Medical Physics

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Browsing Medical Physics by browse.metadata.advisor "Trauernicht, Christoph Jan"

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    The application of a deep convolution neural network for the automated delineation of the target and organs at risk in High Dose Rate Cervical Brachytherapy
    (Stellenbosch : Stellenbosch University, 2022-12) Duprez, Didier Raphael Roger; Trauernicht, Christoph Jan; Stellenbosch University. Faculty of Medicine and Health Sciences. Dept. of Medical Imaging and Clinical Oncology. Medical Physics.
    ENGLISH SUMMARY: Low/middle income countries suffer from large deficits in experienced Radiation Oncologists, Medical Physicists and Radiation Therapists. Due to these deficits, the bottlenecks experienced in the High-dose rate (HDR) cervical brachytherapy treatment planning workflow are amplified. Image-guided HDR cervical brachytherapy is a complex, labour intensive, manual, time-consuming and expertise driven process. Automation in radiotherapy treatment planning, especially in brachytherapy, has the potential to substantially reduce the overall planning time however most of these algorithms require high level of expertise to develop. The aim of this study is to implement the out of the box self-configuring deep neural network package, known as No New U-Net (nnU-Net), for the task of automatically delineating the organs at risk (OARs) and high-risk clinical target volume (HR CTV) for HDR cervical brachytherapy. The computed tomography (CT) scans of 100 previously treated patients were used to train and test three different nnU-Net configurations (2D, 3DFR and 3DCasc). The performance of the models was evaluated by calculating the Sørensen-Dice similarity coefficient, Hausdorff distance (HD), 95th percentile Hausdorff distance, mean surface distance (MSD) and precision score for 20 test patients. The dosimetric accuracy between the manual and predicted contours was assessed by looking at the various dose volume histogram (DVH) parameters and volume differences. Three different radiation oncologists (ROs) scored the predicted bladder, rectum and HR CTV contours generated by the best performing model. The manual contouring, prediction and editing times were recorded. The mean DSC, HD, HD95, MSD and precision scores for our best performing model (3DFR) were 0.92/7.5 mm/3.0 mm/ 0.8 mm/0.91 for the bladder, 0.84/13.8 mm/5.2 mm/1.4 mm/0.84 for the rectum and 0.81/8.5 mm/6.0 mm/2.2 mm/0.80 for the HR CTV. Mean dose differences (D2cc/90%) and volume differences were 0.08 Gy/1.3 cm3 for the bladder, 0.02 Gy/0.7 cm3 for the rectum and 0.33 Gy/1.5 cm3 for the HR CTV. On average, 65 % of the generated contours were clinically acceptable, 33 % requiring minor edits, 2 % required major edits and no contours were rejected. Average manual contouring time was 14.0 minutes, while the average prediction and editing times were 1.6 and 2.1 minutes respectively. Our best performing model (3DFR) provided fast accurate auto generated OARs and HR CTV contours with a large clinical acceptance rate. Future work should focus on including larger datasets to eliminate inconsistencies, as well as focus on automating the generation of treatment plans.
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    Application of gradient dose segmented analysis as a treatment quality indicator for patients undergoing volumetric modulated arc radiotherapy
    (Stellenbosch : Stellenbosch University, 2022-12) van Reenen, Christoffel Jacobus; Trauernicht, Christoph Jan; Stellenbosch University. Faculty of Medicine and Health Sciences. Dept. of Medical Imaging and Clinical Oncology. Medical Physics.
    ENGLISH SUMMARY: The gamma analysis metric is a commonly used metric for volumetric modulated arc radiotherapy (VMAT) plan evaluation. The major drawback of this metric is the lack of correlation between gamma passing rates and dose-volume histogram (DVH) values for planning target volumes (PTV). The novel gradient dose segmented analysis (GDSA) metric was developed by Steers et al. to quantify changes in the PTV mean dose (Dmean) for patients undergoing VMAT. In this study, the GDSA metric was applied to 115 head-and-neck cancer patients treated on the Varian Halcyon v2.0 linear accelerator between August 2019 and July 2020 in the Division of Radiation Oncology. The GDSA indicated that a total of 13 patients had received at least one treatment fraction where the PTV Dmean exceeded 3% compared to the first treatment fraction. The kilovoltage cone-beam computed tomography (kV CBCT) images of these patients were analysed to determine the cause. The maximum predicted change in the PTV Dmean was 4.83%. Measurable changes in anterior-posterior and lateral separations were observed for 8 out the 13 patients (62%) where the change in PTV Dmean exceeded 3%. The maximum calculated effective separation change diameter was calculated as 3.86 cm. In cases where the change in PTV Dmean was less than 3%, no measurable separation changes were observed. The pitch-, roll- and yaw-rotational errors were quantified as the Halcyon treatment couch does not allow for online rotational corrections. The maximum pitch, roll and yaw rotational errors were 3.91º ± 0.89º, 3.07º ± 0.51º and 2.62º ± 0.40º, respectively. The mean errors were 0.9º, 0.45º, and 0.43º, for pitch, roll and yaw, respectively. The obtained results demonstrated that large deviations in PTV Dmean (>3%) were more likely due to change in effective diameter, whereas small deviations in PTV Dmean combined with separation changes less than 1 cm, were more likely caused by errors in pitch for long treatment fields. Weight loss during radiotherapy is well documented and proven to be the highest among head-and-neck cancer patients. The GDSA easily be implemented to identify setup/immobilization errors, as well as aid the department in scheduling new CT scans for patients experiencing continuous weight loss before significant differences in dose delivery occur.