Cone-Beam Computed Tomography (CBCT)-Guided Adaptive Boost Radiotherapy for a Patient With Locally Advanced Cervical Cancer Ineligible for Brachytherapy.

Brachytherapy is a critical component of locally advanced cervical cancer treatment, and patients ineligible for brachytherapy historically have poor outcomes. Delivery of boost with stereotactic body radiation therapy (SBRT) has been studied, though toxicity is a concern. Recent case reports have explored adaptive radiation boost, which can adjust plans for inter-fraction motion using magnetic resonance guidance. Herein, we report the first patient with locally advanced cervical cancer ineligible for brachytherapy who was treated with a cone-beam computed tomography (CBCT)-guided adaptive boost following completion of chemoradiation. A 71-year-old female with locally advanced cervical cancer was treated with chemoradiation and was deemed ineligible for a brachytherapy boost due to tumor size, geometry, and a fistula with a tumor in the bladder. She was prescribed a boost to the primary tumor of 25 Gy in five fractions using CBCT-guided adaptive radiation following the completion of chemoradiation. A simulation was performed using a non-contrast CT fused with a mid-chemoradiation magnetic resonance imaging (MRI) scan to create an initial plan. For each treatment fraction, kilovoltage CBCTs were acquired, contours of organs at risk (OARs) were adjusted to reflect anatomy-of-the-day, and an adapted plan was generated. The initial and adapted plans were compared using dose-volume histogram objectives, and the adapted plan was used if it resolved OAR constraint violations or improved target coverage. The use of the initial treatment plan would have resulted in constraint violations for the rectum, sigmoid, and bladder in all fractions. The adapted plans achieved hard constraints in all fractions for all four critical OARs. The mean total treatment time across all five fractions was 58 minutes. This case demonstrates the feasibility of a CBCT-guided adaptive boost approach and the dosimetric benefits of plan adaptation in this setting. Though larger-scale and longer-term data are needed, CBCT-guided adaptive radiation may present a feasible alternative modality to deliver boost doses for brachytherapy-ineligible patients.

CT-Guided Online Adaptive Radiotherapy Delivered via Personalized Ultrafractionated Stereotactic Adaptive Radiotherapy (PULSAR) for a Bulky Thoracic and Abdominal Mass in Oligometastatic Renal Cell Carcinoma.

In the context of oligometastatic renal cell carcinoma (RCC), local treatment with stereotactic body radiotherapy (SBRT) may improve oncologic outcomes. However, the location and size can often pose a technical challenge in standard SBRT delivery, and the dose is potentially limited by nearby organs at risk (OARs). Online adaptive radiotherapy (oART) improves radiation delivery by personalizing high-dose fractions to account for daily stochastic variations in patient anatomy or setup. The oART process aims to maximize tumor control and enhances precision by tailoring to a more accurate representation of a patient in near-real time. The proceeding re-optimization can mitigate the uncertainty inherent in the traditional radiation delivery workflow and precludes the need for larger margins that account for anatomical variations and setup errors. Here, we describe a case of oligometastatic RCC with a bulky (>300 cm3) pleural-based left lower lobe mass extending into the upper abdomen treated via personalized ultrafractionated stereotactic adaptive radiotherapy (PULSAR). Three fractions were delivered four weeks apart allowing for tumor shrinkage of these bulky lesions, and oART permitted on-table adaptation of the plan without traditional re-simulation and re-planning required during off-line adaptive radiotherapy. The plan was designed for the Ethos linear accelerator (Varian Medical Systems, Inc., Palo Alto, CA, USA). The prescription dose was 36 Gray (Gy) in three fractions, and the adapted plan was selected in each treatment over the scheduled plan due to better target coverage and reversal of OAR dose violations. The adapted plan met all OAR dose constraints, and it achieved higher target coverage in the first two PULSAR fractions compared to the scheduled plan. In the third fraction, the cumulative point dose was approaching the maximum heart tolerance, and target coverage was accordingly compromised based on clinical judgment. There was evidence of tumor regression throughout the course of treatment, and the patient did not develop any significant radiation-related toxicities. Follow-up imaging has demonstrated the overall stable size of her lesion without any evidence of disease progression. Our case reflects the benefit of adaptive SBRT delivery to a bulky mass near multiple OARs in the setting of oligometastatic RCC. The adapted plan allowed for prioritization of critical structures on a fraction-by-fraction basis while preserving the therapeutic intent of SBRT. Further integration of advanced imaging techniques, optimal disease-specific systemic immunotherapies or targeted therapies, and refinement of patient selection will be crucial in identifying which patients would most benefit from an adaptive approach.

On Route to Chimeric Antigen Receptor T-cell (CAR T) Therapy, Less Is More: Adaptive Bridging Radiotherapy in Large B-cell Lymphoma.

CD19-targeted chimeric antigen receptor (CAR) T-cell therapy has appreciably advanced treatment for relapsed or refractory large B-cell lymphoma (LBCL). During the critical interim of four to six weeks, until CAR T-cells are ready, radiation therapy (RT) can be used to control the disease. We present the case of a 64-year-old female with relapsed/refractory diffuse large B-cell lymphoma (DLBCL) who received adaptive RT for bilateral adrenal masses as a bridging strategy before undergoing CAR T-cell therapy and enrolled in an adaptive RT clinical trial. A plan was developed to deliver up to five once-weekly fractions (5 Gy per fraction) of CT-based online adaptive RT (Varian Ethos with HyperSight imaging, Varian Medical Systems, Palo Alto, CA). The patient experienced rapid symptomatic relief, with no RT-related toxicities. The patient received RT at only half of the sessions (two out of four sessions) due to excellent tumor shrinkage on cone-beam CT (CBCT). As such, the patient was treated at a lower total dose (10 Gy) than she otherwise would have received with standard RT. Post-RT PET/CT showed significant disease regression, compatible with partial response, prior to CAR T-cell infusion. This case shows the successful application of adaptive RT as bridging therapy prior to CAR T-cell therapy, and we expect the results of this adaptive RT trial to guide the future of adaptive RT in relapsed/refractory B-cell lymphomas.

Moderated Hypofractionated Online Adaptive Radiotherapy in Locally Advanced Cervical Cancer: A Case Report.

Cervical cancer is one of the most frequent malignant tumors in females. Concurrent chemoradiotherapy is one of the treatment options for cervical cancer. The treatment time of conventional radiotherapy is long. Moderately hypofractionated radiotherapy (MHRT) offers the advantage of shortening the overall treatment duration and enhancing the radiobiological effects on tumors. MHRT shortens the overall treatment duration while enhancing the radiobiological effects on tumors. Previous studies have reported that MHRT of cervical cancer has relatively high toxicity. Daily online adaptive radiation therapy (oART) showed improvements in dosimetry and a decrease in toxicity. To the best of our knowledge, this case was the first reported case of moderated hypofractionated oART used in a cervical cancer patient to date in a prospective clinical trial (NCT05994300). This case serves as a critical reminder that cervical cancer is a potential tumor that may be in MHRT with iterative cone beam computed tomography-guided oART. Further data are needed to confirm the toxicity and efficacy of this technique.

CT-Guided Online Adaptive Hypofractionated Radiotherapy for Primary Bladder Cancer: A Report of Two Cases.

Trimodality treatment for bladder cancer, consisting of maximal transurethral resection of the tumor followed by concurrent chemoradiotherapy, is an attractive management option with curative and organ-sparing intent. However, such treatment can be associated with acute toxicities related to the large treatment margins required due to daily variation in bladder filling, with resultant bladder, bowel, and rectal toxicity. Adaptive radiation, which accounts for inter-fraction variations in bladder size, allows the confident delivery of radiation to bladder cancer with smaller margins, with the potential to reduce toxicities without the associated risk of compromising the target coverage. Herein, we present a case series of two patients with primary bladder cancer who were treated with computed tomography (CT)-based online adaptive hypofractionated radiotherapy using the Ethos system (Varian Medical Systems, Palo Alto, CA, USA). The first is an 83-year-old male with a remote history of prostate cancer treated with radiotherapy, who received adaptive radiotherapy as a means of decreasing the required margin size and optimizing planning based on adjacent bowel to reduce the risk of re-irradiation. The second patient is a 78-year-old male with node-positive bladder cancer, which necessitated whole pelvis radiotherapy, who underwent adaptive treatment (25 fractions) as a means of sparing cumulative dose to the bowel while ensuring suitable target coverage. In both cases, the clinical target volume consisted of the entire bladder (± nodes) with a planning target volume expansion of 7 mm. During treatment, daily cone-beam CT scans were acquired and used to generate adapted plans. These plans were compared to the original plans, with attention to target coverage and dose to organs at risk. For all 45 fractions, the adaptive plan was selected, primarily as a means of improving target coverage. This case series demonstrates that the adaptive Ethos system effectively delivers treatment for primary bladder cancer. Further data are needed for clinical toxicity outcomes and the efficacy of this approach.

Online Adaptive Proton Therapy Facilitated by Artificial Intelligence-based Auto Segmentation in Pencil Beam Scanning Proton Therapy: Prostate oAPT in PBSPT.

PURPOSE: Online adaptive proton therapy (oAPT) is essential to address interfractional anatomical changes in patients receiving pencil beam scanning proton therapy (PBSPT). Artificial intelligence (AI)-based auto-segmentation can increase the efficiency and accuracy. Linear energy transfer (LET)-based biological effect evaluation can potentially mitigate possible adverse events caused by high LET. New spot arrangement based on the verification CT (vCT) can further improve the re-plan quality. We propose an oAPT workflow that incorporates all these functionalities and validate its clinical implementation feasibility with prostate patients. METHODS AND MATERIALS: AI-based auto-segmentation tool AccuContourTM (Manteia, Xiamen, China) was seamlessly integrated into oAPT. Initial spot arrangement tool on the vCT for re-optimization was implemented using raytracing. An LET-based biological effect evaluation tool was developed to assess the overlap region of high dose and high LET in selected OARs. Eleven prostate cancer patients were retrospectively selected to verify the efficacy and efficiency of the proposed oAPT workflow. The time cost of each component in the workflow was recorded for analysis. RESULTS: The verification plan showed significant degradation of the CTV coverage and rectum and bladder sparing due to the interfractional anatomical changes. Re-optimization on the vCT resulted in great improvement of the plan quality. No overlap regions of high dose and high LET distributions were observed in bladder or rectum in re-plans. 3D Gamma analyses in PSQA confirmed the accuracy of the re-plan doses before delivery (Gamma passing rate = 99.57 ± 0.46%), and after delivery (98.59 ± 1.29%). The robustness of the re-plans passed all clinical requirements. The average time for the complete execution of the workflow was 9.12 ± 0.85 minutes, excluding manual intervention time. CONCLUSION: The AI-facilitated oAPT workflow was demonstrated to be both efficient and effective by generating a re-plan that significantly improved the plan quality in prostate cancer treated with PBSPT.

Cone-Beam Computed Tomography-Guided Adaptive Radiotherapy: A Case Report on Dynamic Tumor Response in Lung Adenocarcinoma.

Cone-beam computed tomography (CBCT) is an essential tool in radiotherapy, enhancing patient positioning accuracy and enabling precise treatment delivery by monitoring anatomical changes throughout the treatment process. This case report highlights the significant role of CBCT in managing a patient with lung adenocarcinoma treated with concurrent chemoradiation. The lung mass and lower paratracheal lymph nodes were irradiated with 60 Gy in 30 fractions. During the course of treatment, CBCT allowed us to observe substantial tumor shrinkage, prompting a treatment replanning to ensure optimal targeting of the tumor while minimizing radiation exposure to healthy tissues. This adaptive approach resulted in excellent treatment outcomes with no complications, demonstrating the efficacy of CBCT in modern radiotherapy.

Technical Development and In Silico Implementation of SyntheticMR in Head and Neck Adaptive Radiation Therapy: A Prospective R-IDEAL Stage 0/1 Technology Development Report.

Objective: The purpose of this study was to investigate the technical feasibility of integrating the quantitative maps available from SyntheticMR into the head and neck adaptive radiation oncology workflow. While SyntheticMR has been investigated for diagnostic applications, no studies have investigated its feasibility and potential for MR-Simulation or MR-Linac workflow. Demonstrating the feasibility of using this technique will facilitate rapid quantitative biomarker extraction which can be leveraged to guide adaptive radiation therapy decision making. Approach: Two phantoms, two healthy volunteers, and one patient were scanned using SyntheticMR on the MR-Simulation and MR-Linac devices with scan times between four to six minutes. Images in phantoms and volunteers were conducted in a test/retest protocol. The correlation between measured and reference quantitative T1, T2, and PD values were determined across clinical ranges in the phantom. Distortion was also studied. Contours of head and neck organs-at-risk (OAR) were drawn and applied to extract T1, T2, and PD. These values were plotted against each other, clusters were computed, and their separability significance was determined to evaluate SyntheticMR for differentiating tumor and normal tissue. Main Results: The Lin's Concordance Correlation Coefficient between the measured and phantom reference values was above 0.98 for both the MR-Sim and MR-Linac. No significant levels of distortion were measured. The mean bias between the measured and phantom reference values across repeated scans was below 4% for T1, 7% for T2, and 4% for PD for both the MR-Sim and MR-Linac. For T1 vs. T2 and T1 vs. PD, the GTV contour exhibited perfect purity against neighboring OARs while being 0.7 for T2 vs. PD. All cluster significance levels between the GTV and the nearest OAR, the tongue, using the SigClust method was p < 0.001. Significance: The technical feasibility of SyntheticMR was confirmed. Application of this technique to the head and neck adaptive radiation therapy workflow can enrich the current quantitative biomarker landscape.

MR-linac based radiation therapy in gastrointestinal cancers: a narrative review.

Background and Objective: Magnetic resonance guided radiotherapy (MRgRT) is an emerging technological innovation with more and more institutions gaining clinical experience in this new field of radiation oncology. The ability to better visualize both tumors and healthy tissues due to excellent soft tissue contrast combined with new possibilities regarding motion management and the capability of online adaptive radiotherapy might increase tumor control rates while potentially reducing the risk of radiation-induced toxicities. As conventional computed tomography (CT)-based image guidance methods are insufficient for adaptive workflows in abdominal tumors, MRgRT appears to be an optimal method for this tumor site. The aim of this narrative review is to outline the opportunities and challenges in magnetic resonance guided radiation therapy in gastrointestinal cancers. Methods: We searched for studies, reviews and conceptual articles, including the general technique of MRgRT and the specific utilization in gastrointestinal cancers, focusing on pancreatic cancer, liver metastases and primary liver cancer, rectal cancer and esophageal cancer. Key Content and Findings: This review is highlighting the innovative approach of MRgRT in gastrointestinal cancer and gives an overview of the currently available literature with regard to clinical experiences and theoretical background. Conclusions: MRgRT is a promising new tool in radiation oncology, which can play off several of its beneficial features in the specific field of gastrointestinal cancers. However, clinical data is still scarce. Nevertheless, the available literature points out large potential for improvements regarding dose coverage and escalation as well as the reduction of dose exposure to critical organs at risk (OAR). Further prospective studies are needed to demonstrate the role of this innovative technology in gastrointestinal cancer management, in particular trials that randomly compare MRgRT with conventional CT-based image-guided radiotherapy (IGRT) would be of high value.

Technical note: Phantom-based evaluation of CBCT dose calculation accuracy for use in adaptive radiotherapy.

BACKGROUND: High-quality 3D-anatomy of the day is needed for treatment plan adaptation in radiotherapy. For online x-ray-based CBCT workflows, one approach is to create a synthetic CT or to utilize a fan-beam CT with corresponding registrations. The former potentially introduces uncertainties in the dose calculation if deformable image registration is used. The latter can introduce burden and complexity to the process, the facility, and the patient. PURPOSE: Using the CBCT of the day, acquired on the treatment device, for direct dose calculation and plan adaptation can overcome these limitations. This study aims to assess the accuracy of the calculated dose on the CBCT scans acquired on a Halcyon linear accelerator equipped with HyperSight. METHODS: HyperSight's new CBCT reconstruction algorithm includes improvements in scatter correction, HU calibration of the imager, and beam shape adaptation. Furthermore, HyperSight introduced a new x-ray detector. To show the effect of the implemented improvements, gamma comparisons of 2%/2 mm, 2%/1 mm, and 1%/1 mm were made between the dose distribution in phantoms calculated on the CBCT reconstructions and the simulation CT scans, considering this the standard of care. The resulting gamma passing rates were compared to those obtained with the Halcyon 3.0 reconstruction and hardware without HyperSight's technologies. Various anatomical phantoms for dosimetric evaluations on brain, head and neck, lung, breast, and prostate cases have been used in this study. RESULTS: The overall results demonstrated that HyperSight outperformed the Halcyon 3.0 version. Based on the gamma analysis, the calculated dose using HyperSight was closer to the CT scan-based doses than the calculated dose using iCBCT Halcyon 3.0 for most cases. Over all plans and gamma criteria, Halcyon 3.0 achieved an average passing rate of 92.9%, whereas HyperSight achieved 98.1%. CONCLUSION: Using HyperSight CBCT images for direct dose calculation, for example, in (online) plan adaptation, seems feasible for the investigated cases.

"Under the hood": artificial intelligence in personalized radiotherapy.

This review presents and discusses the ways in which artificial intelligence (AI) tools currently intervene, or could potentially intervene in the future, to enhance the diverse tasks involved in the radiotherapy workflow. The radiotherapy framework is presented on 2 different levels for the personalization of the treatment, distinct in tasks and methodologies. The first level is the clinically well-established anatomy-based workflow, known as adaptive radiation therapy. The second level is referred to as biology-driven workflow, explored in the research literature and recently appearing in some preliminary clinical trials for personalized radiation treatments. A 2-fold role for AI is defined according to these 2 different levels. In the anatomy-based workflow, the role of AI is to streamline and improve the tasks in terms of time and variability reductions compared to conventional methodologies. The biology-driven workflow instead fully relies on AI, which introduces decision-making tools opening uncharted frontiers that were in the past deemed challenging to explore. These methodologies are referred to as radiomics and dosiomics, handling imaging and dosimetric information, or multiomics, when complemented by clinical and biological parameters (ie, biomarkers). The review explicitly highlights the methodologies that are currently incorporated into clinical practice or still in research, with the aim of presenting the AI's growing role in personalized radiotherapy.

Validation of MLC leaf open time calculation methods for PSQA in adaptive radiotherapy with tomotherapy units.

BACKGROUND: Treatment delivery safety and accuracy are essential to control the disease and protect healthy tissues in radiation therapy. For usual treatment, a phantom-based patient specific quality assurance (PSQA) is performed to verify the delivery prior to the treatment. The emergence of adaptive radiation therapy (ART) adds new complexities to PSQA. In fact, organ at risks and target volume re-contouring as well as plan re-optimization and treatment delivery are performed with the patient immobilized on the treatment couch, making phantom-based pretreatment PSQA impractical. In this case, phantomless PSQA tools based on multileaf collimator (MLC) leaf open times (LOTs) verifications provide alternative approaches for the Radixact® treatment units. However, their validity is compromised by the lack of independent and reliable methods for calculating the LOT performed by the MLC during deliveries. PURPOSE: To provide independent and reliable methods of LOT calculation for the Radixact® treatment units. METHODS: Two methods for calculating the LOTs performed by the MLC during deliveries have been implemented. The first method uses the signal recorded by the build-in detector and the second method uses the signal recorded by optical sensors mounted on the MLC. To calibrate the methods to the ground truth, in-phantom ionization chamber LOT measurements have been conducted on a Radixact® treatment unit. The methods were validated by comparing LOT calculations with in-phantom ionization chamber LOT measurements performed on two Radixact® treatment units. RESULTS: The study shows a good agreement between the two LOT calculation methods and the in-phantom ionization chamber measurements. There are no notable differences between the two methods and the same results were observed on the different treatment units. CONCLUSIONS: The two implemented methods have the potential to be part of a PSQA solution for ART in tomotherapy.

Artificial intelligence for treatment delivery: image-guided radiotherapy.

Radiation therapy (RT) is a highly digitized field relying heavily on computational methods and, as such, has a high affinity for the automation potential afforded by modern artificial intelligence (AI). This is particularly relevant where imaging is concerned and is especially so during image-guided RT (IGRT). With the advent of online adaptive RT (ART) workflows at magnetic resonance (MR) linear accelerators (linacs) and at cone-beam computed tomography (CBCT) linacs, the need for automation is further increased. AI as applied to modern IGRT is thus one area of RT where we can expect important developments in the near future. In this review article, after outlining modern IGRT and online ART workflows, we cover the role of AI in CBCT and MRI correction for dose calculation, auto-segmentation on IGRT imaging, motion management, and response assessment based on in-room imaging.

Optimization and validation of low-field MP2RAGE T1 mapping on 0.35T MR-Linac: Toward adaptive dose painting with hypoxia biomarkers.

BACKGROUND: Stereotactic MR-guided Adaptive Radiation Therapy (SMART) dose painting for hypoxia has potential to improve treatment outcomes, but clinical implementation on low-field MR-Linac faces substantial challenges due to dramatically lower signal-to-noise ratio (SNR) characteristics. While quantitative MRI and T1 mapping of hypoxia biomarkers show promise, T1-to-noise ratio (T1NR) optimization at low fields is paramount, particularly for the clinical implementation of oxygen-enhanced (OE)-MRI. The 3D Magnetization Prepared (2) Rapid Gradient Echo (MP2RAGE) sequence stands out for its ability to acquire homogeneous T1-weighted contrast images with simultaneous T1 mapping. PURPOSE: To optimize MP2RAGE for low-field T1 mapping; conduct experimental validation in a ground-truth phantom; establish feasibility and reproducibility of low-field MP2RAGE acquisition and T1 mapping in healthy volunteers. METHODS: The MP2RAGE optimization was performed to maximize the contrast-to-noise ratio (CNR) of T1 values in white matter (WM) and gray matter (GM) brain tissues at 0.35T. Low-field MP2RAGE images were acquired on a 0.35T MR-Linac (ViewRay MRIdian) using a multi-channel head coil. Validation of T1 mapping was performed with a ground-truth Eurospin phantom, containing inserts of known T1 values (400-850 ms), with one and two average (1A and 2A) MP2RAGE scans across four acquisition sessions, resulting in eight T1 maps. Mean (± SD) T1 relative error, T1NR, and intersession coefficient of variation (CV) were determined. Whole-brain MP2RAGE scans were acquired in 5 healthy volunteers across two sessions (A and B) and T1 maps were generated. Mean (± SD) T1 values for WM and GM were determined. Whole-brain T1 histogram analysis was performed, and reproducibility was determined with the CV between sessions. Voxel-by-voxel T1 difference maps were generated to evaluate 3D spatial variation. RESULTS: Low-field MP2RAGE optimization resulted in parameters: MP2RAGETR of 3250 ms, inversion times (TI1/TI2) of 500/1200 ms, and flip angles (α1/α2) of 7/5°. Eurospin T1 maps exhibited a mean (± SD) relative error of 3.45% ± 1.30%, T1NR of 20.13 ± 5.31, and CV of 2.22% ± 0.67% across all inserts. Whole-brain MP2RAGE images showed high anatomical quality with clear tissue differentiation, resulting in mean (± SD) T1 values: 435.36 ± 10.01 ms for WM and 623.29 ± 14.64 ms for GM across subjects, showing excellent concordance with literature. Whole-brain T1 histograms showed high intrapatient and intersession reproducibility with characteristic intensity peaks consistent with voxel-level WM and GM T1 values. Reproducibility analysis revealed a CV of 0.46% ± 0.31% and 0.35% ± 0.18% for WM and GM, respectively. Voxel-by-voxel T1 difference maps show a normal 3D spatial distribution of noise in WM and GM. CONCLUSIONS: Low-field MP2RAGE proved effective in generating accurate, reliable, and reproducible T1 maps with high T1NR in phantom studies and in vivo feasibility established in healthy volunteers. While current work is focused on refining the MP2RAGE protocol to enable clinically efficient OE-MRI, this study establishes a foundation for TOLD T1 mapping for hypoxia biomarkers. This advancement holds the potential to facilitate a paradigm shift toward MR-guided biological adaptation and dose painting by leveraging 3D hypoxic spatial distributions and improving outcomes in conventionally challenging-to-treat cancers.

TransAnaNet: Transformer-based Anatomy Change Prediction Network for Head and Neck Cancer Patient Radiotherapy.

Background: Adaptive radiotherapy (ART) can compensate for the dosimetric impact of anatomic change during radiotherapy of head neck cancer (HNC) patients. However, implementing ART universally poses challenges in clinical workflow and resource allocation, given the variability in patient response and the constraints of available resources. Therefore, early identification of head and neck cancer (HNC) patients who would experience significant anatomical change during radiotherapy (RT) is of importance to optimize patient clinical benefit and treatment resources. Purpose: The purpose of this study is to assess the feasibility of using a vision-transformer (ViT) based neural network to predict radiotherapy induced anatomic change of HNC patients. Methods: We retrospectively included 121 HNC patients treated with definitive RT/CRT. We collected the planning CT (pCT), planned dose, CBCTs acquired at the initial treatment (CBCT01) and fraction 21 (CBCT21), and primary tumor volume (GTVp) and involved nodal volume (GTVn) delineated on both pCT and CBCTs for model construction and evaluation. A UNet-style ViT network was designed to learn the spatial correspondence and contextual information from embedded image patches of CT, dose, CBCT01, GTVp, and GTVn. The deformation vector field between CBCT01 and CBCT21 was estimated by the model as the prediction of anatomic change, and deformed CBCT01 was used as the prediction of CBCT21. We also generated binary masks of GTVp, GTVn and patient body for volumetric change evaluation. We used data from 100 patients for training and validation, and the remaining 21 patients for testing. Image and volumetric similarity metrics including mean square error (MSE), structural similarity index (SSIM), dice coefficient, and average surface distance were used to measure the similarity between the target image and predicted CBCT. Results: The predicted image from the proposed method yielded the best similarity to the real image (CBCT21) over pCT, CBCT01, and predicted CBCTs from other comparison models. The average MSE and SSIM between the normalized predicted CBCT to CBCT21 are 0.009 and 0.933, while the average dice coefficient between body mask, GTVp mask, and GTVn mask are 0.972, 0.792, and 0.821 respectively. Conclusions: The proposed method showed promising performance for predicting radiotherapy induced anatomic change, which has the potential to assist in the decision making of HNC Adaptive RT.

The potential of mixed carbon-helium beams for online treatment verification: a simulation and treatment planning study.

Objective.Recently, a new and promising approach for range verification was proposed. This method requires the use of two different ion species. Due to their equal magnetic rigidity, fully ionized carbon and helium ions can be simultaneously accelerated in accelerators like synchrotrons. At sufficiently high treatment energies, helium ions can exit the patient distally, reaching approximately three times the range of carbon ions at an equal energy per nucleon. Therefore, the proposal involves adding a small helium fluence to the carbon ion beam and utilizing helium as an online range probe during radiation therapy. This work aims to develop a software framework for treatment planning and motion verification in range-guided radiation therapy using mixed carbon-helium beams.Approach.The developed framework is based on the open-source treatment planning toolkit matRad. Dose distributions and helium radiographs were simulated using the open-source Monte Carlo package TOPAS. Beam delivery system parameters were obtained from the Heidelberg Ion Therapy Center, and imaging detectors along with reconstruction were facilitated by ProtonVDA. Methods for reconstructing the most likely patient positioning error scenarios and the motion phase of 4DCT are presented for prostate and lung cancer sites.Main results.The developed framework provides the capability to calculate and optimize treatment plans for mixed carbon-helium ion therapy. It can simulate the treatment process and generate helium radiographs for simulated patient geometry, including small beam views. Furthermore, motion reconstruction based on these radiographs seems possible with preliminary validation.Significance.The developed framework can be applied for further experimental work with the promising mixed carbon-helium ion implementation of range-guided radiotherapy. It offers opportunities for adaptation in particle therapy, improving dose accumulation, and enabling patient anatomy reconstruction during radiotherapy.

A reference-point-method-based online proton treatment plan re-optimization strategy and a novel solution to planning constraint infeasibility problem.

Objective. Propose a highly automated treatment plan re-optimization strategy suitable for online adaptive proton therapy. The strategy includes a rapid re-optimization method that generates quality replans and a novel solution that efficiently addresses the planning constraint infeasibility issue that can significantly prolong the re-optimization process.Approach. We propose a systematic reference point method (RPM) model that minimizes the l-infinity norm from the initial treatment plan in the daily objective space for online re-optimization. This model minimizes the largest objective value deviation among the objectives of the daily replan from their reference values, leading to a daily replan similar to the initial plan. Whether a set of planning constraints is feasible with respect to the daily anatomy cannot be known before solving the corresponding optimization problem. The conventional trial-and-error-based relaxation process can cost a significant amount of time. To that end, we propose an optimization problem that first estimates the magnitude of daily violation of each planning constraint. Guided by the violation magnitude and clinical importance of the constraints, the constraints are then iteratively converted into objectives based on their priority until the infeasibility issue is solved.Main results.The proposed RPM-based strategy generated replans similar to the offline manual replans within the online time requirement for six head and neck and four breast patients. The average targetD95and relevant organ at risk sparing parameter differences between the RPM replans and clinical offline replans were -0.23, -1.62 Gy for head and neck cases and 0.29, -0.39 Gy for breast cases. The proposed constraint relaxation solution made the RPM problem feasible after one round of relaxation for all four patients who encountered the infeasibility issue.Significance. We proposed a novel RPM-based re-optimization strategy and demonstrated its effectiveness on complex cases, regardless of whether constraint infeasibility is encountered.

The effects of mega-voltage CT scan parameters on offline adaptive radiation therapy.

TomoTherapy involves image-guided radiation therapy (IGRT) using Mega-voltage CT (MVCT) for each treatment session. The acquired MVCT images can be utilized for the retrospective assessment of dose distribution. The TomoTherapy provides 18 distinct imaging conditions that can be selected based on a combination of algorithms, acquisition pitch, and slice interval. We investigated the accuracy of dose calculation and deformable image registration (DIR) depending on MVCT scan parameters and their effects on adaptive radiation therapy (ART). We acquired image values for density calibration tables (IVDTs) under 18 different MVCT conditions and compared them. The planning CT (pCT) was performed using a thoracic phantom, and an esophageal intensity-modulated radiation therapy (IMRT) plan was created. MVCT images of the thoracic phantom were acquired under each of the 18 conditions, and dose recalculation was performed. DIR was performed on the MVCT images acquired under each condition. The accuracy of DIR, depending on the MVCT scan parameters, was compared using the mean distance to agreement (MDA) and Dice similarity coefficient (DSC). The dose distribution calculated on the MVCT images was deformed using deformed vector fields (DVF). No significant differences were observed in the results of the 18 IVDTs. The esophageal IMRT plan also showed a small dose difference. Regarding verifying the DIR accuracy, the MDA increased, and the DSC decreased as the acquisition pitch and slice interval increased. The difference between the dose distributions after dose mapping was comparable to that before DIR. The MVCT scan parameters had little effect on ART.

Prospects for online adaptive radiation therapy (ART) for head and neck cancer.

BACKGROUND: The aim of the present study is to examine the impact of kV-CBCT-based online adaptive radiation therapy (ART) on dosimetric parameters in comparison to image-guided-radiotherapy (IGRT) in consecutive patients with tumors in the head and neck region from a prospective registry. METHODS: The study comprises all consecutive patients with tumors in the head and neck area who were treated with kV-CBCT-based online ART or IGRT-modus at the linear-accelerator ETHOS™. As a measure of effectiveness, the equivalent-uniform-dose was calculated for the CTV (EUDCTV) and organs-at-risk (EUDOAR) and normalized to the prescribed dose. As an important determinant for the need of ART the interfractional shifts of anatomic landmarks related to the tongue were analyzed and compared to the intrafractional shifts. The latter determine the performance of the adapted dose distribution on the verification CBCT2 postadaptation. RESULTS: Altogether 59 consecutive patients with tumors in the head-and-neck-area were treated from 01.12.2021 to 31.01.2023. Ten of all 59 patients (10/59; 16.9%) received at least one phase within a treatment course with ART. Of 46 fractions in the adaptive mode, irradiation was conducted in 65.2% of fractions with the adaptive-plan, the scheduled-plan in the remaining. The dispersion of the distributions of EUDCTV-values from the 46 dose fractions differed significantly between the scheduled and adaptive plans (Ansari-Bradley-Test, p = 0.0158). Thus, the 2.5th percentile of the EUDCTV-values by the adaptive plans amounted 97.1% (95% CI 96.6-99.5%) and by the scheduled plans 78.1% (95% CI 61.8-88.7%). While the EUDCTV for the accumulated dose distributions stayed above 95% at PTV-margins of ≥ 3 mm for all 8 analyzed treatment phases the scheduled plans did for margins ≥ 5 mm. The intrafractional anatomic shifts of all 8 measured anatomic landmarks were smaller than the interfractional with overall median values of 8.5 mm and 5.5 mm (p < 0.0001 for five and p < 0.05 for all parameters, pairwise comparisons, signed-rank-test). The EUDOAR-values for the larynx and the parotid gland were significantly lower for the adaptive compared with the scheduled plans (Wilcoxon-test, p < 0.001). CONCLUSIONS: The mobile tongue and tongue base showed considerable interfractional variations. While PTV-margins of 5 mm were sufficient for IGRT, ART showed the potential of decreasing PTV-margins and spare dose to the organs-at-risk.