Does gated beam delivery impact delivery accuracy on an Elekta linac?

In this study, we evaluated the performance of an Elekta linac in the delivery of gated radiotherapy. Delivery accuracy was examined with an emphasis on the impact of using short gating windows (low monitor unit beam-on segments) or long beam hold times. The performance was assessed using a 20cm by 20cm open field with the radiation delivered using a range of beam-on and beam-off time periods. Gated delivery measurements were also performed for two SBRT plans delivered using volumetric modulated arc therapy (VMAT). Tests included both free-breathing based gating (covering a variety of gating windows) and simulated breath-hold based gating. An IBA MatriXX 2D ion chamber array was used for data collection, and the gating accuracy at low MU was evaluated using gamma passing rates. For the 20 cm by 20 cm open field, the measurements generally showed close agreement between the gated and non-gated beam deliveries. Discrepancies, however, began to appear with a 5-to-1 ratio of the beam-off to beam-on times. The discrepancies observed for these tight gating windows can be attributed to the small number of monitor units delivered during each beam-on segment. Dose distribution analysis from the delivery of the two SBRT plans showed gamma passing rates (± 1%, 2%/1 mm) in the range of 95% to 100% for gating windows of 25%, 38%, 50%, 63%, 75%, and 83%. Using a simulated sinusoidal breathing signal with a 4 second period, the gamma passing rate of free-breathing gating and breath-hold gating deliveries were measured in the range of 95.7% to 100%. In conclusion, the results demonstrate that Elekta linacs can accurately deliver respiratory gated treatments for both free-breathing and breath-hold patients. Some caution should be exercised with the use of very tight gating windows

Volumetric CT-based segmentation of NSCLC using 3D-Slicer

Accurate volumetric assessment in non-small cell lung cancer (NSCLC) is critical for adequately informing treatments. In this study we assessed the clinical relevance of a semiautomatic computed tomography (CT)-based segmentation method using the competitive region-growing based algorithm, implemented in the free and public available 3D-Slicer software platform. We compared the 3D-Slicer segmented volumes by three independent observers, who segmented the primary tumour of 20 NSCLC patients twice, to manual slice-by-slice delineations of five physicians. Furthermore, we compared all tumour contours to the macroscopic diameter of the tumour in pathology, considered as the “gold standard”. The 3D-Slicer segmented volumes demonstrated high agreement (overlap fractions > 0.90), lower volume variability (p = 0.0003) and smaller uncertainty areas (p = 0.0002), compared to manual slice-by-slice delineations. Furthermore, 3D-Slicer segmentations showed a strong correlation to pathology (r = 0.89, 95%CI, 0.81–0.94). Our results show that semiautomatic 3D-Slicer segmentations can be used for accurate contouring and are more stable than manual delineations. Therefore, 3D-Slicer can be employed as a starting point for treatment decisions or for high-throughput data mining research, such as Radiomics, where manual delineating often represent a time-consuming bottleneck

Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation

Due to advances in the acquisition and analysis of medical imaging, it is currently possible to quantify the tumor phenotype. The emerging field of Radiomics addresses this issue by converting medical images into minable data by extracting a large number of quantitative imaging features. One of the main challenges of Radiomics is tumor segmentation. Where manual delineation is time consuming and prone to inter-observer variability, it has been shown that semi-automated approaches are fast and reduce inter-observer variability. In this study, a semiautomatic region growing volumetric segmentation algorithm, implemented in the free and publicly available 3D-Slicer platform, was investigated in terms of its robustness for quantitative imaging feature extraction. Fifty-six 3D-radiomic features, quantifying phenotypic differences based on tumor intensity, shape and texture, were extracted from the computed tomography images of twenty lung cancer patients. These radiomic features were derived from the 3D-tumor volumes defined by three independent observers twice using 3D-Slicer, and compared to manual slice-by-slice delineations of five independent physicians in terms of intra-class correlation coefficient (ICC) and feature range. Radiomic features extracted from 3D-Slicer segmentations had significantly higher reproducibility (ICC = 0.85±0.15, p = 0.0009) compared to the features extracted from the manual segmentations (ICC = 0.77±0.17). Furthermore, we found that features extracted from 3D-Slicer segmentations were more robust, as the range was significantly smaller across observers (p = 3.819e-07), and overlapping with the feature ranges extracted from manual contouring (boundary lower: p = 0.007, higher: p = 5.863e-06). Our results show that 3D-Slicer segmented tumor volumes provide a better alternative to the manual delineation for feature quantification, as they yield more reproducible imaging descriptors. Therefore, 3D-Slicer can be employed for quantitative image feature extraction and image data mining research in large patient cohorts

Schematic diagram depicting the overview of the analysis.

<p>A: First, we performed five manual delineations and six 3D-Slicer segmentations (three observers twice) on twenty lung tumors. B: Second, fifty-six radiomic features quantifying tumor intensity, texture and shape were extracted from these segmentations. C: Third, the resulting feature matrices were compared for robustness of the feature values.</p

Box-plot comparing intra- and inter-observer reproducibility (ICC) of radiomic features.

<p>High inter- and intra- observer reproducibility (ICC) was observed for 3D-Slicer segmentations compared to the inter-observer reproducibility (ICC) of manual delineations. From left the first box refers to the manual inter-observer reproducibility (ICC), second and third boxes refer to the inter-observer reproducibility (ICC) of two different 3D-Slicer segmentation runs. Remaining three boxes refer to the intra-observer reproducibility (ICC) of 3D-Slicer segmentations.</p

Feature wise comparison of Intra-class correlation coefficients (ICC) between manual and 3D-Slicer segmentations.

<p>A: First order statistics features. B: Shape based features. C: Textural features.</p

Comparison of normalized feature range between manual and 3D-Slicer segmentation groups.

<p>Radiomic features derived from 3D-Slicer segmentations had significantly smaller and overlapping range compared to that from manual delineations.</p