Characterization of imaging performance of a novel helical kVCT for use in image-guided and adaptive radiotherapy.

ClearRT helical kVCT imaging for the Radixact helical tomotherapy system recently received FDA approval and is available for clinical use. The system is intended to enhance image fidelity in radiation therapy treatment planning and delivery compared to the prior MV-based onboard imaging approach. The purpose of this work was to characterize the imaging performance of this system and compare this performance with that of clinical systems used in image-guided and/or adaptive radiotherapy (ART) or computed tomography (CT) simulation, including Radixact MVCT, TomoTherapy MVCT, Varian TrueBeam kV OBI CBCT, and the Siemens SOMATOM Definition Edge kVCT. A CT image quality phantom was scanned across clinically relevant acquisition modes for each system to evaluate image quality metrics, including noise, uniformity, contrast, spatial resolution, and CT number linearity. Similar noise levels were observed for ClearRT and Siemens Edge, whereas noise for the other systems was ∼1.5-5 times higher. Uniformity was best for Siemens Edge, whereas most scans for ClearRT exhibited a slight "cupping" or "capping" artifact. The ClearRT and Siemens Edge performed best for contrast metrics, which included low-contrast visibility and contrast-to-noise ratio evaluations. Spatial resolution was best for TrueBeam and Siemens Edge, whereas the three kVCT systems exhibited similar CT number linearity. Overall, these results provide an initial indication that ClearRT image quality is adequate for image guidance in radiotherapy and sufficient for delineating anatomic structures, thus enabling its use for ART. ClearRT also showed significant improvement over MVCT, which was previously the only onboard imaging modality available on Radixact. Although the acquisition of these scans does come at the cost of additional patient dose, reported CTDI values indicate a similar or generally reduced machine output for ClearRT compared to the other systems while maintaining comparable or improved image quality overall.

New target volume delineation and PTV strategies to further personalise radiotherapy.

Target volume delineation uncertainty (DU) is arguably one of the largest geometric uncertainties in radiotherapy that are accounted for using planning target volume (PTV) margins. Geometrical uncertainties are typically derived from a limited sample of patients. Consequently, the resultant margins are not tailored to individual patients. Furthermore, standard PTVs cannot account for arbitrary anisotropic extensions of the target volume originating from DU. We address these limitations by developing a method to measure DU for each patient by a single clinician. This information is then used to produce PTVs that account for each patient's unique DU, including any required anisotropic component. We do so using a two-step uncertainty evaluation strategy that does not rely on multiple samples of data to capture the DU of a patient's gross tumour volume (GTV) or clinical target volume. For simplicity, we will just refer to the GTV in the following. First, the clinician delineates two contour sets; one which bounds all voxels believed to have a probability of belonging to the GTV of 1, while the second includes all voxels with a probability greater than 0. Next, one specifies a probability density function for the true GTV boundary position within the boundaries of the two contours. Finally, a patient-specific PTV, designed to account for all systematic errors, is created using this information along with measurements of the other systematic errors. Clinical examples indicate that our margin strategy can produce significantly smaller PTVs than the van Herk margin recipe. Our new radiotherapy target delineation concept allows DUs to be quantified by the clinician for each patient, leading to PTV margins that are tailored to each unique patient, thus paving the way to a greater personalisation of radiotherapy.

Selective Internal Radiation Therapy for Hepatocellular Carcinoma Across the Barcelona Clinic Liver Cancer Stages.

BACKGROUND: Hepatocellular carcinoma (HCC) is the second most common lethal cancer, and there is a need for effective therapies. Selective internal radiation therapy (SIRT) has been increasingly used, but is not supported by guidelines due to a lack of solid evidence. AIMS: Determine the efficacy and safety of SIRT in HCC across the Barcelona Clinic Liver Cancer (BCLC) stages A, B, and C. METHODS: Consecutive patients that received SIRT between 2006 and 2016 at two centers in Canada were evaluated. RESULTS: We analyzed 132 patients, 12 (9%), 62 (47%), and 58 (44%) belonged to BCLC stages A, B, and C; mean age was 61.2 (SD ± 9.2), and 89% were male. Median survival was 12.4 months (95% CI 9.6-16.6), and it was different across the stages: 59.7 (95% CI NA), 12.8 (95% CI 10.2-17.5), and 9.3 months (95% CI 5.9-11.8) in BCLC A, B, and C, respectively (p = 0.009). Independent factors associated with survival were previous HCC treatment (HR 2.01, 95% CI 1.23-3.27, p = 0.005), bi-lobar disease (HR 2.25, 95% CI 1.30-3.89, p = 0.003), ascites (HR 1.77, 95% CI 0.99-3.13, p = 0.05), neutrophil-to-lymphocyte ratio (HR 1.11, 95% CI 1.02-1.20, p = 0.01), Albumin-Bilirubin (ALBI) grade-3 (HR 2.69, 95% CI 1.22-5.92, p = 0.01), tumor thrombus (HR 2.95, 95% CI 1.65-5.24, p < 0.001), and disease control rate (HR 0.62, 95% CI 0.39-0.96, p = 0.03). Forty-four (33%) patients developed severe adverse events, and ALBI-3 was associated with higher risk of these events. CONCLUSIONS: SIRT has the potential to be used across the BCLC stages in cases with preserved liver function. When using it as a rescue treatment, one should consider variables reflecting liver function, HCC extension, and systemic inflammation, which are associated with mortality.

Selective internal radiation therapy of hepatic tumors: Morphologic and functional imaging for voxel-based computer-aided dosimetry.

INTRODUCTION: Selective Internal Radiation Therapy (SIRT) is used for the treatment of hepatic tumors. The aim of this retrospective study was to compare two dosimetric approaches based on 99mTc-MAA SPECT/CT and 90Y PET/CT, using Simplicit90Y™ versus the supplier suggested method of activity calculation. MATERIAL AND METHODS: A total of 19 patients underwent 21 SIRT after baseline angiography and 99mTc-MAA SPECT/CT, followed by 90Y PET/CT. Overlap between 99mTc-MAA and 90Y-microspheres was quantified with different thresholds isocontours. The perfused volume and tumor absorbed dose were estimated using Simplicit90Y™ based on SPECT/CT and PET/CT, then compared with the supplier suggested method. These data were related to overall survival to evaluate their prognostic impact. RESULTS: The overlap between PET/CT and SPECT/CT was dependent on thresholds, decreasing with an increasing threshold. The overlap between the 99mTc-MAA and 90Y-microspheres biodistributions versus the tumor distribution on morphological imaging was suboptimal, in particular for small tumor volume. The tumor absorbed dose estimated after 90Y PET/CT was not different from tumor absorbed dose estimated after SPECT/CT. The Perfused lobe absorbed dose was significantly lower while the volume of the perfused lobe was significantly higher when estimated by Simplicit90Y™ compared to the supplier suggested conventional approach. A statistical parameter based on overlap between tumor and 90Y-microspheres distribution as well as tumoral dosimetry was significantly related to the overall survival. CONCLUSION: Post-treatment imaging remains paramount to estimate the irradiation dosimetry, due to an imperfect overlap. The perfused volume could be estimated from functional imaging, given its impact on dosimetry. Finally, survival seems related to tumoral overlap and dosimetry.

Feasibility of Multiparametric Positron Emission Tomography/Magnetic Resonance Imaging as a One-Stop Shop for Radiation Therapy Planning for Patients with Head and Neck Cancer.

PURPOSE: Multiparametric positron emission tomography (PET)/magnetic resonance imaging (MRI) as a one-stop shop for radiation therapy (RT) planning has great potential but is technically challenging. We studied the feasibility of performing multiparametric PET/MRI of patients with head and neck cancer (HNC) in RT treatment position. As a step toward planning RT based solely on PET/MRI, a deep learning approach was employed to generate synthetic computed tomography (sCT) from MRI. This was subsequently evaluated for dose calculation and PET attenuation correction (AC). METHODS AND MATERIALS: Eleven patients, including 3 pilot patients referred for RT of HNC, underwent PET/MRI in treatment position after a routine fluorodeoxyglucose-PET/CT planning scan. The PET/MRI scan protocol included multiparametric imaging. A convolutional neural network was trained in a leave-one-out process to predict sCT from the Dixon MRI. The clinical CT-based dose plans were recalculated on sCT, and the plans were compared in terms of relative differences in mean, maximum, near-maximum, and near-minimum absorbed doses for different volumes of interest. Comparisons between PET with sCT-based AC and PET with CT-based AC were assessed based on the relative differences in mean and maximum standardized uptake values (SUVmean and SUVmax) from the PET-positive volumes. RESULTS: All 11 patients underwent PET/MRI in RT treatment position. Apart from the 3 pilots, full multiparametric imaging was completed in 45 minutes for 7 out of 8 patients. One patient terminated the examination after 30 minutes. With the exception of 1 patient with an inserted tracheostomy tube, all dosimetric parameters of the sCT-based dose plans were within ±1% of the CT-based dose plans. For PET, the mean difference was 0.4 ± 1.2% for SUVmean and -0.5 ± 1.0% for SUVmax. CONCLUSIONS: Performing multiparametric PET/MRI of patients with HNC in RT treatment position was clinically feasible. The sCT generation resulted in AC of PET and dose calculations sufficiently accurate for clinical use. These results are an important step toward using multiparametric PET/MRI as a one-stop shop for personalized RT planning.

Genomics models in radiotherapy: From mechanistic to machine learning.

Machine learning (ML) provides a broad framework for addressing high-dimensional prediction problems in classification and regression. While ML is often applied for imaging problems in medical physics, there are many efforts to apply these principles to biological data toward questions of radiation biology. Here, we provide a review of radiogenomics modeling frameworks and efforts toward genomically guided radiotherapy. We first discuss medical oncology efforts to develop precision biomarkers. We next discuss similar efforts to create clinical assays for normal tissue or tumor radiosensitivity. We then discuss modeling frameworks for radiosensitivity and the evolution of ML to create predictive models for radiogenomics.

Accurate 3D-dose-based generation of MLC segments for robotic radiotherapy.

Radiotherapy treatment planning requires accurate modeling of the delivered patient dose, including radiation scatter effects, multi-leaf collimator (MLC) leaf transmission, interleaf-leakage, etc. In fluence map optimization (FMO), a simple dose model is used to first generate an intermediate plan based on pencil-beams. In a second step (segmentation phase), this intermediate plan is then converted into a deliverable treatment plan with MLC segments. In this paper, we investigate novel approaches for the use of a clinical dose engine (CDE) for segmentation of FMO plans in robotic radiotherapy. Segments are sequentially added to the plan. Generation of each next segment is based on the total 3D dose distribution, resulting from already selected segments and the desired FMO dose, considering all treatment beams as candidates for delivery of the new segment. Three versions of the segmentation algorithm were investigated with differences in the integration of the CDE. The combined use of pencil-beams and segments in a segmentation method is non-trivial. Therefore, new methods were developed for the use of segment doses calculated with the CDE in combination with pencil-beams, used for the selection of new segments. For 20 patients with prostate cancer and 12 with liver cancer, segmented plans were compared with FMO plans. All three versions of the proposed segmentation algorithm could well mimic FMO dose distributions. Segmentation with a fully integrated CDE provided the best plan quality and lowest numbers of monitor units and segments at the cost of increased calculation time.

Accurate proton treatment planning for pencil beam crossing titanium fixation implants.

PURPOSE: To present a planning strategy for proton pencil-beam scanning when titanium implants need to be crossed by the beam. METHODS: We addressed three issues: the implementation of a CT calibration curve to assign to titanium the correct stopping power; the effect of artefacts on CT images and their reduction by a dedicated algorithm; the differences in dose computation depending on the dose engine, pencil-beam vs Monte-Carlo algorithms. We performed measurement tests on a simple cylinder phantom and on a real implant. These phantoms were irradiated with three geometries (single spots, uniform mono-energetic layer and uniform box), measuring the exit dose either by radio-chromic film or multi-layer ionization chamber. The procedure was then applied on two patients treated for chordoma. RESULTS: We had to set in the calibration curve a mass density equal to 4.37 g/cm3 to saturated Hounsfield Units, in order to have the correct stopping power assigned to titanium in TPS. CT artefact reduction algorithm allowed a better reconstruction of the shape and size of the implant. Monte-Carlo resulted accurate in computing the dose distribution whereas the pencil-beam algorithm failed due to sharp density interfaces between titanium and the surrounding material. Finally, the treatment plans obtained on two patients showed the impact of the dose engine algorithm, with 10-20% differences between pencil-beam and Monte-Carlo in small regions distally to the titanium screws. CONCLUSION: The described combination of CT calibration, artefacts reduction and Monte-Carlo computation provides a reliable methodology to compute dose in patients with titanium implants.

Accuracy assessment of the CNAO dose delivery system in the initial period of clinical activity and impact of later improvements on delivered dose distributions.

PURPOSE: A retrospective analysis of the dose delivery system (DDS) performances of the initial clinical operation at CNAO (Centro Nazionale di Adroterapia Oncologica) is reported, and compared with the dose delivery accuracy following the implementation of a position feedback control. METHODS: Log files and raw data of the DDS were analyzed for every field of patients treated with protons and carbon ions between January 2012 and April 2013 (~3800 fields). To investigate the DDS accuracy, the spot positions and the number of particles per spot measured by the DDS and prescribed by the treatment planning system were compared for each field. The impact of deviations on dose distributions was studied by comparing, through the gamma-index method, 2 three-dimensional (3D) physical dose maps (one for prescribed, one for measured data), generated by a validated dose computation software. The maximum gamma and the percentage of points with gamma ≤ 1 (passing volume) were studied as a function of the treatment day, and correlated with the deviations from the prescription in the measured number of particles and spot positions. Finally, delivered dose distributions of same treatment plans were compared before and after the implementation of a feedback algorithm for the correction of small position deviations, to study the effect on the delivery quality. A double comparison of prescribed and measured 3D maps, before and after feedback implementation, is reported and studied for a representative treatment delivered in 2012, redelivered on a polymethyl methacrylate (PMMA) block in 2018. RESULTS: Systematic deviations of spot positions, mainly due to beam lateral offsets, were always found within 1.5 mm, with the exception of the initial clinical period. The number of particles was very stable, as possible deviations are exclusively related to the quantization error in the conversion from monitor counts to particles. For the chosen representative patient treatment, the gamma-index evaluation of prescribed and measured dose maps, before and after feedback implementation, showed a higher variability of maximum gamma for the 2012 irradiation, with respect to the reirradiation of 2018. However, the 2012 passing volume is >99.8% for the sum of all fields, which is comparable to the value of 2018, with the exception of one day with 98.2% passing volume, probably related to an instability of the accelerating system. CONCLUSIONS: A detailed retrospective analysis of the DDS performances in the initial period of CNAO clinical activity is reported. The spot position deviations are referable to beam lateral offset fluctuations, while almost no deviation was found in the number of particles. The impact of deviations on dose distributions showed that the position feedback implementation and the increased beam control capability acquired after the first years of clinical experience led to an evident improvement in the DDS stability, evaluated in terms of gamma-index as a measure of the impact on dose distributions. However, the clinical effect of the maximum gamma variability found in the 2012 representative irradiation is mitigated by averaging along the number of fractions, and the high percentage of passing volumes confirmed the accuracy of the delivery even before the feedback implementation.

Implementation of the structural SIMilarity (SSIM) index as a quantitative evaluation tool for dose distribution error detection.

PURPOSE: To apply an imaging metric of the structural SIMilarity (SSIM) index to the radiotherapy dose verification field and evaluate its capability to reveal the different types of errors between two dose distributions. METHOD: The SSIM index consists of three sub-indices: luminance, contrast, and structure. Given two images, luminance analysis compares the local mean result, contrast analysis compares the local standard deviation, and the structure index represents the local Pearson correlation. Three test error patterns (absolute dose error, dose gradient error, and dose structure error) were designed to characterize the response of SSIM and its sub-indices and establish the correlation between the indices and different dose error types. After establishing the correlation, four radiotherapy plans (one MLC picket-fence test plan, one brain stereotactic radiotherapy plan, and two head-and-neck plans) were tested by computing each index and compared with the gamma analysis results to determine their similarities and differences. RESULTS: Among the three test error patterns, the luminance index decreased from 1 to 0.1 when the absolute dose agreement fell from 100% to 5%, the contrast index decreased from 1 to 0.36 when the dose gradient agreement fell from 100% to 10%, and the structure index decreased from 1 to 0.23 when the periodical dose pattern shifted (leading to a lower correlation). Thus, the luminance, contrast and structure index can detect the absolute dose error, gradient discrepancy, and dose structure error, respectively. For the four clinical cases, the sub-indices can reveal the type of error when gamma analysis only provided limited information. CONCLUSIONS: The correlation between the subcomponents of the SSIM index and the error types of the dose distribution were established. The SSIM index provides additional error information compared to that provided by gamma analysis.

Many-isocenter optimization for robotic radiotherapy.

Despite significant dosimetric gains, clinical implementation of the 4π non-coplanar radiotherapy on the widely available C-arm gantry system is hindered by limited clearance, and the need to perform complex coordinated gantry and couch motion. A robotic radiotherapy platform would be conducive to such treatment but a new conflict between field size and MLC modulation resolution needs to be managed for versatile applications. This study investigates the dosimetry and delivery efficiency of purposefully creating many isocenters to achieve simultaneously high MLC modulation resolution and large tumor coverage. An integrated optimization framework was proposed for simultaneous beam orientation optimization (BOO), isocenter selection, and fluence map optimization (FMO). The framework includes a least-square dose fidelity objective, a total variation term for regularizing the fluence smoothness, and a group sparsity term for beam selection. A minimal number of isocenters were identified for efficient target coverage. Colliding beams excluded, high-resolution small-field 4π intensity-modulated radiotherapy (IMRT) treatment plans with 50 cm source-to-isocenter distance (SID-50) on 10 Head and Neck (H&N) cancer patients were compared with low-resolution large-field plans with 100 cm SID (SID-100). With the same or better target coverage, the average reduction of [Dmean, Dmax] of 20-beam SID-50 plans from 20-beam SID-100 plans were [2.09 Gy, 1.19 Gy] for organs at risk (OARs) overall, [3.05 Gy, 0.04 Gy] for parotid gland, [3.62 Gy, 5.19 Gy] for larynx, and [3.27 Gy, 1.10 Gy] for mandible. R50 and integral dose were reduced by 5.3% and 9.6%, respectively. Wilcoxon signed-rank test showed significant difference (p  < 0.05) in planning target volume (PTV) homogeneity, PTV Dmax, R50, Integral dose, and OAR Dmean and Dmax. The estimated delivery time of 20-beam [SID-50, SID-100] plans were [19, 18] min and [14, 9] min, assuming 5 fractions and 30 fractions, respectively. With clinically acceptable delivery efficiency, many-isocenter optimization is dosimetrically desirable for treating large targets with high modulation resolution on the robotic platform.

Performance comparison of prediction filters for respiratory motion tracking in radiotherapy.

PURPOSE: In precision radiotherapy, the intrafractional motion causes substantial uncertainty. Traditionally, the target volume is expanded to cover the tumor in all positions. Alternative approaches are gating and adaptive tracking, which require a time delay as small as possible between the actual tumor motion and the reaction to effectively compensate the motion. Current treatment machines often exhibit large time delays. Prediction filters offer a promising means to mitigate these time delays by predicting the future respiratory motion. METHODS: A total of 18 prediction filters were implemented and their hyperparameters optimized for various time delays and noise levels. A set of 93 traces were standardized to a sampling frequency of 25 Hz and smoothed using the Fourier transform with a 3 Hz cutoff frequency. The hyperparameter optimization was carried out with ten traces, and the optimal hyperparameters were evaluated on the remaining 83 traces. RESULTS: For smooth traces, the wavelet least mean squares prediction filter and the linear filter reached normalized root mean square errors of below 0.05 for time delays of 160 and 480 ms, respectively. For noisy signals, the performance of the prediction filters deteriorated and led to similar results. CONCLUSIONS: Linear methods for prediction filters are sufficient for respiratory motion signals. Reducing the measurement noise generally improves the performance of the prediction filters investigated in this study, even during breathing irregularities.

First experimental measurement of the effect of cardio-synchronous brain motion on the dose distribution during microbeam radiation therapy.

PURPOSE: Microbeam radiation therapy (MRT) is an emerging radiation oncology modality ideal for treating inoperable brain tumors. MRT employs quasi-parallel beams of low-energy x rays produced from modern synchrotrons. A tungsten carbide multislit collimator (MSC) spatially fractionates the broad beam into rectangular beams. In this study, the MSC creates beams 50 µm wide ("peaks") separated by a center-to-center distance of 400 µm ("valleys"). The peak to valley dose ratio (PVDR) is of critical importance to the efficacy of MRT. The underlying radiobiological advantage of MRT relies on high peak dose for tumor control and low valley dose for healthy tissue sparing. Cardio synchronous brain motion of the order 100-200 µm is comparable to microbeam width and spacing. The motion can have a detrimental effect on the PVDR, full width at half maximum (FWHM) of the microbeams, and ultimately the dose distribution. We present the first experimental measurement of the effect of brain motion on MRT dose distribution. Dosimetry in MRT is difficult due to the high dose rate (up to 15-20 kGy/s) and small field sizes. METHODS: A real-time dosimetry system based on a single silicon strip detector (SSSD) has been developed with spatial resolution ~10 µm. The SSSD was placed in a water-equivalent phantom and scanned through the microbeam distribution. A monodirectional positioning stage reproduced brain motion during the acquisition. Microbeam profiles were reconstructed from the SSSD and compared with Geant4 simulation and radiochromic HD-V2 film. RESULTS: The SSSD is able to reconstruct dose profiles within 2 µm compared to film. When brain motion is applied the SSSD shows a two time increase in FWHM of profiles and 50% reduction in PVDR. This is confirmed by Geant4 and film data. CONCLUSIONS: Motion-induced misalignment and distortion of microbeams at treatment delivery will result in a reduced PVDR and increased irradiation of additional healthy tissue compromising the radiobiological effectiveness of MRT. The SSSD was able to reconstruct dose profiles under motion conditions and predict similar effects on FWHM and PVDR as by the simulation. The SSSD is a simple to setup, real-time detector which can provide time-resolved high spatial resolution dosimetry of microbeams in MRT.

Development of a robot-assisted ultrasound-guided radiation therapy (USgRT).

PURPOSE: Radiation treatment is improved by the use of image-guided workflows. This work pursues the approach of using ultrasound (US) as a real-time imaging modality. The primary focus of this study is to develop and test a breathing and motion control for a robotic-guided US transducer. All control functions of the robot and the US image processing were then integrated into one software platform enabling US-guided radiation therapy. METHODS: The robot (KUKA LBR iiwa 7 R800) and the US image processing workflows were integrated into the Medical Interaction Toolkit (MITK) (Nolden et al. in Int J Comput Assist Radiol Surg 8(4):607-620, 2013). The positions of the US probe were tracked with an optical tracking system. As a main function of robot positioning control, a highly sensitive breathing and motion compensation method was developed using KUKA's robotic application programming interface. The resulting autonomous robot motions were tested by the use of defined breathing patterns with two volunteers. Furthermore, a filter pipeline for 3D US image processing with MITK was developed. Thus, image registration of US images and previously acquired planning image data was enabled. RESULTS: The implemented breathing and motion compensation feature was successful with the addition of the remote rotating, translating capability of the US probe. Desired force applied to the US probe, and thus to the patient, is stable and enables a continuous US imaging. The developed filter pipeline for image processing facilitates registration and display of planning data and US image data in one graphical user interface. CONCLUSION: A stable and robust method for motion compensation for robot-assisted US imaging was developed and tested successfully. This is a first step toward the safe use of autonomous robot motions in interaction with patients. Furthermore, main software components were integrated into a single platform and used with the purpose of ultrasound-guided radiation therapy.

Validation of a Monte Carlo simulation for Microbeam Radiation Therapy on the Imaging and Medical Beamline at the Australian Synchrotron.

Microbeam Radiation Therapy (MRT) is an emerging cancer treatment modality characterised by the use of high-intensity synchrotron-generated x-rays, spatially fractionated by a multi-slit collimator (MSC), to ablate target tumours. The implementation of an accurate treatment planning system, coupled with simulation tools that allow for independent verification of calculated dose distributions are required to ensure optimal treatment outcomes via reliable dose delivery. In this article we present data from the first Geant4 Monte Carlo radiation transport model of the Imaging and Medical Beamline at the Australian Synchrotron. We have developed the model for use as an independent verification tool for experiments in one of three MRT delivery rooms and therefore compare simulation results with equivalent experimental data. The normalised x-ray spectra produced by the Geant4 model and a previously validated analytical model, SPEC, showed very good agreement using wiggler magnetic field strengths of 2 and 3 T. However, the validity of absolute photon flux at the plane of the Phase Space File (PSF) for a fixed number of simulated electrons was unable to be established. This work shows a possible limitation of the G4SynchrotronRadiation process to model synchrotron radiation when using a variable magnetic field. To account for this limitation, experimentally derived normalisation factors for each wiggler field strength determined under reference conditions were implemented. Experimentally measured broadbeam and microbeam dose distributions within a Gammex RMI457 Solid Water® phantom were compared to simulated distributions generated by the Geant4 model. Simulated and measured broadbeam dose distributions agreed within 3% for all investigated configurations and measured depths. Agreement between the simulated and measured microbeam dose distributions agreed within 5% for all investigated configurations and measured depths.

A Super-Learner Model for Tumor Motion Prediction and Management in Radiation Therapy: Development and Feasibility Evaluation.

In cancer radiation therapy, large tumor motion due to respiration can lead to uncertainties in tumor target delineation and treatment delivery, thus making active motion management an essential step in thoracic and abdominal tumor treatment. In current practice, patients with tumor motion may be required to receive two sets of CT scans - the initial free-breathing 4-dimensional CT (4DCT) scan for tumor motion estimation and a second CT scan under appropriate motion management such as breath-hold or abdominal compression. The aim of this study is to assess the feasibility of a predictive model for tumor motion estimation in three-dimensional space based on machine learning algorithms. The model was developed based on sixteen imaging features extracted from non-4D diagnostic CT images and eleven clinical features extracted from the Electronic Health Record (EHR) database of 150 patients to characterize the lung tumor motion. A super-learner model was trained to combine four base machine learning models including the Random Forest, Multi-Layer Perceptron, LightGBM and XGBoost, the hyper-parameters of which were also optimized to obtain the best performance. The outputs of the super-learner model consist of tumor motion predictions in the Superior-Inferior (SI), Anterior-Posterior (AP) and Left-Right (LR) directions, and were compared against tumor motions measured in the free-breathing 4DCT scans. The accuracy of predictions was evaluated using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) through ten rounds of independent tests. The MAE and RMSE of predictions in the SI direction were 1.23 mm and 1.70 mm; the MAE and RMSE of predictions in the AP direction were 0.81 mm and 1.19 mm, and the MAE and RMSE of predictions in the LR direction were 0.70 mm and 0.95 mm. In addition, the relative feature importance analysis demonstrated that the imaging features are of great importance in the tumor motion prediction compared to the clinical features. Our findings indicate that a super-learner model can accurately predict tumor motion ranges as measured in the 4DCT, and could provide a machine learning framework to assist radiation oncologists in determining the active motion management strategy for patients with large tumor motion.

Accounting for respiratory motion in small serial structures during radiotherapy planning: proof of concept in virtual bronchoscopy-guided lung functional avoidance radiotherapy.

Respiratory motion management techniques in radiotherapy (RT) planning are primarily focused on maintaining tumor target coverage. An inadequately addressed need is accounting for motion in dosimetric estimations in smaller serial structures. Accurate dose estimations in such structures are more sensitive to motion because respiration can cause them to move completely in or out of a high dose-gradient field. In this work, we study three motion management strategies (m1-m3) to find an accurate method to estimate the dosimetry in airways. To validate these methods, we generated a 'ground truth' digital breathing model based on a 4DCT scan from a lung stereotactic ablative radiotherapy (SAbR) patient. We simulated 225 breathing cycles with ±10% perturbations in amplitude, respiratory period, and time per respiratory phase. A high-resolution breath-hold CT (BHCT) was also acquired and used with a research virtual bronchoscopy software to autosegment 239 airways. Contours for planning target volume (PTV) and organs at risk (OARs) were defined on the maximum intensity projection of the 4DCT (CTMIP) and transferred to the average of the 10 4DCT phases (CTAVG). To design the motion management methods, the RT plan was recreated using different images and structure definitions. Methods m1 and m2 recreated the plan using the CTAVG image. In method m1, airways were deformed to the CTAVG. In m2, airways were deformed to each of the 4DCT phases, and union structures were transferred onto the CTAVG. In m3, the RT plan was recreated on each of the 10 phases, and the dose distribution from each phase was deformed to the BHCT and summed. Dose errors (mean [min, max]) in airways were: m1: 21% (0.001%, 93%); m2: 45% (0.1%, 179%); and m3: 4% (0.006%, 14%). Our work suggests that accurate dose estimation in moving small serial structures requires customized motion management techniques (like m3 in this work) rather than current clinical and investigational approaches.

Bochdalek hernia with Diamond-Blackfan anemia associated with RPS19 gene mutation: A case report.

RATIONALE: Diamond-Blackfan anemia (DBA) is a rare inherited marrow disorder, characterized by erythrocyte aplasia and is associated with congenital anomalies and a susceptibility to cancer. Although congenital abnormalities have been observed in ∼50% of DBA patients, the occurrence of an associated congenital diaphragmatic hernia (CDH) has rarely been reported. PATIENT CONCERNS: A 19-month-old male child was referred to our pediatric hematology-oncology outpatient clinic with anemic appearance. He presented to us with recurrent anemia, short stature, and developmental delay. DIAGNOSIS: On bone marrow examination, only erythropoietic cells were markedly decreased in number, whereas other cell lines were unaffected. An abdominal computed tomography scan revealed a Bochdalek type of CDH. A genetic analysis revealed heterozygous mutation of RPS19; therefore, he was diagnosed as having DBA with CDH. INTERVENTIONS: The patient received an initial packed red blood cell transfusion, followed by an administration of oral prednisone. OUTCOMES: The patient is maintained on oral prednisone administered at a dose of 0.3 mg/kg every alternate day and has since a hemoglobin level of >9.0 g/dL without further RBC transfusions. LESSONS: We learned that a Bochdalek type of CDH can manifest in a DBA patient with RPS19 gene mutation. Therefore, patients diagnosed with the latter disorder should also be screened for an early detection of potential CDHs.

The ideal couch tracking system-Requirements and evaluation of current systems.

INTRODUCTION: Intrafractional motion can cause substantial uncertainty in precision radiotherapy. Traditionally, the target volume is defined to be sufficiently large to cover the tumor in every position. With the robotic treatment couch, a real-time motion compensation can improve tumor coverage and organ at risk sparing. However, this approach poses additional requirements, which are systematically developed and which allow the ideal robotic couch to be specified. METHODS AND MATERIALS: Data of intrafractional tumor motion were collected and analyzed regarding motion range, frequency, speed, and acceleration. Using this data, ideal couch requirements were formulated. The four robotic couches Protura, Perfect Pitch, RoboCouch, and RPSbase were tested with respect to these requirements. RESULTS: The data collected resulted in maximum speed requirements of 60 mm/s in all directions and maximum accelerations of 80 mm/s2 in the longitudinal, 60 mm/s2 in the lateral, and 30 mm/s2 in the vertical direction. While the two robotic couches RoboCouch and RPSbase completely met the requirements, even these two showed a substantial residual motion (40% of input amplitude), arguably due to their time delays. CONCLUSION: The requirements for the motion compensation by an ideal couch are formulated and found to be feasible for currently available robotic couches. However, the performance these couches can be improved further regarding the position control if the demanded speed and acceleration are taken into account as well.

Impact of CT-based brachytherapy in elderly patients with cervical cancer.

PURPOSE: Three-dimensional image-guided brachytherapy (3D-IGBT) has become the standard therapy for patients with cervical cancer. However, in this population, the impact of 3D-IGBT in elderly individuals remains unknown. This study assessed the efficacy of 3D-IGBT for elderly patients with cervical cancer. METHODS AND MATERIALS: We performed a retrospective chart review of 105 consecutive patients with cervical squamous cell carcinoma aged ≥70 years who received radiotherapy alone between January 2001 and September 2014. All patients were treated with external beam radiotherapy and high-dose-rate intracavitary brachytherapy. We assessed the treatment outcomes in all patients. We then compared outcomes between two groups: patients treated by changing the Point A dose at brachytherapy (Group A, n = 71) and those treated with 3D-IGBT at least twice (Group B, n = 34). RESULTS: The median followup period was 59 (range, 6-203) months; the median age was 77 years. The 5-year local control and cause-specific survival rates were 89% and 78%, respectively. The 5-year cumulative rates of late toxicities of the rectum and bladder of Grade ≥3 were 2.0% and 4.2%, respectively. No statistically significant differences were observed in the local control and cause-specific survival rates, or in the incidence of rectal toxicities between groups. The 3-year cumulative rates of urinary toxicity of Grade ≥1 were 20.4% and 6.9% in Group A and Group B, respectively (p = 0.035). CONCLUSION: In elderly patients with cervical cancer, 3D-IGBT could be performed safely and effectively and contributed to decreasing urinary toxicity incidence rates.