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.

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.

Purpose To develop and test the suitability and performance of a comprehensive quality assurance (QA) phantom for the Small Animal Radiation Research Platform (SARRP). Methods and materials A QA phantom was developed for carrying out daily, monthly and annual QA tasks including: imaging, dosimetry and treatment planning system (TPS) performance evaluation of the SARRP. The QA phantom consists of 15 (60 × 60 × 5 mm3) kV-energy tissue equivalent solid water slabs. The phantom can incorporate optically stimulated luminescence dosimeters (OSLD), Mosfet or film. One slab, with inserts and another slab with hole patterns are particularly designed for image QA. Results Output constancy measurement results showed daily variations within 3%. Using the Mosfet in phantom as target, results showed that the difference between TPS calculations and measurements was within 5%. Annual QA results for the Percentage depth dose (PDD) curves, lateral beam profiles, beam flatness and beam profile symmetry were found consistent with results obtained at commissioning. PDD curves obtained using film and OSLDs showed good agreement. Image QA was performed monthly, with image-quality parameters assessed in terms of CBCT image geometric accuracy, CT number accuracy, image spatial resolution, noise and image uniformity. Conclusions The results show that the developed QA phantom can be employed as a tool for comprehensive performance evaluation of the SARRP. The study provides a useful reference for development of a comprehensive quality assurance program for the SARRP and other similar small animal irradiators, with proposed tolerances and frequency of required tests.

Purpose: In this study, we evaluated the performance of an Elekta linac in the delivery of gated radiotherapy. We examined whether the use of either a short gating window or a long beam hold impacts the accuracy of the delivery Methods: The performance of an Elekta linac in the delivery of gated radiotherapy was assessed using a 20cmX 20cm open field with the radiation delivered using a range of beam‐on and beam‐off time periods. Two SBRT plans were used to examine the accuracy of gated beam delivery for clinical treatment plans. For the SBRT cases, tests were performed for both free‐breathing based gating and for gated delivery with a simulated breath‐hold. A MatriXX 2D ion chamber array was used for data collection, and the gating accuracy was evaluated using gamma score. Results: For the 20cmX20cm open field, the gated beam delivery agreed closely with the non‐gated delivery results. Discrepancies in the agreement, however, began to appear with a 5‐to‐1 ratio of the beam‐off to beam‐on. For these tight gating windows, each beam‐on segment delivered a small number of monitor units. This finding was confirmed with dose distribution analysis from the delivery of the two VMAT plans where the gamma score(±1%,2%/1mm) showed passing rates 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 score of freebreathing gating and breath‐hold gating deliveries were measured in the range of 95.7% to 100%. Conclusion: The results demonstrate that Elekta linacs can be used to accurately deliver respiratory gated treatments for both free‐breathing and breath‐hold patients. The accuracy of beams delivered in a gated delivery mode at low small MU proved higher than similar deliveries performed in a non‐gated (manually interrupted) fashion.

Purpose: Recent advances in medical imaging technologies provide opportunities to quantify the tumor phenotype throughout the course of treatment non‐invasively. The emerging field of Radiomics addresses this by converting medical images into minable data by applying a large number of quantitative imaging algorithms. Accurate tumor segmentation is one of the main challenges of Radiomics. It has been shown that semiautomatic segmentation approaches efficiently reduce inter‐observer variability as compared to the time consuming manual delineations. In this study, a semiautomatic volumetric segmentation algorithm, implemented in the free and publicly available 3D‐Slicer platform, was investigated in terms of its robustness for Radiomics features quantification. Methods: Fifty‐six 3D‐Radiomics features, quantifying phenotypic differences based on the tumor intensity, shape and texture, were extracted from the computed tomography images of twenty lung cancer patients. These Radiomics 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. Results: Radiomics 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, the features extracted from 3D‐Slicer based segmentations, spread over significantly smaller range across observers as compared to those of the manual delineations (p= 3.819e‐07). Moreover, the features derived from 3D‐Slicer segmentations overlapped in range with those of the manual delineations, as the lower(higher) limit(s) being significantly higher(lower) for the 3D‐Slicer features (p= 0.007, p= 5.863e‐06). Conclusion: 3D‐Slicer based semiautomatic segmentation significantly improves the robustness of radiomics features and thus could serve as a potential alternative to the time consuming manual segmentation process. So 3D‐Slicer can have a large application in Radiomics, and be employed for high‐throughput data mining research of medical imaging in clinical oncology.

The purpose of this work is to develop an user friendly and free-to-download application software that can be employed for modeling Radiotherapy with In-situ Dose-painting (RAID) using high-Z nanoparticles (HZNPs). The RAID APP is software program written in Matlab (Mathworks, Natick, MA, USA) based on deterministic code developed to simulate the space-time intra-tumor HZNPs biodistribution within the tumor, and the corresponding dose enhancement in response to low dose rate (LDR) brachytherapy of I-125, Pd-102, Cs-131 and kilovoltage x-rays such as 50 keV and 100 keV. Through the GUI of RAID APP, the user will be directed to different features to compute various parameters related to the dose enhancement and the biodistribution of NPs within high risk tumor sub-volumes. The software was developed as tool for research purposes with potential for subsequent development to guide dose-painting treatment planning using radiosensitizers such as gold (Au) and platinum (Pt).

Purpose: Surface guided radiation therapy (SGRT) uses stereoscopic video images in combination with patterns projected onto the patient's surface to dynamically capture and reconstruct a 3D surface map. In this work, we used a C‐RAD Catalyst HD system (C‐RAD) to evaluate intrafraction motion in the delivery of lung SBRT. Methods: The surface acquired from the 4DCT images from our preliminary cohort of eight lung cancer patients treated with SBRT were matched to the surface images acquired prior to each treatment. Additionally, a CBCT image set was acquired. A linear regression model was established between the external and internal motion of tumor during pretreatment and used to predict the CBCT deviation during treatment. The shifts determined from CBCT and the shifts from surface map imaging were compared and assessed using Bland‐Altman method. For intrafraction motion, we assessed the percentage of mean errors that fell outside of the threshold of 2 mm, 3 mm, and 5 mm along the translational directions. The required PTV margin was quantified over the course of treatment. The correlation between intrafraction treatment time and mean error of 3D displacement was evaluated using the Pearson coefficient, r Results: A total of 7971 data points were analyzed. Deviations of 2mm, 3mm, and 5mm were observed less than 7%, 2 %, and 0 % of the time along the translational direction. CBCT and Catalyst showed close agreement during patient positioning. Furthermore, the calculated PTV margins were less than our clinical tolerance of 5 mm. Using the Pearson coefficient r,the mean error of 3D displacement showed significant correlation with treatment time (r=0.69, p= 0.000002). Conclusion: SGRT can be used to ensure accurate patient positioning during treatment without an additional delivery of dose to the patient. This study shows that importance of treatment time as a consideration during the treatment planning process.

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.