scholarly journals Quantifying systematic RBE-weighted dose uncertainty arising from multiple variable RBE models in Organ-At-Risk

2021 ◽  
pp. 100844
Author(s):  
Wei Yang Calvin Koh ◽  
Hong Qi Tan ◽  
Yan Yee Ng ◽  
Yen Hwa Lin ◽  
Khong Wei Ang ◽  
...  
Keyword(s):  
At Risk ◽  
Multiple Variable ◽  
Organ At Risk ◽  
Dose Uncertainty

scholarly journals Impact of Geometric Uncertainties on Dose Distribution During Intensity Modulated Radiotherapy of Head-and-neck Cancer: The Need for a Planning Target Volume and A Planning Organ-at-Risk Volume

2006 ◽  
Vol 13 (3) ◽  
pp. 108-115 ◽  
Author(s):  
O. Ballivy ◽  
W. Parker ◽  
T. Vuong ◽  
G. Shenouda ◽  
H. Patrocinio
Keyword(s):  
Spinal Cord ◽  
At Risk ◽  
Neck Cancer ◽  
Intensity Modulated Radiotherapy ◽  
Target Volume ◽  
Ct Scans ◽  
Target Coverage ◽  
Geometric Uncertainties ◽  
Intensity Modulated ◽  
Organ At Risk

We assessed the effect of geometric uncertainties on target coverage and on dose to the organs at risk (OARS) during intensity-modulated radiotherapy (IMRT) for head-and-neck cancer, and we estimated the required margins for the planning target volume (PTV) and the planning organ-at-risk volume (PRV). For eight headand- neck cancer patients, we generated IMRT plans with localization uncertainty margins of 0 mm, 2.5 mm, and 5.0 mm. The beam intensities were then applied on repeat computed tomography (CT) scans obtained weekly during treatment, and dose distributions were recalculated. The dose–volume histogram analysis for the repeat CT scans showed that target coverage was adequate (V100 ≥ 95%) for only 12.5% of the gross tumour volumes, 54.3% of the upper-neck clinical target volumes (CTVS), and 27.4% of the lower-neck CTVS when no margins were added for PTV. The use of 2.5-mm and 5.0-mm margins significantly improved target coverage, but the mean dose to the contralateral parotid increased from 25.9 Gy to 29.2 Gy. Maximum dose to the spinal cord was above limit in 57.7%, 34.6%, and 15.4% of cases when 0-mm, 2.5-mm, and 5.0-mm margins (respectively) were used for PRV. Significant deviations from the prescribed dose can occur during IMRT treatment delivery for headand- neck cancer. The use of 2.5-mm to 5.0-mm margins for PTV and PRV greatly reduces the risk of underdosing targets and of overdosing the spinal cord.

OC-0314 Panoptes - a novel tool for teaching organ at risk delineation to radiotherapy technologists

2021 ◽  
Vol 161 ◽  
pp. S223-S225
Author(s):  
J. Scheurleer ◽  
E.M. Vasquez Osorio ◽  
E. Assendelft ◽  
A. Bel ◽  
I. van Dijk ◽  
...  
Keyword(s):  
At Risk ◽  
Organ At Risk

PO-1066 Quality and efficiency of automated organ at risk delineation on MRI in the brain

2021 ◽  
Vol 161 ◽  
pp. S887-S888
Author(s):  
J. Crouzen ◽  
J. Zindler ◽  
R. Wiggenraad ◽  
M. Mast ◽  
S. Lemmouy ◽  
...  
Keyword(s):  
At Risk ◽  
Organ At Risk ◽  
The Brain

Hearing preservation following fractionated stereotactic radiotherapy for vestibular schwannomas: prognostic implications of cochlear dose

2007 ◽  
Vol 107 (5) ◽  
pp. 917-926 ◽  
Author(s):  
Carys Thomas ◽  
Salvatore Di Maio ◽  
Roy Ma ◽  
Emily Vollans ◽  
Christina Chu ◽  
...  
Keyword(s):  
At Risk ◽  
Prognostic Factors ◽  
Stereotactic Radiotherapy ◽  
Tumor Volume ◽  
Intensity Modulated Radiotherapy ◽  
Hearing Preservation ◽  
Fractionated Stereotactic Radiotherapy ◽  
Significant Prognostic Factor ◽  
Organ At Risk ◽  
Radiotherapy Dose

Object The goal in this study was to evaluate hearing preservation rates and to determine prognostic factors for this outcome following fractionated stereotactic radiotherapy (FSRT) of vestibular schwannoma. Methods Thirty-four consecutive patients with serviceable hearing who received FSRT between May 1998 and December 2003 were identified. Clinical and audiometry data were collected prospectively. The prescription dose was 45 Gy in 25 fractions prescribed to the 90% isodose line. The median follow-up duration was 36.5 months (range 12–85 months). The actuarial 2- and 4-year local control rates were 100 and 95.7%, respectively. Permanent trigeminal and facial nerve complications were 0 and 6%, respectively. The actuarial 2- and 3-year serviceable hearing preservation rates were both 63%. The median loss in speech reception threshold was 15 dB (range −10 to 65 dB). The radiotherapy dose to the cochlea was the only significant prognostic factor for hearing deterioration. Radiotherapy dose to the cochlear nucleus, patient age, sex, pre-FSRT hearing grade, tumor volume, and intracanalicular tumor volume failed to show any significance as prognostic factors. Results Five cases were replanned with four different radiotherapy techniques (namely arcs, dynamic arcs, static conformal fields, and intensity-modulated radiotherapy), with the cochlea defined as an organ at risk. In all cases, replanning resulted in statistically significant reduction in radiation to the cochlea (p = 0.001); however, no single replanning technique was found to be superior. Conclusions The radiation dose to the cochlea is strongly predictive for subsequent hearing deterioration. It is essential for the cochlea to be outlined as an organ at risk, and for radiation techniques to be optimized, to improve long-term hearing preservation.

scholarly journals Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study

10.2196/26151 ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. e26151
Author(s):  
Stanislav Nikolov ◽  
Sam Blackwell ◽  
Alexei Zverovitch ◽  
Ruheena Mendes ◽  
Michelle Livne ◽  
...  
Keyword(s):  
Computed Tomography ◽  
At Risk ◽  
Deep Learning ◽  
Clinical Practice ◽  
Head And Neck ◽  
Organs At Risk ◽  
Data Set ◽  
Computed Tomography Scans ◽  
Neck Anatomy ◽  
Organ At Risk

Background Over half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This planning process can delay treatment while also introducing interoperator variability, resulting in downstream radiation dose differences. Although auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying, and achieving expert performance remain. Objective Adopting a deep learning approach, we aim to demonstrate a 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck organs at risk commonly segmented in clinical practice. Methods The model was trained on a data set of 663 deidentified computed tomography scans acquired in routine clinical practice and with both segmentations taken from clinical practice and segmentations created by experienced radiographers as part of this research, all in accordance with consensus organ at risk definitions. Results We demonstrated the model’s clinical applicability by assessing its performance on a test set of 21 computed tomography scans from clinical practice, each with 21 organs at risk segmented by 2 independent experts. We also introduced surface Dice similarity coefficient, a new metric for the comparison of organ delineation, to quantify the deviation between organ at risk surface contours rather than volumes, better reflecting the clinical task of correcting errors in automated organ segmentations. The model’s generalizability was then demonstrated on 2 distinct open-source data sets, reflecting different centers and countries to model training. Conclusions Deep learning is an effective and clinically applicable technique for the segmentation of the head and neck anatomy for radiotherapy. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways.

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