Unlocking the adaptive advantage: correlation and machine learning classification to identify optimal online adaptive stereotactic partial breast candidates.

OBJECTIVE: Online adaptive radiotherapy (OART) is a promising technique for delivering stereotactic accelerated partial breast irradiation (APBI), as lumpectomy cavities vary in location and size between simulation and treatment. However, OART is resource-intensive, increasing planning and treatment times and decreasing machine throughput compared to the standard of care (SOC). Thus, it is pertinent to identify high-yield OART candidates to best allocate resources. Approach. Reference plans (plans based on simulation anatomy), SOC plans (reference plans recalculated onto daily anatomy), and daily adaptive plans were analyzed for 31 sequential APBI targets, resulting in the analysis of 333 treatment plans. Spearman correlations between 22 reference plan metrics and 10 adaptive benefits, defined as the difference between mean SOC and delivered metrics, were analyzed to select a univariate predictor of OART benefit. A multivariate logistic regression model was then trained to stratify high- and low-benefit candidates. Main Results. Adaptively delivered plans showed dosimetric benefit as compared to SOC plans for most plan metrics, although the degree of adaptive benefit varied per patient. The univariate model showed high likelihood for dosimetric adaptive benefit when the reference plan ipsilateral breast V15Gy exceeds 23.5%. Recursive feature elimination identified 5 metrics that predict high-dosimetric-benefit adaptive patients. Using leave-one-out cross validation, the univariate and multivariate models classified targets with 74.2% and 83.9% accuracy, resulting in improvement in per-fraction adaptive benefit between targets identified as high- and low-yield for 7/10 and 8/10 plan metrics, respectively. Significance. This retrospective, exploratory study demonstrated that dosimetric benefit can be predicted using only ipsilateral breast V15 Gy on the reference treatment plan, allowing for a simple, interpretable model. Using multivariate logistic regression for adaptive benefit prediction led to increased accuracy at the cost of a more complicated model. This work presents a methodology for clinics wishing to triage OART resource allocation. .

Longitudinal brain volumetrics in glioma survivors.

OBJECTIVE: Radiation therapy (RT) is used selectively for patients with low-grade glioma (LGG) given the concerns for potential cognitive effects in survivors, but prior cognitive outcome studies among LGG survivors have had inconsistent findings. Translational studies that characterize changes in brain anatomy and physiology after treatment of LGG may help to both contextualize cognitive findings and improve the overall understanding of radiation effects in normal brain tissue. This study aimed to investigate the hypothesis that patients with LGG who are treated with RT will experience greater brain volume loss than those who do not receive RT. METHODS: This retrospective longitudinal study included all patients with WHO grade 2 glioma who received posttreatment surveillance MRI at the University of Alabama at Birmingham. Volumetric analysis of contralateral cortical white matter (WM), cortical gray matter (GM), and hippocampus was performed on all posttreatment T1-weighted MRI sequences using the SynthSeg script. The effect of clinical and treatment variables on brain volumes was assessed using two-level hierarchical linear models. RESULTS: The final study cohort consisted of 105 patients with 1974 time points analyzed. The median length of imaging follow-up was 4.6 years (range 0.36-18.9 years), and the median number of time points analyzed per patient was 12 (range 2-40). Resection was performed in 79 (75.2%) patients, RT was administered to 61 (58.1%) patients, and chemotherapy was administered to 66 (62.9%) patients. Age at diagnosis (ß = -0.06, p < 0.001) and use of RT (ß = -1.12, p = 0.002) were associated with the slope of the contralateral cortical GM volume model (i.e., change in GM over time). Age at diagnosis (ß = -0.08, p < 0.001), midline involvement (ß = 1.31, p = 0.006), and use of RT (ß = -1.45, p = 0.001) were associated with slope of the contralateral cortical WM volume model. Age (ß = -0.0027, p = 0.001), tumor resection (ß = -0.069, p < 0.001), use of chemotherapy (ß = -0.0597, p = 0.003), and use of RT (ß = -0.0589, p < 0.001) were associated with the slope of the contralateral hippocampus volume model. CONCLUSIONS: This study demonstrated volume loss in contralateral brain structures among LGG survivors, and patients who received RT experienced greater volume loss than those who did not. The results of this study may help to provide context for cognitive outcome research in LGG survivors and inform the design of future strategies to preserve cognition.

Prospective Evaluation of Automated Contouring for CT-Based Brachytherapy for Gynecologic Malignancies.

Purpose: The use of deep learning to auto-contour organs at risk (OARs) in gynecologic radiation treatment is well established. Yet, there is limited data investigating the prospective use of auto-contouring in clinical practice. In this study, we assess the accuracy and efficiency of auto-contouring OARs for computed tomography-based brachytherapy treatment planning of gynecologic malignancies. Methods and Materials: An inhouse contouring tool automatically delineated 5 OARs in gynecologic radiation treatment planning: the bladder, small bowel, sigmoid, rectum, and urethra. Accuracy of each auto-contour was evaluated using a 5-point Likert scale: a score of 5 indicated the contour could be used without edits, while a score of 1 indicated the contour was unusable. During scoring, automated contours were edited and subsequently used for treatment planning. Dice similarity coefficient, mean surface distance, 95% Hausdorff distance, Hausdorff distance, and dosimetric changes between original and edited contours were calculated. Contour approval time and total planning time of a prospective auto-contoured (AC) cohort were compared with times from a retrospective manually contoured (MC) cohort. Results: Thirty AC cases from January 2022 to July 2022 and 31 MC cases from July 2021 to January 2022 were included. The mean (±SD) Likert score for each OAR was the following: bladder 4.77 (±0.58), small bowel 3.96 (±0.91), sigmoid colon 3.92 (±0.81), rectum 4.6 (±0.71), and urethra 4.27 (±0.78). No ACs required major edits. All OARs had a mean Dice similarity coefficient > 0.86, mean surface distance < 0.48 mm, 95% Hausdorff distance < 3.2 mm, and Hausdorff distance < 10.32 mm between original and edited contours. There was no significant difference in dose-volume histogram metrics (D2.0 cc/D0.1 cc) between original and edited contours (P values > .05). The average time to plan approval in the AC cohort was 19% less than the MC cohort. (AC vs MC, 117.0 + 18.0 minutes vs 144.9 ± 64.5 minutes, P = .045). Conclusions: Automated contouring is useful and accurate in clinical practice. Auto-contouring OARs streamlines radiation treatment workflows and decreases time required to design and approve gynecologic brachytherapy plans.

Automatic end-to-end VMAT treatment planning for rectal cancers.

BACKGROUND: The treatment planning process from segmentation to producing a deliverable plan is time-consuming and labor-intensive. Existing solutions automate the segmentation and planning processes individually. The feasibility of combining auto-segmentation and auto-planning for volumetric modulated arc therapy (VMAT) for rectal cancers in an end-to-end process is not clear. PURPOSE: To create and clinically evaluate a complete end-to-end process for auto-segmentation and auto-planning of VMAT for rectal cancer requiring only the gross tumor volume contour and a CT scan as inputs. METHODS: Patient scans and data were retrospectively selected from our institutional records for patients treated for malignant neoplasm of the rectum. We trained, validated, and tested deep learning auto-segmentation models using nnU-Net architecture for clinical target volume (CTV), bowel bag, large bowel, small bowel, total bowel, femurs, bladder, bone marrow, and female and male genitalia. For the CTV, we identified 174 patients with clinically drawn CTVs. We used data for 18 patients for all structures other than the CTV. The structures were contoured under the guidance of and reviewed by a gastrointestinal (GI) radiation oncologist. The predicted results for CTV in 35 patients and organs at risk (OAR) in six patients were scored by the GI radiation oncologist using a five-point Likert scale. For auto-planning, a RapidPlan knowledge-based planning solution was modeled for VMAT delivery with a prescription of 25 Gy in five fractions. The model was trained and tested on 20 and 34 patients, respectively. The resulting plans were scored by two GI radiation oncologists using a five-point Likert scale. Finally, the end-to-end pipeline was evaluated on 16 patients, and the resulting plans were scored by two GI radiation oncologists. RESULTS: In 31 of 35 patients, CTV contours were clinically acceptable without necessary modifications. The CTV achieved a Dice similarity coefficient of 0.85 (±0.05) and 95% Hausdorff distance of 15.25 (±5.59) mm. All OAR contours were clinically acceptable without edits, except for large and small bowel which were challenging to differentiate. However, contours for total, large, and small bowel were clinically acceptable. The two physicians accepted 100% and 91% of the auto-plans. For the end-to-end pipeline, the two physicians accepted 88% and 62% of the auto-plans. CONCLUSIONS: This study demonstrated that the VMAT treatment planning technique for rectal cancer can be automated to generate clinically acceptable and safe plans with minimal human interventions.

nnUNet for Automatic Kidney and Cyst Segmentation in Autosomal Dominant Polycystic Kidney Disease.

BACKGROUND: Autosomal Dominant Polycystic Kidney Disease (ADPKD) is a genetic disorder that causes uncontrolled kidney cyst growth, leading to kidney volume enlargement and renal function loss over time. Total kidney volume (TKV) and cyst burdens have been used as prognostic imaging biomarkers for ADPKD. OBJECTIVE: This study aimed to evaluate nnUNet for automatic kidney and cyst segmentation in T2-weighted (T2W) MRI images of ADPKD patients. METHODS: 756 kidney images were retrieved from 95 patients in the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) cohort (95 patients × 2 kidneys × 4 follow-up scans). The nnUNet model was trained, validated, and tested on 604, 76, and 76 images, respectively. In contrast, all images of each patient were exclusively assigned to either the training, validation, or test sets to minimize evaluation bias. The kidney and cyst regions defined using a semi-automatic method were employed as ground truth. The model performance was assessed using the Dice Similarity Coefficient (DSC), the intersection over union (IoU) score, and the Hausdorff distance (HD). RESULTS: The test DSC values were 0.96±0.01 (mean±SD) and 0.90±0.05 for kidney and cysts, respectively. Similarly, the IoU scores were 0.91± 0.09 and 0.81±0.06, and the HD values were 12.49±8.71 mm and 12.04±10.41 mm, respectively, for kidney and cyst segmentation. CONCLUSION: The nnUNet model is a reliable tool to automatically determine kidney and cyst volumes in T2W MRI images for ADPKD prognosis and therapy monitoring.

Improved Dosimetry and Plan Quality for Accelerated Partial Breast Irradiation Using Online Adaptive Radiation Therapy: A Single Institutional Study.

Purpose: Accelerated partial breast irradiation (APBI) is an attractive treatment modality for eligible patients as it has been shown to result in similar local control and improved cosmetic outcomes compared with whole breast radiation therapy. The use of online adaptive radiation therapy (OART) for APBI is promising as it allows for a reduction of planning target volume margins because breast motion and lumpectomy cavity volume changes are accounted for in daily imaging. Here we present a retrospective, single-institution evaluation on the adequacy of kV-cone beam computed tomography (CBCT) OART for APBI treatments. Methods and Materials: Nineteen patients (21 treatment sites) were treated to 30 Gy in 5 fractions between January of 2022 and May of 2023. Time between simulation and treatment, change in gross tumor (ie, lumpectomy cavity) volume, and differences in dose volume histogram metrics with adaption were analyzed. The Wilcoxon paired, nonparametric test was used to test for dose volume histogram metric differences between the scheduled plans (initial plans recalculated on daily CBCT anatomy) and delivered plans, either the scheduled or adapted plan, which was reoptimized using daily anatomy. Results: Median (interquartile range) time from simulation to first treatment was 26 days (21-32 days). During this same time, median gross tumor volume reduction was 16.0% (7.3%-23.9%) relative to simulation volume. Adaptive treatments took 31.3 minutes (27.4-36.6 minutes) from start of CBCT to treatment session end. At treatment, the adaptive plan was selected for 86% (89/103) of evaluable fractions. In evaluating plan quality, 78% of delivered plans met all target, organs at risk, and conformity metrics evaluated, compared with 34% of scheduled plans. Conclusions: Use of OART for stereotactic linac-based APBI allowed for safe, high-quality treatments in this cohort of 21 treatment courses. Although treatment delivery times were longer than traditional stereotactic body treatments, there were notable improvements in plan quality for APBI using OART.

Investigation of autosegmentation techniques on T2-weighted MRI for off-line dose reconstruction in MR-linac workflow for head and neck cancers.

BACKGROUND: In order to accurately accumulate delivered dose for head and neck cancer patients treated with the Adapt to Position workflow on the 1.5T magnetic resonance imaging (MRI)-linear accelerator (MR-linac), the low-resolution T2-weighted MRIs used for daily setup must be segmented to enable reconstruction of the delivered dose at each fraction. PURPOSE: In this pilot study, we evaluate various autosegmentation methods for head and neck organs at risk (OARs) on on-board setup MRIs from the MR-linac for off-line reconstruction of delivered dose. METHODS: Seven OARs (parotid glands, submandibular glands, mandible, spinal cord, and brainstem) were contoured on 43 images by seven observers each. Ground truth contours were generated using a simultaneous truth and performance level estimation (STAPLE) algorithm. Twenty total autosegmentation methods were evaluated in ADMIRE: 1-9) atlas-based autosegmentation using a population atlas library (PAL) of 5/10/15 patients with STAPLE, patch fusion (PF), random forest (RF) for label fusion; 10-19) autosegmentation using images from a patient's 1-4 prior fractions (individualized patient prior [IPP]) using STAPLE/PF/RF; 20) deep learning (DL) (3D ResUNet trained on 43 ground truth structure sets plus 45 contoured by one observer). Execution time was measured for each method. Autosegmented structures were compared to ground truth structures using the Dice similarity coefficient, mean surface distance (MSD), Hausdorff distance (HD), and Jaccard index (JI). For each metric and OAR, performance was compared to the inter-observer variability using Dunn's test with control. Methods were compared pairwise using the Steel-Dwass test for each metric pooled across all OARs. Further dosimetric analysis was performed on three high-performing autosegmentation methods (DL, IPP with RF and 4 fractions [IPP_RF_4], IPP with 1 fraction [IPP_1]), and one low-performing (PAL with STAPLE and 5 atlases [PAL_ST_5]). For five patients, delivered doses from clinical plans were recalculated on setup images with ground truth and autosegmented structure sets. Differences in maximum and mean dose to each structure between the ground truth and autosegmented structures were calculated and correlated with geometric metrics. RESULTS: DL and IPP methods performed best overall, all significantly outperforming inter-observer variability and with no significant difference between methods in pairwise comparison. PAL methods performed worst overall; most were not significantly different from the inter-observer variability or from each other. DL was the fastest method (33 s per case) and PAL methods the slowest (3.7-13.8 min per case). Execution time increased with a number of prior fractions/atlases for IPP and PAL. For DL, IPP_1, and IPP_RF_4, the majority (95%) of dose differences were within ± 250 cGy from ground truth, but outlier differences up to 785 cGy occurred. Dose differences were much higher for PAL_ST_5, with outlier differences up to 1920 cGy. Dose differences showed weak but significant correlations with all geometric metrics (R2 between 0.030 and 0.314). CONCLUSIONS: The autosegmentation methods offering the best combination of performance and execution time are DL and IPP_1. Dose reconstruction on on-board T2-weighted MRIs is feasible with autosegmented structures with minimal dosimetric variation from ground truth, but contours should be visually inspected prior to dose reconstruction in an end-to-end dose accumulation workflow.

Volumetric brain assessment of long-term head and neck cancer survivors.

BACKGROUND: Radiation therapy (RT) for locally advanced head and neck cancer (HNC) often exposes subcortical brain structures to radiation. We performed this study to assess region-specific brain volumetrics in a population of long term HNC survivors. METHODS AND MATERIALS: Forty HNC survivors were enrolled at a mean of 6.4 years from completion of RT. Patients underwent a research MRI protocol that included a 3D T1- weighted whole-brain scan on a 3 Tesla MRI scanner. Voxel based morphometry was performed using the Computational Anatomy Toolbox with the Neuromorphometrics atlas. Healthy controls from the Human Connectome Project were used as a comparison cohort. Study participants also completed a comprehensive neurocognitive assessment. RESULTS: The final study cohort consisted of 38 participants after excluding 2 participants due to image quality. HNC survivors displayed widespread reduction in gray matter (GM) brain region volumes that included bilateral medial frontal cortex, temporal lobe, hippocampus, supplemental motor area, and cerebellum. Greater radiation exposure was associated with reduced GM volume in the left ventral diencephalon (r = -0.512, p = 0.003). Associations between cognition and regional GM volumes were identified for motor coordination and bilateral cerebellum (left, r = 0.444, p = 0.009; right, r = 0.372, p = 0.030), confrontation naming and left amygdala (r = 0.382, p = 0.026), verbal memory and bilateral thalamus (left, r = 0.435, p = 0.010; right, r = 0.424, p = 0.012), right amygdala (r = 0.339, p = 0.050), and right putamen (r = 0.364, p = 0.034). CONCLUSIONS: Reductions in GM were observed within this cohort of primarily non-nasopharyngeal HNC survivors as compared to a control sample. GM volumes were associated with performance in multiple cognitive domains. Results of this exploratory study support the need for investigation of anatomic brain changes as an important translational corollary to cognitive problems among HNC survivors.

Fully-automated, CT-only GTV contouring for palliative head and neck radiotherapy.

Planning for palliative radiotherapy is performed without the advantage of MR or PET imaging in many clinics. Here, we investigated CT-only GTV delineation for palliative treatment of head and neck cancer. Two multi-institutional datasets of palliative-intent treatment plans were retrospectively acquired: a set of 102 non-contrast-enhanced CTs and a set of 96 contrast-enhanced CTs. The nnU-Net auto-segmentation network was chosen for its strength in medical image segmentation, and five approaches separately trained: (1) heuristic-cropped, non-contrast images with a single GTV channel, (2) cropping around a manually-placed point in the tumor center for non-contrast images with a single GTV channel, (3) contrast-enhanced images with a single GTV channel, (4) contrast-enhanced images with separate primary and nodal GTV channels, and (5) contrast-enhanced images along with synthetic MR images with separate primary and nodal GTV channels. Median Dice similarity coefficient ranged from 0.6 to 0.7, surface Dice from 0.30 to 0.56, and 95th Hausdorff distance from 14.7 to 19.7 mm across the five approaches. Only surface Dice exhibited statistically-significant difference across these five approaches using a two-tailed Wilcoxon Rank-Sum test (p ≤ 0.05). Our CT-only results met or exceeded published values for head and neck GTV autocontouring using multi-modality images. However, significant edits would be necessary before clinical use in palliative radiotherapy.

A real-time contouring feedback tool for consensus-based contour training.

Purpose: Variability in contouring structures of interest for radiotherapy continues to be challenging. Although training can reduce such variability, having radiation oncologists provide feedback can be impractical. We developed a contour training tool to provide real-time feedback to trainees, thereby reducing variability in contouring. Methods: We developed a novel metric termed localized signed square distance (LSSD) to provide feedback to the trainee on how their contour compares with a reference contour, which is generated real-time by combining trainee contour and multiple expert radiation oncologist contours. Nine trainees performed contour training by using six randomly assigned training cases that included one test case of the heart and left ventricle (LV). The test case was repeated 30 days later to assess retention. The distribution of LSSD maps of the initial contour for the training cases was combined and compared with the distribution of LSSD maps of the final contours for all training cases. The difference in standard deviations from the initial to final LSSD maps, ΔLSSD, was computed both on a per-case basis and for the entire group. Results: For every training case, statistically significant ΔLSSD were observed for both the heart and LV. When all initial and final LSSD maps were aggregated for the training cases, before training, the mean LSSD ([range], standard deviation) was -0.8 mm ([-37.9, 34.9], 4.2) and 0.3 mm ([-25.1, 32.7], 4.8) for heart and LV, respectively. These were reduced to -0.1 mm ([-16.2, 7.3], 0.8) and 0.1 mm ([-6.6, 8.3], 0.7) for the final LSSD maps during the contour training sessions. For the retention case, the initial and final LSSD maps of the retention case were aggregated and were -1.5 mm ([-22.9, 19.9], 3.4) and -0.2 mm ([-4.5, 1.5], 0.7) for the heart and 1.8 mm ([-16.7, 34.5], 5.1) and 0.2 mm ([-3.9, 1.6],0.7) for the LV. Conclusions: A tool that uses real-time contouring feedback was developed and successfully used for contour training of nine trainees. In all cases, the utility was able to guide the trainee and ultimately reduce the variability of the trainee's contouring.

Identifying the optimal deep learning architecture and parameters for automatic beam aperture definition in 3D radiotherapy.

PURPOSE: Two-dimensional radiotherapy is often used to treat cervical cancer in low- and middle-income countries, but treatment planning can be challenging and time-consuming. Neural networks offer the potential to greatly decrease planning time through automation, but the impact of the wide range of hyperparameters to be set during training on model accuracy has not been exhaustively investigated. In the current study, we evaluated the effect of several convolutional neural network architectures and hyperparameters on 2D radiotherapy treatment field delineation. METHODS: Six commonly used deep learning architectures were trained to delineate four-field box apertures on digitally reconstructed radiographs for cervical cancer radiotherapy. A comprehensive search of optimal hyperparameters for all models was conducted by varying the initial learning rate, image normalization methods, and (when appropriate) convolutional kernel size, the number of learnable parameters via network depth and the number of feature maps per convolution, and nonlinear activation functions. This yielded over 1700 unique models, which were all trained until performance converged and then tested on a separate dataset. RESULTS: Of all hyperparameters, the choice of initial learning rate was most consistently significant for improved performance on the test set, with all top-performing models using learning rates of 0.0001. The optimal image normalization was not consistent across architectures. High overlap (mean Dice similarity coefficient = 0.98) and surface distance agreement (mean surface distance < 2 mm) were achieved between the treatment field apertures for all architectures using the identified best hyperparameters. Overlap Dice similarity coefficient (DSC) and distance metrics (mean surface distance and Hausdorff distance) indicated that DeepLabv3+ and D-LinkNet architectures were least sensitive to initial hyperparameter selection. CONCLUSION: DeepLabv3+ and D-LinkNet are most robust to initial hyperparameter selection. Learning rate, nonlinear activation function, and kernel size are also important hyperparameters for improving performance.

Deep learning-based dose prediction to improve the plan quality of volumetric modulated arc therapy for gynecologic cancers.

BACKGROUND: In recent years, deep-learning models have been used to predict entire three-dimensional dose distributions. However, the usability of dose predictions to improve plan quality should be further investigated. PURPOSE: To develop a deep-learning model to predict high-quality dose distributions for volumetric modulated arc therapy (VMAT) plans for patients with gynecologic cancer and to evaluate their usability in driving plan quality improvements. METHODS: A total of 79 VMAT plans for the female pelvis were used to train (47 plans), validate (16 plans), and test (16 plans) 3D dense dilated U-Net models to predict 3D dose distributions. The models received the normalized CT scan, dose prescription, and target and normal tissue contours as inputs. Three models were used to predict the dose distributions for plans in the test set. A radiation oncologist specializing in the treatment of gynecologic cancers scored the test set predictions using a 5-point scale (5, acceptable as-is; 4, prefer minor edits; 3, minor edits needed; 2, major edits needed; and 1, unacceptable). The clinical plans for which the dose predictions indicated that improvements could be made were reoptimized with constraints extracted from the predictions. RESULTS: The predicted dose distributions in the test set were of comparable quality to the clinical plans. The mean voxel-wise dose difference was -0.14 ± 0.46 Gy. The percentage dose differences in the predicted target metrics of D 1 % ${D}_{1{\mathrm{\% }}}$ and D 98 % ${D}_{98{\mathrm{\% }}}$ were -1.05% ± 0.59% and 0.21% ± 0.28%, respectively. The dose differences in the predicted organ at risk mean and maximum doses were -0.30 ± 1.66 Gy and -0.42 ± 2.07 Gy, respectively. A radiation oncologist deemed all of the predicted dose distributions clinically acceptable; 12 received a score of 5, and four received a score of 4. Replanning of flagged plans (five plans) showed that the original plans could be further optimized to give dose distributions close to the predicted dose distributions. CONCLUSIONS: Deep-learning dose prediction can be used to predict high-quality and clinically acceptable dose distributions for VMAT female pelvis plans, which can then be used to identify plans that can be improved with additional optimization.

Automated evaluation for rapid implementation of knowledge-based radiotherapy planning models.

PURPOSE: Knowledge-based planning (KBP) offers the ability to predict dose-volume metrics based on information extracted from previous plans, reducing plan variability and improving plan quality. As clinical integration of KBP is increasing there is a growing need for quantitative evaluation of KBP models. A .NET-based application, RapidCompare, was created for automated plan creation and analysis of Varian RapidPlan models. METHODS: RapidCompare was designed to read calculation parameters and a list of reference plans. The tool copies the reference plan field geometry and structure set, applies the RapidPlan model, optimizes the KBP plan, and generates data for quantitative evaluation of dose-volume metrics. A cohort of 85 patients, divided into training (50), testing (10), and validation (25) groups, was used to demonstrate the utility of RapidCompare. After training and tuning, the KBP model was paired with three different optimization templates to compare various planning strategies in the validation cohort. All templates used the same set of constraints for the planning target volume (PTV). For organs-at-risk, the optimization template provided constraints using the whole dose-volume histogram (DVH), fixed-dose/volume points, or generalized equivalent uniform dose (gEUD). The resulting plans from each optimization approach were compared using DVH metrics. RESULTS: RapidCompare allowed for the automated generation of 75 total plans for comparison with limited manual intervention. In comparing optimization techniques, the Dose/Volume and Lines optimization templates generated plans with similar DVH metrics, with a slight preference for the Lines technique with reductions in heart V30Gy and spinal cord max dose. The gEUD model produced high target heterogeneity. CONCLUSION: Automated evaluation allowed for the exploration of multiple optimization templates in a larger validation cohort than would have been feasible using a manual approach. A final KBP model using line optimization objectives produced the highest quality plans without human intervention.

Evaluation and correlation of patient movement during online adaptive radiotherapy with CBCT and a surface imaging system.

PURPOSE: With the clinical implementation of kV-CBCT-based daily online-adaptive radiotherapy, the ability to monitor, quantify, and correct patient movement during adaptive sessions is paramount. With sessions lasting between 20-45 min, the ability to detect and correct for small movements without restarting the entire session is critical to the adaptive workflow and dosimetric outcome. The purpose of this study was to quantify and evaluate the correlation of observed patient movement with machine logs and a surface imaging (SI) system during adaptive radiation therapy. METHODS: Treatment machine logs and SGRT registration data log files for 1972 individual sessions were exported and analyzed. For each session, the calculated shifts from a pre-delivery position verification CBCT were extracted from the machine logs and compared to the SGRT registration data log files captured during motion monitoring. The SGRT calculated shifts were compared to the reported shifts of the machine logs for comparison for all patients and eight disease site categories. RESULTS: The average (±STD) net displacement of the SGRT shifts were 2.6 ± 3.4 mm, 2.6 ± 3.5 mm, and 3.0 ± 3.2 in the lateral, longitudinal, and vertical directions, respectively. For the treatment machine logs, the average net displacements in the lateral, longitudinal, and vertical directions were 2.7 ± 3.7 mm, 2.6 ± 3.7 mm, and 3.2 ± 3.6 mm. The average difference (Machine-SGRT) was -0.1 ± 1.8 mm, 0.2 ± 2.1 mm, and -0.5 ± 2.5 mm for the lateral, longitudinal, and vertical directions. On average, a movement of 5.8 ± 5.6 mm and 5.3 ± 4.9 mm was calculated prior to delivery for the CBCT and SGRT systems, respectively. The Pearson correlation coefficient between CBCT and SGRT shifts was r = 0.88. The mean and median difference between the treatment machine logs and SGRT log files was less than 1 mm for all sites. CONCLUSION: Surface imaging should be used to monitor and quantify patient movement during adaptive radiotherapy.

Benchmarking Automated Machine Learning-Enhanced Planning With Ethos Against Manual and Knowledge-Based Planning for Locally Advanced Lung Cancer.

Purpose: Currently, there is insufficient guidance for standard fractionation lung planning using the Varian Ethos adaptive treatment planning system and its unique intelligent optimization engine. Here, we address this gap in knowledge by developing a methodology to automatically generate high-quality Ethos treatment plans for locally advanced lung cancer. Methods and Materials: Fifty patients previously treated with manually generated Eclipse plans for inoperable stage IIIA-IIIC non-small cell lung cancer were included in this institutional review board-approved retrospective study. Fifteen patient plans were used to iteratively optimize a planning template for the Daily Adaptive vs Non-Adaptive External Beam Radiation Therapy With Concurrent Chemotherapy for Locally Advanced Non-Small Cell Lung Cancer: A Prospective Randomized Trial of an Individualized Approach for Toxicity Reduction (ARTIA-Lung); the remaining 35 patients were automatically replanned without intervention. Ethos plan quality was benchmarked against clinical plans and reoptimized knowledge-based RapidPlan (RP) plans, then judged using standard dose-volume histogram metrics, adherence to clinical trial objectives, and qualitative review. Results: Given equal prescription target coverage, Ethos-generated plans showed improved primary and nodal planning target volume V95% coverage (P < .001) and reduced lung gross tumor volume V5 Gy and esophagus D0.03 cc metrics (P ≤ .003) but increased mean esophagus and brachial plexus D0.03 cc metrics (P < .001) compared with RP plans. Eighty percent, 49%, and 51% of Ethos, clinical, and RP plans, respectively, were "per protocol" or met "variation acceptable" ARTIA-Lung planning metrics. Three radiation oncologists qualitatively scored Ethos plans, and 78% of plans were clinically acceptable to all reviewing physicians, with no plans receiving scores requiring major changes. Conclusions: A standard Ethos template produced lung radiation therapy plans with similar quality to RP plans, elucidating a viable approach for automated plan generation in the Ethos adaptive workspace.

A deep-learning-based dose verification tool utilizing fluence maps for a cobalt-60 compensator-based intensity-modulated radiation therapy system.

Background and purpose: A novel cobalt-60 compensator-based intensity-modulated radiation therapy (IMRT) system was developed for a resource-limited environment but lacked an efficient dose verification algorithm. The aim of this study was to develop a deep-learning-based dose verification algorithm for accurate and rapid dose predictions. Materials and methods: A deep-learning network was employed to predict the doses from static fields related to beam commissioning. Inputs were a cube-shaped phantom, a beam binary mask, and an intersecting volume of the phantom and beam binary mask, while output was a 3-dimensional (3D) dose. The same network was extended to predict patient-specific doses for head and neck cancers using two different approaches. A field-based method predicted doses for each field and combined all calculated doses into a plan, while the plan-based method combined all nine fluences into a plan to predict doses. Inputs included patient computed tomography (CT) scans, binary beam masks, and fluence maps truncated to the patient's CT in 3D. Results: For static fields, predictions agreed well with ground truths with average deviations of less than 0.5% for percent depth doses and profiles. Even though the field-based method showed excellent prediction performance for each field, the plan-based method showed better agreement between clinical and predicted dose distributions. The distributed dose deviations for all planned target volumes and organs at risk were within 1.3 Gy. The calculation speed for each case was within two seconds. Conclusions: A deep-learning-based dose verification tool can accurately and rapidly predict doses for a novel cobalt-60 compensator-based IMRT system.

Commissioning and dosimetric validation of a novel compensator-based Co-60 IMRT system for evaluating suitability to automated treatment planning.

PURPOSE: A novel compensator-based system has been proposed which delivers intensity-modulated radiation therapy (IMRT) with cobalt-60 beams. This could improve access to advanced radiotherapy in low- and middle-income countries. For this system to be clinically viable and to be adapted into the Radiation Planning Assistant (RPA), being developed to offer automated planning services in low- and middle-income countries, it is necessary to commission and validate it in a commercial treatment planning system (TPS). METHODS: The novel treatment device considered here employs a cobalt-60 source and nine compensators. Each compensator is produced by 3-D printing a thin plastic mold which is then filled on-demand within the machine with reusable 2-mm-diameter spherical tungsten balls. This system was commissioned in the Eclipse TPS and validation tests were conducted with Monte Carlo using Geant4 Application for Tomographic Emission for percentage depth dose, in-plane profiles, penumbra, and IMRT dose validation. And the American Association of Physicists in Medicine Task Group 119 benchmarking testing was performed. Additionally, compensator-based cobalt-60 IMRT plans were created for 46 head-and-neck cancer cases and compared to the linac-based volumetric modulated arc therapy (VMAT) plans used clinically, then dosimetric parameters were evaluated. Beam-on time for each field was calculated. In addition, the measurement was also performed in a limited environment and compared with the Monte Carlo simulations. RESULTS: The differences in percent depth doses and in-plane profiles between the Eclipse and Monte Carlo simulations were 0.65% ± 0.41% and 1.02% ± 0.99%, respectively, and the 80%-20% penumbra agreed within 0.46 ± 0.27 mm. For the Task Group 119 validation plans, all treatment planning goals were met and gamma passing rates were >95% (3%/3 mm criteria). In 46 clinical head-and-neck cases, the cobalt-60 compensator-based IMRT plans had planning target volume (PTV) coverages similar to linac-based VMAT plans: all dosimetric values for PTV were within 1.5%. The organs at risk dose parameters were somewhat higher in cobalt-60 compensator-based IMRT plans versus linac-based VMAT plans. The mean dose differences for the spinal cord, brain, and brainstem were 4.43 ± 1.92, 3.39 ± 4.67, and 2.40 ± 3.71 Gy, while those for the rest of the organs were <1 Gy. The average beam-on time per field was 0.42 ± 0.10 min for the 6 MV multi-leaf-collimator plans while those for the cobalt-60 compensator plans were 0.17 ± 0.01 and 0.31 ± 0.01 min at the dose rates of 350 and 175 cGy/min. There was a good agreement between in-plane profiles from measurements and Monte Carlo simulations, which differences are 1.34 ± 1.90% and 0.13 ± 2.16% for two different fields. CONCLUSIONS: A novel compensator-based IMRT system using cobalt-60 beams was commissioned and validated in a commercial TPS. Plan quality with this system was comparable to that of linac-based plans in all test cases with shorter estimated beam-on times. This system enables reliable, high-quality plans with reduced cost and complexity and may have benefits for underserved regions of the world. This system is being integrated into the RPA, a web-based platform for auto-contouring and auto-planning.