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The primary goal of precision medicine is to minimize side effects and optimize efficacy of treatments. Recent advances in medical imaging technology allow the use of more advanced image analysis methods beyond simple measurements of tumor size or radiotracer uptake metrics. The extraction of quantitative features from medical images to characterize tumor pathology or heterogeneity is an interesting process to investigate, in order to provide information that may be useful to guide the therapies and predict survival. This paper discusses the rationale supporting the concept of radiomics and the feasibility of its application to Non-Small Cell Lung Cancer in the field of radiation oncology research. We studied 91 stage III patients treated with concurrent chemoradiation and adaptive approach in case of tumor reduction during treatment. We considered 12 statistics features and 230 textural features extracted from the CT images. In our study, we used an ensemble learning method to classify patients’ data into either the adaptive or non-adaptive group during chemoradiation on the basis of the starting CT simulation. Our data supports the hypothesis that a specific signature can be identified (AUC 0.82). In our experience, a radiomic signature mixing semantic and image-based features has shown promising results for personalized adaptive radiotherapy in non-small cell lung cancer. [ABSTRACT FROM AUTHOR]

Since absorption of X-ray radiation has the possibility of inducing cancerous, genetic and other diseases to patients, researches usually attempt to reduce the radiation dose. However, reduction of the radiation dose associated with CT scans will unavoidably increase the severity of noise and artifacts, which can seriously affect diagnostic confidence. Due to the outstanding performance of deep neural networks in image processing, in this paper, we proposed a Stacked Competitive Network (SCN) approach to noise reduction, which stacks several successive Competitive Blocks (CB). The carefully handcrafted design of the competitive blocks was inspired by the idea of multi-scale processing and improvement the network’s capacity. Qualitative and quantitative evaluations demonstrate the competitive performance of the proposed method in noise suppression, structural preservation, and lesion detection. [ABSTRACT FROM AUTHOR]

Lung 4D computed tomography (4D-CT) plays an important role in high-precision radiotherapy because it characterizes respiratory motion, which is crucial for accurate target definition. However, the manual segmentation of a lung tumor is a heavy workload for doctors because of the large number of lung 4D-CT data slices. Meanwhile, tumor segmentation is still a notoriously challenging problem in computer-aided diagnosis. In this paper, we propose a new method based on an improved graph cut algorithm with context information constraint to find a convenient and robust approach of lung 4D-CT tumor segmentation. We combine all phases of the lung 4D-CT into a global graph, and construct a global energy function accordingly. The sub-graph is first constructed for each phase. A context cost term is enforced to achieve segmentation results in every phase by adding a context constraint between neighboring phases. A global energy function is finally constructed by combining all cost terms. The optimization is achieved by solving a max-flow/min-cut problem, which leads to simultaneous and robust segmentation of the tumor in all the lung 4D-CT phases. The effectiveness of our approach is validated through experiments on 10 different lung 4D-CT cases. The comparison with the graph cut without context constraint, the level set method and the graph cut with star shape prior demonstrates that the proposed method obtains more accurate and robust segmentation results. [ABSTRACT FROM AUTHOR]

Background: Prognostic models based on individual patient characteristics can improve treatment decisions and outcome in the future. In many (radiomic) studies, small size and heterogeneity of datasets is a challenge that often limits performance and potential clinical applicability of these models. The current study is example of a retrospective multi-centric study with challenges and caveats. To highlight common issues and emphasize potential pitfalls, we aimed for an extensive analysis of these multi-center pre-treatment datasets, with an additional 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) scan acquired during treatment. Methods: The dataset consisted of 138 stage II-IV non-small cell lung cancer (NSCLC) patients from four different cohorts acquired from three different institutes. The differences between the cohorts were compared in terms of clinical characteristics and using the so-called ‘cohort differences model’ approach. Moreover, the potential prognostic performances for overall survival of radiomic features extracted from CT or FDG-PET, or relative or absolute differences between the scans at the two time points, were assessed using the LASSO regression method. Furthermore, the performances of five different classifiers were evaluated for all image sets. Results: The individual cohorts substantially differed in terms of patient characteristics. Moreover, the cohort differences model indicated statistically significant differences between the cohorts. Neither LASSO nor any of the tested classifiers resulted in a clinical relevant prognostic model that could be validated on the available datasets. Conclusion: The results imply that the study might have been influenced by a limited sample size, heterogeneous patient characteristics, and inconsistent imaging parameters. No prognostic performance of FDG-PET or CT based radiomics models can be reported. This study highlights the necessity of extensive evaluations of cohorts and of validation datasets, especially in retrospective multi-centric datasets. [ABSTRACT FROM AUTHOR]

We present an approach for fully automatic urinary bladder segmentation in CT images with artificial neural networks in this study. Automatic medical image analysis has become an invaluable tool in the different treatment stages of diseases. Especially medical image segmentation plays a vital role, since segmentation is often the initial step in an image analysis pipeline. Since deep neural networks have made a large impact on the field of image processing in the past years, we use two different deep learning architectures to segment the urinary bladder. Both of these architectures are based on pre-trained classification networks that are adapted to perform semantic segmentation. Since deep neural networks require a large amount of training data, specifically images and corresponding ground truth labels, we furthermore propose a method to generate such a suitable training data set from Positron Emission Tomography/Computed Tomography image data. This is done by applying thresholding to the Positron Emission Tomography data for obtaining a ground truth and by utilizing data augmentation to enlarge the dataset. In this study, we discuss the influence of data augmentation on the segmentation results, and compare and evaluate the proposed architectures in terms of qualitative and quantitative segmentation performance. The results presented in this study allow concluding that deep neural networks can be considered a promising approach to segment the urinary bladder in CT images. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to evaluate the dosimetric accuracy of respiratory gated volumetric modulated arc therapy (VMAT) for lung stereotactic body radiation therapy (SBRT) under simulation conditions similar to the actual clinical situation using patient-specific lung phantoms and realistic target movements. Methods: Six heterogeneous lung phantoms were fabricated using a 3D-printer (3DISON, ROKIT, Seoul, Korea) to be dosimetrically equivalent to actual target regions of lung SBRT cases treated via gated VMAT. They were designed to move realistically via a motion device (QUASAR, Modus Medical Devices, Canada). Using the lung phantoms and a homogeneous phantom (model 500–3315, Modus Medical Devices), film dosimetry was performed with and without respiratory gating for VMAT delivery (TrueBeam STx; Varian Medical Systems, Palo Alto, CA, USA). The measured results were analyzed with the gamma passing rates (GPRs) of 2%/1 mm criteria, by comparing with the calculated dose via the AXB and AAA algorithms of the Eclipse Treatment Planning System (version 10.0.28; Varian Medical Systems). Results: GPRs were greater than the acceptance criteria 80% for all film measurements with the stationary and homogeneous phantoms in conventional QAs. Regardless of the heterogeneity of phantoms, there were no significant differences (p > 0.05) in GPRs obtained with and without target motions; the statistical significance (p = 0.031) was presented between both algorithms under the utilization of heterogeneous phantoms. Conclusions: Dosimetric verification with heterogeneous patient-specific lung phantoms could be successfully implemented as the evaluation method for gated VMAT delivery. In addition, it could be dosimetrically confirmed that the AXB algorithm improved the dose calculation accuracy under patient-specific simulations using 3D printed lung phantoms. [ABSTRACT FROM AUTHOR]

Background: Non-small-cell lung cancer (NSCLC) patients often demonstrate varying clinical courses and outcomes, even within the same tumor stage. This study explores deep learning applications in medical imaging allowing for the automated quantification of radiographic characteristics and potentially improving patient stratification.Methods and Findings: We performed an integrative analysis on 7 independent datasets across 5 institutions totaling 1,194 NSCLC patients (age median = 68.3 years [range 32.5-93.3], survival median = 1.7 years [range 0.0-11.7]). Using external validation in computed tomography (CT) data, we identified prognostic signatures using a 3D convolutional neural network (CNN) for patients treated with radiotherapy (n = 771, age median = 68.0 years [range 32.5-93.3], survival median = 1.3 years [range 0.0-11.7]). We then employed a transfer learning approach to achieve the same for surgery patients (n = 391, age median = 69.1 years [range 37.2-88.0], survival median = 3.1 years [range 0.0-8.8]). We found that the CNN predictions were significantly associated with 2-year overall survival from the start of respective treatment for radiotherapy (area under the receiver operating characteristic curve [AUC] = 0.70 [95% CI 0.63-0.78], p < 0.001) and surgery (AUC = 0.71 [95% CI 0.60-0.82], p < 0.001) patients. The CNN was also able to significantly stratify patients into low and high mortality risk groups in both the radiotherapy (p < 0.001) and surgery (p = 0.03) datasets. Additionally, the CNN was found to significantly outperform random forest models built on clinical parameters-including age, sex, and tumor node metastasis stage-as well as demonstrate high robustness against test-retest (intraclass correlation coefficient = 0.91) and inter-reader (Spearman's rank-order correlation = 0.88) variations. To gain a better understanding of the characteristics captured by the CNN, we identified regions with the most contribution towards predictions and highlighted the importance of tumor-surrounding tissue in patient stratification. We also present preliminary findings on the biological basis of the captured phenotypes as being linked to cell cycle and transcriptional processes. Limitations include the retrospective nature of this study as well as the opaque black box nature of deep learning networks.Conclusions: Our results provide evidence that deep learning networks may be used for mortality risk stratification based on standard-of-care CT images from NSCLC patients. This evidence motivates future research into better deciphering the clinical and biological basis of deep learning networks as well as validation in prospective data. [ABSTRACT FROM AUTHOR]

Background: Four-dimensional computed tomography (4D-CT) ventilation is an emerging imaging modality. Functional avoidance of regions according to 4D-CT ventilation may reduce lung toxicity after radiation therapy. This study evaluated associations between 4D-CT ventilation-based dosimetric parameters and clinical outcomes. Methods: Pre-treatment 4D-CT data were used to retrospectively construct ventilation images for 40 thoracic cancer patients retrospectively. Fifteen patients were treated with conventional radiation therapy, 6 patients with hyperfractionated radiation therapy and 19 patients with stereotactic body radiation therapy (SBRT). Ventilation images were calculated from 4D-CT data using a deformable image registration and Jacobian-based algorithm. Each ventilation map was normalized by converting it to percentile images. Ventilation-based dosimetric parameters (Mean Dose, V5 [percent lung volume receiving ≥5 Gy], and V20 [percent lung volume receiving ≥20 Gy]) were calculated for highly and poorly ventilated regions. To test whether the ventilation-based dosimetric parameters could be used predict radiation pneumonitis of ≥Grade 2, the area under the curve (AUC) was determined from the receiver operating characteristic analysis. Results: For Mean Dose, poorly ventilated lung regions in the 0–30% range showed the highest AUC value (0.809; 95% confidence interval [CI], 0.663–0.955). For V20, poorly ventilated lung regions in the 0–20% range had the highest AUC value (0.774; 95% [CI], 0.598–0.915), and for V5, poorly ventilated lung regions in the 0–30% range had the highest AUC value (0.843; 95% [CI], 0.732–0.954). The highest AUC values for Mean Dose, V20, and V5 were obtained in poorly ventilated regions. There were significant differences in all dosimetric parameters between radiation pneumonitis of Grade 1 and Grade ≥2. Conclusions: Poorly ventilated lung regions identified on 4D-CT had higher AUC values than highly ventilated regions, suggesting that functional planning based on poorly ventilated regions may reduce the risk of lung toxicity in radiation therapy. [ABSTRACT FROM AUTHOR]

The purpose of this study is to investigate the optimal reference geometry for gamma camera calibration. Yet another question of interest was to assess the influence of the number of 3D Ordered Subsets Expectation Maximization (3D-OSEM) updates on activity quantification for SPECT imaging with 177Lu. The accuracy of 177Lu activity quantification was assessed both in small and in large objects. Two different reference geometries, namely a cylindrical homogeneous phantom and a Jaszczak 16 ml sphere surrounded by cold water, were used to determine the gamma camera calibration factor of a commercial SPECT/CT system. Moreover, the noise level and the concentration recovery coefficient were evaluated as a function of the number of 3D-OSEM updates by using the SPECT/CT images of the reference geometry phantoms and those of a cold Jaszczak phantom with three hot spheres (16ml, 8ml and 4ml), respectively. The optimal choice of the number of 3D-OSEM updates was based on a compromise between the noise level achievable in the reconstructed SPECT images and the concentration recovery coefficients. The quantitative accuracy achievable was finally validated on a test phantom, where a spherical insert composed of two concentric spheres was used to simulate a lesion in a warm background. Our data confirm and extend previous observations. Using the calibration factor obtained with the cylindrical homogeneous phantom and the Jaszczak 16 ml sphere, the recovered activity in the test phantom was underestimated by -16.4% and -24.8%, respectively. Our work has led us to conclude that gamma camera calibration performed with large homogeneous phantom outperforms calibration executed with the Jaszczak 16ml sphere. Furthermore, the results obtained support the assumption that approximately 50 OSEM updates represent a good trade-off to reach convergence in small volumes, meanwhile minimizing the noise level. [ABSTRACT FROM AUTHOR]

Development and comparison of spine-shaped phantoms generated by two different 3D-printing technologies, digital light processing (DLP) and Polyjet has been purposed to utilize in patient-specific quality assurance (QA) of stereotactic body radiation treatment. The developed 3D-printed spine QA phantom consisted of an acrylic body phantom and a 3D-printed spine shaped object. DLP and Polyjet 3D printers using a high-density acrylic polymer were employed to produce spine-shaped phantoms based on CT images. Image fusion was performed to evaluate the reproducibility of our phantom, and the Hounsfield units (HUs) were measured based on each CT image. Two different intensity-modulated radiotherapy plans based on both CT phantom image sets from the two printed spine-shaped phantoms with acrylic body phantoms were designed to deliver 16 Gy dose to the planning target volume (PTV) and were compared for target coverage and normal organ-sparing. Image fusion demonstrated good reproducibility of the developed phantom. The HU values of the DLP- and Polyjet-printed spine vertebrae differed by 54.3 on average. The PTV Dmax dose for the DLP-generated phantom was about 1.488 Gy higher than that for the Polyjet-generated phantom. The organs at risk received a lower dose for the 3D printed spine-shaped phantom image using the DLP technique than for the phantom image using the Polyjet technique. Despite using the same material for printing the spine-shaped phantom, these phantoms generated by different 3D printing techniques, DLP and Polyjet, showed different HU values and these differently appearing HU values according to the printing technique could be an extra consideration for developing the 3D printed spine-shaped phantom depending on the patient’s age and the density of the spinal bone. Therefore, the 3D printing technique and materials should be carefully chosen by taking into account the condition of the patient in order to accurately produce 3D printed patient-specific QA phantom. [ABSTRACT FROM AUTHOR]

The combination external-beam radiotherapy and high-dose-rate brachytherapy is a standard form of treatment for patients with locally advanced uterine cervical cancer. Personalized radiotherapy in cervical cancer requires efficient and accurate dose planning and assessment across these types of treatment. To achieve radiation dose assessment, accurate mapping of the dose distribution from HDR-BT onto EBRT is extremely important. However, few systems can achieve robust dose fusion and determine the accumulated dose distribution during the entire course of treatment. We have therefore developed a toolbox (FZUImageReg), which is a user-friendly dose fusion system based on hybrid image registration for radiation dose assessment in cervical cancer radiotherapy. The main part of the software consists of a collection of medical image registration algorithms and a modular design with a user-friendly interface, which allows users to quickly configure, test, monitor, and compare different registration methods for a specific application. Owing to the large deformation, the direct application of conventional state-of-the-art image registration methods is not sufficient for the accurate alignment of EBRT and HDR-BT images. To solve this problem, a multi-phase non-rigid registration method using local landmark-based free-form deformation is proposed for locally large deformation between EBRT and HDR-BT images, followed by intensity-based free-form deformation. With the transformation, the software also provides a dose mapping function according to the deformation field. The total dose distribution during the entire course of treatment can then be presented. Experimental results clearly show that the proposed system can achieve accurate registration between EBRT and HDR-BT images and provide radiation dose warping and fusion results for dose assessment in cervical cancer radiotherapy in terms of high accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Objective: To prospectively evaluate the ability of CT urography with a low-dose multi-phasic excretory phase for opacification of the urinary system. Materials and methods: Thirty-two patients underwent CT urography with low-dose multi-phasic s using adaptive iterative dose reduction 3D acquired at 5-, 10-, and 15-minute delays. Opacification scores of the upper urinary tracts and the urinary bladder were assigned for each excretory phase by two radiologists, who recorded whether adequate (>75%) or complete (100%) opacification of the upper urinary tract and urinary bladder was achieved in each patient. Adequate and complete opacification rates of the upper urinary tracts and the urinary bladder were compared among three excretory phases and among combined multi-phasic excretory phases using Cochran's Q test. Results: There was no significant difference among three excretory phases with 5-, 10-, and 15-minute delays in adequate (56.3, 43.8, and 63.5%, respectively; P = 0.174) and complete opacification rates (9.3, 15.6, and 18.7%, respectively; P = 0.417) of the upper urinary tracts. Combined tri-phasic excretory phases significantly improved adequate and complete opacification rates to 84.4% and 43.8%, respectively (P = 0.002). In contrast, there were significant differences among three excretory phases for the rate of adequate (31.3, 84.4, and 93.8%, respectively; P<0.001) and complete opacification (21.9, 53.1, and 81.3%, respectively; P<0.001) of the urinary bladder. Multi-phasic excretory phases did not improve these rates because opacification was always better with a longer delay. Conclusion: Although multi-phasic acquisition of excretory phases is effective at improving opacification of the upper urinary tracts, complete opacification is difficult even with tri-phasic acquisition. [ABSTRACT FROM AUTHOR]

Radiotherapy of lung and liver lesions has changed from normofractioned 3D-CRT to stereotactic treatment in a single or few fractions, often employing volumetric arc therapy (VMAT)-based techniques. Potential unintended interference of respiratory target motion and dynamically changing beam parameters during VMAT dose delivery motivates establishing 4D quality assurance (4D QA) procedures to assess appropriateness of generated VMAT treatment plans when taking into account patient-specific motion characteristics. Current approaches are motion phantom-based 4D QA and image-based 4D VMAT dose simulation. Whereas phantom-based 4D QA is usually restricted to a small number of measurements, the computational approaches allow simulating many motion scenarios. However, 4D VMAT dose simulation depends on various input parameters, influencing estimated doses along with mitigating simulation reliability. Thus, aiming at routine use of simulation-based 4D VMAT QA, the impact of such parameters as well as the overall accuracy of the 4D VMAT dose simulation has to be studied in detail–which is the topic of the present work. In detail, we introduce the principles of 4D VMAT dose simulation, identify influencing parameters and assess their impact on 4D dose simulation accuracy by comparison of simulated motion-affected dose distributions to corresponding dosimetric motion phantom measurements. Exploiting an ITV-based treatment planning approach, VMAT treatment plans were generated for a motion phantom and different motion scenarios (sinusoidal motion of different period/direction; regular/irregular motion). 4D VMAT dose simulation results and dose measurements were compared by local 3% / 3 mm γ-evaluation, with the measured dose distributions serving as ground truth. Overall γ-passing rates of simulations and dynamic measurements ranged from 97% to 100% (mean across all motion scenarios: 98% ± 1%); corresponding values for comparison of different day repeat measurements were between 98% and 100%. Parameters of major influence on 4D VMAT dose simulation accuracy were the degree of temporal discretization of the dose delivery process (the higher, the better) and correct alignment of the assumed breathing phases at the beginning of the dose measurements and simulations. Given the high γ-passing rates between simulated motion-affected doses and dynamic measurements, we consider the simulations to provide a reliable basis for assessment of VMAT motion effects that–in the sense of 4D QA of VMAT treatment plans–allows to verify target coverage in hypofractioned VMAT-based radiotherapy of moving targets. Remaining differences between measurements and simulations motivate, however, further detailed studies. [ABSTRACT FROM AUTHOR]

Purpose: To develop and validate fully automated generation of VMAT plan-libraries for plan-of-the-day adaptive radiotherapy in locally-advanced cervical cancer. Material and Methods: Our framework for fully automated treatment plan generation (Erasmus-iCycle) was adapted to create dual-arc VMAT treatment plan libraries for cervical cancer patients. For each of 34 patients, automatically generated VMAT plans (autoVMAT) were compared to manually generated, clinically delivered 9-beam IMRT plans (CLINICAL), and to dual-arc VMAT plans generated manually by an expert planner (manVMAT). Furthermore, all plans were benchmarked against 20-beam equi-angular IMRT plans (autoIMRT). For all plans, a PTV coverage of 99.5% by at least 95% of the prescribed dose (46 Gy) had the highest planning priority, followed by minimization of V45Gy for small bowel (SB). Other OARs considered were bladder, rectum, and sigmoid. Results: All plans had a highly similar PTV coverage, within the clinical constraints (above). After plan normalizations for exactly equal median PTV doses in corresponding plans, all evaluated OAR parameters in autoVMAT plans were on average lower than in the CLINICAL plans with an average reduction in SB V45Gy of 34.6% (p<0.001). For 41/44 autoVMAT plans, SB V45Gy was lower than for manVMAT (p<0.001, average reduction 30.3%), while SB V15Gy increased by 2.3% (p = 0.011). AutoIMRT reduced SB V45Gy by another 2.7% compared to autoVMAT, while also resulting in a 9.0% reduction in SB V15Gy (p<0.001), but with a prolonged delivery time. Differences between manVMAT and autoVMAT in bladder, rectal and sigmoid doses were ≤ 1%. Improvements in SB dose delivery with autoVMAT instead of manVMAT were higher for empty bladder PTVs compared to full bladder PTVs, due to differences in concavity of the PTVs. Conclusions: Quality of automatically generated VMAT plans was superior to manually generated plans. Automatic VMAT plan generation for cervical cancer has been implemented in our clinical routine. Due to the achieved workload reduction, extension of plan libraries has become feasible. [ABSTRACT FROM AUTHOR]

Background: Proton beam therapy (PBT) achieves good local control for hepatocellular carcinoma (HCC), and toxicity tends to be lower than for photon radiotherapy. Focal liver parenchymal damage in radiotherapy is described as the focal liver reaction (FLR); the threshold doses (TDs) for FLR in the background liver have been analyzed in stereotactic ablative body radiotherapy and brachytherapy. To develop a safer approach for PBT, both TD and liver volume changes are considered clinically important in predicting the extent of damage before treatment, and subsequently in reducing background liver damage. We investigated appearance time, TDs and volume changes regarding FLR after PBT for HCC. Material and Methods: Patients who were treated using PBT and were followed up using gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid-enhanced magnetic resonance imaging (Gd-EOB-DTPA MRI) after PBT were enrolled. Sixty-eight lesions in 58 patients were eligible for analysis. MRI was acquired at the end of treatment, and at 1, 2, 3 and 6 months after PBT. We defined the FLR as a clearly depicted hypointense area on the hepatobiliary phase of Gd-EOB-DTPA MRI, and we monitored TDs and volume changes in the FLR area and the residual liver outside of the FLR area. Results: FLR was depicted in all lesions at 3 months after PBT. In FLR expressed as the 2-Gy equivalent dose (α/β = 3 Gy), TDs did not differ significantly (27.0±6.4 CGE [10 fractions [Fr] vs. 30.5±7.3 CGE [20 Fr]). There were also no correlations between the TDs and clinical factors, and no significant differences between Child-Pugh A and B scores. The volume of the FLR area decreased and the residual liver volume increased, particularly during the initial 3 months. Conclusion: This study established the FLR dose for liver with HCC, which might be useful in the prediction of remnant liver volume for PBT. [ABSTRACT FROM AUTHOR]

Objective: The study was designed to evaluate diffusion-weighted magnetic resonance imaging (DWI) vs. PET-CT of the thorax in the determination of gross tumor volume (GTV) in radiotherapy planning of non-small-cell lung cancer (NSCLC). Materials and Methods: Eligible patients with NSCLC who were supposed to receive definitive radio(chemo)therapy were prospectively recruited. For MRI, a respiratory gated T2-weighted sequence in axial orientation and non-gated DWI (b = 0, 800, 1,400 and apparent diffusion coefficient map [ADC]) were acquired on a 1.5 Tesla scanner. Primary tumors were delineated on FDG-PET/CT (stGTV) and DWI images (dwGTV). The definition of stGTV was based on the CT and visually adapted to the FDG-PET component if indicated (e.g., in atelectasis). For DWI, dwGTV was visually determined and adjusted for anatomical plausibility on T2w sequences. Beside a statistical comparison of stGTV and dwGTB, spatial agreement was determined with the “Hausdorff-Distance” (HD) and the “Dice Similarity Coefficient” (DSC). Results: Fifteen patients (one patient with two synchronous NSCLC) were evaluated. For 16 primary tumors with UICC stages I (n = 4), II (n = 3), IIIA (n = 2) and IIIB (n = 7) mean values for dwGTV were significantly larger than those of stGTV (76.6 ± 84.5 ml vs. 66.6 ± 75.2 ml, p<0.01). The correlation of stGTV and dwGTV was highly significant (r = 0.995, p<0.001). Yet, some considerable volume deviations between these two methods were observed (median 27.5%, range 0.4–52.1%). An acceptable agreement between dwGTV and stGTV regarding the spatial extent of primary tumors was found (average HD: 2.25 ± 0.7 mm; DC 0.68 ± 0.09). Conclusion: The overall level of agreement between PET-CT and MRI based GTV definition is acceptable. Tumor volumes may differ considerably in single cases. DWI-derived GTVs are significantly, yet modestly, larger than their PET-CT based counterparts. Prospective studies to assess the safety and efficacy of DWI-based radiotherapy planning in NSCLC are warranted. [ABSTRACT FROM AUTHOR]

Introduction: For accurate dose calculations, it is necessary to provide a correct relationship between the CT numbers and electron density in radiotherapy treatment planning systems (TPSs). The purpose of this study was to investigate the energy dependence of measured CT numbers on substituted materials used for CT number calibration of radiotherapy TPSs and the resulting errors in the treatment planning calculation doses. Materials and Methods: In this study, we designed a cylindrical water phantom with different materials used as tissue equivalent materials for the simulation of tissues and obtaining the related CT numbers. For evaluating the effect of CT number variations of substituted materials due to energy changing of scanner (kVp) on the dose calculation of TPS, the slices of the scanned phantom at three kVp's were imported into the desired TPSs (MIRS and CorePLAN). Dose calculations were performed on two TPSs. Results: The mean absolute percentage differences between the CT numbers of CT scanner and two treatment planning systems for all the samples were 3.22%±2.57% for CorePLAN and 2.88%±2.11% for MIRS. It was also found that the maximum absolute percentage difference between all of the calculated doses from each photon beam of linac (6 and 15 MV) at three kVp's was less than 1.2%. Discussion: The present study revealed that, for the materials with effective low atomic number, the mean CT number increased with increasing energy, which was opposite for the materials with an effective high atomic number. We concluded that the tissue substitute materials had a different behavior in the energy ranges from 80 to 130 kVp. So, it is necessary to consider the energy dependence of the substitute materials used for the measurement or calibration of CT number for radiotherapy treatment planning systems. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to improve the image quality of cone-beam computed tomography (CBCT) mounted on the gantry of a linear accelerator used in radiation therapy based on the image information provided by planning multi-detector CT (MDCT). Methods: MDCT-based shading correction for CBCT and virtual monochromatic CT (VMCT) synthesized using the dual-energy method were performed. In VMCT, the high-energy data were obtained from CBCT, while the low-energy data were obtained from MDCT. An electron density phantom was used to investigate the efficacy of shading correction and VMCT on improving the target detectability, Hounsfield unit (HU) accuracy and variation, which were quantified by calculating the contrast-to-noise ratio (CNR), the percent difference (%Diff) and the standard deviation of the CT numbers for tissue equivalent background material, respectively. Treatment plan studies for a chest phantom were conducted to investigate the effects of image quality improvement on dose planning. Results: For the electron density phantom, the mean value of CNR was 17.84, 26.78 and 34.31 in CBCT, shading-corrected CBCT and VMCT, respectively. The mean value of %Diff was 152.67%, 11.93% and 7.66% in CBCT, shading-corrected CBCT and VMCT, respectively. The standard deviation within a uniform background of CBCT, shading-corrected CBCT and VMCT was 85, 23 and 15 HU, respectively. With regards to the chest phantom, the monitor unit (MU) difference between the treatment plan calculated using MDCT and those based on CBCT, shading corrected CBCT and VMCT was 6.32%, 1.05% and 0.94%, respectively. Conclusions: Enhancement of image quality in on-board CBCT can contribute to daily patient setup and adaptive dose delivery, thus enabling higher confidence in patient treatment accuracy in radiation therapy. Based on our results, VMCT has the highest image quality, followed by the shading corrected CBCT and the original CBCT. The research results presented in this study should be able to provide a route to reach a high level of image quality for CBCT imaging in radiation oncology. [ABSTRACT FROM AUTHOR]

Purpose: To study the dosimetric difference between fixed-jaw volumetric modulated radiotherapy (FJ-VMAT) and large-field volumetric modulated radiotherapy (LF-VMAT) for nasopharyngeal carcinoma (NPC) with cervical lymph node metastasis. Methods: Computed tomography (CT) datasets of 10 NPC patients undergoing chemoradiotherapy were used to generate LF-VMAT and FJ-VMAT plans in the Eclipse version 10.0 treatment planning system. These two kinds of plans were then compared with respect to planning-target-volume (PTV) coverage, conformity index (CI), homogeneity index (HI), organ-at-risk sparing, monitor units (MUs) and treatment time (TT). Results: The FJ-VMAT plans provided lower D2% of PGTVnd (PTV of lymph nodes), PTV1 (high-risk PTV) and PTV2 (low-risk PTV) than did the LF-VMAT plans, whereas no significant differences were observed in PGTVnx (PTV of primary nasopharyngeal tumor). The FJ-VMAT plans provided lower doses delivered to the planning organ at risk (OAR) volumes (PRVs) of both brainstem and spinal cord, both parotid glands and normal tissue than did the LF-VMAT plans, whereas no significant differences were observed with respect to the oral cavity and larynx. The MUs of the FJ-VMAT plans (683 ± 87) were increased by 22% ± 12% compared with the LF-VMAT plans (559 ± 62). In terms of the TT, no significant difference was found between the two kinds of plans. Conclusions: FJ-VMAT was similar or slightly superior to LF-VMAT in terms of PTV coverage and was significantly superior in terms of OAR sparing, at the expense of increased MUs. [ABSTRACT FROM AUTHOR]

Purpose: Lung cancer screening with CT has been recently recommended for decreasing lung cancer mortality. The radiation dose of CT, however, must be kept as low as reasonably achievable for reducing potential stochastic risks from ionizing radiation. The purpose of this study was to calculate individual patients’ lung doses and to estimate cancer risks in low-dose CT (LDCT) in comparison with a standard dose CT (SDCT) protocol. Materials and Methods: This study included 47 adult patients (mean age 63.0 ± 5.7 years) undergoing chest CT on a third-generation dual-source scanner. 23/47 patients (49%) had a non-enhanced chest SDCT, 24 patients (51%) underwent LDCT at 100 kVp with spectral shaping at a dose equivalent to a chest x-ray. 3D-dose distributions were obtained from Monte Carlo simulations for each patient, taking into account their body size and individual CT protocol. Based on the dose distributions, patient-specific lung doses were calculated and relative cancer risk was estimated according to BEIR VII recommendations. Results: As compared to SDCT, the LDCT protocol allowed for significant organ dose and cancer risk reductions (p<0.001). On average, lung dose was reduced from 7.7 mGy to 0.3 mGy when using LDCT, which was associated with lowering of the cancer risk from 8.6 to 0.35 per 100’000 cases. A strong linear correlation between lung dose and patient effective diameter was found for both protocols (R2 = 0.72 and R2 = 0.75 for SDCT and LDCT, respectively). Conclusion: Use of a LDCT protocol for chest CT with a dose equivalent to a chest x-ray allows for significant lung dose and cancer risk reduction from ionizing radiation. [ABSTRACT FROM AUTHOR]

Polymer gel dosimeters have been proven useful for dose evaluation in radiotherapy treatments. Previous studies have demonstrated that using a polymer gel dosimeter requires a 24 h reaction time to stabilize and further evaluate the measured dose distribution in two-dimensional dosimetry. In this study, the short-term stability within 24 h and feasibility of N-isopropylacrylamide (NIPAM) polymer gel dosimeters for use in three-dimensional dosimetry were evaluated using magnetic resonance imaging (MRI). NIPAM gels were used to measure the dose volume in a clinical case of intensity-modulated radiation therapy (IMRT). For dose readouts, MR images of irradiated NIPAM gel phantoms were acquired at 2, 5, 12, and 24 h after dose delivery. The mean standard errors of dose conversion from using dose calibration curves (DRC) were calculated. The measured dose volumes at the four time points were compared with those calculated using a treatment planning system (TPS). The mean standard errors of the dose conversion from using the DRCs were lower than 1 Gy. Mean pass rates of 2, 5, 12, and 24 h axial dose maps calculated using gamma evaluation with 3% dose difference and 3 mm distance-to-agreement criteria were 83.5% ± 0.9%, 85.9% ± 0.6%, 98.7% ± 0.3%, and 98.5% ± 0.9%, respectively. Compared with the dose volume histogram of the TPS, the absolute mean relative volume differences of the 2, 5, 12, and 24 h measured dose volumes were lower than 1% for the irradiated region with an absorbed dose higher than 2.8 Gy. It was concluded that a 12 h reaction time was sufficient to acquire accurate dose volume using the NIPAM gels with MR readouts. [ABSTRACT FROM AUTHOR]

In recent years, there has been increased interest in low-dose X-ray cone beam computed tomography (CBCT) in many fields, including dentistry, guided radiotherapy and small animal imaging. Despite reducing the radiation dose, low-dose CBCT has not gained widespread acceptance in routine clinical practice. In addition to performing more evaluation studies, developing a fast and high-quality reconstruction algorithm is required. In this work, we propose an iterative reconstruction method that accelerates ordered-subsets (OS) reconstruction using a power factor. Furthermore, we combine it with the total-variation (TV) minimization method. Both simulation and phantom studies were conducted to evaluate the performance of the proposed method. Results show that the proposed method can accelerate conventional OS methods, greatly increase the convergence speed in early iterations. Moreover, applying the TV minimization to the power acceleration scheme can further improve the image quality while preserving the fast convergence rate. [ABSTRACT FROM AUTHOR]

This study evaluated the setup uncertainties for brain sites when using BrainLAB’s ExacTrac X-ray 6D system for daily pretreatment to determine the optimal planning target volume (PTV) margin. Between August 2012 and April 2015, 28 patients with brain tumors were treated by daily image-guided radiotherapy using the BrainLAB ExacTrac 6D image guidance system of the Novalis-Tx linear accelerator. DUONTM (Orfit Industries, Wijnegem, Belgium) masks were used to fix the head. The radiotherapy was fractionated into 27–33 treatments. In total, 844 image verifications were performed for 28 patients and used for the analysis. The setup corrections along with the systematic and random errors were analyzed for six degrees of freedom in the translational (lateral, longitudinal, and vertical) and rotational (pitch, roll, and yaw) dimensions. Optimal PTV margins were calculated based on van Herk et al.’s [margin recipe = 2.5∑ + 0.7σ - 3 mm] and Stroom et al.’s [margin recipe = 2∑ + 0.7σ] formulas. The systematic errors (∑) were 0.72, 1.57, and 0.97 mm in the lateral, longitudinal, and vertical translational dimensions, respectively, and 0.72°, 0.87°, and 0.83° in the pitch, roll, and yaw rotational dimensions, respectively. The random errors (σ) were 0.31, 0.46, and 0.54 mm in the lateral, longitudinal, and vertical rotational dimensions, respectively, and 0.28°, 0.24°, and 0.31° in the pitch, roll, and yaw rotational dimensions, respectively. According to van Herk et al.’s and Stroom et al.’s recipes, the recommended lateral PTV margins were 0.97 and 1.66 mm, respectively; the longitudinal margins were 1.26 and 3.47 mm, respectively; and the vertical margins were 0.21 and 2.31 mm, respectively. Therefore, daily setup verifications using the BrainLAB ExacTrac 6D image guide system are very useful for evaluating the setup uncertainties and determining the setup margin. [ABSTRACT FROM AUTHOR]

Background: Inter-observer delineation variation has been detailed for many years in almost every tumor location. Inadequate delineation can impair the chance of cure and/or increase toxicity. The aim of our original work was to prospectively improve the homogeneity of delineation among all of the senior radiation oncologists in the Nord-Pas de Calais region, irrespective of the conditions of practice. Methods: All 11 centers were involved. The first studied cancer was prostate cancer. Three clinical cases were studied: a low-risk prostate cancer case (case 1), a high-risk prostate cancer case (pelvic nodes, case 2) and a case of post-operative biochemical elevated PSA (case 3). All of the involved physicians delineated characteristically the clinical target volume (CTV) and organs at risk. The volumes were compared using validated indexes: the volume ratio (VR), common and additional volumes (CV and AV), volume overlap (VO) and Dice similarity coefficient (DSC). A second delineation of the same three cases was performed after discussion of the slice results and the choice of shared guidelines to evaluate homogenization. A comparative analysis of the indexes before and after discussion was conducted using the Wilcoxon test for paired samples. A p-value less than 0.05 was considered to indicate statistical significance. Results: The indexes were not improved in case 1, for which the inter-observer agreement was considered good after the first comparison (DSC = 0.83±0.06). In case 2, the second comparison showed homogenization of the CTV delineation with a significant improvement in CV (81.4±11.7 vs. 88.6±10.26, respectively, p = 0.048), VO (0.41±0.09 vs. 0.47±0.07, respectively; p = 0.009) and DSC (0.58±0.09 vs. 0.63±0.07, respectively; p = 0.0098). In case 3, VR and AV were significantly improved: VR: 1.71(±0.6) vs. 1.34(±0.46), respectively, p = 0.0034; AV: 46.58(±14.50) vs. 38.08(±15.10), respectively, p = 0.0024. DSC was not improved, but it was already superior to 0.6 in the first comparison. Conclusion: Our prospective work showed that a collaborative discussion about clinical cases and the choice of shared guidelines within an established framework improved the homogeneity of CTV delineation among the senior radiation oncologists in our region. [ABSTRACT FROM AUTHOR]

With advances in therapeutic instruments and techniques, three-dimensional dose delivery has been widely used in radiotherapy. The verification of dose distribution in a small field becomes critical because of the obvious dose gradient within the field. The study investigates the dose distributions of various field sizes by using NIPAM polymer gel dosimeter. The dosimeter consists of 5% gelatin, 5% monomers, 3% cross linkers, and 5 mM THPC. After irradiation, a 24 to 96 hour delay was applied, and the gel dosimeters were read by a cone beam optical computed tomography (optical CT) scanner. The dose distributions measured by the NIPAM gel dosimeter were compared to the outputs of the treatment planning system using gamma evaluation. For the criteria of 3%/3 mm, the pass rates for 5 × 5, 3 × 3, 2 × 2, 1 × 1, and 0.5 × 0.5 cm2 were as high as 91.7%, 90.7%, 88.2%, 74.8%, and 37.3%, respectively. For the criteria of 5%/5 mm, the gamma pass rates of the 5 × 5, 3 × 3, and 2 × 2 cm2 fields were over 99%. The NIPAM gel dosimeter provides high chemical stability. With cone-beam optical CT readouts, the NIPAM polymer gel dosimeter has potential for clinical dose verification of small-field irradiation. [ABSTRACT FROM AUTHOR]

Introduction: To quantify interfraction patient setup-errors for radiotherapy based on cone-beam computed tomography and suggest safety margins accordingly. Material and Methods: Positioning vectors of pre-treatment cone-beam computed tomography for different treatment sites were collected (n = 9504). For each patient group the total average and standard deviation were calculated and the overall mean, systematic and random errors as well as safety margins were determined. Results: The systematic (and random errors) in the superior-inferior, left-right and anterior-posterior directions were: for prostate, 2.5(3.0), 2.6(3.9) and 2.9(3.9)mm; for prostate bed, 1.7(2.0), 2.2(3.6) and 2.6(3.1)mm; for cervix, 2.8(3.4), 2.3(4.6) and 3.2(3.9)mm; for rectum, 1.6(3.1), 2.1(2.9) and 2.5(3.8)mm; for anal, 1.7(3.7), 2.1(5.1) and 2.5(4.8)mm; for head and neck, 1.9(2.3), 1.4(2.0) and 1.7(2.2)mm; for brain, 1.0(1.5), 1.1(1.4) and 1.0(1.1)mm; and for mediastinum, 3.3(4.6), 2.6(3.7) and 3.5(4.0)mm. The CTV-to-PTV margins had the smallest value for brain (3.6, 3.7 and 3.3mm) and the largest for mediastinum (11.5, 9.1 and 11.6mm). For pelvic treatments the means (and standard deviations) were 7.3 (1.6), 8.5 (0.8) and 9.6 (0.8)mm. Conclusions: Systematic and random setup-errors were smaller than 5mm. The largest errors were found for organs with higher motion probability. The suggested safety margins were comparable to published values in previous but often smaller studies. [ABSTRACT FROM AUTHOR]