PH-0377 Organ-at-risk sparing in head and neck radiotherapy with dynamic trajectory radiotherapy

2021 ◽  
Vol 161 ◽  
pp. S276-S277
Author(s):  
J. Bertholet ◽  
P. Mackeprang ◽  
S. Mueller ◽  
W. Volken ◽  
D. Frei ◽  
...  
Keyword(s):  
At Risk ◽  
Head And Neck ◽  
Organ At Risk ◽  
Head And Neck Radiotherapy ◽  
Dynamic Trajectory

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.

Organ-at-Risk (OAR) segmentation in head and neck CT using U-RCNN

2020 ◽  
Author(s):  
Yang Lei ◽  
Joseph M. Harms ◽  
Xue Dong ◽  
Tonghe Wang ◽  
Xiangyang Tang ◽  
...  
Keyword(s):  
At Risk ◽  
Head And Neck ◽  
Organ At Risk

scholarly journals EP-2199: Developing Head-and-Neck IMRT Organ-at-risk Sparing Decisions Support System using Model Tree

2018 ◽  
Vol 127 ◽  
pp. S1215
Author(s):  
Y. Sheng ◽  
Q.J. Wu ◽  
J. Zhang ◽  
T. Xie ◽  
F.F. Yin ◽  
...  
Keyword(s):  
At Risk ◽  
Head And Neck ◽  
Support System ◽  
Model Tree ◽  
Organ At Risk

PD-0052: Interobserver variability in organ at risk delineation in head and neck cancer

2020 ◽  
Vol 152 ◽  
pp. S19-S20
Author(s):  
J. Van der Veen ◽  
A. Gulyban ◽  
S. Willems ◽  
F. Maes ◽  
S. Nuyts
Keyword(s):  
At Risk ◽  
Head And Neck Cancer ◽  
Head And Neck ◽  
Neck Cancer ◽  
Interobserver Variability ◽  
Organ At Risk

scholarly journals Interobserver variability in organ at risk delineation in head and neck cancer.

2020 ◽  
Author(s):  
Julie van der Veen ◽  
Akos Gulyban ◽  
Siri Willems ◽  
Frederik Maes ◽  
Sandra Nuyts
Keyword(s):  
Artificial Intelligence ◽  
At Risk ◽  
Head And Neck Cancer ◽  
Head And Neck ◽  
Neck Cancer ◽  
Interobserver Variability ◽  
Treatment Plan ◽  
Consensus Guidelines ◽  
International Consensus ◽  
Organ At Risk

Abstract Background: In radiotherapy inaccuracy in organ at risk (OAR) delineation can impact treatment plan optimisation and treatment plan evaluation. Brouwer et al. showed significant interobserver variability (IOV) in OAR delineation in head and neck cancer (HNC) and published international consensus guidelines (ICG) for OAR delineation in 2015. The aim of our study was to evaluate IOV in the presence of these guidelines. Methods: HNC radiation oncologists (RO) from each Belgian radiotherapy centre were invited to complete a survey and submit contours for 5 HNC cases. Reference contours (OARref) were obtained by a clinically validated artificial intelligence-tool trained using ICG. Dice similarity coefficients (DSC), mean surface distance (MSD) and 95% Hausdorff distances (HD95) were used for comparison.Results: Fourteen of twenty-two RO (64%) completed the survey and submitted delineations. Thirteen (93%) confirmed the use of delineation guidelines, of which six (43%) used the ICG. The OARs whose delineations agreed best with the OARref were mandible (median DSC 0.9, range [0.8-0.9]; median MSD 1.1mm, range [0.8-8.3], median HD95 3.4mm, range [1.5-38.7]), brainstem (median DSC 0.9 [0.6-0.9]; median MSD 1.5mm [1.1-4.0], median HD95 4.0mm [2.3-15.0]), submandibular glands (median DSC 0.8 [0.5-0.9]; median MSD 1.2mm [0.9-2.5], median HD95 3.1mm [1.8-12.2]) and parotids (median DSC 0.9 [0.6-0.9]; median MSD 1.9mm [1.2-4.2], median HD95 5.1mm [3.1-19.2]). Oral cavity, cochleas, PCMs, supraglottic larynx and glottic area showed more variation. RO who used the consensus guidelines showed significantly less IOV (p=0.008).Conclusion: Although ICG for delineation of OARs in HNC exist, they are only implemented by about half of RO participating in this study, which partly explains the delineation variability. However, this study highlights that guidelines alone do not suffice to eliminate IOV and that more effort needs to be done to accomplish further treatment standardisation, for example with artificial intelligence.

Comparison of organ‐at‐risk sparing and plan robustness for spot‐scanning proton therapy and volumetric modulated arc photon therapy in head‐and‐neck cancer

2015 ◽  
Vol 42 (11) ◽  
pp. 6589-6598 ◽  
Author(s):  
Danique L. J. Barten ◽  
Jim P. Tol ◽  
Max Dahele ◽  
Ben J. Slotman ◽  
Wilko F. A. R. Verbakel
Keyword(s):  
At Risk ◽  
Head And Neck Cancer ◽  
Head And Neck ◽  
Neck Cancer ◽  
Proton Therapy ◽  
Spot Scanning ◽  
Organ At Risk
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