Performance evaluation of rural water environment governance based on AHP: a case study of the Beitang River Basin.

Rural water environment governance in China still lacks a systematic and comprehensive assessment protocol to help analyze and improve such governance performance. The Analytic Hierarchy Process (AHP) method was employed in this study to build a governance assessment system that integrates ecological conditions, water pollution control, and public satisfaction. To cover these topics, the assessment system is composed of an indicator layer that is customized to rural water environment governance in China. The Beitang River, located in the rural region of Hangzhou, Zhejiang, China, was presented as a case study. Field investigation provided raw data for this assessment. A questionnaire survey was conducted to interview local residents on the governance performance. An additional survey with executives who played major roles in the governance was performed to reconstruct a water environment assessment on the Beitang River prior to the governance, in order to highlight the effects of the governance through contrast. The results showed consistency in the questionnaire survey and the assessment system. The AHP assessment system was able to reflect the improvement in the water quality, river ecology, and residential welfare after the governance, and suggested limits and future directions in the following upgrade programs for the river basin.

Influence of additional methanol on both pre- and post-denitrification processes in treating municipal wastewater.

Changes in functional properties of biological denitrification in the long-term use of methanol were explored in both pre- and post-denitrification processes. The two systems employed were sequencing batch reactor (SBR) using post-denitrification in temporal sequence, and Carrousel oxidation ditch, which was equipped with a separate pre-denitrification zone. In the SBR, stable nitrate reduction rates reached after 37 days elapsed with addition of methanol (TOC/N = 1.4-1.8) at the start of anoxic phase, and specific denitrification rate increased from 0.378 mgNOx-N·(gVSS·h)-1 to 2.406 mgNOx-N·(gVSS·h)-1. Besides, by means of nitrogen uptake rate (NUR) batch tests based on methanol-adapted sludge, the appropriate range of TOC/N ratios for complete denitrification was estimated to be 1.10-2.68. By comparison, the Carrousel oxidation ditch that was fed with methanol in the anaerobic zone took fewer days (29 days) to obtain a constant effluent nitrate. Moreover, the denitrification yield in ditch was elevated from an initial value of 0.082 mgTN/mgCOD to 0.123 mgTN/mgCOD, and the nitrogen removal efficiency reached up to a level of 68%. The focus on denitrification potential with external methanol is valuable to provide information for developing carbon dosage control, as well as predict the nitrate effluent quality of the plant.

A comparison of two methodologies for radiotherapy treatment plan optimization and QA for clinical trials.

BACKGROUND AND PURPOSE: The efficacy of clinical trials and the outcome of patient treatment are dependent on the quality assurance (QA) of radiation therapy (RT) plans. There are two widely utilized approaches that include plan optimization guidance created based on patient-specific anatomy. This study examined these two techniques for dose-volume histogram predictions, RT plan optimizations, and prospective QA processes, namely the knowledge-based planning (KBP) technique and another first principle (FP) technique. METHODS: This analysis included 60, 44, and 10 RT plans from three Radiation Therapy Oncology Group (RTOG) multi-institutional trials: RTOG 0631 (Spine SRS), RTOG 1308 (NSCLC), and RTOG 0522 (H&N), respectively. Both approaches were compared in terms of dose prediction and plan optimization. The dose predictions were also compared to the original plan submitted to the trials for the QA procedure. RESULTS: For the RTOG 0631 (Spine SRS) and RTOG 0522 (H&N) plans, the dose predictions from both techniques have correlation coefficients of >0.9. The RT plans that were re-optimized based on the predictions from both techniques showed similar quality, with no statistically significant differences in target coverage or organ-at-risk sparing. The predictions of mean lung and heart doses from both methods for RTOG1308 patients, on the other hand, have a discrepancy of up to 14 Gy. CONCLUSIONS: Both methods are valuable tools for optimization guidance of RT plans for Spine SRS and Head and Neck cases, as well as for QA purposes. On the other hand, the findings suggest that KBP may be more feasible in the case of inoperable lung cancer patients who are treated with IMRT plans that have spatially unevenly distributed beam angles.

Retrospective quality metrics review of stereotactic radiosurgery plans treating multiple targets using single-isocenter volumetric modulated arc therapy.

PURPOSE: To characterize key plan quality metrics in multi-target stereotactic radiosurgery (SRS) plans treated using single-isocenter volumetric modulated arc therapy (VMAT) in comparison to dynamic conformal arc (DCA) plans treating single target. To investigate the feasibility of quality improvement in VMAT planning based on previous planning knowledge. MATERIALS AND METHODS: 97 VMAT plans of multi-target and 156 DCA plans of single-target treated in 2017 at a single institution were reviewed. A total of 605 targets were treated with these SRS plans. The prescription dose was normalized to 20 Gy in all plans for this analysis. Two plan quality metrics, target conformity index (CI) and normal tissue volume receiving more than 12 Gy (V12Gy), were calculated for each target. The distribution of V12Gy per target was plotted as a function of the target volume. For multi-target VMAT plans, the number of targets being treated in the same plan and the distance between targets were calculated to evaluate their impact on V12Gy. VMAT plans that had a large deviation of V12Gy from the average level were re-optimized to determine the possibility of reducing the variation of V12Gy in VMAT planning. RESULTS: Conformity index of multi-target VMAT plans were lower than that of DCA plans while the mean values of 12 Gy were comparable. The V12Gy for a target in VMAT plan did not show apparent dependence on the total number of targets or the distance between targets. The distribution of V12Gy exhibited a larger variation in VMAT plans compared to DCA plans. Re-optimization of outlier plans reduced V12 Gy by 33.9% and resulted in the V12Gy distribution in VMAT plans more closely resembling that of DCA plans. CONCLUSION: The benchmark data on key plan quality metrics were established for single-isocenter multi-target SRS planning. It is feasible to use this knowledge to guide VMAT planning and reduce high V12Gy outliers.

Technical Note: Investigation of the dosimetric impact of stray radiation on the Common Control Unit of the IBA Blue Phantom2.

PURPOSE: This technical note aims to investigate the dosimetric impact of stray radiation on the Common Control Unit (CCU) of the IBA Blue Phantom2 and the measured beam data. METHODS: Three CCUs of the same model were used for the study. The primary test CCU was placed at five distances from the radiation beam central axis. At each distance, a set of depth dose and beam profiles for two open and two wedge fields were measured. The field sizes were 10 × 10 cm2 and 30 × 30 cm2 for the open fields, and 30 × 30 cm2 and 15 × 15 cm2 for the 30° and 60° wedges, respectively. The other two CCUs were used to cross check the data of the primary CCU. Assuming the effect of stray radiation on the data measured at the farthest reachable distance 4.5 m is negligible, the dosimetric impact of stray radiation on the CCU and consequently on the measured data can be extracted for analysis by comparing it with those measured at shorter distances. RESULTS: The results of three CCUs were consistent. The dosimetric impact of stray radiation was greater for lower energies at larger field sizes. For open fields, the data variation was up to 4.5% for depth dose curves and 7.1% for beam profiles. For wedge fields, the data variation was up to 9.3% for depth dose curves and 10.6% for beam profiles. Moreover, for wedge field profiles in the wedge direction, they became flatter as the CCU was placed closer to the primary radiation beam, manifesting smaller wedge angles. CONCLUSION: The stray radiation added a uniform background noise on all measured data. The magnitude of the noise is inversely proportional to the square of the distance of the CCU to the primary radiation beam, approximately following the inverse square law.

LINAC based stereotactic radiosurgery for multiple brain metastases: guidance for clinical implementation.

Introduction: Stereotactic radiosurgery (SRS) is a promising treatment option for patients with multiple brain metastases (BM). Recent technical advances have made LINAC based SRS a patient friendly technique, allowing for accurate patient positioning and a short treatment time. Since SRS is increasingly being used for patients with multiple BM, it remains essential that SRS be performed with the highest achievable quality in order to prevent unnecessary complications such as radionecrosis. The purpose of this article is to provide guidance for high-quality LINAC based SRS for patients with BM, with a focus on single isocenter non-coplanar volumetric modulated arc therapy (VMAT). Methods: The article is based on a consensus statement by the study coordinators and medical physicists of four trials which investigated whether patients with multiple BM are better palliated with SRS instead of whole brain radiotherapy (WBRT): A European trial (NCT02353000), two American trials and a Canadian CCTG lead intergroup trial (CE.7). This manuscript summarizes the quality assurance measures concerning imaging, planning and delivery. Results: To optimize the treatment, the interval between the planning-MRI (gadolinium contrast-enhanced, maximum slice thickness of 1.5 mm) and treatment should be kept as short as possible (< two weeks). The BM are contoured based on the planning-MRI, fused with the planning-CT. GTV-PTV margins are minimized or even avoided when possible. To maximize efficiency, the preferable technique is single isocenter (non-)coplanar VMAT, which delivers high doses to the target with maximal sparing of the organs at risk. The use of flattening filter free photon beams ensures a lower peripheral dose and shortens the treatment time. To bench mark SRS treatment plan quality, it is advisable to compare treatment plans between hospitals. Conclusion: This paper provides guidance for quality assurance and optimization of treatment delivery for LINAC-based radiosurgery for patients with multiple BM.

Evaluation of dosimetric uncertainty caused by MR geometric distortion in MRI-based liver SBRT treatment planning.

PURPOSE: MRI-based treatment planning is a promising technique for liver stereotactic-body radiation therapy (SBRT) treatment planning to improve target volume delineation and reduce radiation dose to normal tissues. MR geometric distortion, however, is a source of potential error in MRI-based treatment planning. The aim of this study is to investigate dosimetric uncertainties caused by MRI geometric distortion in MRI-based treatment planning for liver SBRT. MATERIALS AND METHODS: The study was conducted using computer simulations. 3D MR geometric distortion was simulated using measured data in the literature. Planning MR images with distortions were generated by integrating the simulated 3D MR geometric distortion onto planning CT images. MRI-based treatment plans were then generated on the planning MR images with two dose calculation methods: (1) using original CT numbers; and (2) using organ-specific assigned CT numbers. Dosimetric uncertainties of various dose-volume-histogram parameters were determined as their differences between the simulated MRI-based plans and the original clinical CT-based plans for five liver SBRT cases. RESULTS: The average simulated distortion for the five liver SBRT cases was 2.77 mm. In the case of using original CT numbers for dose calculation, the average dose uncertainties for target volumes and critical structures were <0.5 Gy, and the average target volume percentage at prescription dose uncertainties was 0.97%. In the case of using assigned CT numbers, the average dose uncertainties for target volumes and critical structures were <1.0 Gy, and the average target volume percentage at prescription dose uncertainties was 2.02%. CONCLUSIONS: Dosimetric uncertainties caused by MR geometric distortion in MRI-based liver SBRT treatment planning was generally small (<1 Gy) when the distortion is 3 mm.

An in-house protocol for improved flood field calibration of TrueBeam FFF cine imaging.

PURPOSE: TrueBeams equipped with the 40 × 30 cm2 Electronic Portal Imaging Devices (EPIDs) are prone to image saturation at the image center when used with flattening filter free (FFF) photon energies. While cine imaging during treatment may not saturate because the beam is attenuated by the patient, the flood field calibration is affected when the standard calibration procedure is followed. Here, we describe the hardware and protocol to achieve improved image quality for this model of TrueBeam EPID. MATERIALS & METHODS: A stainless steel filter of uniform thickness was designed to have sufficient attenuation to avoid panel saturation. The cine imaging flood field calibration was acquired with the filter in place for the FFF energies under the standard calibration geometry (SID = 150 cm). Image quality during MV cine was assessed with & without the modified flood field calibration using a low contrast resolution phantom and an anthropomorphic phantom. RESULTS: When the flood field is acquired without the filter in place, a pixel gain artifact is clearly present in the image center which may be mis-attributed to panel saturation in the subject image. At the image center, the artifact obscured all low contrast inserts and was also visible on the anthropomorphic phantom. Using the filter for flood field calibration eliminates the artifact. CONCLUSION: TrueBeams equipped with the 40 × 30 cm2 IDU can utilize a modified flood field calibration procedure for FFF photon energies that improves image quality for cine MV imaging.

AAPM-RSS Medical Physics Practice Guideline 9.a. for SRS-SBRT.

The American Association of Physicists in Medicine (AAPM) is a nonprofit professional society whose primary purposes are to advance the science, education, and professional practice of medical physics. The AAPM has more than 8,000 members and is the principal organization of medical physicists in the United States. The AAPM will periodically define new practice guidelines for medical physics practice to help advance the science of medical physics and to improve the quality of service to patients throughout the United States. Existing medical physics practice guidelines will be reviewed for revision or renewal, as appropriate, on their fifth anniversary or sooner. Each medical physics practice guideline represents a policy statement by the AAPM, has undergone a thorough consensus process in which it has been subjected to extensive review, and requires the approval of the Professional Council. The medical physics practice guidelines recognize that the safe and effective use of diagnostic and therapeutic radiology requires specific training, skills, and techniques, as described in each document. Reproduction or modification of the published practice guidelines and technical standards by those entities not providing these services is not authorized. The following terms are used in the AAPM practice guidelines: Must and Must Not: Used to indicate that adherence to the recommendation is considered necessary to conform to this practice guideline. Should and Should Not: Used to indicate a prudent practice to which exceptions may occasionally be made in appropriate circumstances. Approved by AAPM Professional Council 3-31-2017 and Executive Committee 4-4-2017.

An efficient calculation method for pharmacokinetic parameters in brain permeability study using dynamic contrast-enhanced MRI.

PURPOSE: To develop an efficient method for calculating pharmacokinetic (PK) parameters in brain DCE-MRI permeability studies. METHODS: A linear least-squares fitting algorithm based on a derivative expression of the two-compartment PK model was proposed to analytically solve for the PK parameters. Noise in the expression was minimized through low-pass filtering. Simulation studies were conducted in which the proposed method was compared with two existing methods in terms of accuracy and efficiency. Five in vivo brain studies were demonstrated for potential clinical application. RESULTS: In the simulation studies using chosen parameter values, the calculated percent difference of K(trans) by the proposed method was <5.0% with a temporal resolution (Δt) < 5 s, and the accuracies of all parameter results were better or comparable to existing methods. When analyzed within certain parameter intensity ranges, the proposed method was more accurate than the existing methods and improved the efficiency by a factor of up to 458 for a Δt = 1 s and up to 38 for a Δt = 5 s. In the in vivo study, the calculated parameters using the proposed method were comparable to those using the existing methods with improved efficiencies. CONCLUSIONS: An efficient method was developed for the accurate and efficient calculation of parameters in brain DCE-MRI permeability studies.

On the sensitivity of TG-119 and IROC credentialing to TPS commissioning errors.

We investigate the sensitivity of IMRT commissioning using the TG-119 C-shape phantom and credentialing with the IROC head and neck phantom to treatment planning system commissioning errors. We introduced errors into the various aspects of the commissioning process for a 6X photon energy modeled using the analytical anisotropic algorithm within a commercial treatment planning system. Errors were implemented into the various components of the dose calculation algorithm including primary photons, secondary photons, electron contamination, and MLC parameters. For each error we evaluated the probability that it could be committed unknowingly during the dose algorithm commissioning stage, and the probability of it being identified during the verification stage. The clinical impact of each commissioning error was evaluated using representative IMRT plans including low and intermediate risk prostate, head and neck, mesothelioma, and scalp; the sensitivity of the TG-119 and IROC phantoms was evaluated by comparing dosimetric changes to the dose planes where film measurements occur and change in point doses where dosimeter measurements occur. No commissioning errors were found to have both a low probability of detection and high clinical severity. When errors do occur, the IROC credentialing and TG 119 commissioning criteria are generally effective at detecting them; however, for the IROC phantom, OAR point-dose measurements are the most sensitive despite being currently excluded from IROC analysis. Point-dose measurements with an absolute dose constraint were the most effective at detecting errors, while film analysis using a gamma comparison and the IROC film distance to agreement criteria were less effective at detecting the specific commissioning errors implemented here.

Diode-based transmission detector for IMRT delivery monitoring: a validation study.

The purpose of this work was to evaluate the potential of a new transmission detector for real-time quality assurance of dynamic-MLC-based radiotherapy. The accuracy of detecting dose variation and static/dynamic MLC position deviations was measured, as well as the impact of the device on the radiation field (surface dose, transmission). Measured dose variations agreed with the known variations within 0.3%. The measurement of static and dynamic MLC position deviations matched the known deviations with high accuracy (0.7-1.2 mm). The absorption of the device was minimal (~ 1%). The increased surface dose was small (1%-9%) but, when added to existing collimator scatter effects could become significant at large field sizes (≥ 30 × 30 cm2). Overall the accuracy and speed of the device show good potential for real-time quality assurance.

Dosimetric comparison of preoperative single-fraction partial breast radiotherapy techniques: 3D CRT, noncoplanar IMRT, coplanar IMRT, and VMAT.

The purpose of this study was to compare dosimetric parameters of treatment plans among four techniques for preoperative single-fraction partial breast radiotherapy in order to select an optimal treatment technique. The techniques evaluated were noncoplanar 3D conformal radiation therapy (3D CRT), noncoplanar intensity-modulated radiation therapy (IMRTNC), coplanar IMRT (IMRTCO), and volumetric-modulated arc therapy (VMAT). The planning CT scans of 16 patients in the prone position were used in this study, with the single-fraction prescription doses of 15 Gy for the first eight patients and 18 Gy for the remaining eight patients. Six (6) MV photon beams were designed to avoid the heart and contralateral breast. Optimization for IMRT and VMAT was performed to reduce the dose to the skin and normal breast. All plans were normalized such that 100% of the prescribed dose covered greater than 95% of the clinical target volume (CTV) consisting of gross tumor volume (GTV) plus 1.5 cm margin. Mean homogeneity index (HI) was the lowest (1.05 ± 0.02) for 3D CRT and the highest (1.11 ± 0.04) for VMAT. Mean conformity index (CI) was the lowest (1.42 ± 0.32) for IMRTNC and the highest (1.60 ± 0.32) for VMAT. Mean of the maximum point dose to skin was the lowest (73.7 ± 11.5%) for IMRTNC and the highest (86.5 ± 6.68%) for 3D CRT. IMRTCO showed very similar HI, CI, and maximum skin dose to IMRTNC (differences <1%). The estimated mean treatment delivery time, excluding the time spent for patient positioning and imaging, was 7.0 ± 1.0, 8.3 ± 1.1, 9.7 ± 1.0, and 11.0 ± 1.5min for VMAT, IMRTCO, IMRTNC and 3D CRT, respectively. In comparison of all four techniques for preoperative single-fraction partial breast radiotherapy, we can conclude that noncoplanar or coplanar IMRT were optimal in this study as IMRT plans provided homogeneous and conformal target coverage, skin sparing, and relatively short treatment delivery time.

Quantitative comparison of automatic and manual IMRT optimization for prostate cancer: the benefits of DVH prediction.

A recent publication indicated that the patient anatomical feature (PAF) model was capable of predicting optimal objectives based on past experience. In this study, the benefits of IMRT optimization using PAF-predicted objectives as guidance for prostate were evaluated. Three different optimization methods were compared.1) Expert Plan: Ten prostate cases (16 plans) were planned by an expert planner using conventional trial-and-error approach started with institutional modified OAR and PTV constraints. Optimization was stopped at 150 iterations and that plan was saved as Expert Plan. 2) Clinical Plan: The planner would keep working on the Expert Plan till he was satisfied with the dosimetric quality and the final plan was referred to as Clinical Plan. 3) PAF Plan: A third sets of plans for the same ten patients were generated fully automatically using predicted DVHs as guidance. The optimization was based on PAF-based predicted objectives, and was continued to 150 iterations without human interaction. DMAX and D98% for PTV, DMAX for femoral heads, DMAX, D10cc, D25%/D17%, and D40% for bladder/rectum were compared. Clinical Plans are further optimized with more iterations and adjustments, but in general provided limited dosimetric benefits over Expert Plans. PTV D98% agreed within 2.31% among Expert, Clinical, and PAF plans. Between Clinical and PAF Plans, differences for DMAX of PTV, bladder, and rectum were within 2.65%, 2.46%, and 2.20%, respectively. Bladder D10cc was higher for PAF but < 1.54% in general. Bladder D25% and D40% were lower for PAF, by up to 7.71% and 6.81%, respectively. Rectum D10cc, D17%, and D40% were 2.11%, 2.72%, and 0.27% lower for PAF, respectively. DMAX for femoral heads were comparable (< 35 Gy on average). Compared to Clinical Plan (Primary + Boost), the average optimization time for PAF plan was reduced by 5.2 min on average, with a maximum reduction of 7.1min. Total numbers of MUs per plan for PAF Plans were lower than Clinical Plans, indicating better delivery efficiency. The PAF-guided planning process is capable of generating clinical-quality prostate IMRT plans with no human intervention. Compared to manual optimization, this automatic optimization increases planning and delivery efficiency, while maintainingplan quality.

Impact of dose calculation accuracy during optimization on lung IMRT plan quality.

The purpose of this study was to evaluate the effect of dose calculation accuracy and the use of an intermediate dose calculation step during the optimization of intensity-modulated radiation therapy (IMRT) planning on the final plan quality for lung cancer patients. This study included replanning for 11 randomly selected free-breathing lung IMRT plans. The original plans were optimized using a fast pencil beam convolution algorithm. After optimization, the final dose calculation was performed using the analytical anisotropic algorithm (AAA). The Varian Treatment Planning System (TPS) Eclipse v11, includes an option to perform intermediate dose calculation during optimization using the AAA. The new plans were created using this intermediate dose calculation during optimization with the same planning objectives and dose constraints as in the original plan. Differences in dosimetric parameters for the planning target volume (PTV) dose coverage, organs-at-risk (OARs) dose sparing, and the number of monitor units (MU) between the original and new plans were analyzed. Statistical significance was determined with a p-value of less than 0.05. All plans were normalized to cover 95% of the PTV with the prescription dose. Compared with the original plans, the PTV in the new plans had on average a lower maximum dose (69.45 vs. 71.96Gy, p = 0.005), a better homogeneity index (HI) (0.08 vs. 0.12, p = 0.002), and a better conformity index (CI) (0.69 vs. 0.59, p = 0.003). In the new plans, lung sparing was increased as the volumes receiving 5, 10, and 30 Gy were reduced when compared to the original plans (40.39% vs. 42.73%, p = 0.005; 28.93% vs. 30.40%, p = 0.001; 14.11%vs. 14.84%, p = 0.031). The volume receiving 20 Gy was not significantly lower (19.60% vs. 20.38%, p = 0.052). Further, the mean dose to the lung was reduced in the new plans (11.55 vs. 12.12 Gy, p = 0.024). For the esophagus, the mean dose, the maximum dose, and the volumes receiving 20 and 60 Gy were lower in the new plans than in the original plans (17.91 vs. 19.24 Gy, p = 0.004; 57.32vs. 59.81 Gy, p = 0.020; 39.34% vs. 41.59%, p = 0.097; 12.56%vs. 15.35%, p = 0.101). For the heart, the mean dose, the maximum dose, and the volume receiving 40 Gy were also lower in new plans (11.07 vs. 12.04 Gy, p = 0.007; 56.41 vs. 57.7 Gy, p = 0.027; 7.16% vs. 9.37%, p= 0.012). The maximum dose to the spinal cord in the new plans was significantly lower than in the original IMRT plans (29.1 vs. 31.39Gy, p = 0.014). Difference in MU between the IMRT plans was not significant (1216.90 vs. 1198.91, p = 0.328). In comparison to the original plans, the number of iterations needed to meet the optimization objectives in the new plans was reduced by a factor of 2 (2-3 vs. 5-6 iterations). Further, optimization was 30% faster corresponding to an average time savings of 10-15 min for the reoptimized plans. Accuracy of the dose calculation algorithm during optimization has an impact on planning efficiency, as well as on the final plan dosimetric quality. For lung IMRT treatment planning, utilizing the intermediate dose calculation during optimization is feasible for dose homogeneity improvement of the PTV and for improvement of optimization efficiency.

Flattening filter-free accelerators: a report from the AAPM Therapy Emerging Technology Assessment Work Group.

This report describes the current state of flattening filter-free (FFF) radiotherapy beams implemented on conventional linear accelerators, and is aimed primarily at practicing medical physicists. The Therapy Emerging Technology Assessment Work Group of the American Association of Physicists in Medicine (AAPM) formed a writing group to assess FFF technology. The published literature on FFF technology was reviewed, along with technical specifications provided by vendors. Based on this information, supplemented by the clinical experience of the group members, consensus guidelines and recommendations for implementation of FFF technology were developed. Areas in need of further investigation were identified. Removing the flattening filter increases beam intensity, especially near the central axis. Increased intensity reduces treatment time, especially for high-dose stereotactic radiotherapy/radiosurgery (SRT/SRS). Furthermore, removing the flattening filter reduces out-of-field dose and improves beam modeling accuracy. FFF beams are advantageous for small field (e.g., SRS) treatments and are appropriate for intensity-modulated radiotherapy (IMRT). For conventional 3D radiotherapy of large targets, FFF beams may be disadvantageous compared to flattened beams because of the heterogeneity of FFF beam across the target (unless modulation is employed). For any application, the nonflat beam characteristics and substantially higher dose rates require consideration during the commissioning and quality assurance processes relative to flattened beams, and the appropriate clinical use of the technology needs to be identified. Consideration also needs to be given to these unique characteristics when undertaking facility planning. Several areas still warrant further research and development. Recommendations pertinent to FFF technology, including acceptance testing, commissioning, quality assurance, radiation safety, and facility planning, are presented. Examples of clinical applications are provided. Several of the areas in which future research and development are needed are also indicated.

Stereotactic body radiotherapy: a critical review for nonradiation oncologists.

Stereotactic body radiotherapy (SBRT) involves the treatment of extracranial primary tumors or metastases with a few, high doses of ionizing radiation. In SBRT, tumor kill is maximized and dose to surrounding tissue is minimized, by precise and accurate delivery of multiple radiation beams to the target. This is particularly challenging, because extracranial lesions often move with respiration and are irregular in shape, requiring careful treatment planning and continual management of this motion and patient position during irradiation. This review presents the rationale, process workflow, and technology for the safe and effective administration of SBRT, as well as the indications, outcome, and limitations for this technique in the treatment of lung cancer, liver cancer, and metastatic disease.

Quantifying U-Net uncertainty in multi-parametric MRI-based glioma segmentation by spherical image projection.

BACKGROUND: Uncertainty quantification in deep learning is an important research topic. For medical image segmentation, the uncertainty measurements are usually reported as the likelihood that each pixel belongs to the predicted segmentation region. In potential clinical applications, the uncertainty result reflects the algorithm's robustness and supports the confidence and trust of the segmentation result when the ground-truth result is absent. For commonly studied deep learning models, novel methods for quantifying segmentation uncertainty are in demand. PURPOSE: To develop a U-Net segmentation uncertainty quantification method based on spherical image projection of multi-parametric MRI (MP-MRI) in glioma segmentation. METHODS: The projection of planar MRI data onto a spherical surface is equivalent to a nonlinear image transformation that retains global anatomical information. By incorporating this image transformation process in our proposed spherical projection-based U-Net (SPU-Net) segmentation model design, multiple independent segmentation predictions can be obtained from a single MRI. The final segmentation is the average of all available results, and the variation can be visualized as a pixel-wise uncertainty map. An uncertainty score was introduced to evaluate and compare the performance of uncertainty measurements. The proposed SPU-Net model was implemented on the basis of 369 glioma patients with MP-MRI scans (T1, T1-Ce, T2, and FLAIR). Three SPU-Net models were trained to segment enhancing tumor (ET), tumor core (TC), and whole tumor (WT), respectively. The SPU-Net model was compared with (1) the classic U-Net model with test-time augmentation (TTA) and (2) linear scaling-based U-Net (LSU-Net) segmentation models in terms of both segmentation accuracy (Dice coefficient, sensitivity, specificity, and accuracy) and segmentation uncertainty (uncertainty map and uncertainty score). RESULTS: The developed SPU-Net model successfully achieved low uncertainty for correct segmentation predictions (e.g., tumor interior or healthy tissue interior) and high uncertainty for incorrect results (e.g., tumor boundaries). This model could allow the identification of missed tumor targets or segmentation errors in U-Net. Quantitatively, the SPU-Net model achieved the highest uncertainty scores for three segmentation targets (ET/TC/WT): 0.826/0.848/0.936, compared to 0.784/0.643/0.872 using the U-Net with TTA and 0.743/0.702/0.876 with the LSU-Net (scaling factor = 2). The SPU-Net also achieved statistically significantly higher Dice coefficients, underscoring the improved segmentation accuracy. CONCLUSION: The SPU-Net model offers a powerful tool to quantify glioma segmentation uncertainty while improving segmentation accuracy. The proposed method can be generalized to other medical image-related deep-learning applications for uncertainty evaluation.

Prognostic value of different discretization parameters in 18fluorodeoxyglucose positron emission tomography radiomics of oropharyngeal squamous cell carcinoma.

Purpose: We aim to interrogate the role of positron emission tomography (PET) image discretization parameters on the prognostic value of radiomic features in patients with oropharyngeal cancer. Approach: A prospective clinical trial (NCT01908504) enrolled patients with oropharyngeal squamous cell carcinoma (N=69; mixed HPV status) undergoing definitive radiotherapy and evaluated intra-treatment 18fluorodeoxyglucose PET as a potential imaging biomarker of early metabolic response. The primary tumor volume was manually segmented by a radiation oncologist on PET/CT images acquired two weeks into treatment (20 Gy). From this, 54 radiomic texture features were extracted. Two image discretization techniques-fixed bin number (FBN) and fixed bin size (FBS)-were considered to evaluate systematic changes in the bin number ({32, 64, 128, 256} gray levels) and bin size ({0.10, 0.15, 0.22, 0.25} bin-widths). For each discretization-specific radiomic feature space, an LASSO-regularized logistic regression model was independently trained to predict residual and/or recurrent disease. The model training was based on Monte Carlo cross-validation with a 20% testing hold-out, 50 permutations, and minor-class up-sampling to account for imbalanced outcomes data. Performance differences among the discretization-specific models were quantified via receiver operating characteristic curve analysis. A final parameter-optimized logistic regression model was developed by incorporating different settings parameterizations into the same model. Results: FBN outperformed FBS in predicting residual and/or recurrent disease. The four FBN models achieved AUC values of 0.63, 0.61, 0.65, and 0.62 for 32, 64, 128, and 256 gray levels, respectively. By contrast, the average AUC of the four FBS models was 0.53. The parameter-optimized model, comprising features joint entropy (FBN = 64) and information measure correlation 1 (FBN = 128), achieved an AUC of 0.70. Kaplan-Meier analyses identified these features to be associated with disease-free survival (p=0.0158 and p=0.0180, respectively; log-rank test). Conclusions: Our findings suggest that the prognostic value of individual radiomic features may depend on feature-specific discretization parameter settings.

Radiomics on spatial-temporal manifolds via Fokker-Planck dynamics.

BACKGROUND: Delta radiomics is a high-throughput computational technique used to describe quantitative changes in serial, time-series imaging by considering the relative change in radiomic features of images extracted at two distinct time points. Recent work has demonstrated a lack of prognostic signal of radiomic features extracted using this technique. We hypothesize that this lack of signal is due to the fundamental assumptions made when extracting features via delta radiomics, and that other methods should be investigated. PURPOSE: The purpose of this work was to show a proof-of-concept of a new radiomics paradigm for sparse, time-series imaging data, where features are extracted from a spatial-temporal manifold modeling the time evolution between images, and to assess the prognostic value on patients with oropharyngeal cancer (OPC). METHODS: To accomplish this, we developed an algorithm to mathematically describe the relationship between two images acquired at time t = 0 $t = 0$ and t > 0 $t > 0$ . These images serve as boundary conditions of a partial differential equation describing the transition from one image to the other. To solve this equation, we propagate the position and momentum of each voxel according to Fokker-Planck dynamics (i.e., a technique common in statistical mechanics). This transformation is driven by an underlying potential force uniquely determined by the equilibrium image. The solution generates a spatial-temporal manifold (3 spatial dimensions + time) from which we define dynamic radiomic features. First, our approach was numerically verified by stochastically sampling dynamic Gaussian processes of monotonically decreasing noise. The transformation from high to low noise was compared between our Fokker-Planck estimation and simulated ground-truth. To demonstrate feasibility and clinical impact, we applied our approach to 18F-FDG-PET images to estimate early metabolic response of patients (n = 57) undergoing definitive (chemo)radiation for OPC. Images were acquired pre-treatment and 2-weeks intra-treatment (after 20 Gy). Dynamic radiomic features capturing changes in texture and morphology were then extracted. Patients were partitioned into two groups based on similar dynamic radiomic feature expression via k-means clustering and compared by Kaplan-Meier analyses with log-rank tests (p < 0.05). These results were compared to conventional delta radiomics to test the added value of our approach. RESULTS: Numerical results confirmed our technique can recover image noise characteristics given sparse input data as boundary conditions. Our technique was able to model tumor shrinkage and metabolic response. While no delta radiomics features proved prognostic, Kaplan-Meier analyses identified nine significant dynamic radiomic features. The most significant feature was Gray-Level-Size-Zone-Matrix gray-level variance (p = 0.011), which demonstrated prognostic improvement over its corresponding delta radiomic feature (p = 0.722). CONCLUSIONS: We developed, verified, and demonstrated the prognostic value of a novel, physics-based radiomics approach over conventional delta radiomics via data assimilation of quantitative imaging and differential equations.