 ± 0.47 Gy (p = 0.53), and sigmoid ΔD2cc = −0.07 ± 0.47 Gy (p = 0.70). The only significant categorical variable was the attending physician for bladder and rectum ΔD2cc. 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.