Quantitative methods for improved error detection in dose-guided radiotherapy

With the increasing complexity of radiotherapy treatments, it becomes increasingly important to verify that the desired radiation dose is delivered as planned (high dose in tumours, as little as possible in healthy tissues). This dissertation focuses on dose-guided radiotherapy using dose measurements with electronic X-ray cameras (EPID dosimetry) to identify treatments in which errors occur, so that these treatments can be adjusted. This dissertation shows that automatic error detection with EPID dosimetry can be significantly improved. It contributes to this improvement by providing a framework for analysing the uncertainties of dose measurements by quantifying the performance of simple error classification methods, and by applying advanced artificial intelligence algorithms for error classification. These results will ultimately lead to improved radiotherapy treatments

The bilirubin albumin ratio in the management of hyperbilirubinemia in preterm infants to improve neurodevelopmental outcome: A randomized controlled trial - BARTrial

Background and Objective: High bilirubin/albumin (B/A) ratios increase the risk of bilirubin neurotoxicity. The B/A ratio may be a valuable measure, in addition to the total serum bilirubin (TSB), in the management of hyperbilirubinemia. We aimed to assess whether the additional use of B/A ratios in the management of hyperbilirubinemia in preterm infants improved neurodevelopmental outcome. Methods: In a prospective, randomized controlled trial, 615 preterm infants of 32 weeks' gestation or less were randomly assigned to treatment based on either B/A ratio and TSB thresholds (consensus-based), whichever threshold was crossed first, or on the TSB thresholds only. The primary outcome was neurodevelopment at 18 to 24 months' corrected age as assessed with the Bayley Scales of Infant Development III by investigators unaware of treatment allocation. Secondary outcomes included complications of preterm birth and death. Results: Composite motor (100±13 vs. 101±12) and cognitive (101±12 vs. 101±11) scores did not differ between the B/A ratio and TSB groups. Demographic characteristics, maximal TSB levels, B/A ratios, and other secondary outcomes were similar. The rates of death and/or severe neurodevelopmental impairment for th

What is the optimal input information for deep learning-based pre-treatment error identification in radiotherapy?

Background and purpose: Deep learning (DL) provides high sensitivity for detecting and identifying errors in pre-treatment radiotherapy quality assurance (QA). This work’s objective was to systematically evaluate the impact of different dose comparison and image preprocessing methods on DL model performance for error identification in pre-treatment QA. Materials and methods: For 53 volumetric modulated arc therapy (VMAT) and 69 stereotactic body radiotherapy (SBRT) treatment plans of lung cancer patients, mechanical errors were simulated (MLC leaf positions, monitor unit scaling, collimator rotation). Two classification levels were assessed: error type (Level 1) and error magnitude (Level 2). Portal dose images with and without errors were compared using standard (gamma analysis), simple (absolute/relative dose difference, ratio) and alternative (distance-to-agreement, structural similarity index, gradient) dose comparison methods. For preprocessing, different normalization methods (min/max and mean/standard deviation) and image resolutions (32 × 32, 64 × 64 and 128 × 128) were evaluated. All possible combinations of classification level, dose comparison, normalization method and image size resulted in 144 input datasets for DL networks for error identification. Results: Average accuracy was highest for simple dose comparison methods (Level 1: 97.7%, Level 2: 78.1%) while alternative methods scored lowest (Level 1: 91.6%, Level 2: 71.2%). Mean/stdev normalization particularly improved Level 2 classification. Higher image resolution improved error identification, although for SBRT lower image resolution was also sufficient. Conclusions: The choice of dose comparison method has the largest impact on error identification for pre-treatment QA using DL, compared to image preprocessing. Model performance can improve by using simple dose comparison methods, mean/stdev normalization and high image resolution

Treatment plan quality assessment for radiotherapy of rectal cancer patients using prediction of organ-at-risk dose metrics

Background and purpose: Radiotherapy centers frequently lack simple tools for periodic treatment plan verification and feedback on current plan quality. It is difficult to measure treatment quality over different years or during the planning process. Here, we implemented plan quality assurance (QA) by developing a database of dose-volume histogram (DVH) metrics and a prediction model. These tools were used to assess automatically optimized treatment plans for rectal cancer patients, based on cohort analysis. Material and methods: A treatment plan QA framework was established and an overlap volume histogram based model was used to predict DVH parameters for cohorts of patients treated in 2018 and 2019 and grouped according to planning technique. A training cohort of 22 re-optimized treatment plans was used to make the prediction model. The prediction model was validated on 95 automatically generated treatment plans (automatically optimized cohort) and 93 manually optimized plans (manually optimized cohort). Results: For the manually optimized cohort, on average the prediction deviated less than 0.3 ± 1.4 Gy and -4.3 ± 5.5 Gy, for the mean doses to the bowel bag and bladder, respectively; for the automatically optimized cohort a smaller deviation was observed: -0.1 ± 1.1 Gy and -0.2 ± 2.5 Gy, respectively. The interquartile range of DVH parameters was on average smaller for the automatically optimized cohort, indicating less variation within each parameter compared to manual planning. Conclusion: An automated framework to monitor treatment quality with a DVH prediction model was successfully implemented clinically and revealed less variation in DVH parameters for automated in comparison to manually optimized plans. The framework also allowed for individual feedback and DVH estimation

Inter-observer variability of organ contouring for preclinical studies with cone beam Computed Tomography imaging

BACKGROUND AND PURPOSE: In preclinical radiation studies, there is great interest in quantifying the radiation response of healthy tissues. Manual contouring has significant impact on the treatment-planning because of variation introduced by human interpretation. This results in inconsistencies when assessing normal tissue volumes. Evaluation of these discrepancies can provide a better understanding on the limitations of the current preclinical radiation workflow. In the present work, interobserver variability (IOV) in manual contouring of rodent normal tissues on cone-beam Computed Tomography, in head and thorax regions was evaluated. MATERIALS AND METHODS: Two animal technicians performed manually (assisted) contouring of normal tissues located within the thorax and head regions of rodents, 20 cases per body site. Mean surface distance (MSD), displacement of center of mass (ΔCoM), DICE similarity coefficient (DSC) and the 95th percentile Hausdorff distance (HD(95)) were calculated between the contours of the two observers to evaluate the IOV. RESULTS: For the thorax organs, right lung had the lowest IOV (ΔCoM: 0.08 ± 0.04 mm, DSC: 0.96 ± 0.01, MSD:0.07 ± 0.01 mm, HD(95):0.20 ± 0.03 mm) while spinal cord, the highest IOV (ΔCoM:0.5 ± 0.3 mm, DSC:0.81 ± 0.05, MSD:0.14 ± 0.03 mm, HD(95):0.8 ± 0.2 mm). Regarding head organs, right eye demonstrated the lowest IOV (ΔCoM:0.12 ± 0.08 mm, DSC: 0.93 ± 0.02, MSD: 0.15 ± 0.04 mm, HD(95): 0.29 ± 0.07 mm) while complete brain, the highest IOV (ΔCoM: 0.2 ± 0.1 mm, DSC: 0.94 ± 0.02, MSD: 0.3 ± 0.1 mm, HD(95): 0.5 ± 0.1 mm). CONCLUSIONS: Our findings reveal small IOV, within the sub-mm range, for thorax and head normal tissues in rodents. The set of contours can serve as a basis for developing an automated delineation method for e.g., treatment planning