Review of cone beam computed tomography based online adaptive radiotherapy: current trend and future direction

The use of precision radiotherapy for the management of cancer-related pain in the abdomen

PURPOSE OF REVIEW

Abdominal pain due to cancer is a significant and debilitating symptom for cancer patients, which is commonly undertreated. Radiotherapy (RT) for the management of abdominal cancer pain is underused, with limited awareness of its benefit. This review presents a discussion on current precision RT options for the management of cancer pain in the abdomen.

RECENT FINDINGS

Precision RT focuses on delivering targeted and effective radiation doses while minimizing damage to surrounding healthy tissues. In patients with primary or secondary liver cancer, RT has been shown to significantly improve liver related cancer pain in the majority of patients. Also, symptom sequelae of tumour thrombus may be relieved with the use of palliative RT. Similarly, single dose, high precision stereotactic RT to the celiac plexus has been shown to significantly improve pain in patients with pancreatic cancer. Pain response for adrenal metastases has been less commonly investigated, but small series suggest that stereotactic body RT may reduce or alleviate pain.

SUMMARY

RT is an effective option for the treatment of abdominal cancer pain. RT should be considered within the multidisciplinary treatment armamentarium, and may be successfully integrated, alone or in conjunction with other treatment modalities, in abdominal cancer-related pain.

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.

A Dual-Energy Computed Tomography Guided Intelligent Radiation Therapy Platform

PURPOSE

The integration of advanced imaging and artificial intelligence technologies in radiation therapy has revolutionized cancer treatment by enhancing precision and adaptability. This study introduces a novel dual-energy computed tomography (DECT) guided intelligent radiation therapy (DEIT) platform designed to streamline and optimize the radiation therapy process. The DEIT system combines DECT, a newly designed dual-layer multileaf collimator, deep learning algorithms for auto-segmentation, and automated planning and quality assurance capabilities.

METHODS AND MATERIALS

The DEIT system integrates an 80-slice computed tomography (CT) scanner with an 87 cm bore size, a linear accelerator delivering 4 photon and 5 electron energies, and a flat panel imager optimized for megavoltage (MV) cone beam CT acquisition. A comprehensive evaluation of the system's accuracy was conducted using end-to-end tests. Virtual monoenergetic CT images and electron density images of the DECT were generated and compared on both phantom and patient. The system's auto-segmentation algorithms were tested on 5 cases for each of the 99 organs at risk, and the automated optimization and planning capabilities were evaluated on clinical cases.

RESULTS

The DEIT system demonstrated systematic errors of less than 1 mm for target localization. DECT reconstruction showed electron density mapping deviations ranging from -0.052 to 0.001, with stable Hounsfield unit consistency across monoenergetic levels above 60 keV, except for high-Z materials at lower energies. Auto-segmentation achieved dice similarity coefficients above 0.9 for most organs with an inference time of less than 2 seconds. Dose-volume histogram comparisons showed improved dose conformity indices and reduced doses to critical structures in auto-plans compared to manual plans across various clinical cases. In addition, high gamma passing rates at 2%/2 mm in both 2-dimensional (above 97%) and 3-dimensional (above 99%) in vivo analyses further validate the accuracy and reliability of treatment plans.

CONCLUSIONS

The DEIT platform represents a viable solution for radiation treatment. The DEIT system uses artificial intelligence-driven automation, real-time adjustments, and CT imaging to enhance the radiation therapy process, improving efficiency and flexibility.

Cone Beam Online Adaptive Radiation Therapy: A Promising Approach for Gastric Mucosa-Associated Lymphoid Tissue Lymphoma?

Purpose

Daily online adaptive radiation therapy (oART) opens the opportunity to treat gastric mucosa-associated lymphoid tissue (MALT) lymphoma with a reduced margin. This study reports our early experience of cone beam computed tomography (CBCT)-based daily oART treating gastric MALT lymphoma with breath-hold and reduced margins.

Methods and Materials

Ten patients were treated on a CBCT-based oART system. Organs at risk (OARs) and the clinical target volume (CTV) were adjusted based on the daily CBCT. Planning target volume (PTV) was derived from the CTV with a 0.5 to 0.7 cm margin with breath-hold. Multiple beam arrangements were compared during the preplanning phase to ensure minimal monitor unit (MU) for patient comfort and breath-hold reproducibility. For 108 fractions from the 10 patients, the PTV, CTV coverage, and Paddick conformity index (CI) were compared between the adapted and scheduled plans. The MU, Paddick CI, and gradient index were compared using relative percentage differences between the adapted plans and preplans. The OAR doses from 106 fractions across 9 patients were reported for the preplans, adapted plans, and scheduled plans. The time statistics for each step of the clinical workflow were recorded and reported for 93 treatment fractions from 9 patients.

Results

The PTV volume varied from -37.1% to 90.5% (11.7% ± 18.5%) throughout treatments across all patients. The adapted plan was chosen as the treatment plan for each fraction because of superior PTV and CTV coverage while maintaining a similar OAR dose. The PTV and CTV coverage for the adapted and scheduled plans was VRx = 95.0% ± 0.3% versus 64.1 ± 19.6% and VRx = 99.9 ± 0.1% versus 74.0% ± 22.2%, respectively. The adapted plans' MU, Paddick CI, and gradient index were, on average, 4.1%, 0.4%, and -4.2% of the preplan values, respectively. The console's adaptive workflow and physician time were 25 ± 7 and 19 ± 6 minutes, respectively.

Conclusion

A CBCT-based oART system with the proposed workflow is feasible for treating patients with gastric MALT lymphoma using a reduced PTV margin while maintaining excellent target coverage within a reasonable time, resulting in consistent adapted plan quality. This approach can be expanded to a larger cohort of gastrointestinal patients.

Evaluation of artificial intelligence-based autosegmentation for a high-performance cone-beam computed tomography imaging system in the pelvic region

Background and purpose

A novel ring-gantry cone-beam computed tomography (CBCT) imaging system shows improved image quality compared to its conventional version, but its effect on autosegmentation is unknown. This study evaluates the impact of this high-performance CBCT on autosegmentation performance, inter-observer variability, contour correction times and delineation confidence, compared to the conventional CBCT.

Materials and methods

Twenty prostate cancer patients were enrolled in this prospective clinical study. Per patient, one pair of high-performance CBCT and conventional CBCT scans was included. Three observers manually corrected contours generated by the artificial intelligence (AI) model for prostate, seminal vesicles, bladder, rectum and bowel. Differences between AI-based and manual corrected contours were quantified using Dice Similarity Coefficient (DSC) and 95th percentile of Hausdorff distance (HD95). Autosegmentation performance and interobserver variation were compared using a random effects model; correction times and confidence scores using a paired t-test and Wilcoxon signed-rank test, respectively.

Results

Autosegmentation performance showed small, but statistically insignificant differences. Interobserver variability, assessed by the intraclass correlation coefficient, was significantly different across most organs, but these were considered clinically irrelevant (maximum difference = 0.08). Mean contour correction times were similar for both CBCT systems (11:03 versus 11:12 min; p = 0.66). Delineation confidence scores were significantly higher with the high-performance CBCT scans for prostate, seminal vesicles and rectum (4.5 versus 3.5, 4.3 versus 3.5, 4.8 versus 4.3; all p < 0.001).

Conclusion

The high-performance CBCT did not (clinically) improve autosegmentation performance, inter-observer variability or contour correction time compared to conventional CBCT. However, it clearly enhanced user confidence in organ delineation for prostate, seminal vesicles and rectum.

Cone Beam Computed Tomography-Based Online Adaptive Radiation Therapy of Esophageal Cancer: First Clinical Experience and Dosimetric Benefits

Purpose

Radiation therapy (RT) plays a key role in the management of esophageal cancer (EC). However, toxicities caused by proximity of organs at risk (OAR) and daily target coverage caused by interfractional anatomic changes are of concern. Daily online adaptive RT (oART) addresses these concerns and has the potential to increase OAR sparing and improve target coverage. We present the first clinical experience and dosimetric investigations of cone beam CT-based oART in EC using the ETHOS platform.

Methods and Materials

Treatment fractions of the first 10 EC patients undergoing cone beam CT-based oART at our institution were retrospectively analyzed. The prescription dose was 50.4 Gy in 28 fractions. The same clinical target volume (CTV) and planning target volume (PTV) margins as for nonadaptive treatments were used. For all sessions, the timestamp of each oART workflow step, PTV size, target volume doses, mean heart dose, and lung V20Gy of both the scheduled and the adapted treatment plan were analyzed.

Results

Following automatic propagation, the CTV was adapted by the physician in 164 (59%) fractions. The adapted treatment plan was selected in 276 (99%) sessions. The median time needed for an oART session was 28 minutes (range, 14.8-43.3). Compared to the scheduled plans, a significant relative reduction of 9.5% in mean heart dose (absolute, 1.6 Gy; P = .006) and 16.9% reduction in mean lung V20Gy (absolute, 2.3%; P < .001) was achieved with the adapted treatment plans. Simultaneously, we observed a significant relative improvement in D99%PTV and D99%CTV by 15.3% (P < .001) and 5.0% (P = .008), respectively, along with a significant increase in D95%PTV by 5.1% (P = .003).

Conclusions

Although being resource-intensive, oART for EC is feasible in a reasonable timeframe and results in increased OAR sparing and improved target coverage, even without a reduction of margins. Further studies are planned to evaluate the potential clinical benefits.

A systematic review of the role of artificial intelligence in automating computed tomography-based adaptive radiotherapy for head and neck cancer

Purpose

Adaptive radiotherapy (ART) may improve treatment quality by monitoring variations in patient anatomy and incorporating them into the treatment plan. This systematic review investigated the role of artificial intelligence (AI) in computed tomography (CT)-based ART for head and neck (H&N) cancer.

Methods

A comprehensive search of main electronic databases was conducted until April 2024. Titles and abstracts were reviewed to evaluate the compliance with inclusion criteria: CT-based imaging for photon ART of H&N patients and AI applications. 17 original retrospective studies with samples sizes ranging from 37 to 239 patients were included. The quality of the studies was evaluated with the Quality Assessment of Diagnostic Accuracy Studies-2 and the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) tools. Key metrics were examined to evaluate the performances of the proposed AI-methods.

Results

Overall, the risk of bias was low. The average CLAIM score was 70%. A major finding was that generated synthetic CTs improved similarity metrics with planning CT compared to original cone-beam CTs, with average mean absolute error up to 39 HU and maximum improvement of 80%. Auto-segmentation provided an efficient and accurate option for organ-at-risk delineation, with average Dice similarity coefficient ranging from 80 to 87%. Finally, AI models could be trained using clinical and radiomic features to predict the effectiveness of ART with accuracy above 80%.

Conclusions

Automation of processes in ART for H&N cancer is very promising throughout the entire chain, from the generation of synthetic CTs and auto-segmentation to predict the effectiveness of ART.

Experience with patient-specific quality assurance of dosimetrist-led online adaptive prostate SBRT

INTRODUCTION

The aim of this study was to assess the results of the local pre-treatment verifications of online adaptive prostate SBRT plans performed by dosimetrists METHODS AND MATERIALS: Prostate SBRT treatments are planned in our department using an online adaptive method developed and validated by our group. The adaptive plans were computed on the daily CBCT scan using the Acuros XB v. 16.1 algorithm of the Varian Eclipse treatment planning system. Adaptive plans consisted of a single VMAT with 6 MV flattening-filter-free (FFF) energy performed on a Varian TrueBeam linac. Pre-treatment verification of the adaptive "plan-of-the-day" (POD) created in each treatment session was performed using the Mobius 3D v. 3.1 secondary dose calculation program (M3D). Commissioning of M3D included the tuning of the dosimetric leaf gap correction (DLGc) parameter. Generic and specific DLGc values were then derived using a set of plans for typical sites (prostate, head and neck, brain, lung and bone palliative) and another set were determined for specific online SBRT PODs (gDLGc and sDLGc, respectively). The first 50 prostate patients treated with the PACE-B schedule (5 × 7.25 Gy) were included, i.e., 250 adaptive SBRT PODs were collected in this study. For each online adaptive POD, a global 3D gamma comparison between the Eclipse 3D dose and the M3D dose in the patient CBCT was performed. Gamma passing rates (GPRs) for the whole external patient contour (Body) and the PTV were recorded, using the 5 % global /3 mm criteria. The target mean dose and target coverage differences between the Eclipse and M3D doses were also analyzed (ΔDmean and ΔD90 %, respectively). The accuracy of M3D was assessed against PRIMO Monte Carlo software. Twenty-five online prostate SBRT PODs were randomly selected from the set of 250 adaptive plans and simulated with PRIMO.

RESULTS

Values of -1 mm and -0.14 mm were found as optimal gDLGc and sDLGc, respectively. Over the 250 online adaptive PODs, excellent GPR values ∼ 100 % were obtained for the Body and PTV structures, regardless the type of DLGc used. The use of the sDLGc instead of the gDLGc provided better results for ΔDmean (0.1 % ± 0.5% vs. -1.9 ± 0.7 %) and ΔD90 % (-1.0 % ± 0.5 %. vs. -3.5 % ± 0.8 %). This issue was also observed when M3D calculations were compared to PRIMO simulations.

CONCLUSIONS

M3D can be effectively used for independent pre-treatment verifications of online adaptive prostate SBRT plans. The use of a specific DLGc value is advised for this SBRT online adaptive technique.

Improving Deformable Image Registration Accuracy through a Hybrid Similarity Metric and CycleGAN Based Auto-Segmentation

Purpose

Deformable image registration (DIR) plays a critical role in adaptive radiation therapy (ART) to accommodate anatomical changes. However, conventional intensity-based DIR methods face challenges when registering images with unequal image intensities. In these cases, DIR accuracy can be improved using a hybrid image similarity metric which matches both image intensities and the location of known structures. This study aims to assess DIR accuracy using a hybrid similarity metric and leveraging CycleGAN-based intensity correction and auto-segmentation and comparing performance across three DIR workflows.

Methods

The proposed approach incorporates a hybrid image similarity metric combining a point-to-distance (PD) score and intensity similarity score. Synthetic CT (sCT) images were generated using a 2D CycleGAN model trained on unpaired CT and CBCT images, improving soft-tissue contrast in CBCT images. The performance of the approach was evaluated by comparing three DIR workflows: (1) traditional intensity-based (No PD), (2) auto-segmented contours on sCT (CycleGAN PD), and (3) expert manual contours (Expert PD). A 3D U-Net model was then trained on two datasets comprising 56 3D images and validated on 14 independent cases to segment the prostate, bladder, and rectum. DIR accuracy was assessed using Dice Similarity Coefficient (DSC), 95% Hausdorff Distance (HD), and fiducial separation metrics.

Results

The hybrid similarity metric significantly improved DIR accuracy. For the prostate, DSC increased from 0.61 ± 0.18 (No PD) to 0.82 ± 0.13 (CycleGAN PD) and 0.89 ± 0.05 (Expert PD), with corresponding reductions in 95% HD from 11.75 mm to 4.86 mm and 3.27 mm, respectively. Fiducial separation was also reduced from 8.95 mm to 4.07 mm (CycleGAN PD) and 4.11 mm (Expert PD) (p < 0.05). Improvements in alignment were also observed for the bladder and rectum, highlighting the method's robustness.

Conclusion

A hybrid similarity metric that uses CycleGAN-based auto-segmentation presents a promising avenue for advancing DIR accuracy in ART. The study's findings suggest the potential for substantial enhancements in DIR accuracy by combining AI-based image correction and auto-segmentation with classical DIR.

Unsupervised Deep Learning for Synthetic CT Generation from CBCT Images for Proton and Carbon Ion Therapy for Paediatric Patients

Image-guided treatment adaptation is a game changer in oncological particle therapy (PT), especially for younger patients. The purpose of this study is to present a cycle generative adversarial network (CycleGAN)-based method for synthetic computed tomography (sCT) generation from cone beam CT (CBCT) towards adaptive PT (APT) of paediatric patients. Firstly, 44 CBCTs of 15 young pelvic patients were pre-processed to reduce ring artefacts and rigidly registered on same-day CT scans (i.e., verification CT scans, vCT scans) and then inputted to the CycleGAN network (employing either Res-Net and U-Net generators) to synthesise sCT. In particular, 36 and 8 volumes were used for training and testing, respectively. Image quality was evaluated qualitatively and quantitatively using the structural similarity index metric (SSIM) and the peak signal-to-noise ratio (PSNR) between registered CBCT (rCBCT) and vCT and between sCT and vCT to evaluate the improvements brought by CycleGAN. Despite limitations due to the sub-optimal input image quality and the small field of view (FOV), the quality of sCT was found to be overall satisfactory from a quantitative and qualitative perspective. Our findings indicate that CycleGAN is promising to produce sCT scans with acceptable CT-like image texture in paediatric settings, even when CBCT with narrow fields of view (FOV) are employed.

Enhancing Delivery Efficiency on the Magnetic Resonance-Linac: A Comprehensive Evaluation of Prostate Stereotactic Body Radiation Therapy Using Volumetric Modulated Arc Therapy

PURPOSE

Long treatment sessions are a limitation within magnetic resonance imaging guided adaptive radiation therapy (MRIgART). This work aims for significantly enhancing the delivery efficiency on the magnetic resonance linear accelerator (MR-linac) by introducing dedicated optimization and delivery techniques for volumetric modulated arc therapy (VMAT). VMAT plan and delivery quality during MRIgART is compared with step-and-shoot intensity-modulated radiation therapy (IMRT) for prostate stereotactic body radiation therapy.

METHODS AND MATERIALS

Ten patients with prostate cancer previously treated on a 1.5T MR-linac were retrospectively replanned to 36.25 Gy in 5 fractions using step-and-shoot IMRT and the clinical Hyperion optimizer within Monaco (Hyp-IMRT), the same optimizer with a VMAT technique (Hyp-VMAT), and a research-based optimizer called optimal fluence levels and pseudo gradient descent with VMAT (OFL+PGD-VMAT). The plans were then adapted onto each daily magnetic resonance imaging data set using 2 different optimization strategies to evaluate the adapt-to-position workflow: "optimize weights" (IMRT-Weights and VMAT-Weights) and "optimize shapes" (IMRT-Shapes and VMAT-Shapes). Treatment efficiency was evaluated by measuring optimization time, delivery time, and total time (optimization+delivery). Plan quality was assessed by evaluating organ at risk sparing. Ten patient plans were measured using a modified linac control system to assess delivery accuracy via a gamma analysis (2%/2 mm). Delivery efficiency was calculated as average dose rate divided by maximum dose rate.

RESULTS

For Hyp-VMAT and OFL+PGD-VMAT, the total time was reduced by 124 ± 140 seconds (P = .020) and 459 ± 110 seconds (P < .001), respectively, as compared with the clinical Hyp-IMRT group. Speed enhancements were also measured for adapt-to-position with reductions in total time of 404 ± 55 (P < .001) for VMAT-Weights as compared with the clinical IMRT-Shapes group. Bladder and rectum dosimetric volume histogram (DVH) points were within 1.3% or 0.8 cc for each group. All VMAT plans had gamma passing rates greater than 96%. The delivery efficiency of VMAT plans was 89.7 ± 2.7 % compared with 50.0 ± 2.2 % for clinical IMRT.

CONCLUSIONS

Incorporating VMAT into MRIgART will significantly reduce treatment session times while maintaining equivalent plan quality.

Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report

Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data crucial for accurate dose calculations. However, accurately representing patient anatomy is challenging, especially in adaptive radiotherapy, where CT is not acquired daily. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast. Still, it lacks electron density information, while cone beam CT (CBCT) lacks direct electron density calibration and is mainly used for patient positioning. Adopting MRI-only or CBCT-based adaptive radiotherapy eliminates the need for CT planning but presents challenges. Synthetic CT (sCT) generation techniques aim to address these challenges by using image synthesis to bridge the gap between MRI, CBCT, and CT. The SynthRAD2023 challenge was organized to compare synthetic CT generation methods using multi-center ground truth data from 1080 patients, divided into two tasks: (1) MRI-to-CT and (2) CBCT-to-CT. The evaluation included image similarity and dose-based metrics from proton and photon plans. The challenge attracted significant participation, with 617 registrations and 22/17 valid submissions for tasks 1/2. Top-performing teams achieved high structural similarity indices (≥0.87/0.90) and gamma pass rates for photon (≥98.1%/99.0%) and proton (≥97.3%/97.0%) plans. However, no significant correlation was found between image similarity metrics and dose accuracy, emphasizing the need for dose evaluation when assessing the clinical applicability of sCT. SynthRAD2023 facilitated the investigation and benchmarking of sCT generation techniques, providing insights for developing MRI-only and CBCT-based adaptive radiotherapy. It showcased the growing capacity of deep learning to produce high-quality sCT, reducing reliance on conventional CT for treatment planning.

Custom-Trained Deep Learning-Based Auto-Segmentation for Male Pelvic Iterative CBCT on C-Arm Linear Accelerators

PURPOSE

The purpose of this investigation was to evaluate the clinical applicability of a commercial artificial intelligence-driven deep learning auto-segmentation (DLAS) tool on enhanced iterative cone beam computed tomography (iCBCT) acquisitions for intact prostate and prostate bed treatments.

METHODS AND MATERIALS

DLAS models were trained using 116 iCBCT data sets with manually delineated organs at risk (bladder, femoral heads, and rectum) and target volumes (intact prostate and prostate bed) adhering to institution-specific contouring guidelines. An additional 25 intact prostate and prostate bed iCBCT data sets were used for model testing. Segmentation accuracy relative to a reference structure set was quantified using various geometric comparison metrics and qualitatively evaluated by trained physicists and physicians. These results were compared with those obtained for an additional DLAS-based model trained on planning computed tomography (pCT) data sets and for a deformable image registration (DIR)-based automatic contour propagation method.

RESULTS

In most instances, statistically significant differences in the Dice similarity coefficient (DSC), 95% directed Hausdorff distance, and mean surface distance metrics were observed between the models, as the iCBCT-trained DLAS model outperformed the pCT-trained DLAS model and DIR-based method for all organs at risk and the intact prostate target volume. Mean DSC values for the proposed method were ≥0.90 for these volumes of interest. The iCBCT-trained DLAS model demonstrated a relatively suboptimal performance for the prostate bed segmentation, as the mean DSC value was <0.75 for this target contour. Overall, 90% of bladder, 93% of femoral head, 67% of rectum, and 92% of intact prostate contours generated by the proposed method were deemed clinically acceptable based on qualitative scoring, and approximately 63% of prostate bed contours required moderate or major manual editing to adhere to institutional contouring guidelines.

CONCLUSIONS

The proposed method presents the potential for improved segmentation accuracy and efficiency compared with the DIR-based automatic contour propagation method as commonly applied in CBCT-based dose evaluation and calculation studies.

An operator-independent quality assurance system for automatically generated structure sets

Objective. This study describes geometry-based and intensity-based tools for quality assurance (QA) of automatically generated structures for online adaptive radiotherapy, and designs an operator-independent traffic light system that identifies erroneous structure sets.Approach.A cohort of eight head and neck (HN) patients with daily CBCTs was selected for test development. Radiotherapy contours were propagated from planning computed tomography (CT) to daily cone beam CT (CBCT) using deformable image registration. These propagated structures were visually verified for acceptability. For each CBCT, several error scenarios were used to generate what were judged unacceptable structures. Ten additional HN patients with daily CBCTs and different error scenarios were selected for validation. A suite of tests based on image intensity, intensity gradient, and structure geometry was developed using acceptable and unacceptable HN planning structures. Combinations of one test applied to one structure, referred to as structure-test combinations, were selected for inclusion in the QA system based on their discriminatory power. A traffic light system was used to aggregate the structure-test combinations, and the system was evaluated on all fractions of the ten validation HN patients.Results.The QA system distinguished between acceptable and unacceptable fractions with high accuracy, labeling 294/324 acceptable fractions as green or yellow and 19/20 unacceptable fractions as yellow or red.Significance.This study demonstrates a system to supplement manual review of radiotherapy planning structures. Automated QA is performed by aggregating results from multiple intensity- and geometry-based tests.

Adaptive-Driven CT Simulation-Free Soft Tissue Stereotactic Body Radiation Therapy: A Single-Patient Case Report

Online adaptive radiotherapy (ART) enables accommodation for variations in patient setup and anatomical changes, allowing for fractional replanning for target coverage, organ at risk (OAR) sparing, and application of CT simulation-free (SF) workflows. SF workflows bypass the conventional simulation CT scan at the potential trade-off in dosimetric uncertainty. ART can alleviate many of these uncertainties, and this work extends previous experience with an Ethos adaptive cone-beam computed tomography (CBCT)-based SF process to treating a unique bony and soft tissue case with stereotactic body radiation therapy (SBRT). The patient is an 83-year-old male with metastatic prostate cancer, presenting with metastases near the right posterior ischium and a right perirectal lymph node. The patient's history includes multiple radiation treatments and androgen deprivation therapy (ADT). Rising prostate-specific antigen(PSA) levels and new metastases identified via positron emission tomography (PET)/CT prostate-specific membrane antigen (PSMA) led to SBRT re-irradiation, considered safe due to the time lapse since previous treatments. Using a HyperSight-equipped Ethos ART system, an SF SBRT workflow utilized the patient's recent PET/CT images for target and OAR delineation. A nine-field adaptive intensity-modulated radiotherapy(IMRT) treatment plan was generated to deliver 3600 Gy in three fractions with a primary focus to limit the dose to proximal OARs and the previously treated region. At the adaptive treatment, the patient is positioned based on anatomical marks, and axial images from HyperSight CBCT are used to contour the OARs and targets. These modified contours accommodate daily variations and are used to recalculate the reference plan and generate a new adapted plan. The adapted plan is selected if coverage improvement and OAR sparing are achieved. For each newly adapted plan, Ethos-generated synthetic CT is reviewed prior to treatment to verify no errors occurred in the deformable propagation between the reference image and the fractional CBCT. For this patient, the adapted plan was selected for all fractions due to improved target coverage, particularly of the soft tissue target, and OAR sparing. The patient tolerated the treatment well and demonstrated a good response on three-month follow-up PSMA PET/CT imaging. This case highlights the efficacy of CBCT-driven SF ART in complex re-irradiation scenario. Future enhancements in the Ethos treatment planning system, including direct dose computation on HyperSight CBCT images, will streamline SF workflows and expand their applicability. Careful consideration of potential on-unit OAR changes and target motion remains crucial for successful SF ART applications.

Implementation of Cone Beam Computed Tomography-Guided Online Adaptive Radiotherapy for Challenging Trimodal Therapy in Bladder Preservation: A Report of Two Cases

Muscle invasive bladder cancer (MIBC) is an aggressive disease with a high risk of metastasis. Bladder preservation with trimodality therapy (TMT) is an option for well-selected patients or poor cystectomy candidates. Cone beam computed tomography (CBCT)-guided online adaptive radiotherapy (oART) shows promise in improving the dose to treatment targets while better sparing organs at risk (OARs). The following series presents two cases in which the capabilities of a CBCT-guided oART platform were leveraged to meet clinical challenges. The first case describes a patient with synchronous MIBC and high-risk prostate cancer with challenging target-OAR interfaces. The second recounts the case of a patient with a history of low dose rate (LDR) brachytherapy to the prostate who was later diagnosed with MIBC and successfully treated with CBCT-guided oART with reduced high-dose volume bladder targeting. To date, both patients report minimal side effects and are without disease recurrence. These cases illustrate how CBCT-guided online adaptive systems may efficiently aid radiation oncologists in treating patients with more complex clinical scenarios who desire bladder-sparing therapy.

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.

Harnessing the Power of Radiotherapy for Lung Cancer: A Narrative Review of the Evolving Role of Magnetic Resonance Imaging Guidance

Compared with computed tomography (CT), magnetic resonance imaging (MRI) traditionally plays a very limited role in lung cancer management, although there is plenty of room for improvement in the current CT-based workflow, for example, in structures such as the brachial plexus and chest wall invasion, which are difficult to visualize with CT alone. Furthermore, in the treatment of high-risk tumors such as ultracentral lung cancer, treatment-associated toxicity currently still outweighs its benefits. The advent of MR-Linac, an MRI-guided radiotherapy (RT) that combines MRI with a linear accelerator, could potentially address these limitations. Compared with CT-based technologies, MR-Linac could offer superior soft tissue visualization, daily adaptive capability, real-time target tracking, and an early assessment of treatment response. Clinically, it could be especially advantageous in the treatment of central/ultracentral lung cancer, early-stage lung cancer, and locally advanced lung cancer. Increasing demands for stereotactic body radiotherapy (SBRT) for lung cancer have led to MR-Linac adoption in some cancer centers. In this review, a broad overview of the latest research on imaging-guided radiotherapy (IGRT) with MR-Linac for lung cancer management is provided, and development pertaining to artificial intelligence is also highlighted. New avenues of research are also discussed.

Clinical practicality and patient performance for surface-guided automated VMAT gating for DIBH breast cancer radiotherapy

BACKGROUND AND PURPOSE

To evaluate the performance of automated surface-guided gating for left-sided breast cancer with DIBH and VMAT.

MATERIALS AND METHODS

Patients treated in the first year after introduction of DIBH with VMAT were retrospectively considered for analysis. With automated surface-guided gating the beam automatically switches on/off, if the surface region of interest moved in/out the gating tolerance (±3 mm, ±3°). Patients were coached to hold their breath as long as comfortably possible. Depending on the patient's preference, patients received audio instructions during treatment delivery. Real-time positional variations of the breast/chest wall surface with respect to the reference surface were collected, for all three orthogonal directions. The durations and number of DIBHs needed to complete dose delivery, and DIBH position variations were determined. To evaluate an optimal gating window threshold, smaller tolerances of ±2.5 mm, ±2.0 mm, and ±1.5 mm were simulated.

RESULTS

525 fractions from 33 patients showed that median DIBH duration was 51 s (range: 30-121 s), and median 4 DIBHs per fraction were needed to complete VMAT dose delivery. Median intra-DIBH stability and intrafractional DIBH reproducibility approximated 1.0 mm in each direction. No large differences were found between patients who preferred to perform the DIBH procedure with (n = 21) and without audio-coaching (n = 12). Simulations demonstrated that gating window tolerances could be reduced from ±3.0 mm to ±2.0 mm, without affecting beam-on status.

CONCLUSION

Independent of the use of audio-coaching, this study demonstrates that automated surface-guided gating with DIBH and VMAT proved highly efficient. Patients' DIBH performance far exceeded our expectations compared to earlier experiences and literature. Furthermore, gating window tolerances could be reduced.

Evaluation of Motion Artifact Correction Technique for Cone-Beam Computed Tomography Image Considering Blood Vessel Geometry

Background: In this study, we present a quantitative method to evaluate the motion artifact correction (MAC) technique through the morphological analysis of blood vessels in the images before and after MAC. Methods: Cone-beam computed tomography (CBCT) scans of 37 patients who underwent transcatheter chemoembolization were obtained, and images were reconstructed with and without the MAC technique. First, two interventional radiologists selected the blood vessels corrected by MAC. We devised a motion-corrected index (MCI) metric that analyzed the morphology of blood vessels in 3D space using information on the centerline of blood vessels, and the blood vessels selected by the interventional radiologists were quantitatively evaluated using MCI. In addition, these blood vessels were qualitatively evaluated by two interventional radiologists. To validate the effectiveness of the devised MCI, we compared the MCI values in a blood vessel corrected by MAC and one non-corrected by MAC. Results: The visual evaluation revealed that motion correction was found in the images of 23 of 37 patients (62.2%), and a performance evaluation of MAC was performed with 54 blood vessels in 23 patients. The visual grading analysis score was 1.56 ± 0.57 (radiologist 1) and 1.56 ± 0.63 (radiologist 2), and the proposed MCI was 0.67 ± 0.11, indicating that the vascular morphology was well corrected by the MAC. Conclusions: We verified that our proposed method is useful for evaluating the MAC technique of CBCT, and the MAC technique can correct the blood vessels distorted by the patient's movement and respiration.

The accuracy of artificial intelligence deformed nodal structures in cervical online cone-beam-based adaptive radiotherapy

Background and Purpose

Online cone-beam-based adaptive radiotherapy (ART) adjusts for anatomical changes during external beam radiotherapy. However, limited cone-beam image quality complicates nodal contouring. Despite this challenge, artificial-intelligence guided deformation (AID) can auto-generate nodal contours. Our study investigated the optimal use of such contours in cervical online cone-beam-based ART.

Materials and Methods

From 136 adaptive fractions across 21 cervical cancer patients with nodal disease, we extracted 649 clinically-delivered and AID clinical target volume (CTV) lymph node boost structures. We assessed geometric alignment between AID and clinical CTVs via dice similarity coefficient, and 95% Hausdorff distance, and geometric coverage of clinical CTVs by AID planning target volumes by false positive dice. Coverage of clinical CTVs by AID contour-based plans was evaluated using D100, D95, V100%, and V95%.

Results

Between AID and clinical CTVs, the median dice similarity coefficient was 0.66 and the median 95 % Hausdorff distance was 4.0 mm. The median false positive dice of clinical CTV coverage by AID planning target volumes was 0. The median D100 was 1.00, the median D95 was 1.01, the median V100% was 1.00, and the median V95% was 1.00. Increased nodal volume, fraction number, and daily adaptation were associated with reduced clinical CTV coverage by AID-based plans.

Conclusion

In one of the first reports on pelvic nodal ART, AID-based plans could adequately cover nodal targets. However, physician review is required due to performance variation. Greater attention is needed for larger, daily-adapted nodes further into treatment.

Fractionation versus Adaptation for Compensation of Target Volume Changes during Online Adaptive Radiotherapy for Bladder Cancer: Answers from a Prospective Registry

Online adaptive radiotherapy (ART) allows adaptation of the dose distribution to the anatomy captured by with pre-adaptation imaging. ART is time-consuming, and thus intra-fractional deformations can occur. This prospective registry study analyzed the effects of intra-fraction deformations of clinical target volume (CTV) on the equivalent uniform dose (EUDCTV) of focal bladder cancer radiotherapy. Using margins of 5-10 mm around CTV on pre-adaptation imaging, intra-fraction CTV-deformations found in a second imaging study reduced the 10th percentile of EUDCTV values per fraction from 101.1% to 63.2% of the prescribed dose. Dose accumulation across fractions of a series was determined with deformable-image registration and worst-case dose accumulation that maximizes the correlation of cold spots. A strong fractionation effect was demonstrated-the EUDCTV was above 95% and 92.5% as determined by the two abovementioned accumulation methods, respectively, for all series of dose fractions. A comparison of both methods showed that the fractionation effect caused the EUDCTV of a series to be insensitive to EUDCTV-declines per dose fraction, and this could be explained by the small size and spatial variations of cold spots. Therefore, ART for each dose fraction is unnecessary, and selective ART for fractions with large inter-fractional deformations alone is sufficient for maintaining a high EUDCTV for a radiotherapy series.