Dataset Information

A cardiac contouring atlas for radiotherapy.

ABSTRACT:

Background and purpose

The heart is a complex anatomical organ and contouring the cardiac substructures is challenging. This study presents a reproducible method for contouring left ventricular and coronary arterial segments on radiotherapy CT-planning scans.

Material and methods

Segments were defined from cardiology models and agreed by two cardiologists. Reference atlas contours were delineated and written guidelines prepared. Six radiation oncologists tested the atlas. Spatial variation was assessed using the DICE similarity coefficient (DSC) and the directed Hausdorff average distance (d?_H,avg). The effect of spatial variation on doses was assessed using six different breast cancer regimens.

Results

The atlas enabled contouring of 15 cardiac segments. Inter-observer contour overlap (mean DSC) was 0.60-0.73 for five left ventricular segments and 0.10-0.53 for ten coronary arterial segments. Inter-observer contour separation (mean d?_H,avg) was 1.5-2.2mm for left ventricular segments and 1.3-5.1mm for coronary artery segments. This spatial variation resulted in <1Gy dose variation for most regimens and segments, but 1.2-21.8Gy variation for segments close to a field edge.

Conclusions

This cardiac atlas enables reproducible contouring of segments of the left ventricle and main coronary arteries to facilitate future studies relating cardiac radiation doses to clinical outcomes.

SUBMITTER: Duane F

PROVIDER: S-EPMC5356506 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Publications

A cardiac contouring atlas for radiotherapy.

Duane Frances F Aznar Marianne C MC Bartlett Freddie F Cutter David J DJ Darby Sarah C SC Jagsi Reshma R Lorenzen Ebbe L EL McArdle Orla O McGale Paul P Myerson Saul S Rahimi Kazem K Vivekanandan Sindu S Warren Samantha S Taylor Carolyn W CW

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology 20170221 3

<h4>Background and purpose</h4>The heart is a complex anatomical organ and contouring the cardiac substructures is challenging. This study presents a reproducible method for contouring left ventricular and coronary arterial segments on radiotherapy CT-planning scans.<h4>Material and methods</h4>Segments were defined from cardiology models and agreed by two cardiologists. Reference atlas contours were delineated and written guidelines prepared. Six radiation oncologists tested the atlas. Spatial ...[more]

PMID: 28233564

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Project description:Background and purposeThe implementation of MRI-guided online adaptive radiotherapy has facilitated the extension of therapeutic radiographers' roles to include contouring, thus releasing the clinician from attending daily treatment. Following undergoing a specifically designed training programme, an online interobserver variability study was performed.Materials and methods117 images from six patients treated on a MR Linac were contoured online by either radiographer or clinician and the same images contoured offline by the alternate profession. Dice similarity coefficient (DSC), mean distance to agreement (MDA), Hausdorff distance (HD) and volume metrics were used to analyse contours. Additionally, the online radiographer contours and optimised plans (n = 59) were analysed using the offline clinician defined contours. After clinical implementation of radiographer contouring, target volume comparison and dose analysis was performed on 20 contours from five patients.ResultsComparison of the radiographers' and clinicians' contours resulted in a median (range) DSC of 0.92 (0.86 - 0.99), median (range) MDA of 0.98 mm (0.2-1.7) and median (range) HD of 6.3 mm (2.5-11.5) for all 117 fractions. There was no significant difference in volume size between the two groups. Of the 59 plans created with radiographer online contours and overlaid with clinicians' offline contours, 39 met mandatory dose constraints and 12 were acceptable because 95 % of the high dose PTV was covered by 95 % dose, or the high dose PTV was within 3 % of online plan. A clinician blindly reviewed the eight remaining fractions and, using trial quality assurance metrics, deemed all to be acceptable. Following clinical implementation of radiographer contouring, the median (range) DSC of CTV was 0.93 (0.88-1.0), median (range) MDA was 0.8 mm (0.04-1.18) and HD was 5.15 mm (2.09-8.54) respectively. Of the 20 plans created using radiographer online contours overlaid with clinicians' offline contours, 18 met the dosimetric success criteria, the remaining 2 were deemed acceptable by a clinician.ConclusionRadiographer and clinician prostate and seminal vesicle contours on MRI for an online adaptive workflow are comparable and produce clinically acceptable plans. Radiographer contouring for prostate treatment on a MR-linac can be effectively introduced with appropriate training and evaluation. A DSC threshold for target structures could be implemented to streamline future training.

Project description:Automatic boundary detection of 4D ultrasound (4DUS) cardiac data is a promising yet challenging application at the intersection of machine learning and medicine. Using recently developed murine 4DUS cardiac imaging data, we demonstrate here a set of three machine learning models that predict left ventricular wall kinematics along both the endo- and epi-cardial boundaries. Each model is fundamentally built on three key features: (1) the projection of raw US data to a lower dimensional subspace, (2) a smoothing spline basis across time, and (3) a strategic parameterization of the left ventricular boundaries. Model 1 is constructed such that boundary predictions are based on individual short-axis images, regardless of their relative position in the ventricle. Model 2 simultaneously incorporates parallel short-axis image data into their predictions. Model 3 builds on the multi-slice approach of model 2, but assists predictions with a single ground-truth position at end-diastole. To assess the performance of each model, Monte Carlo cross validation was used to assess the performance of each model on unseen data. For predicting the radial distance of the endocardium, models 1, 2, and 3 yielded average R2 values of 0.41, 0.49, and 0.71, respectively. Monte Carlo simulations of the endocardial wall showed significantly closer predictions when using model 2 versus model 1 at a rate of 48.67%, and using model 3 versus model 2 at a rate of 83.50%. These finding suggest that a machine learning approach where multi-slice data are simultaneously used as input and predictions are aided by a single user input yields the most robust performance. Subsequently, we explore the how metrics of cardiac kinematics compare between ground-truth contours and predicted boundaries. We observed negligible deviations from ground-truth when using predicted boundaries alone, except in the case of early diastolic strain rate, providing confidence for the use of such machine learning models for rapid and reliable assessments of murine cardiac function. To our knowledge, this is the first application of machine learning to murine left ventricular 4DUS data. Future work will be needed to strengthen both model performance and applicability to different cardiac disease models.

Dataset Information

A cardiac contouring atlas for radiotherapy.

Background and purpose

Material and methods

Results

Conclusions

Publications

A cardiac contouring atlas for radiotherapy.

Similar Datasets

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