Deep match: A zero-shot framework for improved fiducial-free respiratory motion tracking

BACKGROUND AND PURPOSE

Motion management is essential to reduce normal tissue exposure and maintain adequate tumor dose in lung stereotactic body radiation therapy (SBRT). Lung SBRT using an articulated robotic arm allows dynamic tracking during radiation dose delivery. Two stereoscopic X-ray tracking modes are available - fiducial-based and fiducial-free tracking. Although X-ray detection of implanted fiducials is robust, the implantation procedure is invasive and inapplicable to some patients and tumor locations. Fiducial-free tracking relies on tumor contrast, which challenges the existing tracking algorithms for small (e.g., <15 mm) and/or tumors obscured by overlapping anatomies. To markedly improve the performance of fiducial-free tracking, we proposed a deep learning-based template matching algorithm - Deep Match.

METHOD

Deep Match consists of four self-definable stages - training-free feature extractor, similarity measurements for location proposal, local refinements, and uncertainty level prediction for constructing a more trustworthy and versatile pipeline. Deep Match was validated on a 10 (38 fractions; 2661 images) patient cohort whose lung tumor was trackable on one X-ray view, while the second view did not offer sufficient conspicuity for tumor tracking using existing methods. The patient cohort was stratified into subgroups based on tumor sizes (<10 mm, 10-15 mm, and >15 mm) and tumor locations (with/without thoracic anatomy overlapping).

RESULTS

On X-ray views that conventional methods failed to track the lung tumor, Deep Match achieved robust performance as evidenced by >80 % 3 mm-Hit (detection within 3 mm superior/inferior margin from ground truth) for 70 % of patients and <3 mm superior/inferior distance (SID) ∼1 mm standard deviation for all the patients.

CONCLUSION

Deep Match is a zero-shot learning network that explores the intrinsic neural network benefits without training on patient data. With Deep Match, fiducial-free tracking can be extended to more patients with small tumors and with tumors obscured by overlapping anatomy.

Intra-fractional geometric and dose/volume metric variations of magnetic resonance imaging-guided stereotactic radiotherapy of prostate bed after radical prostatectomy

Background and purpose

Magnetic Resonance Imaging (MRI)-guided Stereotactic body radiotherapy (SBRT) treatment to prostate bed after radical prostatectomy has garnered growing interests. The aim of this study is to evaluate intra-fractional anatomic and dose/volume metric variations for patients receiving this treatment.

Materials and methods

Nineteen patients who received 30-34 Gy in 5 fractions on a 0.35T MR-Linac were included. Pre- and post-treatment MRIs were acquired for each fraction (total of 75 fractions). The Clinical Target Volume (CTV), bladder, rectum, and rectal wall were contoured on all images. Volumetric changes, Hausdorff distance, Mean Distance to Agreement (MDA), and Dice similarity coefficient (DSC) for each structure were calculated. Median value and Interquartile range (IQR) were recorded. Changes in target coverage and Organ at Risk (OAR) constraints were compared and evaluated using Wilcoxon rank sum tests at a significant level of 0.05.

Results

Bladder had the largest volumetric changes, with a median volume increase of 48.9 % (IQR 28.9-76.8 %) and a median MDA of 5.1 mm (IQR 3.4-7.1 mm). Intra-fractional CTV volume remained stable with a median volume change of 1.2 % (0.0-4.8 %). DSC was 0.97 (IQR 0.94-0.99). For the dose/volume metrics, there were no statistically significant changes observed except for an increase in bladder hotspot and a decrease of bladder V32.5 Gy and mean dose. The CTV V95% changed from 99.9 % (IQR 98.8-100 %) to 99.6 % (IQR 93.9-100 %).

Conclusion

Despite intra-fractional variations of OARs, CTV coverage remained stable during MRI-guided SBRT treatments for the prostate bed.

Nodule detection and generation on chest X-rays: NODE21 Challenge

Pulmonary nodules may be an early manifestation of lung cancer, the leading cause of cancer-related deaths among both men and women. Numerous studies have established that deep learning methods can yield high-performance levels in the detection of lung nodules in chest X-rays. However, the lack of gold-standard public datasets slows down the progression of the research and prevents benchmarking of methods for this task. To address this, we organized a public research challenge, NODE21, aimed at the detection and generation of lung nodules in chest X-rays. While the detection track assesses state-of-the-art nodule detection systems, the generation track determines the utility of nodule generation algorithms to augment training data and hence improve the performance of the detection systems. This paper summarizes the results of the NODE21 challenge and performs extensive additional experiments to examine the impact of the synthetically generated nodule training images on the detection algorithm performance.

Impact of Interfractional Bladder and Trigone Displacement and Deformation on Radiation Exposure and Subsequent Acute Genitourinary Toxicity: A Post Hoc Analysis of Patients Treated with Magnetic Resonance Imaging-Guided Prostate Stereotactic Body Radiation Therapy in a Phase 3 Randomized Trial

PURPOSE

Emerging data suggest that trigone dosimetry may be more associated with poststereotactic body radiation therapy (SBRT) urinary toxicity than whole bladder dosimetry. We quantify the dosimetric effect of interfractional displacement and deformation of the whole bladder and trigone during prostate SBRT using on-board, pretreatment 0.35T magnetic resonance images (MRI).

METHODS AND MATERIALS

Seventy-seven patients treated with MRI-guided prostate SBRT (40 Gy/5 fractions) on the MRI arm of a phase 3 single-center randomized trial were included. Bladder and trigone structures were contoured on images obtained from a 0.35T simulation MRI and 5 on-board pretreatment MRIs. Dice similarity coefficient (DSC) scores and changes in volume between simulation and daily treatments were calculated. Dosimetric parameters including Dmax, D0.03 cc, Dmean, V40 Gy, V39 Gy, V38 Gy, and V20 Gy for the bladder and trigone for the simulation and daily treatments were collected. Both physician-scored (Common Terminology Criteria for Adverse Events, version 4.03 scale) as well as patient-reported (International Prostate Symptom Scores and the Expanded Prostate Cancer Index Composite-26 scores) acute genitourinary (GU) toxicity outcomes were collected and analyzed.

RESULTS

The average treatment bladder volume was about 30% smaller than the simulation bladder volume; however, the trigone volume remained fairly consistent despite being positively correlated with total bladder volume. Overall, the trigone accounted for <2% of the bladder volume. Median DSC for the bladder was 0.79, whereas the median DSC of the trigone was only 0.33. No statistically significant associations between our selected bladder and trigonal dosimetric parameters and grade ≥2 GU toxicity were identified, although numerically, patients with GU toxicity (grade ≥2) had higher intermediate doses to the bladder (V20 Gy and Dmean) and larger volumes exposed to higher doses in the trigone (V40 Gy, V39 Gy, and V38 Gy).

CONCLUSIONS

The trigone exhibits little volume change, but considerable interfractional displacement/deformation. As a result, the relative volume of the trigone receiving high doses during prostate SBRT varies substantially between fractions, which could influence GU toxicity and may not be predicted by radiation planning dosimetry.

Robust treatment planning for small animal radio-neuromodulation using focused kV x-ray beams

BACKGROUND

In preclinical radio-neuromodulation research, small animal experiments are pivotal for unraveling radiobiological mechanism, investigating prescription and planning techniques, and assessing treatment effects and toxicities. However, the target size inside a rat brain is typically in the order of sub-millimeters. The small target inside the visual cortex neural region in rat brain with a diameter of around 1 mm was focused in this work to observe the physiological change of this region. Delivering uniform doses to the small target while sparing health tissues is challenging. Focused kV x-ray technique based on modern x-ray polycapillary focusing lens is a promising modality for small animal radio-neuromodulation.

PURPOSE

The current manual planning method could lead to sub-optimal plans, and the positioning uncertainties due to mechanical accuracy limitations, animal immobilization, and robotic arm motion are not considered. This work aims to design a robust inverse planning method to optimize the intensities of focused kV x-ray beams located in beam trajectories to irradiate small mm-sized targets in rat brains for radio-neuromodulation.

METHODS

Focused kV x-ray beams were generated through polycapillary x-ray focusing lenses on achieving small (≤0.3 mm) focus perpendicular to the beam. The beam trajectories were manually designed in 3D space in scanning-while-rotating mode. Geant4 Monte Carlo (MC) simulation generated a dose calculation matrix for each focused kV x-ray beam located in beam trajectories. In the proposed robust inverse planning method, an objective function combining a voxel-wise stochastic programming approach and L1 norm regularization was established to overcome the positioning uncertainties and obtain a high-quality plan. The fast iterative shrinkage thresholding algorithm (FISTA) was utilized to solve the objective function and obtain the optimal intensities. Four cases were employed to validate the feasibility and effectiveness of the proposed method. The manual and non-robust inverse planning methods were also implemented for comparison.

RESULTS

The proposed robust inverse planning method achieved superior dose homogeneity and higher robustness against positioning uncertainties. On average, the clinical target volume (CTV) homogeneity index (HI) of robust inverse plan improved to 13.3 from 22.9 in non-robust inverse plan and 53.8 in manual plan if positioning uncertainties were also present. The average bandwidth at D90 was reduced by 6.5 Gy in the robust inverse plan, compared to 9.6 Gy in non-robust inverse plan and 12.5 Gy in manual plan. The average bandwidth at D80 was reduced by 3.4 Gy in robust inverse plan, compared to 5.5 Gy in non-robust inverse plan and 8.5 Gy in manual plan. Moreover, the dose delivery time of manual plan was reduced by an average reduction of 54.7% with robust inverse plan and 29.0% with non-robust inverse plan.

CONCLUSION

Compared to manual and non-robust inverse planning methods, the robust inverse planning method improved the dose homogeneity and delivery efficiency and was resistant to the uncertainties, which are crucial for radio-neuromodulation utilizing focused kV x-rays.

Hypoxia-informed RBE-weighted beam orientation optimization for intensity modulated proton therapy

BACKGROUND

Variable relative biological effectiveness (RBE) models in treatment planning have been proposed to optimize the therapeutic ratio of proton therapy. It has been reported that proton RBE decreases with increasing tumor oxygen level, offering an opportunity to address hypoxia-related radioresistance with RBE-weighted optimization.

PURPOSE

Here, we obtain a voxel-level estimation of partial oxygen pressure to weigh RBE values in a single biologically informed beam orientation optimization (BOO) algorithm.

METHODS

Three glioblastoma patients with [18 F]-fluoromisonidazole (FMISO)-PET/CT images were selected from the institutional database. Oxygen values were derived from tracer uptake using a nonlinear least squares curve fitting. McNamara RBE, calculated from proton dose, was then weighed using oxygen enhancement ratios (OER) for each voxel and incorporated into the dose fidelity term of the BOO algorithm. The nonlinear optimization problem was solved using a split-Bregman approach, with FISTA as the solver. The proposed hypoxia informed RBE-weighted method (HypRBE) was compared to dose fidelity terms using the constant RBE of 1.1 (cRBE) and the normoxic McNamara RBE model (RegRBE). Tumor homogeneity index (HI), maximum biological dose (Dmax), and D95%, as well as OAR therapeutic index (TI = gEUDCTV /gEUDOAR ) were evaluated along with worst-case statistics after normalization to normal tissue isotoxicity.

RESULTS

Compared to [cRBE, RegRBE], HypRBE increased tumor HI, Dmax, and D95% across all plans by on average [31.3%, 31.8%], [48.6%, 27.1%], and [50.4%, 23.8%], respectively. In the worst-case scenario, the parameters increase on average by [12.5%, 14.7%], [7.3%,-8.9%], and [22.3%, 2.1%]. Despite increased OAR Dmean and Dmax by [8.0%, 3.0%] and [13.1%, -0.1%], HypRBE increased average TI by [22.0%, 21.1%]. Worst-case OAR Dmean, Dmax, and TI worsened by [17.9%, 4.3%], [24.5%, -1.2%], and [9.6%, 10.5%], but in the best cases, HypRBE escalates tumor coverage significantly without compromising OAR dose, increasing the therapeutic ratio.

CONCLUSIONS

We have developed an optimization algorithm whose dose fidelity term accounts for hypoxia-informed RBE values. We have shown that HypRBE selects bE:\Alok\aaeams better suited to deliver high physical dose to low RBE, hypoxic tumor regions while sparing the radiosensitive normal tissue.

Dosimetric predictors for genitourinary toxicity in MR-guided stereotactic body radiation therapy (SBRT): Substructure with fraction-wise analysis

BACKGROUND

MR-guided radiation therapy (MRgRT) systems provide superior soft tissue contrast than x-ray based systems and can acquire real-time cine for treatment gating. These features allow treatment planning margins to be reduced, allowing for improved critical structure sparing and reduced treatment toxicity. Despite this improvement, genitourinary (GU) toxicity continues to affect many patients.

PURPOSE

(1) To identify dosimetric predictors, potentially in combination with clinical parameters, of GU toxicity following SBRT by leveraging MRgRT to accurately monitor daily dose, beyond predicted dose calculated during planning. (2) Improve awareness of toxicity-sensitive bladder substructures, specifically the trigone and urethra.

METHODS

Sixty-nine prostate cancer patients (NCT04384770 clinical trial) were treated on a ViewRay MRIdian MRgRT system, with 40 Gy prescribed to 95% of the PTV in over five fractions. Overall, 17 (24.6%) prostate patients reported acute grade 2 GU toxicity. The CTV, PTV, bladder, bladder wall, trigone, urethra, rectum, and rectal wall were contoured on the planning and daily treatment MRIs. Planning and daily treatment DVHs (0.1 Gy increments), organ doses (min, max, mean), and organ volumes were recorded. Daily dose was estimated by transferring the planning dose distributions to the daily MRI based on the daily setup alignment. Patients were partitioned into a training (55) and testing set (14). Dose features were pre-filtered using a t-test followed by maximum relevance minimum redundancy (MRMR) algorithm. Logistic regression was investigated with regularization to select dosimetric predictors. Specifically, two approaches: time-group least absolute shrinkage and selection (LASSO), and interactive grouped greedy algorithm (IGA) were investigated. Shared features across the planning and five treatment fractions were grouped to encourage consistency and stability. The conventional flat non-temporally grouped LASSO was also evaluated to provide a solid benchmark. After feature selection, a final logistic regression model was trained. Dosimetric regression models were compared to a clinical regression model with only clinical parameters (age, baseline IPSS, prostate gland size, ADT usage, etc.) and a hybrid model, combining the best performing dosimetric features with the clinical parameters, was evaluated. Final model performance was evaluated on the testing set using accuracy, sensitivity, and specificity determined by the optimal threshold of the training set.

RESULTS

IGA had the best testing performance with an accuracy/sensitivity/specificity of 0.79/0.67/0.82, selecting 12 groups covering the bladder (V19.8 Gy, V20.5 Gy), bladder wall (19.7 Gy), trigone (15.9, 18.2, 43.3 Gy), urethra (V41.4 Gy, V41.7 Gy), CTV (V41.9 Gy), rectum (V8.5 Gy), and rectal wall (1.2, 44.1 Gy) dose features. Absolute bladder V19.8 Gy and V20.5 Gy were the most important features, followed by relative trigone 15.9  and 18.2 Gy. Inclusion of clinical parameters in the hybrid model with IGA did not significantly change regression performance.

CONCLUSION

Overall, IGA feature selection resulted in the best GU toxicity prediction performance. This exploratory study demonstrated the feasibility of identification and analysis of dosimetric toxicity predictors with awareness to sensitive substructures and daily dose to potentially provide consistent and stable dosimetric metrics to guide treatment planning. Further patient accruement is warranted to further assess dosimetric predictor and perform validation.

Technical note: Multi-MATE, a high-throughput platform for automated image-guided small-animal irradiation

BACKGROUND

Small animal irradiation is essential to study the radiation response of new interventions before or parallel to human therapy. Image-guided radiotherapy (IGRT) and intensity-modulated radiotherapy (IMRT) are recently adopted in small animal irradiation to more closely mimic human treatments. However, sophisticated techniques require exceedingly high time, resources, and expertize that are often impractical.

PURPOSE

We propose a high throughput and high precision platform named Multiple Mouse Automated Treatment Environment (Multi-MATE) to streamline image-guided small animal irradiation.

METHODS

Multi-MATE consists of six parallel and hexagonally arranged channels, each equipped with a transfer railing, a 3D-printed immobilization pod, and an electromagnetic control unit, computer-controlled via an Arduino interface. The mouse immobilization pods are transferred along the railings between the home position outside the radiation field and the imaging/irradiation position at the irradiator isocenter. All six immobilization pods are transferred to the isocenter in the proposed workflow for parallel CBCT scans and treatment planning. The immobilization pods are then sequentially transported to the imaging/therapy position for dose delivery. The positioning reproducibility of Multi-MATE are evaluated using CBCT and radiochromic films.

RESULTS

While parallelizing and automating the image-guided small animal radiation delivery, Multi-MATE achieved the average pod position reproducibility of 0.17 ± 0.04 mm in the superior-inferior direction, 0.20 ± 0.04 mm in the left-right direction, and 0.12 ± 0.02mm in the anterior-posterior direction in repeated CBCT tests. Additionally, in image-guided dose delivery tasks, Multi-MATE demonstrated the positioning reproducibility of 0.17 ± 0.06 mm in the superior-inferior direction, 0.19 ± 0.06 mm in the left-right direction.

CONCLUSIONS

We designed, fabricated, and tested a novel automated irradiation platform, Multi-MATE to accelerate and automate image-guided small animal irradiation. The automated platform minimizes human operation and achieves high setup reproducibility and image-guided dose delivery accuracy. Multi-MATE thus removes a major barrier to implementing high-precision preclinical radiation research.

Hypoxia Informed RBE-Weighted Beam Orientation Optimization for Intensity Modulated Proton Therapy Using [18F]-FMISO-PET Estimation of pO2

PURPOSE/OBJECTIVE(S)

Variable relative biological effectiveness (RBE) models have previously informed proton therapy dose optimization algorithms, but few models have incorporated hypoxia's increase on radioresistance. Here, we obtain voxel-based estimation of partial oxygen pressure to weigh RBE values in a single biologically informed beam orientation optimization (BOO) algorithm.

MATERIALS/METHODS

Four brain cancer patients with [18F]-FMISO-PET/CT images were selected from an HCP database. Oxygen values were derived from tracer uptake using a non-linear least squares curve fitting. RBE dose was then weighted using oxygen enhancement ratios (OER) for each structure and substituted into the dose fidelity term of our BOO algorithm. The nonlinear optimization problem was solved using a split-Bregman approach, with FISTA as the solver. This method (HypRBE) was compared dose fidelity terms using the Rorvik RBE model (RegRBE), without OER. Tumor homogeneity index (HI), Dmax, and D95% were evaluated along with worst-case statistics after normalization to normal tissue isotoxicity.

RESULTS

Compared to RegRBE, HypRBE increased tumor [HI, Dmax, D95%] on average by [0.5%, 2.0%, 2.5%] and improved worst-case tumor [HI, Dmax, D95%] by [5.3%, 16.2%, 9.6%]. HypRBE shows an increase in therapeutic ratio, and is notably robust against uncertainty scenarios.

CONCLUSION

We have developed an optimization algorithm whose dose fidelity term is weighted by hypoxia informed RBE values. We have shown that HypRBE selects beams that are better suited to protect low RBE, well-oxygenated normal tissue while maintaining high dose to high RBE, hypoxic tumor cells.

Contouring Analysis on Synthetic Contrast-Enhanced MR from GRMM-GAN and Implications on MR-Guide Radiation Therapy

PURPOSE/OBJECTIVE(S)

MR-guided linear accelerators have been commercialized making MR-only planning and adaptation an appealing alternative circumventing MR-CT registration. However, obtaining daily contrast-enhanced MR images can be prohibitive due to the increased risk of side effects from repeated contrast injections. In this work, we evaluate the quality of contrast-enhanced multi-modal MR image synthesis network GRMM-GAN (gradient regularized multi-modal multi-discrimination sparse-attention fusion generative adversarial network) for MR-guided radiation therapy.

MATERIALS/METHODS

With IRB approval, we trained the GRMM-GAN based on 165 abdominal MR studies from 65 patients. Each study included T2, T1 pre-contrast (T1pre), and T1 contrast enhanced (T1ce) images. The two pre-contrast MR modalities, T2 and T1pre images were adopted as inputs for GRMM-GAN, and the T1ce image at the portal venous phase was used as an output. Ten MR scans containing 21 liver tumors were selected for contouring analysis. A Turing test was first given to six radiation oncologists, in which 100 real T1ce and synthetic T1ce image slices are randomly given to the radiation oncologists to determine the authenticity of the synthesis. We then invited two radiation oncologists (RadOnc 1 and RadOnc2) to manually contour the 21 liver tumors independently on the real T1ce images. RadOnc2 then performed contouring on the respective synthetic T1ce MRs. DICE coefficient (defined as the intersection over the average of two volumes) and Hausdorff distance (HD, measuring how far two volumes are from each other) were used as analysis metrics. The DICE coefficients were calculated from the two radiation oncologists' contours on the real T1ce MR for each tumor. The DICE coefficients were also calculated from RadOnc 2's contours on real and synthetic MRs. Besides, tumor center shifts were extracted. The tumor center of mass coordinates was extracted from real and synthetic volumes. The difference in the coordinates indicated the shifts in the superior-inferior (SI), right-left (RL), and anterior-posterior (AP) directions between real and synthetic tumor volumes.

RESULTS

An average of 52.3% test score was achieved from the six radiation oncologists, which is close to random guessing. RadOnc 1 and RadOnc 2, who had participated in the contouring analysis, achieved an average DICE of 0.91±0.02 from tumor volumes drawn on the real T1ce MRs. This result sets the inter-operator uncertainty baseline in the real clinical setting. RadOnc 2 achieved an average DICE (real vs. synth) of 0.90±0.04 and HD of 4.76±1.82 mm. Only sub-millimeter (SI: 0.67 mm, RL: 0.41 mm, AP: 0.39 mm) tumor center shifts were observed in all three directions.

CONCLUSION

The GRMM-GAN method has the potential for MR-guided liver radiation when contrast agents cannot be administered daily and provide synthetic contrast-enhanced MR for better tumor targeting. The network can produce synthetic MR images with satisfactory contour agreement and geometric integrity.

Towards Automated Dosimetric Analysis of the Bladder Trigone: Deep-Learning-Based Joint Segmentation and Landmark Localization

PURPOSE/OBJECTIVE(S)

The bladder trigone dosimetry is hypothesized to have a stronger correlation with post-SBRT urinary toxicity than that of the entire bladder. However, the trigone tends to move significantly between simulation and daily treatment. Its small size, large daily motion, and proximity to the target lead to potentially consequential but unaccounted-for dosimetric uncertainties. Manual segmentation of the structure can be inconsistent and time-consuming, even with MR-guided RT. Here, we propose and demonstrate a deep-learning-based framework for joint segmentation and landmark localization to support deformable registration and comprehensive dosimetric analysis.

MATERIALS/METHODS

A total of 30 patients were randomly selected for training, and 20 were held out for testing. Each patient had 1 simulation and 5 daily pre-treatment images obtained from a clinical 0.35T MR Linac. The trigone is defined as the triangular bladder section among three landmarks (2 ureteral orifices and the internal urethral orifice). In the manual contouring process, the 3 landmarks were identified first, followed by trigone segmentation. The proposed joint method uses a modified 3D nnU-Net with 2 decoders, one for segmentation and the other for landmark localization. The shared encoder is expected to extract features useful for both tasks. The joint framework was compared with a baseline method using two separate 3D nnU-Nets for landmark localization and trigone segmentation, respectively. Since the trigone is small (∼2% of the bladder volume), we further experimented with a second-stage prediction mimicking the human contouring process. The predicted landmarks from the first stage were used to crop the trigone region, and a second network was trained on cropped images. Evaluation metrics included the Dice score, 95% Hausdorff distance (HD95), and average surface distance (ASD) for segmentation, and Euclidean distance (ED) between the predicted and ground truth landmarks for localization.

RESULTS

The quantification metrics are summarized in the table below. The joint approach shows similar Dice performance to the baseline method but markedly better HD95 by 13%. For landmark localization, the proposed method is similar to the baseline, but the integration of the segmentation task stabilizes the training process. The two-stage approach further improves HD95, ASD, and ED by 27%, 24%, and 19%.

CONCLUSION

Combining segmentation and landmark localization exhibits a synergistic effect. The proposed two-stage approach provided additional improvement. Future studies will explore the deformable registration of the trigone based on the segmentation and landmark detection, as well as analyze cumulated dose distribution.

Analytical HDR prostate brachytherapy planning with automatic catheter and isotope selection

BACKGROUND

High dose rate (HDR) brachytherapy is commonly used to treat prostate cancer. Existing HDR planning systems solve the dwell time problem for predetermined catheters and a single energy source.

PURPOSE

Additional degrees of freedom can be obtained by relaxing the catheters' pre-designation and introducing more source types, and may have a dosimetric benefit, particularly in improving conformality to spare the urethra. This study presents a novel analytical approach to solving the corresponding HDR planning problem.

METHODS

The catheter and dual-energy source selection problem was formulated as a constrained optimization problem with a non-convex group sparsity regularization. The optimization problem was solved using the fast-iterative shrinkage-thresholding algorithm (FISTA). Two isotopes were considered. The dose rates for the HDR 4140 Ytterbium (Yb-169) source and the Elekta Iridium (Ir-192) HDR Flexisource were modeled according to the TG-43U1 formalism and benchmarked accordingly. Twenty-two retrospective HDR prostate brachytherapy patients treated with Ir-192 were considered. An Ir-192 only (IRO), Yb-169 only (YBO), and dual-source (DS) plan with optimized catheter location was created for each patient with N catheters, where N is the number of catheters used in the clinically delivered plans. The DS plans jointly optimized Yb-169 and Ir-192 dwell times. All plans and the clinical plans were normalized to deliver a 15 Gy prescription (Rx) dose to 95% of the clinical treatment volume (CTV) and evaluated for the CTV D90%, V150%, and V200%, urethra D0.1cc and D1cc, bladder V75%, and rectum V75%. Dose-volume histograms (DVHs) were generated for each structure.

RESULTS

The DS plans ubiquitously selected Ir-192 as the only treatment source. IRO outperformed YBO in organ at risk (OARs) OAR sparing, reducing the urethra D0.1cc and D1cc by 0.98% (p = 2.22 ∗ 10 - 9 $p\ = \ 2.22*{10^{ - 9}}$ ) and 1.09% (p = 1.22 ∗ 10 - 10 $p\ = \ 1.22*{10^{ - 10}}$ ) of the Rx dose, respectively, and reducing the bladder and rectum V75% by 0.09 (p = 0.0023 $p\ = \ 0.0023$ ) and 0.13 cubic centimeters (cc) (p = 0.033 $p\ = \ 0.033$ ), respectively. The YBO plans delivered a more homogenous dose to the CTV, with a smaller V150% and V200% by 3.20 (p = 4.67 ∗ 10 - 10 $p\ = \ 4.67*{10^{ - 10}}$ ) and 1.91 cc (p = 5.79 ∗ 10 - 10 $p\ = \ 5.79*{10^{ - 10}}$ ), respectively, and a lower CTV D90% by 0.49% (p = 0.0056 $p\ = \ 0.0056$ ) of the prescription dose. The IRO plans reduce the urethral D1cc by 2.82% (p = 1.38 ∗ 10 - 4 $p\ = \ 1.38*{10^{ - 4}}$ ) of the Rx dose compared to the clinical plans, at the cost of increased bladder and rectal V75% by 0.57 (p = 0.0022 $p\ = \ 0.0022$ ) and 0.21 cc (p = 0.019 $p\ = \ 0.019$ ), respectively, and increased CTV V150% by a mean of 1.46 cc (p = 0.010 $p\ = \ 0.010$ ) and CTV D90% by an average of 1.40% of the Rx dose (p = 8.80 ∗ 10 - 8 $p\ = \ 8.80*{10^{ - 8}}$ ). While these differences are statistically significant, the clinical differences between the plans are minimal.

CONCLUSIONS

The proposed analytical HDR planning algorithm integrates catheter and isotope selection with dwell time optimization for varying clinical goals, including urethra sparing. The planning method can guide HDR implants and identify promising isotopes for specific HDR clinical goals, such as target conformality or OAR sparing.

A unified path seeking algorithm for IMRT and IMPT beam orientation optimization

Objective. Fully automated beam orientation optimization (BOO) for intensity-modulated radiotherapy and intensity modulated proton therapy (IMPT) is gaining interest, since achieving optimal plan quality for an unknown number of fixed beam arrangements is tedious. Fast group sparsity-based optimization methods have been proposed to find the optimal orientation, but manual tuning is required to eliminate the exact number of beams from a large candidate set. Here, we introduce a fast, automated gradient descent-based path-seeking algorithm (PathGD), which performs fluence map optimization for sequentially added beams, to visualize the dosimetric benefit of one added field at a time.Approach. Several configurations of 2-4 proton and 5-15 photon beams were selected for three head-and-neck patients using PathGD, which was compared to group sparsity-regularized BOO solved with the fast iterative shrinkage-thresholding algorithm (GS-FISTA), and manually selected IMPT beams or one coplanar photon VMAT arc (MAN). Once beams were chosen, all plans were compared on computational efficiency, dosimetry, and for proton plans, robustness.Main results. With each added proton beam, Clinical Target Volume (CTV) and organs at risk (OAR) dosimetric cost improved on average across plans by [1.1%, 13.6%], and for photons, [0.6%, 2.0%]. Comparing algorithms, beam selection for PathGD was faster than GS-FISTA on average by 35%, and PathGD matched the CTV coverage of GS-FISTA plans while reducing OAR mean and maximum dose in all structures by an average of 13.6%. PathGD was able to improve CTV [Dmax, D95%] by [2.6%, 5.2%] and reduced worst-case [max, mean] dose in OARs by [11.1%, 13.1%].Significance. The benefit of a path-seeking algorithm is the beam-by-beam analysis of dosimetric cost. PathGD was shown to be most efficient and dosimetrically desirable amongst group sparsity and manual BOO methods, and highlights the sensitivity of beam addition for IMPT in particular.

Minimum reporting standards should be expected for preclinical radiobiology irradiators and dosimetry in the published literature

The cornerstones of science advancement are rigor in performing scientific research, reproducibility of research findings and unbiased reporting of design and results of the experiments. For radiation research, this requires rigor in describing experimental details as well as the irradiation protocols for accurate, precise and reproducible dosimetry. Most institutions conducting radiation biology research in in vitro or animal models do not have describe experimental irradiation protocols in sufficient details to allow for balanced review of their publication nor for other investigators to replicate published experiments. The need to increase and improve dosimetry standards, traceability to National Institute of Standards and Technology (NIST) standard beamlines, and to provide dosimetry harmonization within the radiation biology community has been noted for over a decade both within the United States and France. To address this requirement subject matter experts have outlined minimum reporting standards that should be included in published literature for preclinical irradiators and dosimetry.

Gell: A GPU-powered 3D hybrid simulator for large-scale multicellular system

As a powerful but computationally intensive method, hybrid computational models study the dynamics of multicellular systems by evolving discrete cells in reacting and diffusing extracellular microenvironments. As the scale and complexity of studied biological systems continuously increase, the exploding computational cost starts to limit large-scale cell-based simulations. To facilitate the large-scale hybrid computational simulation and make it feasible on easily accessible computational devices, we develop Gell (GPU Cell), a fast and memory-efficient open-source GPU-based hybrid computational modeling platform for large-scale system modeling. We fully parallelize the simulations on GPU for high computational efficiency and propose a novel voxel sorting method to further accelerate the modeling of massive cell-cell mechanical interaction with negligible additional memory footprint. As a result, Gell efficiently handles simulations involving tens of millions of cells on a personal computer. We compare the performance of Gell with a state-of-the-art paralleled CPU-based simulator on a hanging droplet spheroid growth task and further demonstrate Gell with a ductal carcinoma in situ (DCIS) simulation. Gell affords ~150X acceleration over the paralleled CPU method with one-tenth of the memory requirement.

Contrast-Enhanced Liver Magnetic Resonance Image Synthesis Using Gradient Regularized Multi-Modal Multi-Discrimination Sparse Attention Fusion GAN

PURPOSES

To provide abdominal contrast-enhanced MR image synthesis, we developed an gradient regularized multi-modal multi-discrimination sparse attention fusion generative adversarial network (GRMM-GAN) to avoid repeated contrast injections to patients and facilitate adaptive monitoring.

METHODS

With IRB approval, 165 abdominal MR studies from 61 liver cancer patients were retrospectively solicited from our institutional database. Each study included T2, T1 pre-contrast (T1pre), and T1 contrast-enhanced (T1ce) images. The GRMM-GAN synthesis pipeline consists of a sparse attention fusion network, an image gradient regularizer (GR), and a generative adversarial network with multi-discrimination. The studies were randomly divided into 115 for training, 20 for validation, and 30 for testing. The two pre-contrast MR modalities, T2 and T1pre images, were adopted as inputs in the training phase. The T1ce image at the portal venous phase was used as an output. The synthesized T1ce images were compared with the ground truth T1ce images. The evaluation metrics include peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean squared error (MSE). A Turing test and experts' contours evaluated the image synthesis quality.

RESULTS

The proposed GRMM-GAN model achieved a PSNR of 28.56, an SSIM of 0.869, and an MSE of 83.27. The proposed model showed statistically significant improvements in all metrics tested with p-values < 0.05 over the state-of-the-art model comparisons. The average Turing test score was 52.33%, which is close to random guessing, supporting the model's effectiveness for clinical application. In the tumor-specific region analysis, the average tumor contrast-to-noise ratio (CNR) of the synthesized MR images was not statistically significant from the real MR images. The average DICE from real vs. synthetic images was 0.90 compared to the inter-operator DICE of 0.91.

CONCLUSION

We demonstrated the function of a novel multi-modal MR image synthesis neural network GRMM-GAN for T1ce MR synthesis based on pre-contrast T1 and T2 MR images. GRMM-GAN shows promise for avoiding repeated contrast injections during radiation therapy treatment.

An Efficient and Robust Method for Chest X-ray Rib Suppression That Improves Pulmonary Abnormality Diagnosis

BACKGROUND

Suppression of thoracic bone shadows on chest X-rays (CXRs) can improve the diagnosis of pulmonary disease. Previous approaches can be categorized as either unsupervised physical models or supervised deep learning models. Physical models can remove the entire ribcage and preserve the morphological lung details but are impractical due to the extremely long processing time. Machine learning (ML) methods are computationally efficient but are limited by the available ground truth (GT) for effective and robust training, resulting in suboptimal results.

PURPOSE

To improve bone shadow suppression, we propose a generalizable yet efficient workflow for CXR rib suppression by combining physical and ML methods.

MATERIALS AND METHOD

Our pipeline consists of two stages: (1) pair generation with GT bone shadows eliminated by a physical model in spatially transformed gradient fields; and (2) a fully supervised image denoising network trained on stage-one datasets for fast rib removal from incoming CXRs. For stage two, we designed a densely connected network called SADXNet, combined with a peak signal-to-noise ratio and a multi-scale structure similarity index measure as the loss function to suppress the bony structures. SADXNet organizes the spatial filters in a U shape and preserves the feature map dimension throughout the network flow.

RESULTS

Visually, SADXNet can suppress the rib edges near the lung wall/vertebra without compromising the vessel/abnormality conspicuity. Quantitively, it achieves an RMSE of ~0 compared with the physical model generated GTs, during testing with one prediction in <1 s. Downstream tasks, including lung nodule detection as well as common lung disease classification and localization, are used to provide task-specific evaluations of our rib suppression mechanism. We observed a 3.23% and 6.62% AUC increase, as well as 203 (1273 to 1070) and 385 (3029 to 2644) absolute false positive decreases for lung nodule detection and common lung disease localization, respectively.

CONCLUSION

Through learning from image pairs generated from the physical model, the proposed SADXNet can make a robust sub-second prediction without losing fidelity. Quantitative outcomes from downstream validation further underpin the superiority of SADXNet and the training ML-based rib suppression approaches from the physical model yielded dataset. The training images and SADXNet are provided in the manuscript.

Transformative Technology for FLASH Radiation Therapy

The general concept of radiation therapy used in conventional cancer treatment is to increase the therapeutic index by creating a physical dose differential between tumors and normal tissues through precision dose targeting, image guidance, and radiation beams that deliver a radiation dose with high conformality, e.g., protons and ions. However, the treatment and cure are still limited by normal tissue radiation toxicity, with the corresponding side effects. A fundamentally different paradigm for increasing the therapeutic index of radiation therapy has emerged recently, supported by preclinical research, and based on the FLASH radiation effect. FLASH radiation therapy (FLASH-RT) is an ultra-high-dose-rate delivery of a therapeutic radiation dose within a fraction of a second. Experimental studies have shown that normal tissues seem to be universally spared at these high dose rates, whereas tumors are not. While dose delivery conditions to achieve a FLASH effect are not yet fully characterized, it is currently estimated that doses delivered in less than 200 ms produce normal-tissue-sparing effects, yet effectively kill tumor cells. Despite a great opportunity, there are many technical challenges for the accelerator community to create the required dose rates with novel compact accelerators to ensure the safe delivery of FLASH radiation beams.

The protective roles of liraglutide on Kawasaki disease via AMPK/mTOR/NF-κB pathway

Kawasaki disease (KD) is an acute febrile rash illness among children of unknown etiology, with coronary artery injury. The main purpose of this study was to investigate the protective effects of liraglutide on KD, and elucidate the underlying mechanisms. The candida albicans water-soluble fraction (CAWS)-induced coronary arteritis of mouse KD model in vivo and tumor necrosis factor α (TNF-α) induced endothelial cell injury of human umbilical vein endothelial cell (HUVEC) model in vitro were used to explore the anti-inflammation and anti-apoptosis effects of liraglutide on KD. In vivo results showed that liraglutide could significantly alleviate the coronary artery injury of KD mice, as evidenced by the reduction of inflammatory infiltration around the coronary arteries, downregulation of inflammatory cytokines and chemokines expressions, and decrease of TUNEL (Terminal deoxynucleotidyl transferase dUTP nick end labeling) positive cell rates. The results in vitro also displayed that liraglutide could markedly relieve the inflammatory of TNF-α induced HUVECs through downregulating the expressions of inflammatory and chemokine indicators as well as inhibit TNF-α induced HUVEC apoptosis by the less ratio of apoptotic cells, the more loss of mitochondrial membrane potential (△Ψm), the lower level of intracellular reactive oxygen species (ROS), and the more ratio of BCL-2/BAX. Further in vivo and in vitro studies demonstrated that liraglutide could rescue endothelial cell injury through AMPK/mTOR/NF-κB pathway. In conclusion, liraglutide could play protective roles on KD through inhibiting endothelial cell inflammation and apoptosis via the activation of AMPK/mTOR/NF-κB pathway.

Tomographic detection of photon pairs produced from high-energy X-rays for the monitoring of radiotherapy dosing

Measuring the radiation dose reaching a patient's body is difficult. Here we report a technique for the tomographic reconstruction of the location of photon pairs originating from the annihilation of positron-electron pairs produced by high-energy X-rays travelling through tissue. We used Monte Carlo simulations on pre-recorded data from tissue-mimicking phantoms and from a patient with a brain tumour to show the feasibility of this imaging modality, which we named 'pair-production tomography', for the monitoring of radiotherapy dosing. We simulated three image-reconstruction methods, one applicable to a pencil X-ray beam scanning through a region of interest, and two applicable to the excitation of tissue volumes via broad beams (with temporal resolution sufficient to identify coincident photon pairs via filtered back projection, or with higher temporal resolution sufficient for the estimation of a photon's time-of-flight). In addition to the monitoring of radiotherapy dosing, we show that image contrast resulting from pair-production tomography is highly proportional to the material's atomic number. The technique may thus also allow for element mapping and for soft-tissue differentiation.

Magnetic Resonance Imaging-Guided vs Computed Tomography-Guided Stereotactic Body Radiotherapy for Prostate Cancer: The MIRAGE Randomized Clinical Trial

Importance

Magnetic resonance imaging (MRI) guidance offers multiple theoretical advantages in the context of stereotactic body radiotherapy (SBRT) for prostate cancer. However, to our knowledge, these advantages have yet to be demonstrated in a randomized clinical trial.

Objective

To determine whether aggressive margin reduction with MRI guidance significantly reduces acute grade 2 or greater genitourinary (GU) toxic effects after prostate SBRT compared with computed tomography (CT) guidance.

Design, Setting, and Participants

This phase 3 randomized clinical trial (MRI-Guided Stereotactic Body Radiotherapy for Prostate Cancer [MIRAGE]) enrolled men aged 18 years or older who were receiving SBRT for clinically localized prostate adenocarcinoma at a single center between May 5, 2020, and October 1, 2021. Data were analyzed from January 15, 2021, through May 15, 2022. All patients had 3 months or more of follow-up.

Interventions

Patients were randomized 1:1 to SBRT with CT guidance (control arm) or MRI guidance. Planning margins of 4 mm (CT arm) and 2 mm (MRI arm) were used to deliver 40 Gy in 5 fractions.

Main Outcomes and Measures

The primary end point was the incidence of acute (≤90 days after SBRT) grade 2 or greater GU toxic effects (using Common Terminology Criteria for Adverse Events, version 4.03 [CTCAE v4.03]). Secondary outcomes included CTCAE v4.03-based gastrointestinal toxic effects and International Prostate Symptom Score (IPSS)-based and Expanded Prostate Cancer Index Composite-26 (EPIC-26)-based outcomes.

Results

Between May 2020 and October 2021, 156 patients were randomized: 77 to CT (median age, 71 years [IQR, 67-77 years]) and 79 to MRI (median age, 71 years [IQR, 68-75 years]). A prespecified interim futility analysis conducted after 100 patients reached 90 or more days after SBRT was performed October 1, 2021, with the sample size reestimated to 154 patients. Thus, the trial was closed to accrual early. The incidence of acute grade 2 or greater GU toxic effects was significantly lower with MRI vs CT guidance (24.4% [95% CI, 15.4%-35.4%] vs 43.4% [95% CI, 32.1%-55.3%]; P = .01), as was the incidence of acute grade 2 or greater gastrointestinal toxic effects (0.0% [95% CI, 0.0%-4.6%] vs 10.5% [95% CI, 4.7%-19.7%]; P = .003). Magnetic resonance imaging guidance was associated with a significantly smaller percentage of patients with a 15-point or greater increase in IPSS at 1 month (6.8% [5 of 72] vs 19.4% [14 of 74]; P = .01) and a significantly reduced percentage of patients with a clinically significant (≥12-point) decrease in EPIC-26 bowel scores (25.0% [17 of 68] vs 50.0% [34 of 68]; P = .001) at 1 month.

Conclusions and Relevance

In this randomized clinical trial, compared with CT-guidance, MRI-guided SBRT significantly reduced both moderate acute physician-scored toxic effects and decrements in patient-reported quality of life. Longer-term follow-up will confirm whether these notable benefits persist.

Trial Registration

ClinicalTrials.gov Identifier: NCT04384770.

Fully automated segmentation of prostatic urethra for MR-guided radiation therapy

PURPOSE

Accurate delineation of the urethra is a prerequisite for urethral dose reduction in prostate radiotherapy. However, even in magnetic resonance-guided radiation therapy (MRgRT), consistent delineation of the urethra is challenging, particularly in online adaptive radiotherapy. This paper presented a fully automatic MRgRT-based prostatic urethra segmentation framework.

METHODS

Twenty-eight prostate cancer patients were included in this study. In-house 3D half fourier single-shot turbo spin-echo (HASTE) and turbo spin echo (TSE) sequences were used to image the Foley-free urethra on a 0.35 T MRgRT system. The segmentation pipeline uses 3D nnU-Net as the base and innovatively combines ground truth and its corresponding radial distance (RD) map during training supervision. Additionally, we evaluate the benefit of incorporating a convolutional long short term memory (LSTM-Conv) layer and spatial recurrent convolution layer (RCL) into nnU-Net. A novel slice-by-slice simple exponential smoothing (SEPS) method specifically for tubular structures was used to post-process the segmentation results.

RESULTS

The experimental results show that nnU-Net trained using a combination of Dice, cross-entropy and RD achieved a Dice score of 77.1 ± 2.3% in the testing dataset. With SEPS, Hausdorff distance (HD) and 95% HD were reduced to 2.95 ± 0.17 mm and 1.84 ± 0.11 mm, respectively. LSTM-Conv and RCL layers only minimally improved the segmentation precision.

CONCLUSION

We present the first Foley-free MRgRT-based automated urethra segmentation study. Our method is built on a data-driven neural network with novel cost functions and a post-processing step designed for tubular structures. The performance is consistent with the need for online and offline urethra dose reduction in prostate radiotherapy.

Dose and dose rate objectives in Bragg peak and shoot-through beam orientation optimization for FLASH proton therapy

PURPOSE

The combined use of Bragg peak (BP) and shoot-through (ST) beams has previously been shown to increase the normal tissue volume receiving FLASH dose rates while maintaining dose conformality compared to conventional intensity-modulated proton therapy (IMPT) methods. However, the fixed beam optimization method has not considered the effects of beam orientation on the dose and dose rates. To maximize the proton FLASH effect, here, we incorporate dose rate objectives into our beam orientation optimization framework.

METHODS

From our previously developed group-sparsity dose objectives, we add upper and lower dose rate terms using a surrogate dose-averaged dose rate definition and solve using the fast-iterative shrinking threshold algorithm. We compare the dosimetry for three head-and-neck cases between four techniques: (1) spread-out BP IMPT (BP), (2) dose rate optimization using BP beams only (BP-DR), (3) dose rate optimization using ST beams only (ST-DR), and (4) dose rate optimization using combined BP and ST (BPST-DR), with the goal of sparing organs at risk without loss of tumor coverage and maintaining high dose rate within a 10 mm region of interest (ROI) surrounding the clinical target volume (CTV).

RESULTS

For BP, BP-DR, ST-DR, and BPST-DR, CTV homogeneity index and Dmax were found to be on average 0.886, 0.867, 0.687, and 0.936 and 107%, 109%, 135%, and 101% of prescription, respectively. Although ST-DR plans were not able to meet dosimetric standards, BPST-DR was able to match or improve either maximum or mean dose in the right submandibular gland, left and right parotids, constrictors, larynx, and spinal cord compared to BP plans. Volume of ROIs receiving greater than 40 Gy/s ( V γ 0 ) ${V_{\gamma 0}})$ was 51.0%, 91.4%, 95.5%, and 92.1% on average.

CONCLUSIONS

The dose rate techniques, particularly BPST-DR, were able to significantly increase dose rate without compromising physical dose compared with BP. Our algorithm efficiently selects beams that are optimal for both dose and dose rate.

An efficient rectangular optimization method for sparse orthogonal collimator based small animal irradiation

Objective.Intensity-modulated radiotherapy (IMRT) is widely used in clinical radiotherapy, treating varying malignancies with conformal doses. As the test field for clinical translation, preclinical small animal experiments need to mimic the human radiotherapy condition, including IMRT. However, small animal IMRT is a systematic challenge due to the lack of corresponding hardware and software for miniaturized targets.Approach.The sparse orthogonal collimators (SOC) based on the direct rectangular aperture optimization (RAO) substantially simplified the hardware for miniaturization. This study investigates and evaluates a significantly improved RAO algorithm for complex mouse irradiation using SOC. Because the Kronecker product representation of the rectangular aperture is the main limitation of the computational performance, we reformulated matrix multiplication in the data fidelity term using multiplication with small matrices instead of the Kronecker product of the dose loading matrices. Solving the optimization problem was further accelerated using the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA).Main results.Four mouse cases, including a liver, a brain tumor, a concave U-target, and a complex total marrow irradiation (TMI) case, were included in this study with manually delineated targets and OARs. Seven coplanar-field SOC IMRT (sIMRT) plans were compared with idealistic fluence map based IMRT (iIMRT) plans. For the first three cases with simpler and smaller targets, the differences between sIMRT plans and iIMRT plans in the planning target volumes (PTV) statistics are within 1%. For the TMI case, the sIMRT plans are superior in reducing hot spots (also termedDmax) of PTV, kidneys, lungs, heart, and bowel by 20.5%, 31.5%, 24.67%, 20.13%, and 17.78%, respectively. On average, in four cases in this study, the sIMRT plan conformity is comparable to that of the iIMRT's with lightly increased R50 and Integral Dose by 2.23% and 2.78%.Significance.The significantly improved sIMRT optimization method allows fast plan creation in under 1 min for smaller targets and makes complex TMI planning feasible while achieving comparable dosimetry to idealistic IMRT with fluence map optimization.

Feasibility of a Novel Sparse Orthogonal Collimator-Based Preclinical Total Marrow Irradiation for Enhanced Dosimetric Conformality

Total marrow irradiation (TMI) has significantly improved radiation conditioning for hematopoietic cell transplantation in hematologic diseases by reducing conditioning-induced toxicities and improving survival outcomes in relapsed/refractory patients. Recently, preclinical three-dimensional image-guided TMI has been developed to enhance mechanistic understanding of the role of TMI and to support the development of experimental therapeutics. However, a dosimetric comparison between preclinical and clinical TMI reveals that the preclinical TMI treatment lacks the ability to reduce the dose to some of the vital organs that are very close to the skeletal system and thus limits the ability to evaluate radiobiological relevance. To overcome this limit, we introduce a novel Sparse Orthogonal Collimator (SOC)-based TMI and evaluate its ability to enhance dosimetric conformality. The SOC-TMI-based dose modulation technique significantly improves TMI treatment planning by reducing radiation exposures to critical organs that are close to the skeletal system that leads to reducing the gap between clinical and preclinical TMI.

Urethral Interfractional Geometric and Dosimetric Variations of Prostate Cancer Patients: A Study Using an Onboard MRI

Purpose

For a cohort of prostate cancer patients treated on an MR-guided radiotherapy (MRgRT) system, we retrospectively analyzed urethral interfractional geometric and dosimetric variations based on onboard MRIs acquired at different timepoints and evaluated onboard prostatic urethra visualization for urethra-focused online adaptive RT.

Methods

Twenty-six prostate cancer patients were prospectively scanned on a 0.35-T MRgRT system using an optimized T2-weighted HASTE sequence at simulation and final fraction. Two radiation oncologists (RO1 and RO2) contoured the urethras on all HASTE images. The simulation and final fraction HASTE images were rigidly registered, and urethral interobserver and interfractional geometric variation was evaluated using the 95th percentile Hausdorff distance (HD95), mean distance to agreement (MDA), center-of-mass shift (COMS), and DICE coefficient. For dosimetric analysis, simulation and final fraction HASTE images were registered to the 3D bSSFP planning MRI and 3D bSSFP final setup MRI, respectively. Both ROs’ urethra contours were transferred from HASTE images for initial treatment plan optimization and final fraction dose estimation separately. Stereotactic body radiotherapy (SBRT) plans, 40 Gy in 5 fractions, were optimized to meet clinical constraints, including urethral V42Gy ≤0.03 cc, on the planning MRI. The initial plan was then forward calculated on the final setup MRI to estimate urethral dose on the final fraction and evaluate urethral dosimetric impact due to anatomy change.

Results

The average interobserver HD95, MDA, COMS, and DICE were 2.85 ± 1.34 mm, 1.02 ± 0.36 mm, 3.16 ± 1.61 mm, and 0.58 ± 0.15, respectively. The average interfractional HD95, MDA, COMS, and DICE were 3.26 ± 1.54 mm, 1.29 ± 0.54 mm, 3.34 ± 2.01 mm, and 0.49 ± 0.18, respectively. All patient simulation MRgRT plans met all clinical constraints. For RO1 and RO2, 23/26 (88%) and 21/26 (81%) patients’ final fraction estimated urethral dose did not meet the planned constraint. The average urethral V42Gy change was 0.48 ± 0.58 cc.

Conclusion

Urethral interfractional motion and anatomic change can result in daily treatment violating urethral constraints. Onboard MRI with good visualization of the prostatic urethra can be a valuable tool to help better protect the urethra through patient setup or online adaptive RT.

Voxelwise Prediction of Recurrent High-Grade Glioma via Proximity Estimation-Coupled Multidimensional Support Vector Machine

PURPOSE

To provide early and localized glioblastoma (GBM) recurrence prediction, we introduce a novel postsurgery multiparametric magnetic resonance-based support vector machine (SVM) method coupling with stem cell niche (SCN) proximity estimation.

METHODS AND MATERIALS

This study used postsurgery magnetic resonance imaging (MRI) scans from 50 patients with recurrent GBM, obtained approximately 2 months before clinically diagnosed recurrence. The main prediction pipeline consisted of a proximity-based estimator to identify regions with high risk of recurrence (HRRs) and an SVM classifier to provide voxelwise prediction in HRRs. The HRRs were estimated using the weighted sum of inverse distances to 2 possible origins of recurrence-the SCN and the tumor cavity. Subsequently, multiparametric voxels (from T1, T1 contrast-enhanced, fluid-attenuated inversion recovery, T2, and apparent diffusion coefficient) within the HRR were grouped into recurrent (warped from the clinical diagnosis) and nonrecurrent subregions and fed into the proximity estimation-coupled SVM classifier (SVM_PE). The cohort was randomly divided into 40% and 60% for training and testing, respectively. The trained SVM_PE was then extrapolated to an earlier time point for earlier recurrence prediction. As an exploratory analysis, the SVM_PE predictive cluster sizes and the image intensities from the 5 magnetic resonance sequences were compared across time to assess the progressive subclinical traces.

RESULTS

On 2-month prerecurrence MRI scans from 30 test cohort patients, the SVM_PE classifier achieved a recall of 0.80, a precision of 0.69, an F1-score of 0.73, and a mean boundary distance of 7.49 mm. Exploratory analysis at early time points showed spatially consistent but significantly smaller subclinical clusters and significantly increased T1 contrast-enhanced and apparent diffusion coefficient values over time.

CONCLUSIONS

We demonstrated a novel voxelwise early prediction method, SVM_PE, for GBM recurrence based on clinical follow-up MR scans. The SVM_PE is promising in localizing subclinical traces of recurrence 2 months ahead of clinical diagnosis and may be used to guide more effective personalized early salvage therapy.

Reformulated McNamara RBE-weighted beam orientation optimization for intensity modulated proton therapy

PURPOSE

Empirical relative biological effectiveness (RBE) models have been used to estimate the biological dose in proton therapy but do not adequately capture the factors influencing RBE values for treatment planning. We reformulate the McNamara RBE model such that it can be added as a linear biological dose fidelity term within our previously developed sensitivity-regularized and heterogeneity-weighted beam orientation optimization (SHBOO) framework.

METHODS

Based on our SHBOO framework, we formulated the biological optimization problem to minimize total McNamara RBE dose to OARs. We solve this problem using two optimization algorithms: FISTA (McNam-FISTA) and Chambolle-Pock (McNam-CP). We compare their performances with a physical dose optimizer assuming RBE = 1.1 in all structures (PHYS-FISTA) and an LET-weighted dose model (LET-FISTA). Three head and neck patients were planned with the four techniques and compared on dosimetry and robustness.

RESULTS

Compared to Phys-FISTA, McNam-CP was able to match CTV [HI, Dmax, D95%, D98%] by [0.00, 0.05%, 1.4%, 0.8%]. McNam-FISTA and McNam-CP were able to significantly improve overall OAR [Dmean, Dmax] by an average of [36.1%,26.4%] and [29.6%, 20.3%], respectively. Regarding CTV robustness, worst [Dmax, V95%, D95%, D98%] improvement of [-6.6%, 6.2%, 6.0%, 4.8%] was reported for McNam-FISTA and [2.7%, 2.7%, 5.3%, -4.3%] for McNam-CP under combinations of range and setup uncertainties. For OARs, worst [Dmax, Dmean] were improved by McNam-FISTA and McNam-CP by an average of [25.0%, 19.2%] and [29.5%, 36.5%], respectively. McNam-FISTA considerably improved dosimetry and CTV robustness compared to LET-FISTA, which achieved better worst-case OAR doses.

CONCLUSION

The four optimization techniques deliver comparable biological doses for the head and neck cases. Besides modest CTV coverage and robustness improvement, OAR biological dose and robustness were substantially improved with both McNam-FISTA and McNam-CP, showing potential benefit for directly incorporating McNamara RBE in proton treatment planning.

Fixed Beamline Optimization for Intensity Modulated Carbon-Ion Therapy

A major obstacle for the adoption of heavy ion therapy is the cost and technical difficulties to construct and maintain a rotational gantry. Many heavy ion treatment facilities instead choose to construct fixed beamlines as a compromise, which we propose to mitigate with optimized treatment couch angle. We formulate the integrated beam orientation and scanning spot optimization problem as a quadratic cost function with a group sparsity regularization term. The optimization problem is efficiently solved using fast iterative shrinkage-thresholding algorithm (FISTA). To test the method, we created the fixed beamline plans with couch rotation (FBCR) and without couch rotation (FB) for intensity modulated carbon-ion therapy (IMCT) and compared with the ideal scenario where both the couch and gantry have 360 degrees of freedom (GCR). FB, FBCR, and GCR IMCT plans were compared for ten pancreas cases. The FBCR plans show comparable PTV coverage and OAR doses for each pancreas case. In conclusion, the dosimetric limitation of fixed beams in heavy ion radiotherapy may be largely mitigated with integrated beam orientation optimization of the couch rotation.

Magnetic resonance imaging-guided versus computed tomography-guided stereotactic body radiotherapy for prostate cancer (MIRAGE): Interim analysis of a phase III randomized trial

255

Background: Magnetic resonance imaging (MRI) guidance offers several theoretical advantages over computed tomography (CT) guidance in the context of stereotactic body radiotherapy (SBRT) for intact prostate cancer. Here we report the results of an interim analysis of the phase III MIRAGE trial, which directly compared MRI- and CT-guidance with a pragmatic primary endpoint of acute grade ≥2 genitourinary (GU) toxicity. Methods: MIRAGE is a single center, randomized phase 3 trial. Men undergoing SBRT for localized prostate cancer were randomly assigned to either CT-guidance or MRI-guidance. Planning margins of 4 mm (CT-arm) and 2 mm (MRI-arm) were placed around the prostate and proximal seminal vesicles, and this volume received 40 Gy in five fractions. Elective nodal radiotherapy and rectal spacers were allowed per physician discretion. The primary outcome was the incidence of acute (i.e., within 90 days of SBRT) grade ≥2 GU physician-reported toxicity (by CTCAE version 4.03). Secondary outcomes of interest included the incidence of acute grade ≥2 GU physician-reported toxicity, changes in IPSS scores at 1 and 3 months, and changes in EPIC-26 bowel domain summary scores at 1 and 3 months. A pre-specified efficacy analysis was planned once the 100th patient was eligible for evaluation of the primary endpoint. Results: On 9/1/2021, 100 patients became eligible for evaluation for the interim analysis (51 CT arm, 49 MRI arm). Acute grade ≥2 GU toxicity was significantly reduced in men receiving MRI-guided SBRT (incidence of 24 (47.1%) vs. 11 (22.4%), p = 0.01). Acute grade ≥2 GI toxicity was also significantly reduced in men receiving MRI-guided SBRT (incidence of 7 (13.7%) vs. 0 (0%), p = 0.01.). The increase in IPSS scores from baseline was significantly higher in men receiving CT-guided SBRT at 1 month post-SBRT (median change of 10 vs. 6, p = 0.03), but not at 3 months (median change of 3 vs. 2, p = 0.3). The decrement in EPIC-26 bowel domain scores was significantly greater at 1 month in men receiving CT-guided SBRT (median change of -8.3 vs. 0, p = 0.03), but not at 3 months (median change of -2.3 vs. 0, p = 0.4). Given the large primary endpoint signal seen, our protocol was amended to reduce the projected sample size to 154 while still maintaining 89% power to detect a difference. Conclusions: This interim analysis demonstrates a statistically significant reduction in acute grade ≥2 GU toxicity with MRI-guidance versus CT-guidance in the context of prostate SBRT. Patient-reported urinary and bowel function metrics are also better preserved at the 1 month time point with MRI-guidance, though this difference dissipates (potentially due to side-effect management) at the 3 month time point. Accrual has been completed as of October 2021 and a final analysis for the primary endpoint is anticipated in early 2022. Clinical trial information: NCT04384770.

A Prospective Phase II Study of Automated Non-Coplanar VMAT for Recurrent Head and Neck Cancer: Initial Report of Feasibility, Safety, and Patient-Reported Outcomes

Simple Summary

The delivery of higher radiation doses has been shown to increase local control, and ultimately survival, for head and neck cancer patients, but highly conformal dose distributions are necessary to minimize normal tissue toxicity. Varian’s HyperArc non-coplanar automated treatment planning and delivery technique has been shown to improve dose conformity for intracranial treatment, but its safety and efficacy for head and neck cancer treatment has yet to be verified. This study evaluates the initial results of a prospective clinical trial using HyperArc for recurrent head and neck cancer patients. We demonstrated that HyperArc can enable significant tumor dose escalation compared to conventional volumetric modulated arc therapy (VMAT) planning while minimizing the dose to organs at risk. Treatment delivery was feasible and safe, with minimal treatment-related toxicities and positive patient-reported quality of life measures.

Abstract

This study reports the initial results for the first 15 patients on a prospective phase II clinical trial exploring the safety, feasibility, and efficacy of the HyperArc technique for recurrent head and neck cancer treatment. Eligible patients were simulated and planned with both conventional VMAT and HyperArc techniques and the plan with superior dosimetry was selected for treatment. Dosimetry, delivery feasibility and safety, treatment-related toxicity, and patient-reported quality of life (QOL) were all evaluated. HyperArc was chosen over conventional VMAT for all 15 patients and enabled statistically significant increases in dose conformity (R50% reduced by 1.2 ± 2.1, p < 0.05) and mean PTV and GTV doses (by 15.7 ± 4.9 Gy, p < 0.01 and 17.1 ± 6.0 Gy, p < 0.01, respectively). The average HyperArc delivery was 2.8 min longer than conventional VMAT (p < 0.01), and the mean intrafraction motion was ≤ 0.5 ± 0.4 mm and ≤0.3 ± 0.1°. With a median follow-up of 12 months, treatment-related toxicity was minimal (only one grade 3 acute toxicity above baseline) and patient-reported QOL metrics were favorable. HyperArc enabled superior dosimetry and significant target dose escalation compared to conventional VMAT planning, and treatment delivery was feasible, safe, and well-tolerated by patients.

Bladder surface dose modeling in prostate cancer radiotherapy: An analysis of motion-induced variations and the cumulative dose across the treatment

PURPOSE

To introduce a novel surface-based dose mapping method to improve quantitative bladder dosimetric assessment in prostate cancer (PC) radiotherapy.

METHODS

Based on the planning and daily pre and postfraction MRIs of 12 PC patients, bladder surface models (SMs) were generated on manually delineated contours and regionally aligned via surface-based registration. Subsequently, bladder surface dose models (SDMs) were created using face-wise dose sampling. To determine the bladder intrafractional and interfractional motion and dose variation, we performed a pose analysis between pre and postfraction bladder SMs, as well as surface mapping for fractional SMs. Discrepancies between the received dose, accumulated from daily SDMs, and the planned dose were then assessed on the corresponding SDMs. Complementary to the surface dose mapping, dose surface histogram (DSH)-based comparisons were also performed.

RESULTS

The intrafraction pose analysis revealed a significant (p < 0.05) bladder expansion, as well as an anterior/superior drift during the treatment. The intrafraction motion substantially altered dose to mid-bladder body, but not the bladder surface areas distal to or contiguous with the target. A similar pattern of dose variations was also detected by interfraction comparisons. With surface registration to the common SM, the cumulative bladder dose significantly differs from the planned dose. The discrepancy is evident in the mid-posterior range that corresponds to a mid- to high-dose region. The received DSH significantly differs from the planned DSH after permutation correction (p = 0.0122), while the overall surface-based comparison after multiple comparison correction is nonsignificant (p = 0.0800).

CONCLUSIONS

We developed a novel surface-based intra and interdose mapping framework applied to a unique daily MR dataset for image-guided radiotherapy. The framework identified significant intrafraction bladder positional changes, localized the intra and interfraction variations, and quantified planned versus received dose differences on the bladder surface. The result indicates the importance of adopting the motion-integrated bladder SDM for bladder dose management.

Quantitative Characterization of Tumor Proximity to Stem Cell Niches: Implications on Recurrence and Survival in GBM Patients

PURPOSE

Emerging evidence has linked glioblastoma multiforme (GBM) recurrence and survival to stem cell niches (SCNs). However, the traditional tumor-ventricle distance is insufficiently powered for an accurate prediction. We aimed to use a novel inverse distance map for improved prediction.

METHODS AND MATERIALS

Two T1-magnetic resonance imaging data sets were included for a total of 237 preoperative scans for prognostic stratification and 55 follow-up scans for recurrent pattern identification. SCN, including the subventricular zone (SVZ) and subgranular zone (SGZ), were manually defined on a standard template. A proximity map was generated using the summed inverse distances to all SCN voxels. The mean and maximum proximity scores (PS_m-SCN and PS_max-SCN) were calculated for each primary/recurrent tumor, deformably transformed into the template. The prognostic capacity of proximity score (PS)-derived metrics was assessed using Cox regression and log-rank tests. To evaluate the impact of SCNs on recurrence patterns, we performed group comparisons of PS-derived metrics between the primary and recurrent tumors. For comparison, the same analyses were conducted on PS derived from SVZ alone and traditional edge/center-to-ventricle metrics.

RESULTS

Among all SCN-derived features, PS_m-SCN was the strongest survival predictor (P < .0001). PS_max-SCN was the best in risk stratification, using either evenly sorted (P = .0001) or k-means clustering methods (P = .0045). PS metrics based on SVZ only also correlated with overall survival and risk stratification, but to a lesser degree of significance. In contrast, edge/center-to-ventricle metrics showed weak to no prediction capacities in either task. Moreover, PS_m-SCN,PS_m-SVZ, and center-to-ventricle metrics revealed a significantly closer SCN distribution of recurrence than primary tumors.

CONCLUSIONS

We introduced a novel inverse distance-based metric to comprehensively capture the anatomic relationship between GBM tumors and SCN zones. The derived metrics outperformed traditional edge or center distance-based measurements in overall survival prediction, risk stratification, and recurrent pattern differentiation. Our results reveal the potential role of SGZ in recurrence aside from SVZ.

Using neural networks to extend cropped medical images for deformable registration among images with differing scan extents

PURPOSE

Missing or discrepant imaging volume is a common challenge in deformable image registration (DIR). To minimize the adverse impact, we train a neural network to synthesize cropped portions of head and neck CT's and then test its use in DIR.

METHODS

Using a training dataset of 409 head and neck CT's, we trained a generative adversarial network to take in a cropped 3D image and output an image with synthesized anatomy in the cropped region. The network used a 3D U-Net generator along with Visual Geometry Group (VGG) deep feature losses. To test our technique, for each of the 53 test volumes, we used Elastix to deformably register combinations of a randomly cropped, full, and synthetically full volume to a single cropped, full, and synthetically full target volume. We additionally tested our method's robustness to crop extent by progressively increasing the amount of cropping, synthesizing the missing anatomy using our network, and then performing the same registration combinations. Registration performance was measured using 95% Hausdorff distance across 16 contours.

RESULTS

We successfully trained a network to synthesize missing anatomy in superiorly and inferiorly cropped images. The network can estimate large regions in an incomplete image, far from the cropping boundary. Registration using our estimated full images was not significantly different from registration using the original full images. The average contour matching error for full image registration was 9.9 mm, whereas our method was 11.6, 12.1, and 13.6 mm for synthesized-to-full, full-to-synthesized, and synthesized-to-synthesized registrations, respectively. In comparison, registration using the cropped images had errors of 31.7 mm and higher. Plotting the registered image contour error as a function of initial preregistered error shows that our method is robust to registration difficulty. Synthesized-to-full registration was statistically independent of cropping extent up to 18.7 cm superiorly cropped. Synthesized-to-synthesized registration was nearly independent, with a -0.04 mm of change in average contour error for every additional millimeter of cropping.

CONCLUSIONS

Different or inadequate in scan extent is a major cause of DIR inaccuracies. We address this challenge by training a neural network to complete cropped 3D images. We show that with image completion, the source of DIR inaccuracy is eliminated, and the method is robust to varying crop extent.

Clinical assessment of geometric distortion for a 0.35T MR-guided radiotherapy system

Purpose

To estimate the overall spatial distortion on clinical patient images for a 0.35 T MR‐guided radiotherapy system.

Methods

Ten patients with head‐and‐neck cancer underwent CT and MR simulations with identical immobilization. The MR images underwent the standard systematic distortion correction post‐processing. The images were rigidly registered and landmark‐based analysis was performed by an anatomical expert. Distortion was quantified using Euclidean distance between each landmark pair and tagged by tissue interface: bone‐tissue, soft tissue, or air‐tissue. For baseline comparisons, an anthropomorphic phantom was imaged and analyzed.

Results

The average spatial discrepancy between CT and MR landmarks was 1.15 ± 1.14 mm for the phantom and 1.46 ± 1.78 mm for patients. The error histogram peaked at 0–1 mm. 66% of the discrepancies were <2 mm and 51% <1 mm. In the patient data, statistically significant differences (p‐values < 0.0001) were found between the different tissue interfaces with averages of 0.88 ± 1.24 mm, 2.01 ± 2.20 mm, and 1.41 ± 1.56 mm for the air/tissue, bone/tissue, and soft tissue, respectively. The distortion generally correlated with the in‐plane radial distance from the image center along the longitudinal axis of the MR.

Conclusion

Spatial distortion remains in the MR images after systematic distortion corrections. Although the average errors were relatively small, large distortions observed at bone/tissue interfaces emphasize the need for quantitative methods for assessing and correcting patient‐specific spatial distortions.

Interfractional Geometric Variations and Dosimetric Benefits of Stereotactic MRI Guided Online Adaptive Radiotherapy (SMART) of Prostate Bed after Radical Prostatectomy: Post-Hoc Analysis of a Phase II Trial

PURPOSE

To evaluate geometric variations of patients receiving stereotactic body radiotherapy (SBRT) after radical prostatectomy and the dosimetric benefits of stereotactic MRI guided adaptive radiotherapy (SMART) to compensate for these variations.

MATERIALS/METHODS

The CTV and OAR were contoured on 55 MRI setup scans of 11 patients treated with an MR-LINAC and enrolled in a phase II trial of post-prostatectomy SBRT. All patients followed institutional bladder and rectum preparation protocols and received five fractions of 6-6.8 Gy to the prostate bed. Interfractional changes in volume were calculated and shape deformation was quantified by the Dice similar coefficient (DSC). Changes in CTV-V95%, bladder and rectum maximum dose, V32.5Gy and V27.5Gy were predicted by recalculating the initial plan on daily MRI. SMART was retrospectively simulated if the predicted dose exceeded pre-set criteria.

RESULTS

The CTV volume and shape remained stable with a median volumetric change of 3.0% (IQR -3.0% to 11.5%) and DSC of 0.83 (IQR 0.79 to 0.88). Relatively large volumetric changes in bladder (median -24.5%, IQR -34.6% to 14.5%) and rectum (median 5.4%, IQR - 9.7% to 20.7%) were observed while shape changes were moderate (median DSC of 0.79 and 0.73, respectively). The median CTV-V95% was 98.4% (IQR 94.9% to 99.6%) for the predicted doses. However, SMART would have been deemed beneficial for 78.2% of the 55 fractions based on target undercoverage (16.4%), exceeding OAR constraints (50.9%), or both (10.9%). Simulated SMART improved the dosimetry and met dosimetric criteria in all fractions. Moderate correlations were observed between the CTV-V95% and target DSC (R2 = 0.73) and bladder mean dose versus volumetric changes (R2 = 0.61).

CONCLUSIONS

Interfractional dosimetric variations resulting from anatomic deformation are commonly encountered with post-prostatectomy RT and can be mitigated with SMART.

Magnetic resonance imaging-guided stereotactic body radiotherapy for prostate cancer (mirage): a phase iii randomized trial

Background

Stereotactic body radiotherapy (SBRT) is becoming increasingly used in treating localized prostate cancer (PCa), with evidence showing similar toxicity and efficacy profiles when compared with longer courses of definitive radiation. Magnetic resonance imaging (MRI)-guided radiotherapy has multiple potential advantages over standard computed tomography (CT)-guided radiotherapy, including enhanced prostate visualization (abrogating the need for fiducials and MRI fusion), enhanced identification of the urethra, the ability to track the prostate in real-time, and the capacity to perform online adaptive planning. However, it is unknown whether these potential advantages translate into improved outcomes. This phase III randomized superiority trial is designed to prospectively evaluate whether toxicity is lower after MRI-guided versus CT-guided SBRT.

Methods

Three hundred men with localized PCa will be randomized in a 1:1 ratio to SBRT using CT or MRI guidance. Randomization will be stratified by baseline International Prostate Symptom Score (IPSS) (≤15 or > 15) and prostate gland volume (≤50 cc or > 50 cc). Five fractions of 8 Gy will be delivered to the prostate over the course of fourteen days, with or without hormonal therapy and elective nodal radiotherapy (to a dose of 5 Gy per fraction) as per the investigator’s discretion. The primary endpoint is the incidence of physician-reported acute grade ≥ 2 genitourinary (GU) toxicity (during the first 90 days after SBRT), as assessed by the CTCAE version 4.03 scale. Secondary clinical endpoints include incidence of acute grade ≥ 2 gastrointestinal (GI) toxicity, 5-year cumulative incidences of physician-reported late grade ≥ 2 GU and GI toxicity, temporal changes in patient-reported quality of life (QOL) outcomes, 5-year biochemical recurrence-free survival and the proportion of fractions of MRI-guided SBRT in which online adaptive radiotherapy is used.

Discussion

The MIRAGE trial is the first randomized trial comparing MRI-guided with standard CT-guided SBRT for localized PCa. The primary hypothesis is that MRI-guided SBRT will lead to an improvement in the cumulative incidence of acute grade ≥ 2 GU toxicity when compared to CT-guided SBRT. The pragmatic superiority design focused on an acute toxicity endpoint will allow an early comparison of the two technologies.

Trial registration

Clinicaltrials.gov identifier: NCT04384770. Date of registration: May 12, 2020.

https://clinicaltrials.gov/ct2/show/NCT04384770

Protocol version

Version 2.1, Aug 28, 2020.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08281-x.

Automated Non-Coplanar VMAT for Dose Escalation in Recurrent Head and Neck Cancer Patients

Simple Summary

The ability to escalate the radiation dose to head and neck tumors has been shown to offer improved local control, and consequently, survival for recurrent head and neck cancer (rHNC) patients. This study evaluates the HyperArc automated non-coplanar planning technique (originally developed for intracranial treatment) for 20 rHNC patients, and compares this technique to conventional planning methods. HyperArc enables significant tumor dose escalation, with average increases in mean target dose of over 11.5 Gy (26%), while maintaining clinically-equivalent doses to nearby organs. Our results show that the average probability of tumor control is 23% higher for HyperArc than conventional techniques.

Abstract

This study evaluates the potential for tumor dose escalation in recurrent head and neck cancer (rHNC) patients with automated non-coplanar volumetric modulated arc therapy (VMAT) stereotactic body radiation therapy (SBRT) planning (HyperArc). Twenty rHNC patients are planned with conventional VMAT SBRT to 40 Gy while minimizing organ-at-risk (OAR) doses. They are then re-planned with the HyperArc technique to match these minimal OAR doses while escalating the target dose as high as possible. Then, we compare the dosimetry, tumor control probability (TCP), and normal tissue complication probability (NTCP) for the two plan types. Our results show that the HyperArc technique significantly increases the mean planning target volume (PTV) and gross tumor volume (GTV) doses by 10.8 ± 4.4 Gy (25%) and 11.5 ± 5.1 Gy (26%) on average, respectively. There are no clinically significant differences in OAR doses, with maximum dose differences of <2 Gy on average. The average TCP is 23% (± 21%) higher for HyperArc than conventional plans, with no significant differences in NTCP for the brainstem, cord, mandible, or larynx. HyperArc can achieve significant tumor dose escalation while maintaining minimal OAR doses in the head and neck—potentially enabling improved local control for rHNC SBRT patients without increased risk of treatment-related toxicities.

Weak Magnetic Fields Enhance the Efficacy of Radiation Therapy

Purpose

The clinical efficacy of radiation therapy is mechanistically linked to ionization-induced free radicals that cause cell and tissue injury through direct and indirect mechanisms. Free radical reaction dynamics are influenced by many factors and can be manipulated by static weak magnetic fields (WMF) that perturb singlet-triplet state interconversion. Our study exploits this phenomenon to directly increase ionizing radiation (IR) dose absorption in tumors by combining WMF with radiation therapy as a new and effective method to improve treatment.

Methods and Materials

Coils were custom made to produce both homogeneous and gradient magnetic fields. The gradient coil enabled simultaneous in vitro assessment of free radical/reactive oxygen species reactivity across multiple field strengths from 6 to 66 G. First, increases in IR-induced free radical concentrations using oxidant-sensitive fluorescent dyes in a cell-free system were measured and verified. Next, human and murine cancer cell lines were evaluated in in vitro and in vivo models after exposure to clinically relevant doses of IR in combination with WMF.

Results

Cellular responses to IR and WMF were field strength and cell line dependent. WMF was able to enhance IR effects on reactive oxygen species formation, DNA double-strand break formation, cell death, and tumor growth.

Conclusions

We demonstrate that the external presence of a magnetic field enhances radiation-induced cancer cell injury and death in vitro and in vivo. The effect extends beyond the timeframe when free radicals are induced in the presence of radiation into the window when endogenous free radicals are produced and therefore extends the applicability of this novel adjunct to cancer therapy in the context of radiation treatment.

Treating Glioblastoma Multiforme (GBM) with super hyperfractionated radiation therapy: Implication of temporal dose fractionation optimization including cancer stem cell dynamics

PURPOSE

A previously developed ordinary differential equation (ODE) that models the dynamic interaction and distinct radiosensitivity between cancer stem cells (CSC) and differentiated cancer cells (DCC) was used to explain the definitive treatment failure in Glioblastoma Multiforme (GBM) for conventionally and hypo-fractionated treatments. In this study, optimization of temporal dose modulation based on the ODE equation is performed to explore the feasibility of improving GBM treatment outcome.

METHODS

A non-convex optimization problem with the objective of minimizing the total cancer cell number while maintaining the normal tissue biological effective dose (BEDnormal) at 100 Gy, equivalent to the conventional 2 Gy × 30 dosing scheme was formulated. With specified total number of dose fractions and treatment duration, the optimization was performed using a paired simulated annealing algorithm with fractional doses delivered to the CSC and DCC compartments and time intervals between fractions as variables. The recurrence time, defined as the time point at which the total tumor cell number regrows to 2.8×109 cells, was used to evaluate optimization outcome. Optimization was performed for conventional treatment time frames equivalent to currently and historically utilized fractionation schemes, in which limited improvement in recurrence time delay was observed. The efficacy of a super hyperfractionated approach with a prolonged treatment duration of one year was therefore tested, with both fixed regular and optimized variable time intervals between dose fractions corresponding to total number of fractions equivalent to weekly, bi-weekly, and monthly deliveries (n = 53, 27, 13). Optimization corresponding to BEDnormal of 150 Gy was also obtained to evaluate the possibility in further recurrence delay with dose escalation.

RESULTS

For the super hyperfractionated schedules with dose fraction number equivalent to weekly, bi-weekly, and monthly deliveries, the recurrence time points were found to be 430.5, 423.9, and 413.3 days, respectively, significantly delayed compared with the recurrence time of 250.3 days from conventional fractionation. Results show that optimal outcome was achieved by first delivering infrequent fractions followed by dense once per day fractions in the middle and end of the treatment course, with sparse and low dose treatments in the between. The dose to the CSC compartment was held relatively constant throughout while larger dose fractions to the DCC compartment were observed in the beginning and final fractions that preceded large time intervals. Dose escalation to BEDnormal of 150 Gy was shown capable of further delaying recurrence time to 452 days.

CONCLUSION

The development and utilization of a temporal dose fractionation optimization framework in the context of CSC dynamics have demonstrated that substantial delay in GBM local tumor recurrence could be achieved with a super hyperfractionated treatment approach. Preclinical and clinical studies are needed to validate the efficacy of this novel treatment delivery method.

ROAD: ROtational direct Aperture optimization with a Decoupled ring-collimator for FLASH radiotherapy

Ultra-high dose rate in radiotherapy (FLASH) has been shown to increase the therapeutic index with markedly reduced normal tissue toxicity and the same or better tumor cell killing. The challenge to achieve FLASH using x-rays, besides developing a high output linac, is to intensity-modulate the high-dose-rate x-rays so that the biological gain is not offset by the lack of physical dose conformity. In this study, we develop the ROtational direct Aperture optimization with a Decoupled ring-collimator (ROAD) to achieve simultaneous ultrafast delivery and complex dose modulation. The ROAD design includes a fast-rotating slip-ring linac and a decoupled collimator-ring with 75 pre-shaped multi-leaf-collimator (MLC) modules. The ring-source rotates at 1 rotation per second (rps) clockwise while the ring-collimator is either static or rotating at 1 rps counterclockwise, achieving 75 (ROAD-75) or 150 (ROAD-150) equal-angular beams for one full arc. The Direct Aperture Optimization (DAO) for ROAD was formulated to include a least-square dose fidelity, an anisotropic total variation term, and a single segment term. The FLASH dose (FD) and FLASH biological equivalent dose (FBED) were computed voxelwise, with the latter using a spatiotemporal model accounting for radiolytic oxygen depletion. ROAD was compared with clinical volumetric modulated arc therapy (VMAT) on a brain, a lung, a prostate, and a head and neck cancer patient. The mean dose rate of ROAD-75 and ROAD-150 are 76.2 Gy s^-1 and 112 Gy s^-1 respectively to deliver 25 Gy single-fraction dose in 1 s. With improved PTV homogeneity, ROAD-150 reduced (max, mean) OAR physical dose by (4.8 Gy, 6.3 Gy). The average R50 and integral dose of (VMAT, ROAD-75, ROAD-150) are (4.8, 3.2, 3.2) and (89, 57, 56) Gy×Liter, respectively. The FD and FBED showed model dependent FLASH effects. The novel ROAD design achieves ultrafast dose delivery and improves physical dosimetry compared with clinical VMAT, providing a potentially viable engineering solution for x-ray FLASH radiotherapy.

DeepMC: a deep learning method for efficient Monte Carlo beamlet dose calculation by predictive denoising in magnetic resonance-guided radiotherapy

Emerging magnetic resonance (MR) guided radiotherapy affords significantly improved anatomy visualization and, subsequently, more effective personalized treatment. The new therapy paradigm imposes significant demands on radiation dose calculation quality and speed, creating an unmet need for the acceleration of Monte Carlo (MC) dose calculation. Existing deep learning approaches to denoise the final plan MC dose fail to achieve the accuracy and speed requirements of large-scale beamlet dose calculation in the presence of a strong magnetic field for online adaptive radiotherapy planning. Our deep learning dose calculation method, DeepMC, addresses these needs by predicting low-noise dose from extremely noisy (but fast) MC-simulated dose and anatomical inputs, thus enabling significant acceleration. DeepMC simultaneously reduces MC sampling noise and predicts corrupted dose buildup at tissue-air material interfaces resulting from MR-field induced electron return effects. Here we demonstrate our model's ability to accelerate dose calculation for daily treatment planning by a factor of 38 over traditional low-noise MC simulation with clinically meaningful accuracy in deliverable dose and treatment delivery parameters. As a post-processing approach, DeepMC provides compounded acceleration of large-scale dose calculation when used alongside established MC acceleration techniques in variance reduction and graphics processing unit-based MC simulation.

Multi-task edge-recalibrated network for male pelvic multi-organ segmentation on CT images

Automated male pelvic multi-organ segmentation on CT images is highly desired for applications, including radiotherapy planning. To further improve the performance and efficiency of existing automated segmentation methods, in this study, we propose a multi-task edge-recalibrated network (MTER-Net), which aims to overcome the challenges, including blurry boundaries, large inter-patient appearance variations, and low soft-tissue contrast. The proposed MTER-Net is equipped with the following novel components. (a) To exploit the saliency and stability of femoral heads, we employed a light-weight localization module to locate the target region and efficiently remove the complex background. (b) We add an edge stream to the regular segmentation stream to focus on processing the edge-related information, distinguish the organs with blurry boundaries, and then boost the overall segmentation performance. Between the regular segmentation stream and edge stream, we introduce an edge recalibration module at each resolution level to connect the intermediate layers and deliver the higher-level activations from the regular stream to the edge stream to denoise the irrelevant activations. (c) Finally, using a 3D Atrous Spatial Pyramid Pooling (ASPP) feature fusion module, we fuse the features at different scales in the regular stream and the predictions from the edge stream to form the final segmentation result. The proposed segmentation network was evaluated on 200 prostate cancer patient CT images with manually delineated contours of bladder, rectum, seminal vesicle, and prostate. The segmentation performance of the proposed method was quantitatively evaluated using three metrics including Dice similarity coefficient (DSC), average surface distance (ASD), and 95% surface distance (95SD). The proposed MTER-Net achieves average DSC of 86.35%, ASD of 1.09 mm, and 95SD of 3.53 mm on the four organs, which outperforms the state-of-the-art segmentation networks by a large margin. Specifically, the quantitative DSC evaluation results of the four organs are 96.49% (bladder), 86.39% (rectum), 76.38% (seminal vesicle), and 86.14% (prostate), respectively. In conclusion, we demonstrate that the proposed MTER-Net efficiently attains superior performance to state-of-the-art pelvic organ segmentation methods.

Automatic detection and segmentation of multiple brain metastases on magnetic resonance image using asymmetric UNet architecture

Detection of brain metastases is a paramount task in cancer management due both to the number of high-risk patients and the difficulty of achieving consistent detection. In this study, we aim to improve the accuracy of automated brain metastasis (BM) detection methods using a novel asymmetric UNet (asym-UNet) architecture. An end-to-end asymmetric 3D-UNet architecture, with two down-sampling arms and one up-sampling arm, was constructed to capture the imaging features. The two down-sampling arms were trained using two different kernels (3 × 3 × 3 and 1 × 1 × 3, respectively) with the kernel (1 × 1 × 3) dominating the learning. As a comparison, vanilla single 3D UNets were trained with different kernels and evaluated using the same datasets. Voxel-based Dice similarity coefficient (DSC_v), sensitivity (S _v), precision (P _v), BM-based sensitivity (S _BM), and false detection rate (F _BM) were used to evaluate model performance. Contrast-enhanced T1 MR images from 195 patients with a total of 1034 BMs were solicited from our institutional stereotactic radiosurgery database. The patient cohort was split into training (160 patients, 809 lesions), validation (20 patients, 136 lesions), and testing (15 patients, 89 lesions) datasets. The lesions in the testing dataset were further divided into two subgroups based on the diameters (small S = 1-10 mm, large L = 11-26 mm). In the testing dataset, there were 72 and 17 BMs in the S and L sub-groups, respectively. Among all trained networks, asym-UNet achieved the highest DSC_v of 0.84 and lowest F _BM of 0.24. Although vanilla 3D-UNet with a single 1 × 1 × 3 kernel achieved the highest sensitivities for the S group, it resulted in the lowest precision and highest false detection rate. Asym-UNet was shown to balance sensitivity and false detection rate as well as keep the segmentation accuracy high. The novel asym-UNet segmentation network showed overall competitive segmentation performance and more pronounced improvement in hard-to-detect small BMs comparing to the vanilla single 3D UNet.