Technical Innovations and Patient Support in Radiation Oncology最新文献

Background and purpose

End expiration breath hold (EEBH) is the preferred motion management method for abdominal Stereotactic Ablative Body Radiotherapy (SABR) treatments. However, multiple short EEBHs are required to complete a single treatment session. The study aimed to determine the efficacy of preoxygenation with hyperventilation in extending an EEBH duration.

Materials and methods

We randomised 10 healthy participants into two arms, each included breathing room air and oxygen at a rate of 10 L per minute (l/min) without hyperventilation for four minutes, and normally for four minutes and with hyperventilation for one minute at a rate of 20 breaths/minute for hyperventilation. The type of gas was blinded from the participants for each test. EEBH durations were then recorded, as well as systolic blood pressure, SpO₂ and heart rate. A discomfort rating was also recorded after each breath hold.

Results

A significant increase in duration of almost 50% was observed between normal breathing of room air and breathing oxygen normally followed by hyperventilation. Vital signs remained consistent between the 4 tests. The tests were well tolerated with 75% of participants recording none or minimal discomfort.

Conclusion

Preoxygenation with hyperventilation could be used to increase the EEBH duration for abdominal SABR patients which would assist in the accuracy of these treatments and possibly resulting in a reduction of overall treatment times.

Purpose

/Objective(S)

A low-cost, prior knowledge-based individualized dose-constraint generator for organs-at-risk has been developed for prostate cancer radiation therapy (RT) planning. In this study, we aimed to evaluate the feasibility and improvements in organs-at-risk (OAR) doses in prostate cancer RT planning using this tool served on a web application.

Materials And Methods

A set of previously treated prostate cancer cases planned and treated with generic constraints were replanned using individualized dose constraints derived from a library of cases with similar volumes of target, OAR, and overlap regions and served on the web-based application. The goal was to assess the reduction in mean dose, specified dose volumes (V59Gy, V56Gy, V53Gy, V47Gy, and V40Gy), and generalized equivalent uniform dose (gEUD) to the rectum and bladder. Planners and assessors were blinded to the initial achieved doses and penalties. Sample size estimation was based on improvement in V53Gy for the rectum and bladder with a paired evaluation.

Results

Twenty-four patients were replanned. All the plans had a PTV D95 of at least 97% of the prescribed dose. The individualized OAR constraints could be met for 87.5% of patients for all dose levels. The mean dose, V59Gy, V53Gy, and V47Gy for the bladder was reduced by 7.5 Gy, 1.12%, 5.51%, and 10.53% respectively. Similarly for the rectum, the mean dose, V59Gy, V53Gy, V47Gy and was reduced by 5.5 Gy, 4.34%, 6.97%, and 11.61% respectively. All dose reductions were statistically significant. The gEUD of the bladder was reduced by 2.47 Gy (p < 0.001) and the rectum by 3.21 Gy (p < 0.001).

Conclusion

Treatment planning based on individualized dose constraints served on a web application is feasible and leads to improvement at clinically important dose volumes in prostate cancer RT planning. This application can be served publicly for improvements in RT plan quality.

Introduction

Deep learning (DL) models are increasingly developed for auto-segmentation in radiotherapy. Qualitative analysis is of great importance for clinical implementation, next to quantitative. This study evaluates a DL segmentation model for left- and right-sided locally advanced breast cancer both quantitatively and qualitatively.

Methods

For each side a DL model was trained, including primary breast CTV (CTVp), lymph node levels 1–4, heart, lungs, humeral head, thyroid and esophagus. For evaluation, both automatic segmentation, including correction of contours when needed, and manual delineation was performed and both processes were timed. Quantitative scoring with dice-similarity coefficient (DSC), 95% Hausdorff Distance (95%HD) and surface DSC (sDSC) was used to compare both the automatic (not-corrected) and corrected contours with the manual contours. Qualitative scoring was performed by five radiotherapy technologists and five radiation oncologists using a 3-point Likert scale.

Results

Time reduction was achieved using auto-segmentation in 95% of the cases, including correction. The time reduction (mean ± std) was 42.4% ± 26.5% and 58.5% ± 19.1% for OARs and CTVs, respectively, corresponding to an absolute mean reduction (hh:mm:ss) of 00:08:51 and 00:25:38. Good quantitative results were achieved before correction, e.g. mean DSC for the right-sided CTVp was 0.92 ± 0.06, whereas correction statistically significantly improved this contour by only 0.02 ± 0.05, respectively. In 92% of the cases, auto-contours were scored as clinically acceptable, with or without corrections.

Conclusions

A DL segmentation model was trained and was shown to be a time-efficient way to generate clinically acceptable contours for locally advanced breast cancer.

Purpose

To understand the impact of radiotherapy educational sessions with virtual reality on oncologic adult patients’ psychological and cognitive outcomes related to the treatment experience.

Methods

This review was performed according to the Preferred Reporting Items for Systematic Reviews guidelines. A systematic electronic search in three databases, MEDLINE, Scopus, and Web of Science, was conducted in December 2021 to find interventional studies with adult patients undergoing external radiotherapy who received an educational session with virtual reality before or during the treatment. The studies that provided qualitative or quantitative information about the impact of educational sessions on patients’ psychological and cognitive dimensions related to RT experience were retained for analysis.

Results

Of the 25 records found, eight articles about seven studies were analysed that involved 376 patients with different oncological pathologies. Most studies evaluated knowledge and treatment-related anxiety, mainly through self-reported questionnaires. The analysis showed a significant improvement in patients’ knowledge and comprehension of radiotherapy treatment. Anxiety levels also decreased with virtual reality educational sessions and throughout the treatment in almost all the studies, although with less homogeneous results.

Conclusion

Virtual reality methods in standard educational sessions can enhance cancer patients' preparation for radiation therapy by increasing their understanding of treatment and reducing anxiety.

The implementation of MRI-guided online adaptive radiotherapy has enabled extension of therapeutic radiographers’ roles to include contouring. An offline interobserver variability study compared five radiographers’ and five clinicians’ contours on 10 MRIs acquired on a MR-Linac from 10 patients. All contours were compared to a “gold standard” created from an average of clinicians’ contours. The median (range) DSC of radiographers’ and clinicians’ contours compared to the “gold standard” was 0.91 (0.86–0.96), and 0.93 (0.88–0.97) respectively illustrating non-inferiority of the radiographers’ contours to the clinicians. There was no significant difference in HD, MDA or volume size between the groups.

Purpose

Oncological Information Systems (OIS) manage information in radiotherapy (RT) departments. Due to database structure limitations, stored information can rarely be directly used except for vendor-specific purposes. Our aim is to enable the use of such data in various external applications by creating a tool for automatic data extraction, cleaning and formatting. Methods and materials: We used OIS data from a nine-linac RT department in Sweden (70 weeks, 2015–16). Extracted data included patients’ referrals and appointments with details for RT sub-tasks. The data extraction tool to prepare the data for external use was built in C# programming language. It used excel-automation queries to remove unassigned/duplicated values, substitute missing data and perform application-specific calculations. Descriptive statistics were used to verify the output with the manually prepared dataset from the corresponding time period. Results: From the initial raw data, 2030 (51 %)/907 (23 %) patients had known curative and palliative treatment intent for 84 different cancer diagnoses. After removal of incomplete entries, 373 (10 %) patients had unknown treatment intents which were substituted based on the known curative/palliative ratio. Automatically- and manuallyprepared datasets differed < 1 % for Mould, Treatment planning, Quality assurance and ± 5 % for Fractions and Magnetic resonance imaging with overestimations in 80/140 (57 %) entries by the tool. Conclusion: We successfully implemented a software tool to prepare ready-to-use OIS datasets for external applications. Our evaluations showed overall results close to the manually-prepared dataset. The time taken to prepare the dataset using our automated strategy can reduce the time for manual preparation from weeks to seconds.