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PurposeMotion management of tumors within the lung and abdomen is challenging because it requires balancing tissue sparing with accuracy of hitting the target, while considering treatment delivery efficiency. Physicists can play an important role in analyzing four-dimensional computed tomography (4DCT) data to recommend the optimal respiratory gating parameters for a patient. The goal of this work was to develop a standardized procedure for making recommendations regarding gating parameters and planning margins for lung and gastrointestinal stereotactic body radiotherapy (SBRT) treatments. In doing so, we hoped to simplify decision-making and analysis, and provide a tool for troubleshooting complex cases.MethodsFactors that impact gating decisions and planning target volume (PTV) margins were identified. The gating options included gating on exhale with approximately a 50% duty cycle (Gate3070), exhale gating with a reduced duty cycle (Gate4060), and treating for most of respiration, excluding only extreme inhales and exhales (Gate100). A standard operating procedure was developed, as well as a physics consult document to communicate motion management recommendations to other members of the treatment team. This procedure was implemented clinically for 1 year and results are reported below.ResultsIdentified factors that impact motion management included the magnitude of motion observed on 4DCT, the regularity of breathing and quality of 4DCT data, and ability to observe the target on fluoroscopy. These were collated into two decision tables-one specific to lung tumors and another for gastrointestinal tumors-such that a physicist could answer a series of questions to determine the optimal gating and PTV margin. The procedure was used clinically for 252 sites from 213 patients treated with respiratory-gated SBRT and standardized practice across our 12-member physics team.ConclusionImplementation of a standardized procedure for respiratory gating had a positive impact in our clinic, improving efficiency and ease of 4DCT analysis and standardizing gating decision-making amongst physicists.

PurposeThe purpose of this work was to develop a knowledge-based dose prediction system using a convolution neural network (CNN) for cervical brachytherapy treatments with a tandem-and-ovoid applicator.MethodsA 3D U-NET CNN was utilized to make voxel-wise dose predictions based on organ-at-risk (OAR), high-risk clinical target volume (HRCTV), and possible source location geometry. The model comprised 395 previously treated cases: training (273), validation (61), test (61). To assess voxel prediction accuracy, we evaluated dose differences in all cohorts across the dose range of 20-130% of prescription, mean (SD) and standard deviation (σ), as well as isodose dice similarity coefficients for clinical and/or predicted dose distributions. We examined discrete Dose-Volume Histogram (DVH) metrics utilized for brachytherapy plan quality assessment (HRCTV D90%; bladder, rectum, and sigmoid D2cc) with ΔDx=Dx,actual-Dx,predicted mean, standard deviation, and Pearson correlation coefficient further quantifying model performance.ResultsRanges of voxel-wise dose difference accuracy (δD¯±σ) for 20-130% dose interval in training (test) sets ranged from [-0.5% ± 2.0% to +2.0% ± 14.0%] ([-0.1% ± 4.0% to +4.0% ± 26.0%]) in all voxels, [-1.7% ± 5.1% to -3.5% ± 12.8%] ([-2.9% ± 4.8% to -2.6% ± 18.9%]) in HRCTV, [-0.02% ± 2.40% to +3.2% ± 12.0%] ([-2.5% ± 3.6% to +0.8% ± 12.7%]) in bladder, [-0.7% ± 2.4% to +15.5% ± 11.0%] ([-0.9% ± 3.2% to +27.8% ± 11.6%]) in rectum, and [-0.7% ± 2.3% to +10.7% ± 15.0%] ([-0.4% ± 3.0% to +18.4% ± 11.4%]) in sigmoid. Isodose dice similarity coefficients ranged from [0.96,0.91] for training and [0.94,0.87] for test cohorts. Relative DVH metric prediction in the training (test) set were HRCTV ΔD¯90±σΔD = -0.19 ± 0.55Gy (-0.09 ± 0.67 Gy), bladder ΔD¯2cc±σΔD = -0.06 ± 0.54Gy (-0.17 ± 0.67 Gy), rectum ΔD¯2cc±σΔD= -0.03 ± 0.36Gy (-0.04 ± 0.46 Gy), and sigmoid ΔD¯2cc±σΔD = -0.01 ± 0.34Gy (0.00 ± 0.44 Gy).ConclusionsA 3D knowledge-based dose predictions provide voxel-level and DVH metric estimates that could be used for treatment plan quality control and data-driven plan guidance.

PurposeThe use of interstitial needles, combined with intracavitary applicators, enables customized dose distributions and is beneficial for complex cases, but increases procedure time. Overall, applicator selection is not standardized and depends on physician expertise and preference. The purpose of this study is to determine whether dose prediction models can guide needle supplementation decision-making for cervical cancer.Materials and methodsIntracavitary knowledge-based models for organ-at-risk (OAR) dose estimation were trained and validated for tandem-and-ring/ovoids (T&R/T&O) implants. Models were applied to hybrid cases with 1-3 implanted needles to predict OAR dose without needles. As a reference, 70/67 hybrid T&R/T&O cases were replanned without needles, following a standardized procedure guided by dose predictions. If a replanned dose exceeded the dose objective, the case was categorized as requiring needles. Receiver operating characteristic (ROC) curves of needle classification accuracy were generated. Optimal classification thresholds were determined from the Youden Index.ResultsNeedle supplementation reduced dose to OARs. However, 67%/39% of replans for T&R/T&O met all dose constraints without needles. The ROC for T&R/T&O models had an area-under-curve of 0.89/0.86, proving high classification accuracy. The optimal threshold of 99%/101% of the dose limit for T&R/T&O resulted in classification sensitivity and specificity of 78%/86% and 85%/78%.ConclusionsNeedle supplementation reduced OAR dose for most cases but was not always required to meet standard dose objectives, particularly for T&R cases. Our knowledge-based dose prediction model accurately identified cases that could have met constraints without needle supplementation, suggesting that such models may be beneficial for applicator selection.

PurposeCurrently, there is a lack of patient-specific tools to guide brachytherapy planning and applicator choice for cervical cancer. The purpose of this study is to evaluate the accuracy of organ-at-risk (OAR) dose predictions using knowledge-based intracavitary models, and the use of these models and clinical data to determine the dosimetric differences of tandem-and-ring (T&R) and tandem-and-ovoids (T&O) applicators.Materials and methodsKnowledge-based models, which predict organ D2cc, were trained on 77/75 cases and validated on 32/38 for T&R/T&O applicators. Model performance was quantified using ΔD2cc=D2cc,actual-D2cc,predicted, with standard deviation (σ(ΔD2cc)) representing precision. Model-predicted applicator dose differences were determined by applying T&O models to T&R cases, and vice versa, and compared to clinically-achieved D2cc differences. Applicator differences were assessed using a Student's t-test (p < 0.05 significant).ResultsValidation T&O/T&R model precision was 0.65/0.55 Gy, 0.55/0.38 Gy, and 0.43/0.60 Gy for bladder, rectum and sigmoid, respectively, and similar to training. When applying T&O/T&R models to T&R/T&O cases, bladder, rectum and sigmoid D2cc values in EQD2 were on average 5.69/2.62 Gy, 7.31/6.15 Gy and 3.65/0.69 Gy lower for T&R, with similar HRCTV volume and coverage. Clinical data also showed lower T&R OAR doses, with mean EQD2 D2cc deviations of 0.61 Gy, 7.96 Gy (p < 0.01) and 5.86 Gy (p < 0.01) for bladder, rectum and sigmoid.ConclusionsAccurate knowledge-based dose prediction models were developed for two common intracavitary applicators. These models could be beneficial for standardizing and improving the quality of brachytherapy plans. Both models and clinical data suggest that significant OAR sparing can be achieved with T&R over T&O applicators, particularly for the rectum.

PurposeThe purpose of this study is to explore knowledge-based organ-at-risk dose estimation for intracavitary brachytherapy planning for cervical cancer. Using established external-beam knowledge-based dose-volume histogram (DVH) estimation methods, we sought to predict bladder, rectum, and sigmoid D2cc for tandem and ovoid treatments.Methods and materialsA total of 136 patients with loco-regionally advanced cervical cancer treated with 456 (356:100 training:validation ratio) CT-based tandem and ovoid brachytherapy fractions were analyzed. Single fraction prescription doses were 5.5-8 Gy with dose criteria for the high-risk clinical target volume, bladder, rectum, and sigmoid. DVH estimations were obtained by subdividing training set organs-at-risk into high-risk clinical target volume boundary distance subvolumes and computing cohort-averaged differential DVHs. Full DVH estimation was then performed on the training and validation sets. Model performance was quantified by ΔD2cc = D2cc(actual)-D2cc(predicted) (mean and standard deviation). ΔD2cc between training and validation sets were compared with a Student's t test (p < 0.01 significant). Categorical variables (physician, fraction-number, total fractions, and case complexity) that might explain model variance were examined using an analysis of variance test (Bonferroni-corrected p < 0.01 threshold).ResultsTraining set deviations were bladder ΔD2cc = -0.04 ± 0.61 Gy, rectum ΔD2cc = 0.02 ± 0.57 Gy, and sigmoid ΔD2cc = -0.05 ± 0.52 Gy. Model predictions on validation set did not statistically differ: bladder ΔD2cc = -0.02 ± 0.46 Gy (p = 0.80), rectum ΔD2cc = -0.007 ± 0.47 Gy (p = 0.53), and sigmoid ΔD2cc = -0.07 ± 0.47 Gy (p = 0.70). The only significant categorical variable was the attending physician for bladder and rectum ΔD2cc. CONCLUSION: A simple boundary distance-driven knowledge-based DVH estimation exhibited promising results in predicting critical brachytherapy dose metrics. Future work will examine the utility of these predictions for quality control and automated brachytherapy planning.

PurposeTo evaluate the performance and stability of Elekta Agility multi-leaf collimator (MLC) leaf positioning using a daily, automated quality control (QC) test based on megavoltage (MV) images in combination with statistical process control tools, and identify special causes of variations in performance.MethodsLeaf positions were collected daily for 13 Elekta linear accelerators over 11-37 months using the automated QC test, which analyzes 23 MV images to determine the location of MLC leaves relative to radiation isocenter. Leaf positioning stability was assessed using individual and moving range control charts. Specification levels of ±0.5, ±1, and ±1.5 mm were tested to determine positional accuracy. The durations between out-of-control and out-of-specification events were determined. Peaks in out-of-control leaf positions were identified and correlated to servicing events recorded for the whole duration of data collection.ResultsMean leaf position error was -0.01 mm (range -1.3-1.6). Data stayed within ±1 mm specification for 457 days on average (range 3-838) and within ±1.5 mm for the entire date range. Measurements stayed within ±0.5 mm for 1 day on average (range 0-17); however, our MLC leaves were not calibrated to this level of accuracy. Leaf position varied little over time, as confirmed by tight individual (mean ±0.19 mm, range 0.09-0.43) and moving range (mean 0.23 mm, range 0.10-0.53) control limits. Due to sporadic out-of-control events, the mean in-control duration was 2.8 days (range 1-28.5). A number of factors were found to contribute to leaf position errors and out-of-control behavior, including servicing events, beam spot motion, and image artifacts.ConclusionsThe Elekta Agility MLC model was found to perform with high stability, as evidenced by the tight control limits. The in-specification durations support the current recommendation of monthly MLC QC tests with a ±1 mm tolerance. Future work is on-going to determine if performance can be optimized further using high-frequency QC test results to drive recalibration frequency.

Background: 3D neural network dose predictions are useful for automating brachytherapy (BT) treatment planning for cervical cancer. Cervical BT can be delivered with numerous applicators, which necessitates developing models that generalize to multiple applicator types. The variability and scarcity of data for any given applicator type poses challenges for deep learning. Purpose: The goal of this work was to compare three methods of neural network training—a single model trained on all applicator data, fine‐tuning the combined model to each applicator, and individual (IDV) applicator models—to determine the optimal method for dose prediction. Methods: Models were produced for four applicator types—tandem‐and‐ovoid (T&O), T&O with 1–7 needles (T&ON), tandem‐and‐ring (T&R) and T&R with 1–4 needles (T&RN). First, the combined model was trained on 859 treatment plans from 266 cervical cancer patients treated from 2010 onwards. The train/validation/test split was 70%/16%/14%, with approximately 49%/10%/19%/22% T&O/T&ON/T&R/T&RN in each dataset. Inputs included four channels for anatomical masks (high‐risk clinical target volume [HRCTV], bladder, rectum, and sigmoid), a mask indicating dwell position locations, and applicator channels for each applicator component. Applicator channels were created by mapping the 3D dose for a single dwell position to each dwell position and summing over each applicator component with uniform dwell time weighting. A 3D Cascade U‐Net, which consists of two U‐Nets in sequence, and mean squared error loss function were used. The combined model was then fine‐tuned to produce four applicator‐specific models by freezing the first U‐Net and encoding layers of the second and resuming training on applicator‐specific data. Finally, four IDV models were trained using only data from each applicator type. Performance of these three model types was compared using the following metrics for the test set: mean error (ME, representing model bias) and mean absolute error (MAE) over all dose voxels and ME of clinical metrics (HRCTV D90% and D2cc of bladder, rectum, and sigmoid), averaged over all patients. A positive ME indicates the clinical dose was higher than predicted. 3D global gamma analysis with the prescription dose as reference value was performed. Dice similarity coefficients (DSC) were computed for each isodose volume. Results: Fine‐tuned and combined models showed better performance than IDV applicator training. Fine‐tuning resulted in modest improvements in about half the metrics, compared to the combined model, while the remainder were mostly unchanged. Fine‐tuned MAE = 3.98%/2.69%/5.36%/3.80% for T&O/T&R/T&ON/T&RN, and ME over all voxels = –0.08%/–0.89%/–0.59%/1.42%. ME D2cc were bladder = –0.77%/1.00%/–0.66%/–1.53%, rectum = 1.11%/–0.22%/–0.29%/–3.37%, sigmoid = –0.47%/–0.06%/–2.37%/–1.40%, and ME D90 = 2.6%/–4.4%/4.8%/0.0%. Gamma pass rates (3%/3 mm) were 86%/91%/83%/89%. Mean DSCs were 0.92%/0.92%/0.88%/0.91% for isodoses ≤ 150% of prescription. Conclusions: 3D BT dose was accurately predicted for all applicator types, as indicated by the low MAE and MEs, high gamma scores and high DSCs. Training on all treatment data overcomes challenges with data scarcity in each applicator type, resulting in superior performance than can be achieved by training on IDV applicators alone. This could presumably be explained by the fact that the larger, more diverse dataset allows the neural network to learn underlying trends and characteristics in dose that are common to all treatment applicators. Accurate, applicator‐specific dose predictions could enable automated, knowledge‐based planning for any cervical brachytherapy treatment. [ABSTRACT FROM AUTHOR]

Objective. To lay the foundation for automated knowledge-based brachytherapy treatment planning using 3D dose estimations, we describe an optimization framework to convert brachytherapy dose distributions directly into dwell times (DTs). Approach. A dose rate kernel d ̇ (r , θ , φ) was produced by exporting 3D dose for one dwell position from the treatment planning system and normalizing by DT. By translating and rotating this kernel to each dwell position, scaling by DT and summing over all dwell positions, dose was computed (D calc). We used a Python-coded COBYLA optimizer to iteratively determine the DTs that minimize the mean squared error between D calc and reference dose D ref, computed using voxels with D ref 80%–120% of prescription. As validation of the optimization, we showed that the optimizer replicates clinical plans when D ref = clinical dose in 40 patients treated with tandem-and-ovoid (T&O) or tandem-and-ring (T&R) and 0–3 needles. Then we demonstrated automated planning in 10 T&O using D ref = dose predicted from a convolutional neural network developed in past work. Validation and automated plans were compared to clinical plans using mean absolute differences ( MAD = 1 N ∑ n = 1 N abs x n − x n ′ ) over all voxels (xn = Dose, N = #voxels) and DTs (xn = DT, N = #dwell positions), mean differences (MD) in organ D 2cc and high-risk CTV D90 over all patients (where positive indicates higher clinical dose), and mean Dice similarity coefficients (DSC) for 100% isodose contours. Main results. Validation plans agreed well with clinical plans (MADdose = 1.1%, MADDT = 4 s or 0.8% of total plan time, D 2cc MD = −0.2% to 0.2% and D90 MD = −0.6%, DSC = 0.99). For automated plans, MADdose = 6.5% and MADDT = 10.3 s (2.1%). The slightly higher clinical metrics in automated plans (D 2cc MD = −3.8% to 1.3% and D90 MD = −5.1%) were due to higher neural network dose predictions. The overall shape of the automated dose distributions were similar to clinical doses (DSC = 0.91). Significance. Automated planning with 3D dose predictions could provide significant time savings and standardize treatment planning across practitioners, regardless of experience. [ABSTRACT FROM AUTHOR]

Background and Purpose: Quantitative T1 and diffusion tensor imaging (DTI) may provide information about pathological changes underlying disability and progression in diseases like multiple sclerosis (MS). Imaging the corpus callosum (CC), a primary site of damage in MS with a critical role in interhemispheric connectivity, may be useful for assessing overall brain health, prognosis, and therapy efficacy. We assessed the feasibility of multisite clinical trials using advanced MRI by examining the intra and intersite reproducibility of T1 and DTI measurements in the CC and segmented white matter (WM).Methods: Five healthy volunteers were scanned twice within 24 hours at six 3T sites. Coefficients of variation (COVs) and intraclass correlation coefficients (ICCs) for CC and WM T1 , fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (Dax ), and radial diffusivity (Drad ) assessed intrasite and intersite reliability.Results: CC and WM T1 showed excellent intrasite reproducibility with low COVs (mean = .90% and .89%, respectively) and good ICCs (CC = .78, WM = .90). T1 also demonstrated intersite reliability (low COVs: CC = 2.4%, WM = 1.8%; moderate ICCs: CC = .43, WM = .69). DTI had low intrasite COVs (CC: FA = 1.3%, MD = 1.5%, Dax = 1.4%, Drad = 2.2%; WM: FA = .9%, MD = .9%, Dax = .7%, Drad = 1.2%) and high intrasite ICCs (CC: FA = .95, MD = .97, Dax = .94, Drad = .97; CC: FA = .9, MD = .66, Dax = .88, Drad = .63), indicating excellent intrasite reproducibility. DTI also showed excellent intersite reliability with low COVs (CC: FA = 2.1%, MD = 4.1%, Dax = 3.4%, Drad = 5.3%, WM: FA = 1.3%, MD = 1.9%, Dax = 1.8%, Drad = 2.1%,) and good ICCs (CC: FA = .90, MD = .84, Dax = .72, Drad = .90; WM: FA = .83, MD = .34, Dax = .62, Drad = .41).Conclusions: T1 and DTI measures are reproducible using equivalent MRI scanners and sequence protocols. Using a similar MR system, it is feasible to carry out multicenter studies using T1 and DTI to evaluate changes within the CC and WM. [ABSTRACT FROM AUTHOR]

Purpose To assess the reproducibility of myelin water fraction (MWF) and geometric mean T2 (GMT2), which are in vivo markers of pathological changes underlying disability and progression in diseases such as multiple sclerosis. Materials and Methods Five healthy volunteers were scanned twice within 24 hours at six different sites using the same manufacturer's 3T magnetic resonance (MR) system. T2 distributions were produced by fitting multiecho 3D T2 data using non-negative least squares, with stimulated echo correction. MWF, the fraction of signal with T2 between 15 and 40 msec to the entire signal, and GMT2, the mean T2 on a logarithmic scale from T2 between 40 and 200 msec, were examined in white matter. Results Intrasite coefficients of variation (COVs) were low (mean 3.99% for MWF and 0.51% for GMT2), as were intersite COVs (mean 4.68% for MWF, 0.31% for GMT2). Scan-rescan intraclass correlation coefficients (ICCs) (0.76 for MWF and 0.93 for GMT2) and Bland-Altman plots indicated good agreement between single site scans. Intersite ICCs were relatively high (0.69 for MWF and 0.92 for GMT2), revealing good intersite reliability. Conclusion MWF and GMT2 measures are reproducible between scans and across sites with an equivalent MR scanner and sequence protocol. Multicenter clinical trials using quantitative T2 relaxation are feasible. J. Magn. Reson. Imaging 2013;38:1445-1453. © 2013 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Background: The quality of treatment plans for breast cancer can vary greatly. This variation could be reduced by using dose prediction to automate treatment planning. Our work investigates novel methods for training deep-learning models that are capable of producing high-quality dose predictions for breast cancer treatment planning., Purpose: The goal of this work was to compare the performance impact of two novel techniques for deep learning dose prediction models for tangent field treatments for breast cancer. The first technique, a "glowing" mask algorithm, encodes the distance from a contour into each voxel in a mask. The second, a gradient-weighted mean squared error (MSE) loss function, emphasizes the error in high-dose gradient regions in the predicted image., Methods: Four 3D U-Net deep learning models were trained using the planning CT and contours of the heart, lung, and tumor bed as inputs. The dataset consisted of 305 treatment plans split into 213/46/46 training/validation/test sets using a 70/15/15% split. We compared the impact of novel "glowing" anatomical mask inputs and a novel gradient-weighted MSE loss function to their standard counterparts, binary anatomical masks, and MSE loss, using an ablation study methodology. To assess performance, we examined the mean error and mean absolute error (ME/MAE) in dose across all within-body voxels, the error in mean dose to heart, ipsilateral lung, and tumor bed, dice similarity coefficient (DSC) across isodose volumes defined by 0%-100% prescribed dose thresholds, and gamma analysis (3%/3 mm)., Results: The combination of novel glowing masks and gradient weighted loss function yielded the best-performing model in this study. This model resulted in a mean ME of 0.40%, MAE of 2.70%, an error in mean dose to heart and lung of -0.10 and 0.01 Gy, and an error in mean dose to the tumor bed of -0.01%. The median DSC at 50/95/100% isodose levels were 0.91/0.87/0.82. The mean 3D gamma pass rate (3%/3 mm) was 93%., Conclusions: This study found the combination of novel anatomical mask inputs and loss function for dose prediction resulted in superior performance to their standard counterparts. These results have important implications for the field of radiotherapy dose prediction, as the methods used here can be easily incorporated into many other dose prediction models for other treatment sites. Additionally, this dose prediction model for breast radiotherapy has sufficient performance to be used in an automated planning pipeline for tangent field radiotherapy and has the major benefit of not requiring a PTV for accurate dose prediction., (© 2024 American Association of Physicists in Medicine.)

Background and Purpose: Quantitative T 1 and diffusion tensor imaging (DTI) may provide information about pathological changes underlying disability and progression in diseases like multiple sclerosis (MS). Imaging the corpus callosum (CC), a primary site of damage in MS with a critical role in interhemispheric connectivity, may be useful for assessing overall brain health, prognosis, and therapy efficacy. We assessed the feasibility of multisite clinical trials using advanced MRI by examining the intra and intersite reproducibility of T 1 and DTI measurements in the CC and segmented white matter (WM)., Methods: Five healthy volunteers were scanned twice within 24 hours at six 3T sites. Coefficients of variation (COVs) and intraclass correlation coefficients (ICCs) for CC and WM T 1 , fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (D ax ), and radial diffusivity (D rad ) assessed intrasite and intersite reliability., Results: CC and WM T 1 showed excellent intrasite reproducibility with low COVs (mean = .90% and .89%, respectively) and good ICCs (CC = .78, WM = .90). T 1 also demonstrated intersite reliability (low COVs: CC = 2.4%, WM = 1.8%; moderate ICCs: CC = .43, WM = .69). DTI had low intrasite COVs (CC: FA = 1.3%, MD = 1.5%, D ax = 1.4%, D rad = 2.2%; WM: FA = .9%, MD = .9%, D ax = .7%, D rad = 1.2%) and high intrasite ICCs (CC: FA = .95, MD = .97, D ax = .94, D rad = .97; CC: FA = .9, MD = .66, D ax = .88, D rad = .63), indicating excellent intrasite reproducibility. DTI also showed excellent intersite reliability with low COVs (CC: FA = 2.1%, MD = 4.1%, D ax = 3.4%, D rad = 5.3%, WM: FA = 1.3%, MD = 1.9%, D ax = 1.8%, D rad = 2.1%,) and good ICCs (CC: FA = .90, MD = .84, D ax = .72, D rad = .90; WM: FA = .83, MD = .34, D ax = .62, D rad = .41)., Conclusions: T 1 and DTI measures are reproducible using equivalent MRI scanners and sequence protocols. Using a similar MR system, it is feasible to carry out multicenter studies using T 1 and DTI to evaluate changes within the CC and WM., (© 2018 by the American Society of Neuroimaging.)