Multicenter Multivendor Evaluation of Dose Volume Histogram Creation Consistencies for 8 Commercial Radiation Therapy Dosimetric Systems

PURPOSE

To evaluate dose volume histogram (DVH) construction differences across 8 major commercial treatment planning systems (TPS) and dose reporting systems for clinically treated plans of various anatomic sites and target sizes.

METHODS AND MATERIALS

Dose files from 10 selected clinically treated plans with a hypofractionation, stereotactic radiation therapy prescription or sharp dose gradients such as head and neck plans ranging from prescription doses of 18 Gy in 1 fraction to 70 Gy in 35 fractions, each calculated at 0.25 and 0.125 cm grid size, were created and anonymized in Eclipse TPS, and exported to 7 other major TPS (Pinnacle, RayStation, and Elements) and dose reporting systems (MIM, Mobius, ProKnow, and Velocity) systems for comparison. Dose-volume constraint points of clinical importance for each plan were collected from each evaluated system (D0.03 cc [Gy], volume, and the mean dose were used for structures without specified constraints). Each reported constraint type and structure volume was normalized to the value from Eclipse for a pairwise comparison. A Wilcoxon rank-sum test was used for statistical significance and a multivariable regression model was evaluated adjusting for plan, grid size, and distance to target center.

RESULTS

For all DVH points relative to Eclipse, all systems reported median values within 1.0% difference of each other; however, they were all different from Eclipse. Considering mean values, Pinnacle, RayStation, and Elements averaged at 1.038, 1.046, and 1.024, respectively, while MIM, Mobius, ProKnow, and Velocity reported 1.026, 1.050, 1.033, and 1.022, respectively relative to Eclipse. Smaller dose grid size improved agreement between the systems marginally without statistical significance. For structure volumes relative to Eclipse, larger differences are seen across all systems with a range in median values up to 3.0% difference and mean up to 10.1% difference.

CONCLUSIONS

Large variations were observed between all systems. Eclipse generally reported, at statistically significant levels, lower values than all other evaluated systems. The nonsignificant change resulting from lowering the dose grid resolution indicates that this resolution may be less important than other aspects of calculating DVH curves, such as the 3-dimensional modeling of the structure.

Evaluation of risk factors for the development of bacteremia and complications in patients with brucellosis: Is it possible to predict the clinical course?

INTRODUCTION

Brucellosis is often confused with other diseases or accompanies the conditions it imitates. It causes treatment delays, failure, relapse, and complications. This study aimed to investigate bacteremia and complication predictors in Brucellosis patients. Early detection may help reduce relapse rates, length of hospital stay, and surgical intervention rates by providing appropriate treatment.

METHODOLOGY

We examined 220 adult patients diagnosed with Brucellosis in our tertiary care hospital in the Black Sea Region between January 01, 2010, and January 01, 2022. Patients with and without bacteremia and complications were compared regarding demographic characteristics, clinical features, and laboratory parameters.

RESULTS

The mean age was 46.4 ± 15.8 years (18-96 years), and 61% were male. Low back pain and absence of muscle pain were independent risk factors for predicting bacteremia (p = 0.049, p = 0.043, respectively). Weakness /fatigue, weight loss, and 1/320 Standard Tube Agglutination Test (STAT) or Brucella Coombs Gel Test (BCGT) titers were independent risk factors that reduced the risk of complications; in contrast, low back pain and splenomegaly were independent risk factors for development of complications. (p = 0.025, p = 0.007, p = 0.008, p = 0.003, p = 0.021 respectively). Thrombocytopenia was related to complications. When the platelet cut-off value was taken as 160,000/µL in predicting complications, the sensitivity was 31.30%, and the specificity was 97.73% (p = 0.011).

CONCLUSIONS

The risk of clinical progression and complications could be predicted with symptoms and signs such as myalgia, low back pain, weakness/fatigue, weight loss, splenomegaly, and easily accessible laboratory parameters such as serum STAT/BCGT titer and platelet level.

Head and neck synthetic CT generated from ultra-low-dose cone-beam CT following Image Gently Protocol using deep neural network

PURPOSE

Image guidance is used to improve accuracy of radiation therapy delivery but results in increased dose to patients. This is of particular concern in children who need be treated per Pediatric Image Gently Protocols due to long term risks from radiation exposure. The purpose of this study is to design a deep neural network (DNN) architecture and loss function for improving soft-tissue contrast and preserving small anatomical features in ultra-low-dose cone-beam CTs (CBCT) of head and neck cancer (HNC) imaging.

METHODS

A 2-D compound U-Net architecture (modified U-Net++) with different depths was proposed to enhance the network capability of capturing small-volume structures. A mask weighted loss function (Mask-Loss) was applied to enhance soft-tissue contrast. Fifty-five paired CBCT and CT images of HNC patients were retrospectively collected for network training and testing. The output enhanced CBCT images from the present study were evaluated with quantitative metrics including mean absolute error (MAE), signal-to-noise ratio (SNR), and structural similarity (SSIM), and compared with those from the previously proposed network architectures (U-Net and wide U-Net) using MAE loss functions. A visual assessment of ten selected structures in the enhanced CBCT images of each patient was performed to evaluate image quality improvement, blindly scored by an experienced radiation oncologist specialized in HN cancer.

RESULTS

All the enhanced CBCT images showed reduced artifactual distortion and image noise. U-Net++ outperformed the U-Net and wide U-Net in terms of MAE, contrast near structure boundaries, and small structures. The proposed Mask-Loss improved image contrast and accuracy of the soft-tissue regions. The enhanced CBCT images predicted by U-Net++ and Mask-Loss demonstrated improvement compared to the U-Net in terms of average MAE (52.41 vs. 42.85 HU), SNR (14.14 vs. 15.07 dB), and SSIM (0.84 vs. 0.87), respectively (p < 0.01, in all paired t-tests). The visual assessment showed that the proposed U-Net++ and Mask-Loss significantly improved original CBCTs (p < 0.01), compared to the U-Net and MAE loss.

CONCLUSIONS

The proposed network architecture and loss function effectively improved image quality in soft-tissue contrast, organ boundary, and small structures preservation for ultra-low-dose CBCT following Image Gently Protocol. This method has potential to provide sufficient anatomical representation on the enhanced CBCT images for accurate treatment delivery and potentially fast online-adaptive re-planning for HN cancer patients. This article is protected by copyright. All rights reserved.

Clinical Enhancement in AI-Based Post-processed Fast-Scan Low-Dose CBCT for Head and Neck Adaptive Radiotherapy

Purpose: To assess image quality and uncertainty in organ-at-risk segmentation on cone beam computed tomography (CBCT) enhanced by deep-learning convolutional neural network (DCNN) for head and neck cancer.

Methods: An in-house DCNN was trained using forty post-operative head and neck cancer patients with their planning CT and first-fraction CBCT images. Additional fifteen patients with repeat simulation CT (rCT) and CBCT scan taken on the same day (oCBCT) were used for validation and clinical utility assessment. Enhanced CBCT (eCBCT) images were generated from the oCBCT using the in-house DCNN. Quantitative imaging quality improvement was evaluated using HU accuracy, signal-to-noise-ratio (SNR), and structural similarity index measure (SSIM). Organs-at-risk (OARs) were delineated on o/eCBCT and compared with manual structures on the same day rCT. Contour accuracy was assessed using dice similarity coefficient (DSC), Hausdorff distance (HD), and center of mass (COM) displacement. Qualitative assessment of users’ confidence in manual segmenting OARs was performed on both eCBCT and oCBCT by visual scoring.

Results: eCBCT organs-at-risk had significant improvement on mean pixel values, SNR (p < 0.05), and SSIM (p < 0.05) compared to oCBCT images. Mean DSC of eCBCT-to-rCT (0.83 ± 0.06) was higher than oCBCT-to-rCT (0.70 ± 0.13). Improvement was observed for mean HD of eCBCT-to-rCT (0.42 ± 0.13 cm) vs. oCBCT-to-rCT (0.72 ± 0.25 cm). Mean COM was less for eCBCT-to-rCT (0.28 ± 0.19 cm) comparing to oCBCT-to-rCT (0.44 ± 0.22 cm). Visual scores showed OAR segmentation was more accessible on eCBCT than oCBCT images.

Conclusion: DCNN improved fast-scan low-dose CBCT in terms of the HU accuracy, image contrast, and OAR delineation accuracy, presenting potential of eCBCT for adaptive radiotherapy.