Abstract
Purpose: Dosimetric predictors of toxicity after Stereotactic Body Radiation Therapy (SBRT) are not well-established. We sought to develop a multivariate model that predicts Common Terminology Criteria for Adverse Events (CTCAE) late grade 2 or greater genitourinary (GU) toxicity by interrogating the entire dose-volume histogram (DVH) from a large cohort of prostate cancer patients treated with SBRT on prospective trials.Methods: Three hundred and thirty-nine patients with late CTCAE toxicity data treated with prostate SBRT were identified and analyzed. All patients received 40 Gy in five fractions, every other day, using volumetric modulated arc therapy. For each patient, we examined 910 candidate dosimetric features including maximum dose, volumes of each organ [CTV, organs at risk (OARs)], V100%, and other granular volumetric/dosimetric indices at varying volumetric/dosimetric values from the entire DVH as well as ADT use to model and predict toxicity from SBRT. Training and validation subsets were generated with 90 and 10% of the patients in our cohort, respectively. Predictive accuracy was assessed by calculating the area under the receiver operating curve (AROC). Univariate analysis with student t-test was first performed on each candidate DVH feature. We subsequently performed advanced machine-learning multivariate analyses including classification and regression tree (CART), random forest, boosted tree, and multilayer neural network.Results: Median follow-up time was 32.3 months (range 3–98.9 months). Late grade ≥2 GU toxicity occurred in 20.1% of patients in our series. No single dosimetric parameter had an AROC for predicting late grade ≥2 GU toxicity on univariate analysis that exceeded 0.599. Optimized CART modestly improved prediction accuracy, with an AROC of 0.601, whereas other machine learning approaches did not improve upon univariate analyses.Conclusions: CART-based machine learning multivariate analyses drawing from 910 dosimetric features and ADT use modestly improves upon clinical prediction of late GU toxicity alone, yielding an AROC of 0.601. Biologic predictors may enhance predictive models for identifying patients at risk for late toxicity after SBRT.
Highlights
The recently published HYPO-RT-PC trial provides level I evidence that ultrahypofractionated radiotherapy for prostate cancer offers similar 5-year biochemical control and toxicity rates compared with conventionally fractionated radiotherapy [1]
Three hundred and thirty-nine patients treated with stereotactic body radiotherapy (SBRT) at our tertiary academic institution from 2010 to 2017 with late Common Terminology Criteria for Adverse Events (CTCAE) toxicity data were identified and included in our analysis
Grade ≥2 late toxicity was assessed according to the GU domain of the CTCAE, version 4.03 and was defined as the worst CTCAE grade scored
Summary
The recently published HYPO-RT-PC trial provides level I evidence that ultrahypofractionated radiotherapy for prostate cancer offers similar 5-year biochemical control and toxicity rates compared with conventionally fractionated radiotherapy [1]. While the rates of long-term severe toxicity [i.e., grade 3 or greater on the Radiation Therapy Oncology Group (RTOG) or Common Terminology Criteria for Adverse Events (CTCAE) scales] are low, they still occur, and lower grade toxicities—which may not require intervention but can degrade quality of life—may occur in a notable minority of patients. These toxicities are likely dosedependent, and two recently published prospective studies have suggested a toxicity dose-response for prostate SBRT [4, 5]. Doses of up to 40 Gy in five fractions may become increasingly utilized
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
