Brachial Plexus Contouring Guideline Assessed with Inter Observer Variability during Image Guided IMRT for Head and Neck Cancer

e17013 Background: Despite modern day imaging techniques and guidelines for delineation of the clinical target volume, there remains significant inter-observer variability in delineating the CTV. With the use of IMRT, the target volume receives a significant tumoricidal dose while the regions just outside the target receive unpredictable doses. In this report, the dose to the region just outside the planning target volume (PTV) (defined as volume of uncertainty [VOU]), presumed to represent the regions subject to maximum inter-observer variability, was studied. Methods: The IMRT plans of 12 patients with head and neck cancer were used to determine the dose just outside the high-risk CTV by growing volumes around CTV with 3 mm, 5 mm, and 7 mm margins. These volumes were edited at regions close to skin/air and bone. PTVs were subsequently grown using the same margins as used in the original plans. With the Boolean operations, each of these volumes was subtracted from the existing PTV to generate the volumes of uncertainty (VOU) in 3 dimensions. The dose to these VOUs was analyzed. D95, D90 and median dose which are the doses received by 95%, 90%, and 50% of the target volume respectively were studied. Results: The median prescribed dose was 68 Gy (60 Gy-72 Gy). The median percentage D95 for 3mm, 5mm and 7mm VOU was 82.5% ± 4.95, 77.25% ± 5.53, and 69% ± 6.93, respectively. The median percentage D90 for these VOU's was 87.7% ± 3.53, 83.2% ± 4.61, and 79% ± 4.5, respectively. The median dose to each of these VOU”s was 96% ± 1.6, 94.5% ± 1.95, and 92.5% ± 1.85 respectively. Conclusions: This study documents that the volumes of uncertainty surrounding the PTV, which could contain subclinical disease, in fact receive a significant amount of RT dose. Hence, despite a large amount of evidence for inter-observer variability in target delineation for head and neck cancer,the majority of locoregional recurrences are within the high dose region and not marginal failures. No significant financial relationships to disclose.

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Auto-segmentation of the parotid glands on MR images of head and neck cancer patients with deep learning strategies

10.1101/2020.12.19.20248376 ◽

2020 ◽

Author(s):

Jennifer P. Kieselmann ◽

Clifton D. Fuller ◽

Oliver J. Gurney-Champion ◽

Uwe Oelfke

Keyword(s):

Head And Neck Cancer ◽

Deep Learning ◽

Head And Neck ◽

Neck Cancer ◽

Dice Similarity Coefficient ◽

Observer Variability ◽

Parotid Glands ◽

Mr Images ◽

Inter Observer Variability ◽

Mri Guided

AbstractAdaptive online MRI-guided radiotherapy of head and neck cancer requires the reliable segmentation of the parotid glands as important organs at risk in clinically acceptable time frames. This can hardly be achieved by manual contouring. We therefore designed deep learning-based algorithms which automatically perform this task.Imaging data comprised two datasets: 27 patient MR images (T1-weighted and T2-weighted) and eight healthy volunteer MR images (T2-weighted), together with manually drawn contours by an expert. We used four different convolutional neural network (CNN) designs that each processed the data differently, varying the dimensionality of the input. We assessed the segmentation accuracy calculating the Dice similarity coefficient (DSC), Hausdorff distance (HD) and mean surface distance (MSD) between manual and auto-generated contours. We benchmarked the developed methods by comparing to the inter-observer variability and to atlas-based segmentation. Additionally, we assessed the generalisability, strengths and limitations of deep learning-based compared to atlas-based methods in the independent volunteer test dataset.With a mean DSC of 0.85± 0.11 and mean MSD of 1.82 ±1.94 mm, a 2D CNN could achieve an accuracy comparable to that of an atlas-based method (DSC: 0.85 ±0.05, MSD: 1.67 ±1.21 mm) and the inter-observer variability (DSC: 0.84 ±0.06, MSD: 1.50 ±0.77 mm) but considerably faster (<1s v.s. 45 min). Adding information (adjacent slices, fully 3D or multi-modality) did not further improve the accuracy. With additional preprocessing steps, the 2D CNN was able to generalise well for the fully independent volunteer dataset (DSC: 0.79 ±0.10, MSD: 1.72 ±0.96 mm)We demonstrated the enormous potential for the application of CNNs to segment the parotid glands for online MRI-guided radiotherapy. The short computation times render deep learning-based methods suitable for online treatment planning workflows.

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