The use of dose surface maps as a tool to investigate spatial dose delivery accuracy for the rectum during prostate radiotherapy.

PURPOSE: This study aims to address the lack of spatial dose comparisons of planned and delivered rectal doses during prostate radiotherapy by using dose-surface maps (DSMs) to analyze dose delivery accuracy and comparing these results to those derived using DVHs. METHODS: Two independent cohorts were used in this study: twenty patients treated with 36.25 Gy in five fractions (SBRT) and 20 treated with 60 Gy in 20 fractions (IMRT). Daily delivered rectum doses for each patient were retrospectively calculated using daily CBCT images. For each cohort, planned and average-delivered DVHs were generated and compared, as were planned and accumulated DSMs. Permutation testing was used to identify DVH metrics and DSM regions where significant dose differences occurred. Changes in rectal volume and position between planning and delivery were also evaluated to determine possible correlation to dosimetric changes. RESULTS: For both cohorts, DVHs and DSMs reported conflicting findings on how planned and delivered rectum doses differed from each other. DVH analysis determined average-delivered DVHs were on average 7.1% ± 7.6% (p ≤ 0.002) and 5.0 ± 7.4% (p ≤ 0.021) higher than planned for the IMRT and SBRT cohorts, respectively. Meanwhile, DSM analysis found average delivered posterior rectal wall dose was 3.8 ± 0.6 Gy (p = 0.014) lower than planned in the IMRT cohort and no significant dose differences in the SBRT cohort. Observed dose differences were moderately correlated with anterior-posterior rectal wall motion, as well as PTV superior-inferior motion in the IMRT cohort. Evidence of both these relationships were discernable in DSMs. CONCLUSION: DSMs enabled spatial investigations of planned and delivered doses can uncover associations with interfraction motion that are otherwise masked in DVHs. Investigations of dose delivery accuracy in radiotherapy may benefit from using DSMs over DVHs for certain organs such as the rectum.

More than one way to skin a dose volume: the impact of dose-surface map calculation approach on study reproducibility.

Objective.Dose-surface maps (DSMs) provide spatial representations of the radiation dose to organ surfaces during radiotherapy and are a valuable tool for identifying dose deposition patterns that are predictive of radiation toxicities. Over the years, many different DSM calculation approaches have been introduced and used in dose-outcome studies. However, little consideration has been given to how these calculation approaches may be impacting the reproducibility of studies in the field. Therefore, we conducted an investigation to determine the level of equivalence of DSMs calculated with different approaches and their subsequent impact on study results.Approach.Rectum and bladder DSMs were calculated for 20 prostate radiotherapy patients using combinations of the most common slice orientation and spacing styles in the literature. Equivalence of differently calculated DSMs was evaluated using pixel-wise comparisons and DSM features (rectum only). Finally, mock cohort comparison studies were conducted with DSMs calculated using each approach to determine the level of dosimetric study reproducibility between calculation approaches.Main results.We found that rectum DSMs calculated using the planar and non-coplanar orientation styles were non-equivalent in the posterior rectal region and that equivalence of DSMs calculated with different slice spacing styles was conditional on the choice of inter-slice distance used. DSM features were highly sensitive to choice of slice orientation style and DSM sampling resolution. Finally, while general result trends were consistent between the comparison studies performed using different DSMs, statisitically significant subregions and features could vary greatly in position and magnitude.Significance.We have determined that DSMs calculated with different calculation approaches are frequently non-equivalent and can lead to differing conclusions between studies performed using the same dataset. We recommend that the DSM research community work to establish consensus calculation approaches to ensure reproducibility within the field.

Technical note: rtdsm-An open-source software for radiotherapy dose-surface map generation and analysis.

BACKGROUND: Dose-outcome studies in radiation oncology have historically excluded spatial information due to dose-volume histograms being the most dominant source of dosimetric information. In recent years, dose-surface maps (DSMs) have become increasingly popular for characterization of spatial dose distributions and identification of radiosensitive subregions for hollow organs. However, methodological variations and lack of open-source, publicly offered code-sharing between research groups have limited reproducibility and wider adoption. PURPOSE: This paper presents rtdsm, an open-source software for DSM calculation with the intent to improve the reproducibility of and the access to DSM-based research in medical physics and radiation oncology. METHODS: A literature review was conducted to identify essential functionalities and prevailing calculation approaches to guide development. The described software has been designed to calculate DSMs from DICOM data with a high degree of user customizability and to facilitate DSM feature analysis. Core functionalities include DSM calculation, equivalent dose conversions, common DSM feature extraction, and simple DSM accumulation. RESULTS: A number of use cases were used to qualitatively and quantitatively demonstrate the use and usefulness of rtdsm. Specifically, two DSM slicing methods, planar and noncoplanar, were implemented and tested, and the effects of method choice on output DSMs were demonstrated. An example comparison of DSMs from two different treatments was used to highlight the use cases of various built-in analysis functions for equivalent dose conversion and DSM feature extraction. CONCLUSIONS: We developed and implemented rtdsm as a standalone software that provides all essential functionalities required to perform a DSM-based study. It has been made freely accessible under an open-source license on Github to encourage collaboration and community use.