A phantom assessment of achievable contouring concordance across multiple treatment planning systems

In this paper, the highest level of inter- and intra-observer conformity achievable with different treatment planning systems (TPSs), contouring tools, shapes, and sites have been established for metrics including the Dice similarity coefficient (DICE) and Hausdorff Distance. High conformity values, e.g. DICEBreast_Shape = 0.99 ± 0.01, were achieved. Decreasing image resolution decreased contouring conformity

Comparison of magnetic resonance imaging and computed tomography for breast target volume delineation in prone and supine positions

Purpose To\ua0determine whether T2-weighted MRI improves seroma cavity (SC) and whole breast (WB) interobserver conformity for radiation therapy purposes, compared with the gold standard of CT, both in the prone and supine positions. Methods and Materials Eleven observers (2 radiologists and 9 radiation oncologists) delineated SC and WB clinical target volumes (CTVs) on T2-weighted MRI and CT supine and prone scans (4 scans per patient) for 33 patient datasets. Individual observer's volumes were compared using the Dice similarity coefficient, volume overlap index, center of mass shift, and Hausdorff distances. An average cavity visualization score was also determined. Results Imaging modality did not affect interobserver variation for WB CTVs. Prone WB CTVs were larger in volume and more conformal than supine CTVs (on both MRI and CT). Seroma cavity volumes were larger on CT than on MRI. Seroma cavity volumes proved to be comparable in interobserver conformity in both modalities (volume overlap index of 0.57\ua0(95% Confidence Interval (CI) 0.54-0.60) for CT supine and 0.52\ua0(95% CI 0.48-0.56) for MRI supine, 0.56\ua0(95% CI 0.53-0.59) for CT prone and 0.55\ua0(95% CI 0.51-0.59) for MRI prone); however, after registering modalities together the intermodality variation (Dice similarity coefficient of 0.41\ua0(95% CI 0.36-0.46) for supine and 0.38\ua0(0.34-0.42) for prone) was larger than the interobserver variability for SC, despite the location typically remaining constant. Conclusions Magnetic resonance imaging interobserver variation was comparable to CT for the WB CTV and SC delineation, in both prone and supine positions. Although the cavity visualization score and interobserver concordance was not significantly higher for MRI than for CT, the SCs were smaller on MRI, potentially owing to clearer SC definition, especially on T2-weighted MR images

A proposed framework for consensus-based lung tumour volume auto-segmentation in 4D computed tomography imaging.

This work aims to propose and validate a framework for tumour volume auto-segmentation based on ground-truth estimates derived from multi-physician input contours to expedite 4D-CT based lung tumour volume delineation. 4D-CT datasets of ten non-small cell lung cancer (NSCLC) patients were manually segmented by 6 physicians. Multi-expert ground truth (GT) estimates were constructed using the STAPLE algorithm for the gross tumour volume (GTV) on all respiratory phases. Next, using a deformable model-based method, multi-expert GT on each individual phase of the 4D-CT dataset was propagated to all other phases providing auto-segmented GTVs and motion encompassing internal gross target volumes (IGTVs) based on GT estimates (STAPLE) from each respiratory phase of the 4D-CT dataset. Accuracy assessment of auto-segmentation employed graph cuts for 3D-shape reconstruction and point-set registration-based analysis yielding volumetric and distance-based measures. STAPLE-based auto-segmented GTV accuracy ranged from (81.51  ±  1.92) to (97.27  ±  0.28)% volumetric overlap of the estimated ground truth. IGTV auto-segmentation showed significantly improved accuracies with reduced variance for all patients ranging from 90.87 to 98.57% volumetric overlap of the ground truth volume. Additional metrics supported these observations with statistical significance. Accuracy of auto-segmentation was shown to be largely independent of selection of the initial propagation phase. IGTV construction based on auto-segmented GTVs within the 4D-CT dataset provided accurate and reliable target volumes compared to manual segmentation-based GT estimates. While inter-/intra-observer effects were largely mitigated, the proposed segmentation workflow is more complex than that of current clinical practice and requires further development

Evaluating and Improving 4D-CT Image Segmentation for Lung Cancer Radiotherapy

Lung cancer is a high-incidence disease with low survival despite surgical advances and concurrent chemo-radiotherapy strategies. Image-guided radiotherapy provides for treatment measures, however, significant challenges exist for imaging, treatment planning, and delivery of radiation due to the influence of respiratory motion. 4D-CT imaging is capable of improving image quality of thoracic target volumes influenced by respiratory motion. 4D-CT-based treatment planning strategies requires highly accurate anatomical segmentation of tumour volumes for radiotherapy treatment plan optimization. Variable segmentation of tumour volumes significantly contributes to uncertainty in radiotherapy planning due to a lack of knowledge regarding the exact shape of the lesion and difficulty in quantifying variability. As image-segmentation is one of the earliest tasks in the radiotherapy process, inherent geometric uncertainties affect subsequent stages, potentially jeopardizing patient outcomes. Thus, this work assesses and suggests strategies for mitigation of segmentation-related geometric uncertainties in 4D-CT-based lung cancer radiotherapy at pre- and post-treatment planning stages