ART2Dose: A comprehensive dose verification platform for online adaptive radiotherapy.

BACKGROUND: Online adaptive radiotherapy (ART) involves the development of adaptable treatment plans that consider patient anatomical data obtained right prior to treatment administration, facilitated by cone-beam computed tomography guided adaptive radiotherapy (CTgART) and magnetic resonance image-guided adaptive radiotherapy (MRgART). To ensure accuracy of these adaptive plans, it is crucial to conduct calculation-based checks and independent verification of volumetric dose distribution, as measurement-based checks are not practical within online workflows. However, the absence of comprehensive, efficient, and highly integrated commercial software for secondary dose verification can impede the time-sensitive nature of online ART procedures. PURPOSE: The main aim of this study is to introduce an efficient online quality assurance (QA) platform for online ART, and subsequently evaluate it on Ethos and Unity treatment delivery systems in our clinic. METHODS: To enhance efficiency and ensure compliance with safety standards in online ART, ART2Dose, a secondary dose verification software, has been developed and integrated into our online QA workflow. This implementation spans all online ART treatments at our institution. The ART2Dose infrastructure comprises four key components: an SQLite database, a dose calculation server, a report generator, and a web portal. Through this infrastructure, file transfer, dose calculation, report generation, and report approval/archival are seamlessly managed, minimizing the need for user input when exporting RT DICOM files and approving the generated QA report. ART2Dose was compared with Mobius3D in pre-clinical evaluations on secondary dose verification for 40 adaptive plans. Additionally, a retrospective investigation was conducted utilizing 1302 CTgART fractions from ten treatment sites and 1278 MRgART fractions from seven treatment sites to evaluate the practical accuracy and efficiency of ART2Dose in routine clinical use. RESULTS: With dedicated infrastructure and an integrated workflow, ART2Dose achieved gamma passing rates that were comparable to or higher than those of Mobius3D. Additionally, it significantly reduced the time required to complete pre-treatment checks by 3-4 min for each plan. In the retrospective analysis of clinical CTgART and MRgART fractions, ART2Dose demonstrated average gamma passing rates of 99.61 ± 0.83% and 97.75 ± 2.54%, respectively, using the 3%/2 mm criteria for region greater than 10% of prescription dose. The average calculation times for CTgART and MRgART were approximately 1 and 2 min, respectively. CONCLUSION: Overall, the streamlined implementation of ART2Dose notably enhances the online ART workflow, offering reliable and efficient online QA while reducing time pressure in the clinic and minimizing labor-intensive work.

Modeling gamma knife radiosurgical toxicity for multiple brain metastases.

BACKGROUND AND PURPOSE: Radiation oncology protocols for single fraction radiosurgery recommend setting dosing criteria based on assumed risk of radionecrosis, which can be predicted by the 12 Gy normal brain volume (V12). In this study, we show that tumor surface area (SA) and a simple power-law model using only preplan variables can estimate and minimize radiosurgical toxicity. MATERIALS AND METHODS: A 245-patient cohort with 1217 brain metastases treated with single or distributed Gamma Knife sessions was reviewed retrospectively. Univariate and multivariable linear regression models and power-law models determined which modeling parameters best predicted V12. The V12 power-law model, represented by a product of normalized Rx dose Rxn, and tumor longest axial dimension LAD (V12 âˆ¼ Rxn1.5*LAD2), was independently validated using a secondary 63-patient cohort with 302 brain metastases. RESULTS: Surface area was the best univariate linear predictor of V12 (adjR2 = 0.770), followed by longest axial dimension (adjR2 = 0.755) and volume (adjR2 = 0.745). The power-law model accounted for 90% variance in V12 for 1217 metastatic lesions (adjR2 = 0.906) and 245 patients (adjR2 = 0.896). The average difference ΔV12 between predicted and measured V12s was (0.28 ± 0.55) cm3 per lesion and (1.0 ± 1.2) cm3 per patient. The power-law predictive capability was validated using a secondary 63-patient dataset (adjR2 = 0.867) with 302 brain metastases (adjR2 = 0.825). CONCLUSION: Surface area was the most accurate univariate predictor of V12 for metastatic lesions. We developed a preplan model for brain metastases that can help better estimate radionecrosis risk, determine prescription doses given a target V12, and provide safe dose escalation strategies without the use of any planning software.

Bony structure enhanced synthetic CT generation using Dixon sequences for pelvis MR-only radiotherapy.

BACKGROUND: MRI-only radiotherapy planning (MROP) is beneficial to patients by avoiding MRI/CT registration errors, simplifying the radiation treatment simulation workflow and reducing exposure to ionizing radiation. MRI is the primary imaging modality for soft tissue delineation. Treatment planning CTs (i.e., CT simulation scan) are redundant if a synthetic CT (sCT) can be generated from the MRI to provide the patient positioning and electron density information. Unsupervised deep learning (DL) models like CycleGAN are widely used in MR-to-sCT conversion, when paired patient CT and MR image datasets are not available for model training. However, compared to supervised DL models, they cannot guarantee anatomic consistency, especially around bone. PURPOSE: The purpose of this work was to improve the sCT accuracy generated from MRI around bone for MROP. METHODS: To generate more reliable bony structures on sCT images, we proposed to add bony structure constraints in the unsupervised CycleGAN model's loss function and leverage Dixon constructed fat and in-phase (IP) MR images. Dixon images provide better bone contrast than T2-weighted images as inputs to a modified multi-channel CycleGAN. A private dataset with a total of 31 prostate cancer patients were used for training (20) and testing (11). RESULTS: We compared model performance with and without bony structure constraints using single- and multi-channel inputs. Among all the models, multi-channel CycleGAN with bony structure constraints had the lowest mean absolute error, both inside the bone and whole body (50.7 and 145.2 HU). This approach also resulted in the highest Dice similarity coefficient (0.88) of all bony structures compared with the planning CT. CONCLUSION: Modified multi-channel CycleGAN with bony structure constraints, taking Dixon-constructed fat and IP images as inputs, can generate clinically suitable sCT images in both bone and soft tissue. The generated sCT images have the potential to be used for accurate dose calculation and patient positioning in MROP radiation therapy.

Effect of Hollow 304 Stainless Steel Fiber on Corrosion Resistance and Mechanical Properties of Ultra-High Performance Concrete (UHPC).

This study investigated the effect of hollow 304 stainless-steel fiber on the corrosion resistance and mechanical properties of ultra-high-performance concrete (UHPC), and prepared copper-coated-fiber-reinforced UHPC as the control group. The electrochemical performance of the prepared UHPC was compared with the results of X-ray computed tomography (X-CT). The results reveal that cavitation can improve the distribution of steel fibers in the UHPC. Compared with solid steel fibers, the compressive strength of UHPC with hollow stainless-steel fibers did not exhibit significant change, but the maximum flexural strength increased by 45.2% (2 vol% content, length-diameter ratio of 60). Hollow stainless-steel fiber could better improve the durability of UHPC compared with copper-plated steel fiber, and the gap between the two continued to increase as the durability test progressed. After the dry-wet cycle test, the flexural strength of the copper-coated-fiber-reinforced UHPC was 26 MPa, marking a decrease of 21.9%, while the flexural strength of the UHPC mixed with hollow stainless-steel fibers was 40.1 MPa, marking a decrease of only 5.6%. When the salt spray test had run for seven days, the difference in the flexural strength between the two was 18.4%, but when the test ended (180 days), the difference increased to 34%. The electrochemical performance of the hollow stainless-steel fiber improved, owing to the small carrying capacity of the hollow structure, and more uniform distribution in the UHPC and lower interconnection probability were achieved. According to the AC impedance test results, the charge transfer impedance of the UHPC doped with solid steel fiber is 5.8 KΩ, while that of the UHPC doped with hollow stainless-steel fiber is 8.8 KΩ.

Comparison of the Mechanical Properties and Crack Expansion Mechanism of Different Content and Shapes of Brass-Coated Steel Fiber-Reinforced Ultra-High-Performance Concrete.

Steel fiber-reinforced ultra-high-performance concrete (UHPC) is becoming an important type of concrete reinforcement. After mixing with the reinforced steel fibers, the UHPC has perfect flex resistance, shear strength, crack resistance, shock resistance, and anti-seepage. In this study, the influence of straight, corrugated, and hooked brass-coated steel fibers (BCSFs) on the microstructure, mechanical properties, and crack expansion mechanism of ultra-high-performance concrete (UHPC) with varying content of 1-6 wt.% under different curing times were investigated. Field emission scanning electron microscopy and energy dispersive X-ray spectrometry were employed to characterize the microstructure of the BCSF-reinforced UHPC mix specimens. X-ray computed tomography was employed to determine the porosity of the BCSF-reinforced UHPC mix specimens. The obtained results indicate the flexural strength and compressive strength of BCSF-reinforced UHPC mix specimens are enhanced, along with increasing the content of BCSFs reinforcement with different shapes (straight, corrugated, and hooked). The embedded BCSFs play a major role in the adhesive property and stress transfer of the BCSFs-UHPC matrix interface. Different from many studies, the flexural strength of mix UHPC with straight BCSFs is higher than those with corrugated and hooked BCSFs. However, the compressive strength of UHPC with corrugated BCSFs is higher than those with straight and hooked BCSFs. The flexural strength of mix UHPC with 6 wt.% straight BCSFs at 28 days reaches the maximum value of 26.2 MPa, and the compressive strength of UHPC with 6 wt.% corrugated BCSFs at 28 days reaches the maximum value of 142.3 MPa. With the increase in straight BCSF content from 1 wt.% to 6 wt.%, the porosity in mix UHPC reduces gradually from 18.4% to 8.3%. The length of average crack spacing is dependent on the straight BCSF content. With the increase in straight BCSF content from 1 wt.% to 6 wt.%, the average crack length reduces gradually from 34.2 mm to 12.1 mm, and the average crack width reduces gradually from 0.78 mm to less than 0.1 mm. During crack extension, part of the energy in the UHPC mixture specimen with the 6 wt.% BCSF content flows into the crack tip region converted into the work dissipated during the bridging process. The crack propagation resistance of the UHPC mixture with straight BCSFs was improved compared with those with corrugated and hooked BCSFs. The bond strength between the BCSFs and UHPC matrix was enhanced by using vibrational mixing, and the brass film coated on the BCSFs contributes to increase the flexural and compressive strength of the UHPC mixture.

Preliminary Evaluation of PTV Margins for Online Adaptive Radiation Therapy of the Prostatic Fossa.

PURPOSE: In modern trials, traditional planning target volume (PTV) margins for postoperative prostate radiation therapy have been large (7-10 mm) to account for both daily changes in patient positioning and target deformation. With daily adaptive radiation therapy, these interfractional changes could be minimized, potentially reducing the margins required for treatment and improving adjacent normal-tissue dosimetry. METHODS AND MATERIALS: A single-center retrospective study was conducted from March 2021 to November 2021. Patients receiving conventionally fractionated postoperative radiation therapy (PORT) for prostate cancer with pretreatment and posttreatment cone beam computed tomography (CBCT) imaging (pre-CBCT and post-CBCT, respectively) were included (248 paired images). Pretreatment and posttreatment clinical target volumes (pre-CTVs and post-CTVs) were contoured by a single observer on all CBCTs and verified by a second observer. Motion was calculated from pre-CTV to that of the post-CTV, and predicted margins were calculated with van Herk's formula. Adequate coverage of the proposed planning target volume (PTV) margin expansions (pre-PTV) were verified by determining overlap with post-CTV. In a smaller cohort (25 paired images), dosimetric changes with the proposed online adaptive margins were compared with conventional plans in the Ethos emulator environment. RESULTS: The estimated margins predicted to achieve ≥95% CTV coverage for 90% of the population were 1.6 mm, 2.0 mm, and 2.2 mm (x-, y-, and z -xes, respectively), with 95% of the absolute region of interest displacement being within 1.9 mm, 2.8 mm, and 2.1 mm. After symmetrically expanding all pre-CTVs by 3 mm, the percentage of paired images achieving ≥95% CTV coverage was 97.1%. When comparing adaptive plans (3-mm margins) with scheduled plans (7-mm margins), rectum dosimetry significantly improved, with an average relative reduction in V40Gy[cc] of 59.2% and V65Gy[cc] of 79.5% (where V40Gy and V65Gy are defined as the volumes receiving 40 Gy and 65 Gy or higher dose, respectively). CONCLUSIONS: Online daily adaptive radiation therapy could significantly decrease PTV margins for prostatic PORT and improve rectal dosimetry, with a symmetrical expansion of 3 mm achieving excellent coverage in this cohort. These results need to be validated in a larger prospective cohort.

Ensemble learning and personalized training for the improvement of unsupervised deep learning-based synthetic CT reconstruction.

BACKGROUND: The growing adoption of magnetic resonance imaging (MRI)-guided radiation therapy (RT) platforms and a focus on MRI-only RT workflows have brought the technical challenge of synthetic computed tomography (sCT) reconstruction to the forefront. Unpaired-data deep learning-based approaches to the problem offer the attractive characteristic of not requiring paired training data, but the gap between paired- and unpaired-data results can be limiting. PURPOSE: We present two distinct approaches aimed at improving unpaired-data sCT reconstruction results: a cascade ensemble that combines multiple models and a personalized training strategy originally designed for the paired-data setting. METHODS: Comparisons are made between the following models: (1) the paired-data fully convolutional DenseNet (FCDN), (2) the FCDN with the Intentional Deep Overfit Learning (IDOL) personalized training strategy, (3) the unpaired-data CycleGAN, (4) the CycleGAN with the IDOL training strategy, and (5) the CycleGAN as an intermediate model in a cascade ensemble approach. Evaluation of the various models over 25 total patients is carried out using a five-fold cross-validation scheme, with the patient-specific IDOL models being trained for the five patients of fold 3, chosen at random. RESULTS: In both the paired- and unpaired-data settings, adopting the IDOL training strategy led to improvements in the mean absolute error (MAE) between true CT images and sCT outputs within the body contour (mean improvement, paired- and unpaired-data approaches, respectively: 38%, 9%) and in regions of bone (52%, 5%), the peak signal-to-noise ratio (PSNR; 15%, 7%), and the structural similarity index (SSIM; 6%, <1%). The ensemble approach offered additional benefits over the IDOL approach in all three metrics (mean improvement over unpaired-data approach in fold 3; MAE: 20%; bone MAE: 16%; PSNR: 10%; SSIM: 2%), and differences in body MAE between the ensemble approach and the paired-data approach are statistically insignificant. CONCLUSIONS: We have demonstrated that both a cascade ensemble approach and a personalized training strategy designed initially for the paired-data setting offer significant improvements in image quality metrics for the unpaired-data sCT reconstruction task. Closing the gap between paired- and unpaired-data approaches is a step toward fully enabling these powerful and attractive unpaired-data frameworks.

Machine characterization and central axis depth dose data of a superficial x-ray radiotherapy unit.

Objectives. The purpose of this study is to present data from the clinical commissioning of an Xstrahl 150 x-ray unit used for superficial radiotherapy,Methods. Commissioning tasks included vendor acceptance tests, timer reproducibility, linearity and end-effect measurements, half-value layer (HVL) measurements, inverse square law verification, head-leakage measurements, and beam output calibration. In addition, percent depth dose (PDD) curves were determined for different combinations of filter/kV settings and applicators. Automated PDD water phantom scans were performed utilizing four contemporary detectors: a microDiamond detector, a microSilicon detector, an EDGE detector, and a PinPoint ionization chamber. The measured PDD data were compared to the published values in BJR Supplement 25,Results. The x-ray unit's mechanical, safety, and radiation characteristics were within vendor-stated specifications. Across sixty commissioned x-ray beams, the PDDs determined in water using solid state detectors were in excellent agreement with the BJR 25 data. For the lower (<100 kVp) and medium-energy (≥100 kVp) superficial beams the average agreement was within [-3.6,+0.4]% and [-3.7,+1.4]% range, respectively. For the high-energy superficial (low-energy orthovoltage) x-rays at 150 kVp, the average difference for the largest 20 × 20 cm2collimator was (-0.7 ± 1.0)%,Conclusions. This study presents machine characterization data collected for clinical use of a superficial x-ray unit. Special focus was placed on utilizing contemporary detectors and techniques for the relative PDD measurements using a motorized water phantom. The results in this study confirm that the aggregate values published in the BJR 25 report still serve as a valid benchmark when comparing data from site-specific measurements, or the reference data for clinical utilization without such measurements,Advances in knowledge. This paper presents comprehensive data from the acceptance and commissioning of a modern kilovoltage superficial x-ray radiotherapy machine. Comparisons between the PDD data measured in this study using different detectors and BJR 25 data are highlighted.

Effects of regional transport from different potential pollution areas on volatile organic compounds (VOCs) in Northern Beijing during non-heating and heating periods.

Despite the adoption of air quality control measures, the influence of regional transport on volatile organic compounds (VOCs) pollution has gradually increased in Beijing. In this study, the whole observation period (September 24 to December 12, 2020) was divided into heating period and non-heating period to explore the impact of changing VOCs sources in different observation periods, and also classified into "Type-N" and "Type-S" periods both in non-heating period and heating period to explore the impact of regional transport from the northern and southern regions of sampling site, respectively. The average VOCs concentrations in northern Beijing during observation period were 22.6 ± 12.6 ppbv, which showed a decrease trend in recent years compared with other studies. And higher VOCs concentrations were observed in Type-S than in Type-N period. The positive matrix factorization results showed that vehicular exhaust dominated VOCs (26.1%-33.7%), but coal combustion could not be ignored in heating period, when it was twice that in non-heating period. In particular, coal combustion dominated VOCs in southern trajectories (30.9%) in heating period. The analysis of concentration weighted trajectory showed that coal combustion was affected by regional transport from the southeast regions of Beijing, while vehicular exhaust was affected by urban and the southeast regions of Beijing. Regarding human health risks, the carcinogenic risks of benzene and ethylbenzene exceeded the acceptable cancer risk value (1 × 10-6), and were higher in Type-S than in Type-N period. The results indicated that regional transport from urban areas and the areas south of Beijing had a significant impact on VOCs in northern Beijing. Thus, targeted control measures for different potential pollution regions are important for controlling VOCs pollution in Beijing.

Combining Serial and Parallel Functionality in Functional Lung Avoidance Radiation Therapy.

PURPOSE: Functional lung avoidance (FLA) radiation therapy (RT) aims to minimize post-RT pulmonary toxicity by preferentially avoiding dose to high-functioning lung (HFL) regions. A common limitation is that FLA approaches do not consider the conducting architecture for gas exchange. We previously proposed the functionally weighted airway sparing (FWAS) method to spare airways connected to HFL regions, showing that it is possible to substantially reduce risk of radiation-induced airway injury. Here, we compare the performance of FLA and FWAS and propose a novel method combining both approaches. METHODS: We used breath-hold computed tomography (BHCT) and simulation 4-dimensional computed tomography (4DCT) from 12 lung stereotactic ablative radiation therapy patients. Four planning strategies were examined: (1) Conventional: no sparing other than clinical dose-volume constraints; (2) FLA: using a 4DCT-based ventilation map to delineate the HFL, plans were optimized to reduce mean dose and V13.50 in HFL; (3) FWAS: we autosegemented 11 to 13 generations of individual airways from each patient's BHCT and assigned priorities based on the relative contribution of each airway to total ventilation. We used these priorities in the optimization along with airway dose constraints, estimated as a function of airway diameter and 5% probability of collapse; and (4) FLA + FWAS: we combined information from the 2 strategies. We prioritized clinical dose constraints for organs at risk and planning target volume in all plans. We performed the evaluation in terms of ventilation preservation accounting for radiation-induced damage to both lung parenchyma and airways. RESULTS: We observed average ventilation preservation for FLA, FWAS, and FLA + FWAS as 3%, 8.5%, and 14.5% higher, respectively, than for Conventional plans for patients with ventilation preservation in Conventional plans <90%. Generalized estimated equations showed that all improvements were statistically significant (P ≤ .036). We observed no clinically relevant improvements in outcomes of the sparing techniques in patients with ventilation preservation in Conventional plans ≥90%. CONCLUSIONS: These initial results suggest that it is crucial to consider the parallel and the serial nature of the lung to improve post-radiation therapy lung function and, consequently, quality of life for patients.

Volumetric Modulated Arc Therapy Enabled Total Body Irradiation (VMAT-TBI): Six-year Clinical Experience and Treatment Outcomes.

Total body irradiation is an important part of the conditioning regimens frequently used to prepare patients for allogeneic hematopoietic stem cell transplantation (SCT). Volumetric-modulated arc therapy enabled total body irradiation (VMAT-TBI), an alternative to conventional TBI (cTBI), is a novel radiotherapy treatment technique that has been implemented and investigated in our institution. The purpose of this study is to (1) report our six-year clinical experience in terms of treatment planning strategy and delivery time and (2) evaluate the clinical outcomes and toxicities in our cohort of patients treated with VMAT-TBI. This is a retrospective single center study. Forty-four patients at our institution received VMAT-TBI and chemotherapy conditioning followed by allogeneic SCT between 2014 and 2020. Thirty-two patients (73%) received standard-dose TBI (12-13.2 Gy in 6-8 fractions twice daily), whereas 12 (27%) received low-dose TBI (2-4 Gy in one fraction). Treatment planning, delivery, and treatment outcome data including overall survival (OS), relapse-free survival (RFS), and toxicities were analyzed. The developed VMAT-TBI planning strategy consistently generated plans satisfying our dose constraints, with planning target volume coverage >90%, mean lung dose ∼50% to 75% of prescription dose, and minimal hotspots in critical organs. Most of the treatment deliveries were <100 minutes (range 33-147, mean 72). The median follow-up was 26 months. At the last follow-up, 34 of 44 (77%) of patients were alive, with 1- and 2-year OS of 90% and 79% and RFS of 88% and 71%, respectively. The most common grade 3+ toxicities observed were mucositis (31 patients [71%]) and nephrotoxicity (6 patients [13%]), both of which were deemed multifactorial in cause. Four patients (9%) in standard-dose cohort developed grade 3+ pneumonitis, with 3 cases in the setting of documented respiratory infection and only 1 (2%) deemed likely related to radiation alone. VMAT-TBI provides a safe alternative to cTBI. The dose modulation capability of VMAT-TBI may lead to new treatment strategies, such as simultaneous boost and further critical organ sparing, for better malignant cell eradication, immune suppression, and lower toxicities.

Accelerated Hypofractionated Image-Guided vs Conventional Radiotherapy for Patients With Stage II/III Non-Small Cell Lung Cancer and Poor Performance Status: A Randomized Clinical Trial.

IMPORTANCE: A significant subset of patients with stage II/III non-small cell lung cancer (NSCLC) cannot receive standard concurrent chemoradiotherapy owing to the risk of toxic effects outweighing potential benefits. Without concurrent chemotherapy, however, the efficacy of conventional radiotherapy is reduced. OBJECTIVE: To determine whether hypofractionated image-guided radiotherapy (IGRT) would improve overall survival in patients with stage II/III NSCLC who could not receive concurrent chemoradiotherapy and therefore were traditionally relegated to receiving only conventionally fractionated radiotherapy (CFRT). DESIGN, SETTING, AND PARTICIPANTS: This nonblinded, phase 3 randomized clinical study enrolled 103 patients and analyzed 96 patients with stage II/III NSCLC and Zubrod performance status of at least 2, with greater than 10% weight loss in the previous 6 months, and/or who were ineligible for concurrent chemoradiotherapy after oncology consultation. Enrollment occurred at multiple US institutions. Patients were enrolled from November 13, 2012, to August 28, 2018, with a median follow-up of 8.7 (3.6-19.9) months. Data were analyzed from September 14, 2018, to April 11, 2021. INTERVENTIONS: Eligible patients were randomized to hypofractionated IGRT (60 Gy in 15 fractions) vs CFRT (60 Gy in 30 fractions). MAIN OUTCOMES AND MEASURES: The primary end point was 1-year overall survival. RESULTS: A total of 103 patients (96 of whom were analyzed [63 men (65.6%); mean (SD) age, 71.0 (10.2) years (range, 50-90 years)]) were randomized to hypofractionated IGRT (n = 50) or CFRT (n = 46) when a planned interim analysis suggested futility in reaching the primary end point, and the study was closed to further accrual. There was no statistically significant difference between the treatment groups for 1-year overall survival (37.7% [95% CI, 24.2%-51.0%] for hypofractionated IGRT vs 44.6% [95% CI, 29.9%-58.3%] for CFRT; P = .29). There were also no significant differences in median overall survival, progression-free survival, time to local failure, time to distant metastasis, and toxic effects of grade 3 or greater between the 2 treatment groups. CONCLUSIONS AND RELEVANCE: This phase 3 randomized clinical trial found that hypofractionated IGRT (60 Gy in 15 fractions) was not superior to CFRT (60 Gy in 30 fractions) for patients with stage II/III NSCLC ineligible for concurrent chemoradiotherapy. Further studies are needed to verify equivalence between these radiotherapy regimens. Regardless, for well-selected patients with NSCLC (ie, peripheral primary tumors and limited mediastinal/hilar adenopathy), the convenience of hypofractionated radiotherapy regimens may offer an appropriate treatment option. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01459497.

Development of a real-time indoor location system using bluetooth low energy technology and deep learning to facilitate clinical applications.

PURPOSE: An indoor, real-time location system (RTLS) can benefit both hospitals and patients by improving clinical efficiency through data-driven optimization of procedures. Bluetooth-based RTLS systems are cost-effective but lack accuracy because Bluetooth signal is subject to significant fluctuation. We aim to improve the accuracy of RTLS using the deep learning technique. METHODS: We installed a Bluetooth sensor network in a three-floor clinic building to track patients, staff, and devices. The Bluetooth sensors measured the strength of the signal broadcasted from Bluetooth tags, which was fed into a deep neural network to calculate the location of the tags. The proposed deep neural network consists of a long short-term memory (LSTM) network and a deep classifier for tracking moving objects. Additionally, a spatial-temporal constraint algorithm was implemented to further increase the accuracy and stability of the results. To train the neural network, we divided the building into 115 zones and collected training data in each zone. We further augmented the training data to generate cross-zone trajectories, mimicking the real-world scenarios. We tuned the parameters for the proposed neural network to achieve relatively good accuracy. RESULTS: The proposed deep neural network achieved an overall accuracy of about 97% for tracking objects in each individual zone in the whole three-floor building, 1.5% higher than the baseline neural network that was proposed in an earlier paper, when using 10 s of signals. The accuracy increased with the density of Bluetooth sensors. For tracking moving objects, the proposed neural network achieved stable and accurate results. When latency is less of a concern, we eliminated the effect of latency from the accuracy and gained an accuracy of 100% for our testing trajectories, significantly improved from the baseline method. CONCLUSIONS: The proposed deep neural network composed of a LSTM, a deep classifier and a posterior constraint algorithm significantly improved the accuracy and stability of RTLS for tracking moving objects.

Radiation Therapy for Pediatric Brain Tumors using Robotic Radiation Delivery System and Intensity Modulated Proton Therapy.

PURPOSE: This study recruited 2 centers with expertise in treating pediatric brain tumors with robotic radiation delivery system photon therapy and proton therapy, respectively, to study the plan quality and dose deposition characteristics of robotic radiation delivery system photon therapy and intensity modulated proton therapy (IMPT) plans. METHODS AND MATERIALS: A total of 18 patients clinically treated with the robotic radiation delivery system were planned with IMPT. Cases were planned per the standard of care of each institution but respected the same planning objectives. The comparison included 3 aspects: plan quality, dose fall-off characteristics around the target volume, and the volume of the high-, intermediate-, and low-dose baths. RESULTS: All robotic radiation delivery system and IMPT plans met the planning objectives. However, IMPT significantly reduced the maximum dose to organs at risk away from the planning target volume (PTV), such as the cochlea and eye (P < .05), and the mean dose to the normal brain (P < .05). No statistically significant difference was observed in the maximum dose to the optical pathway and brain stem. Robotic radiation delivery system plans demonstrated a sharper dose fall-off within 5 mm around the PTV (P < .05), whereas IMPT significantly lowered the dose to the normal tissue beyond 10 mm from the PTV (P < .05). The robotic radiation delivery system offers a smaller high-dose bath whereas IMPT offers a smaller low-dose bath (P < .05). However, the difference in intermediate dose is not statistically significant. CONCLUSIONS: In general, robotic radiation delivery system plans exhibit reduced high-dose exposure to normal tissue, and IMPT plans have considerably smaller volumes of low-dose exposure with differences in medium-range dose baths increasingly favoring protons as tumor size increases.

Benchmarking techniques for stereotactic body radiotherapy for early-stage glottic laryngeal cancer: LINAC-based non-coplanar VMAT vs. Cyberknife planning.

INTRODUCTION: Stereotactic body radiation therapy (SBRT) was found effective in treating laryngeal cancer with only five treatment fractions by a recent clinical trial (NCT01984502, ClinicalTrials.gov). Nevertheless, this trial used the Cyberknife system, which is not widely accessible enough to benefit all patients affected by laryngeal cancer. Our study investigates the feasibility of larynx SBRT treatment planning on a conventional gantry-based LINAC and compares its plan quality with that from the Cyberknife. MATERIALS & METHODS: Ten larynx SBRT cases were originally treated by Cyberknife using fixed cones in our institution, with plans created and optimized using the Monte-Carlo algorithm in the MultiPlan treatment planning system. These cases were retrospectively planned in the Eclipse planning system for a LINAC with the same prescription dose. We used volumetric modulated arc therapy (VMAT) for larynx SBRT planning in Eclipse and incorporated non-coplanar arcs to approach the Cyberknife's large solid angle delivery space. We used both anisotropic analytical algorithm (AAA) and Acuros XB (AXB) algorithm for dose calculation and compared their accuracy by measurements on an in-house larynx phantom. We compared the LINAC VMAT plans (VMAT-AAA and VMAT-AXB) with the original Cyberknife plans using dosimetric endpoints such as the conformity index, gradient indices (R50, R20), OAR maximum/mean doses, and the monitor units. RESULTS: Phantom measurement showed that both the AAA and the AXB algorithms provided adequate dose calculation accuracy (94.7% gamma pass rate on 2%/2 mm criteria for AAA vs. 97.3% for AXB), though AXB provided better accuracy in the air cavity. The LINAC-based VMAT plans achieved similar dosimetric endpoints as the Cyberknife planning, and all plans met the larynx SBRT dosimetric constraints. Cyberknife plans achieved an average conformity index of 1.13, compared to 1.20 of VMAT-AXB and 1.19 of VMAT-AAA. The VMAT plans spared the thyroid gland better with average Dmean of 2.4 Gy (VMAT-AXB) and 2.7 Gy (VMAT-AAA), as compared to 4.3 Gy for Cyberknife plans. The VMAT-AAA plans had a slightly lower contralateral arytenoid Dmax (average: 15.2 Gy) than Cyberknife plans (average: 17.9 Gy) with statistical significance, while the contralateral arytenoid Dmax was similar between VMAT-AXB and Cyberknife plans with no statistically significant difference. Cyberknife plans offered slightly better R50 (average: 5.0) than VMAT-AXB (5.9) and VMAT-AAA (5.7) plans. The VMAT plans substantially reduced the plan MUs to less than 1/3 of the Cyberknife plans, and the differences were statistically significant. The other metrics were similar between VMAT and Cyberknife plans with no statistically significant differences. CONCLUSIONS: Gantry-based LINACs can achieve similar plan quality to Cyberknife systems. Treatment outcome with both methods remains to be investigated.

Considerations of target surface area and the risk of radiosurgical toxicity.

OBJECTIVE: The goal of this study was to explore conceptual benefits of characterizing delineated target volumes based on surface area and to utilize the concept for assessing risk of therapeutic toxicity in radiosurgery. METHODS AND MATERIALS: Four computer-generated targets, a sphere, a cylinder, an ellipsoid and a box, were designed for two distinct scenarios. In the first scenario, all targets had identical volumes, and in the second one, all targets had identical surface areas. High quality stereotactic radiosurgery plans with at least 95% target coverage and selectivity were created for each target in both scenarios. Normal brain volumes V12Gy, V14Gy and V16Gy corresponding to received dose of 12 Gy, 14 Gy and 16 Gy, respectively, were computed and analyzed. Additionally, V12Gy and V14Gy volumes and values for seven prospective toxicity variables were recorded for 100 meningioma patients after Gamma Knife radiosurgery. Multivariable stepwise linear regression and best subset linear regression analyses were performed in two statistical software packages, SAS/STAT and R, respectively. RESULTS: In a phantom study, for the constant volume targets, the volumes of 12 Gy, 14 Gy and 16 Gy isodose clouds were the lowest for the spherical target as an expected corollary of the isoperimetric inequality. For the constant surface area targets, a conventional wisdom is confirmed, as the target volume increases the corresponding volumes V12Gy, V14Gy and V16Gy also increase. In the 100-meningioma patient cohort, the best univariate model featured tumor surface area as the most significantly associated variable with both V12Gy and V14Gy volumes, corresponding to the adjusted R2 values of 0.82 and 0.77, respectively. Two statistical methods converged to matching multivariable models. CONCLUSIONS: In a univariate model, target surface area is a better predictor of spilled dose to normal tissue than target largest dimension or target volume itself. In complex multivariate models, target surface area is an independent variable for modeling radiosurgical normal tissue toxicity risk.

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.

Comparison of treatment planning approaches for spatially fractionated irradiation of deep tumors.

PURPOSE: The purpose of this work was to compare the dosimetry and delivery times of 3D-conformal (3DCRT)-, volumetric modulated arc therapy (VMAT)-, and tomotherapy-based approaches for spatially fractionated radiation therapy for deep tumor targets. METHODS: Two virtual GRID phantoms were created consisting of 7 "target" cylinders (1-cm diameter) aligned longitudinally along the tumor in a honey-comb pattern, mimicking a conventional GRID block, with 2-cm center-to-center spacing (GRID2 cm ) and 3-cm center-to-center spacing (GRID3 cm ), all contained within a larger cylinder (8 and 10 cm in diameter for the GRID2 cm and GRID3 cm , respectively). In a single patient, a GRID3 cm structure was created within the gross tumor volume (GTV). Tomotherapy, VMAT (6 MV + 6 MV-flattening-filter-free) and multi-leaf collimator segment 3DCRT (6 MV) plans were created using commercially available software. Two tomotherapy plans were created with field widths (TOMO2.5 cm ) 2.5 cm and (TOMO5 cm ) 5 cm. Prescriptions for all plans were set to deliver a mean dose of 15 Gy to the GRID targets in one fraction. The mean dose to the GRID target and the heterogeneity of the dose distribution (peak-to-valley and peak-to-edge dose ratios) inside the GRID target were obtained. The volume of normal tissue receiving 7.5 Gy was determined. RESULTS: The peak-to-valley ratios for GRID2 cm /GRID3 cm /Patient were 2.1/2.3/2.8, 1.7/1.5/2.8, 1.7/1.9/2.4, and 1.8/2.0/2.8 for the 3DCRT, VMAT, TOMO5 cm , and TOMO2.5 cm plans, respectively. The peak-to-edge ratios for GRID2 cm /GRID3 cm /Patient were 2.8/3.2/5.4, 2.1/1.8/5.4, 2.0/2.2/3.9, 2.1/2.7/5.2 and for the 3DCRT, VMAT, TOMO5 cm , and TOMO2.5 cm plans, respectively. The volume of normal tissue receiving 7.5 Gy was lowest in the TOMO2.5 cm plan (GRID2 cm /GRID3 cm /Patient = 54 cm3 /19 cm3 /10 cm3 ). The VMAT plans had the lowest delivery times (GRID2 cm /GRID3 cm /Patient = 17 min/8 min/9 min). CONCLUSION: Our results present, for the first time, preliminary evidence comparing IMRT-GRID approaches which result in high-dose "islands" within a target, mimicking what is achieved with a conventional GRID block but without high-dose "tail" regions outside of the target. These approaches differ modestly in their ability to achieve high peak-to-edge ratios and also differ in delivery times.

Automated Text Message Reminders Improve Radiation Therapy Compliance.

PURPOSE: Protraction of radiation therapy courses can lead to lower cancer control and cancer-specific survival rates. The requirement for daily, consecutive radiation treatments coupled with the complexities of multimodality cancer care and quality assurance can occasionally lead to missed patient appointments or clinical inefficiency. To determine whether an automated text messaging (short message service [SMs]) platform could improve patient compliance with scheduled radiation therapy delivery, we created an automated SMS platform to send daily reminders of radiation therapy appointments. METHODS AND MATERIALS: An automated SMS text messaging program was used from July 2016 to January 2017 to deliver daily appointment time reminders to patients on an elective basis. Automated text messages were sent 2 hours before treatment appointments with appointment-specific information. We analyzed for compliance with radiation therapy appointments for patients who elected to receive SMS reminders versus those who did not. RESULTS: Multivariate analysis of >37,000 encounters involving ∼3400 patients demonstrated that of the factors considered, nonreceipt of SMS appointment reminders had a strong association with 15- to 60-minute tardiness (odds ratio [OR], 1.25; 95% confidence interval [CI], 1.13-1.38; P < .0001), >60-minute tardiness (OR, 1.56; 95% CI, 1.34-1.82; P < .0001) and no-shows (OR, 6.77; 95% CI, 5.45-8.41; P < .0001). Other demographic factors associated with decreased compliance included being early in a radiation therapy course, having an appointment earlier in the day, younger age, and male sex. Receipt of an SMS message did not correlate with overall treatment package time. CONCLUSIONS: Receipt of text messages correlates with compliance for radiation therapy appointments. Prospective randomized trials would be required to determine conclusively whether SMS is an effective intervention for improving compliance in populations at risk for being late to or missing radiation therapy appointments.

Accurate real time localization tracking in a clinical environment using Bluetooth Low Energy and deep learning.

Deep learning has started to revolutionize several different industries, and the applications of these methods in medicine are now becoming more commonplace. This study focuses on investigating the feasibility of tracking patients and clinical staff wearing Bluetooth Low Energy (BLE) tags in a radiation oncology clinic using artificial neural networks (ANNs) and convolutional neural networks (CNNs). The performance of these networks was compared to relative received signal strength indicator (RSSI) thresholding and triangulation. By utilizing temporal information, a combined CNN+ANN network was capable of correctly identifying the location of the BLE tag with an accuracy of 99.9%. It outperformed a CNN model (accuracy = 94%), a thresholding model employing majority voting (accuracy = 95%), and a triangulation classifier utilizing majority voting (accuracy = 95%). Future studies will seek to deploy this affordable real time location system in hospitals to improve clinical workflow, efficiency, and patient safety.