Adaptive Radiotherapy Research Articles

Future of Multidisciplinary Team in the Context of Adaptive Therapy

The implementation and early adoption of online adaptive radiotherapy (oART) has required the presence of clinicians, physicists and radiation therapists (RTT) at the treatment console. The impact on each of them is unique to their profession and must be considered for safe and efficient implementation. In the short term future, widespread adoption will depend on the development of innovative workflows, and rethinking of traditional roles and responsibilities may be required. For the future, technologies such as artificial intelligence promise to change the workflow significantly in terms of speed, automation and decision-making. However, overall communication within the team will persist in being one of the most important aspects.

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.

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. 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. 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. 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.

Supervised deep learning-based synthetic computed tomography from kilovoltage cone-beam computed tomography images for adaptive radiation therapy in head and neck cancer.

To generate and investigate a supervised deep learning algorithm for creating synthetic computed tomography (sCT) images from kilovoltage cone-beam computed tomography (kV-CBCT) images for adaptive radiation therapy (ART) in head and neck cancer (HNC). This study generated the supervised U-Net deep learning model using 3,491 image pairs from planning computed tomography (pCT) and kV-CBCT datasets obtained from 40 HNC patients. The dataset was split into 80% for training and 20% for testing. The evaluation of the sCT images compared to pCT images focused on three aspects: Hounsfield units accuracy, assessed using mean absolute error (MAE) and root mean square error (RMSE); image quality, evaluated using the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) between sCT and pCT images; and dosimetric accuracy, encompassing 3D gamma passing rates for dose distribution and percentage dose difference. MAE, RMSE, PSNR, and SSIM showed improvements from their initial values of 53.15 ± 40.09, 153.99 ± 79.78, 47.91 ± 4.98 dB, and 0.97 ± 0.02 to 41.47 ± 30.59, 130.39 ± 78.06, 49.93 ± 6.00 dB, and 0.98 ± 0.02, respectively. Regarding dose evaluation, 3D gamma passing rates for dose distribution within sCT images under 2%/2 mm, 3%/2 mm, and 3%/3 mm criteria, yielded passing rates of 92.1% ± 3.8%, 93.8% ± 3.0%, and 96.9% ± 2.0%, respectively. The sCT images exhibited minor variations in the percentage dose distribution of the investigated target and structure volumes. However, it is worth noting that the sCT images exhibited anatomical variations when compared to the pCT images. These findings highlight the potential of the supervised U-Net deep learningmodel in generating kV-CBCT-based sCT images for ART in patients with HNC.

Online adaptive radiotherapy in stereotactic body radiotherapy for pancreatic cancer patients.

Stereotactic radiation therapy (SBRT) has emerged as a promising treatment modality for locally advanced pancreatic cancer. The aim of this study is to assess the dosimetric efficacy of online adaptive radiotherapy (ART) in comparison to image-guided radiation therapy (IGRT) for pancreatic cancer. We conducted a retrospective analysis involving 8 patients diagnosed with locally advanced pancreatic cancer. The gross tumor volume (GTV) delineates the visible extent of the tumor on imaging, while the planning tumor volume (PTV) was generated by expanding 5mm from the GTV and ensuring a 3mm distance from the small intestine, duodenum, and stomach simultaneously. Treatment planning was executed using the United Imaging Healthcare Treatment Planning System workstation. The control group underwent evaluation based on daily validated fan-beam CT (FBCT) scans, assessing both the dose delivered to actual organs at risk (OARs) and the target volume. Radiotherapy plans were developed utilizing simulation CT, and conventional radiotherapy with daily image-guided radiation therapy (IGRT) was administered using FBCT-Linac. Conversely, patients in the study group received daily validated FBCT-guided adaptive radiotherapy plans, with a focus on mean dose assessment of both the target volume and OARs. Subsequently, we compared the average outcomes of each treatment fraction between IGRT and online adaptive radiotherapy (ART). Comparison between ART and IGRT treatment plans revealed significant differences in various dosimetric parameters: For PTV: V98%: ART (96.28%) vs IGRT (89.73%), p = 0.000, V95%: ART (96.28%) vs IGRT (89.73%), p = 0.031, V90%: ART (98.58%) vs IGRT (93.65%), p = 0.000, Dmean: ART (4912.91) vs IGRT (4804.11), p = 0.000. For GTV: V100%: ART (97.96%) vs IGRT (94.85%), p = 0.314, V98%: ART (100.00%) vs IGRT (96.83%), p = 0.000, V90%: ART (100.00%) vs IGRT (97.75%), p = 0.000, Dmean: ART (4972.17) vs IGRT (4907.23), p = 0.000. For the duodenum: D0.5cc: ART (2883.92) vs IGRT (3359.35), p = 0.000, D1cc: ART (2726.32) vs IGRT (3128.66), p = 0.001, D5cc: ART (2051.96) vs IGRT (2273.93), p = 0.015, D10cc: ART (1650.73) vs IGRT (1731.74), p = 0.211. For the small bowel: D0.5cc: ART (3022.3) vs IGRT (3142.64), p = 0.037. D5cc: ART (2151.09) vs IGRT (2389.15), p = 0.043, D10cc: ART (1775.20) vs IGRT (1942.00), p = 0.079. For the stomach: D0.5cc: ART (3353.92) vs IGRT (4117.85), p = 0.000, D5cc: ART (2860.20) vs IGRT (3235.41), p = 0.000, D10cc: ART (2553.72) vs IGRT (2836.73), p = 0.000. For the Dmean of the left kidney and right kidney: Left kidney: ART (248.28) vs IGRT (239.65), p = 0.100. Right kidney: ART (314.55) vs IGRT (307.17), p = 0.345. These results suggest significant improvements in PTV coverage and sparing of OARs with ART compared to IGRT, indicating the potential of ART in optimizing treatment outcomes for pancreatic cancer patients.Compared to conventional IGRT-guided SBRT programs, ART-based SBRT for pancreatic cancer not only enhances the dose distribution to the target volume but also mitigates the radiation exposure to critical organs-at-risk (OARs) such as the duodenum, small intestine, and stomach. This approach may offer a more favorable safety profile while concurrently enhancing treatment efficacy.

MR-Linac On-Line Weekly Adaptive Radiotherapy for High Grade Glioma (HGG): Results from the UNITED Single Arm Phase II Trial

MR-Linac On-Line Weekly Adaptive Radiotherapy for High Grade Glioma (HGG): Results from the UNITED Single Arm Phase II Trial

Pseudo‐CT synthesis in adaptive radiotherapy based on a stacked coarse‐to‐fine model: Combing diffusion process and spatial‐frequency convolutions

AbstractBackgroundCone beam computed tomography (CBCT) provides critical anatomical information for adaptive radiotherapy (ART), especially for tumors in the pelvic region that undergo significant deformation. However, CBCT suffers from inaccurate Hounsfield Unit (HU) values and lower soft tissue contrast. These issues affect the accuracy of pelvic treatment plans and implementation of the treatment, hence requiring correction.PurposeA novel stacked coarse‐to‐fine model combining Denoising Diffusion Probabilistic Model (DDPM) and spatial‐frequency domain convolution modules is proposed to enhance the imaging quality of CBCT images.MethodsThe enhancement of low‐quality CBCT images is divided into two stages. In the coarse stage, the improved DDPM with U‐ConvNeXt architecture is used to complete the denoising task of CBCT images. In the fine stage, the deep convolutional network model jointly constructed by fast Fourier and dilated convolution modules is used to further enhance the image quality in local details and global imaging. Finally, the accurate pseudo‐CT (pCT) images consistent with the size of the original data are obtained. Two hundred fifty paired CBCT‐CT images from cervical and rectal cancer, combined with 200 public dataset cases, were used collectively for training, validation, and testing.ResultsTo evaluate the anatomical consistency between pCT and real CT, we have used the mean(std) of structure similarity index measure (SSIM), peak signal to noise ratio (PSNR), and normalized cross‐correlation (NCC). The numerical results for the above three metrics comparing the pCT synthesized by the proposed model against real CT for cervical cancer cases were 87.14% (2.91%), 34.02 dB (1.35 dB), and 88.01% (1.82%), respectively. For rectal cancer cases, the corresponding results were 86.06% (2.70%), 33.50 dB (1.41 dB), and 87.44% (1.95%). The paired t‐test analysis between the proposed model and the comparative models (ResUnet, CycleGAN, DDPM, and DDIM) for these metrics revealed statistically significant differences (p &lt; 0.05). The visual results also showed that the anatomical structures between the real CT and the pCT synthesized by the proposed model were closer. For the dosimetric verification, mean absolute error of dosimetry (MAEdoes) values for the maximum dose (Dmax), the minimum dose (Dmin), and the mean dose (Dmean) in the planning target volume (PTV) were analyzed, with results presented as mean (lower quartile, upper quartile). The experimental results show that the values of the above three dosimetry indexes (Dmin, Dmax, and Dmean) for the pCT images synthesized by the proposed model were 0.90% (0.48%, 1.29%), 0.82% (0.47%, 1.17%), and 0.57% (0.44%, 0.67%). Compared with 10 cases of the original CBCT image by Mann–Whitney test (p &lt; 0.05), it also proved that pCT can significantly improve the accuracy of HU values for the dose calculation.ConclusionThe pCT synthesized by the proposed model outperforms the comparative models in numerical accuracy and visualization, promising for ART of pelvic cancers.

Anatomical and dosimetric variations during volumetric modulated arc therapy in patients with locally advanced nasopharyngeal carcinoma after induction therapy: Implications for adaptive radiation therapy

PurposeTo investigate anatomical and dosimetric changes during volumetric modulated arc therapy (VMAT) in patients with locally advanced nasopharyngeal carcinoma (LA-NPC) after induction therapy (IT) and explore characteristics of patients with notable variations. Materials and methodsFrom July 2021 to June 2023, 60 LA-NPC patients undergoing VMAT after IT were retrospectively recruited. Adaptive computed tomography (aCT), reconstructed from weekly cone-beam computed tomography(CBCT), facilitates recontouring and planning transplantation. Volume, dice similarity coefficients, and dose to target volumes and organs at risk(OARs) on planning CT(pCT) and aCT were compared to identify changing patterns. Multivariate logistic regression was used to investigate risk factors. ResultsThe volumes of PGTVnasopharynx (PGTVp), PGTVnode (PGTVn), ipsilateral and contralateral parotid glands decreased during VMAT, with reductions of 2.25 %, 6.98 %, 20.09 % and 18.00 %, respectively, at 30 fractions from baseline (P < 0.001). After 25 fractions, D99 and D95 of PGTVn decreased by 7.94 % and 4.18 % from baseline, respectively, while the Dmean of ipsilateral and contralateral parotid glands increased by 7.80 % and 6.50 %, marking the peak rates of dosimetric variations (P < 0.001). The dosimetric fluctuations in PGTVp, the brainstem, and the spinal cord remained within acceptable limits. Furthermore, an initial BMI ≥ 23.5 kg/m2 and not-achieving objective response (OR) after IT were regarded as risk factors for a remarkable PGTVn dose reduction in the later stages of VMAT. ConclusionsReplanning for post-IT LA-NPC patients appears reasonable at 25F during VMAT. Patients with an initial BMI ≥ 23.5 kg/m2 and not-achieving OR after IT should be considered for adaptive radiation therapy to stabilize the delivered dose.

Real-time tracking of the Bragg peak during proton therapy via 3D protoacoustic Imaging in a clinical scenario

Proton radiotherapy favored over X-ray photon therapy due to its reduced radiation exposure to surrounding healthy tissues, is highly dependent on the accurate positioning of the Bragg peak. Existing methods like PET and prompt gamma imaging to localize Bragg peak face challenges of low precision and high complexity. Here we introduce a 3D protoacoustic imaging with a 2D matrix array of 256 ultrasound transducers compatible with 256 parallel data acquisition channels provides real-time imaging capability (up to 75 frames per second with 10 averages), achieving high precision (5 mm/5% Gamma index shows accuracy better than 95.73%) at depths of tens of centimeters. We have successfully implemented this method in liver treatment with 5 pencil beam scanning and in prostate cancer treatment on a human torso phantom using a clinical proton machine. This demonstrates its capability to accurately identify the Bragg peak in practical clinical scenarios. It paves the way for adaptive radiotherapy with real-time feedback, potentially revolutionizing radiotherapy by enabling closed-loop treatment for improved patient outcomes.

Prior-image-based low-dose CT reconstruction for adaptive radiation therapy.

The study aims to reduce the imaging radiation dose in Adaptive Radiotherapy (ART) while maintaining high-quality CT images, critical for effective treatment planning and monitoring. We developed the Prior-aware Learned Primal-Dual Network (pLPD-UNet), which uses prior CT images to enhance reconstructions from low-dose scans. The network was separately trained on thorax and abdomen datasets to accommodate the unique imaging requirements of each anatomical region. The pLPD-UNet demonstrated improved reconstruction accuracy and robustness in handling sparse data compared to traditional methods. It effectively maintained image quality essential for precise organ delineation and dose calculation, while achieving a significant reduction in radiation exposure. This method offers a significant advancement in the practice of ART by integrating prior imaging data, potentially setting a new standard for balancing radiation safety with the need for high-resolution imaging in cancer treatment planning.

Incorporating patient-specific information for the development of rectal tumor auto-segmentation models for online adaptive magnetic resonance Image-guided radiotherapy

Background and purposeIn online adaptive magnetic resonance image (MRI)-guided radiotherapy (MRIgRT), manual contouring of rectal tumors on daily images is labor-intensive and time-consuming. Automation of this task is complex due to substantial variation in tumor shape and location between patients. The aim of this work was to investigate different approaches of propagating patient-specific prior information to the online adaptive treatment fractions to improve deep-learning based auto-segmentation of rectal tumors. Materials and methods243 T2-weighted MRI scans of 49 rectal cancer patients treated on the 1.5T MR-Linear accelerator (MR-Linac) were utilized to train models to segment rectal tumors. As benchmark, an MRI_only auto-segmentation model was trained. Three approaches of including a patient-specific prior were studied: 1. include the segmentations of fraction 1 as extra input channel for the auto-segmentation of subsequent fractions, 2. fine-tuning of the MRI_only model to fraction 1 (PSF_1) and 3. fine-tuning of the MRI_only model on all earlier fractions (PSF_cumulative). Auto-segmentations were compared to the manual segmentation using geometric similarity metrics. Clinical impact was assessed by evaluating post-treatment target coverage. ResultsAll patient-specific methods outperformed the MRI_only segmentation approach. Median 95th percentile Hausdorff (95HD) were 22.0 (range: 6.1–76.6) mm for MRI_only segmentation, 9.9 (range: 2.5–38.2) mm for MRI+prior segmentation, 6.4 (range: 2.4–17.8) mm for PSF_1 and 4.8 (range: 1.7–26.9) mm for PSF_cumulative. PSF_cumulative was found to be superior to PSF_1 from fraction 4 onward (p = 0.014). ConclusionPatient-specific fine-tuning of automatically segmented rectal tumors, using images and segmentations from all previous fractions, yields superior quality compared to other auto-segmentation approaches.

The InterVision Framework: An Enhanced Fine-Tuning Deep Learning Strategy for Auto-Segmentation in Head and Neck.

Adaptive radiotherapy (ART) workflows are increasingly adopted to achieve dose escalation and tissue sparing under dynamic anatomical conditions. However, recontouring and time constraints hinder the implementation of real-time ART workflows. Various auto-segmentation methods, including deformable image registration, atlas-based segmentation, and deep learning-based segmentation (DLS), have been developed to address these challenges. Despite the potential of DLS methods, clinical implementation remains difficult due to the need for large, high-quality datasets to ensure model generalizability. This study introduces an InterVision framework for segmentation. The InterVision framework can interpolate or create intermediate visuals between existing images to generate specific patient characteristics. The InterVision model is trained in two steps: (1) generating a general model using the dataset, and (2) tuning the general model using the dataset generated from the InterVision framework. The InterVision framework generates intermediate images between existing patient image slides using deformable vectors, effectively capturing unique patient characteristics. By creating a more comprehensive dataset that reflects these individual characteristics, the InterVision model demonstrates the ability to produce more accurate contours compared to general models. Models are evaluated using the volumetric dice similarity coefficient (VDSC) and the Hausdorff distance 95% (HD95%) for 18 structures in 20 test patients. As a result, the Dice score was 0.81 ± 0.05 for the general model, 0.82 ± 0.04 for the general fine-tuning model, and 0.85 ± 0.03 for the InterVision model. The Hausdorff distance was 3.06 ± 1.13 for the general model, 2.81 ± 0.77 for the general fine-tuning model, and 2.52 ± 0.50 for the InterVision model. The InterVision model showed the best performance compared to the general model. The InterVision framework presents a versatile approach adaptable to various tasks where prior information is accessible, such as in ART settings. This capability is particularly valuable for accurately predicting complex organs and targets that pose challenges for traditional deep learning algorithms.

Machine learning automated treatment planning for online magnetic resonance guided adaptive radiotherapy of prostate cancer

Background and purposeNo best practices currently exist for achieving high quality radiation therapy (RT) treatment plan adaptation during magnetic resonance (MR) guided RT of prostate cancer. This study validates the use of machine learning (ML) automated RT treatment plan adaptation and benchmarks it against current clinical RT plan adaptation methods. Materials and methodsWe trained an atlas-based ML automated treatment planning model using reference MR RT treatment plans (42.7 Gy in 7 fractions) from 46 patients with prostate cancer previously treated at our institution. For a held-out test set of 38 patients, retrospectively generated ML RT plans were compared to clinical human-generated adaptive RT plans for all 266 fractions. Differences in dose-volume metrics and clinical objective pass rates were evaluated using Wilcoxon tests (p < 0.05) and Exact McNemar tests (p < 0.05), respectively. ResultsCompared to clinical RT plans, ML RT plans significantly increased sparing and objective pass rates of the rectum, bladder, and left femur. The mean ± standard deviation of rectum D20 and D50 in ML RT plans were 2.5 ± 2.2 Gy and 1.6 ± 1.3 Gy lower than clinical RT plans, respectively, with 14 % higher pass rates; bladder D40 was 4.6 ± 2.9 Gy lower with a 20 % higher pass rate; and the left femur D5 was 0.8 ± 1.8 Gy lower with a 7 % higher pass rate. ConclusionsML automated RT treatment plan adaptation increases robustness to interfractional anatomical changes compared to current clinical adaptive RT practices by increasing compliance to treatment objectives.

Autodelineation methods in a simulated fully automated proton therapy workflow for esophageal cancer

Background and purposeProton Online Adaptive RadioTherapy (ProtOnART) harnesses the dosimetric advantage of protons and immediately acts upon anatomical changes. Here, we simulate the clinical application of delineation and planning within a ProtOnART-workflow for esophageal cancer. We aim to identify the most appropriate technique for autodelineation and evaluate full automation by replanning on autodelineated contours. Materials and methodsWe evaluated 15 patients who started treatment between 11-2022 and 01-2024, undergoing baseline and three repeat computed tomography (CT) scans in treatment position. Quantitative and qualitative evaluations compared different autodelineation methods. For Organs-at-risk (OAR) deep learning segmentation (DLS), rigid and deformable propagation from baseline to repeat CT-scans were considered. For the clinical target volume (CTV), rigid and three deformable propagation methods (default, heart as controlling structure and with focus region) were evaluated. Adaptive treatment plans with 7 mm (ATP7mm) and 3 mm (ATP3mm) setup robustness were generated using best-performing autodelineated contours. Clinical acceptance of ATPs was evaluated using goals encompassing ground-truth CTV-coverage and OAR-dose. ResultsDeformation was preferred for autodelineation of heart, lungs and spinal cord. DLS was preferred for all other OARs. For CTV, deformation with focus region was the preferred method although the difference with other deformation methods was small. Nominal ATPs passed evaluation goals for 87 % of ATP7mm and 67 % of ATP3mm. This dropped to respectively 2 % and 29 % after robust evaluation. Insufficient CTV-coverage was the main reason for ATP-rejection. ConclusionAutodelineation aids a ProtOnART-workflow for esophageal cancer. Currently available tools regularly require manual annotations to generate clinically acceptable ATPs.

Evaluation of the geometric and dosimetric accuracies of deformable image registration of targets and critical organs in prostate CBCT-guided adaptive radiotherapy.

Kilovoltage cone beam computed tomography (kVCBCT)-guided adaptive radiation therapy (ART) uses daily deformed CT (dCT), which is generated automatically through deformable registration methods. These registration methods may perform poorly in reproducing volumes of the target organ, rectum, and bladder during treatment. We analyzed the registration errors between the daily kVCBCTs and corresponding dCTs for these organs using the default optical flow algorithm and two registration procedures. We validated the effectiveness of these registration methods in replicating the geometry for dose calculation on kVCBCT for ART. We evaluated three deformable image registration (DIR) methods to assess their registration accuracy and dose calculation effeciency in mapping target and critical organs. The DIR methods include (1) default intensity-based deformable registration, (2) hybrid deformable registration, and (3) a two-step deformable registration process. Each technique was applied to a computerized imaging reference system (CIRS) phantom (Model 062M) and to five patients who received volumetric modulated arc therapy to the prostate. Registration accuracy was assessed using the 95% Hausdorff distance (HD95) and Dice similarity coefficient (DSC), and each method was compared with the intensity-based registration method. The improvement in the dCT image quality of the CIRS phantom and five patients was assessed by comparing dCT with kVCBCT. Image quality quantitative metrics for the phantom included the signal-to-noise ratio (SNR), uniformity, and contrast-to-noise ratio (CNR), whereas those for the patients included the mean absolute error (MAE), mean error, peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). To determine dose metric differences, we used a dose-volume histogram (DVH) and 3.0%/0.3mm gamma analysis to compare planning computed tomography (pCT) and kVCBCT recalculations with restimulated CT images used as a reference. The dCT images generated by the hybrid (dCTH) and two-step (dCTC) registration methods resulted in significant improvements compared to kVCBCT in the phantom model. Specifically, the SNR improved by 107% and 107.2%, the uniformity improved by 90% and 75%, and the CNR improved by 212.2% and 225.6 for dCTH and dCTC methods, respectively. For the patient images, the MAEs improved by 98% and 94%, the PSNRs improved by 16.3% and 22.9%, and the SSIMs improved by 1% and 1% in the dCTH and dCTC methods, respectively. For the geometric evaluation, only the two-step registration method improved registration accuracy. The dCTH method yielded an average HD95 of 12mm and average DSC of 0.73, whereas dCTC yielded an average HD95 of 2.9mm and average DSC of 0.902. The DVH showed that the dCTC-based dose calculations differed by <2% from the expected results for treatment targets and volumes of organs at risk. Additionally, gamma indices for dCTC-based treatment plans were >95% at all points, whereas they were <95% for kVCBCT-based treatment plans. The two-step registration method outperforms the intensity-based and hybrid registration methods. While the hybrid and two-step-based methods improved the image quality of kVCBCT in a linear accelerator, only the two-step method improved the registration accuracy of the corresponding structures among the pCT and kVCBCT datasets. A two-step registration process is recommended for applying kVCBCT to ART, which achieves better registration accuracy for local and global image structures. This method appears to be beneficial for radiotherapy dose calculation in patients with pelvic cancer.

Efficacy and safety of MR-guided adaptive simultaneous integrated boost radiotherapy to primary lesions and positive lymph nodes in the neoadjuvant treatment of locally advanced rectal cancer: a randomized controlled phase III trial

BackgroundIn locally advanced rectal cancer (LARC), optimizing neoadjuvant strategies, including the addition of concurrent chemotherapy and dose escalation of radiotherapy, is essential to improve tumor regression and subsequent implementation of anal preservation strategies. Currently, dose escalation studies in rectal cancer have focused on the primary lesions. However, a common source of recurrence in LARC is the metastasis of cancer cells to the proximal lymph nodes. In our trial, we implement simultaneous integrated boost (SIB) to both primary lesions and positive lymph nodes in the experimental group based on magnetic resonance-guided adaptive radiotherapy (MRgART), which allows for more precise (and consequently intense) targeting while sparing neighboring healthy tissue. The objective of this study is to evaluate the efficacy and safety of MRgART dose escalation to both primary lesions and positive lymph nodes, in comparison with the conventional radiotherapy of long-course concurrent chemoradiotherapy (LCCRT) group, in the neoadjuvant treatment of LARC.MethodsThis is a multi-center, randomized, controlled phase III trial (NCT06246344). 128 patients with LARC (cT3-4/N+) will be enrolled. During LCCRT, patients will be randomized to receive either MRgART with SIB (60–65 Gy in 25–28 fractions to primary lesions and positive lymph nodes; 50–50.4 Gy in 25–28 fractions to the pelvis) or intensity-modulated radiotherapy (50–50.4 Gy in 25–28 fractions). Both groups will receive concurrent chemotherapy with capecitabine and consolidation chemotherapy of either two cycles of CAPEOX or three cycles of FOLFOX between radiotherapy and surgery. The primary endpoints are pathological complete response (pCR) rate and surgical difficulty, while the secondary endpoints are clinical complete response (cCR) rate, 3-year and 5-year disease-free survival (DFS) and overall survival (OS) rates, acute and late toxicity and quality of life.DiscussionSince dose escalation of both primary lesions and positive nodes in LARC is rare, we propose conducting a phase III trial to evaluate the efficacy and safety of SIB for both primary lesions and positive nodes in LARC based on MRgART.Trial registrationThe study was registered at ClinicalTrials.gov with the Identifier: NCT06246344 (Registered 7th Feb 2024).

Characterization of kilovoltage x-ray image guidance system with a novel post-processing algorithm on a new slip ring-mounted radiotherapy system.

This study evaluates the performance of a kilovoltage x-ray image-guidance system equipped with a novel post-processing optimization algorithm on the newly introduced TAICHI linear accelerator (Linac). A comparative study involving image quality tests and radiation dose measurements was conducted across six scanning protocols of the kV-cone beam computed tomography (CBCT) system on the TAICHI Linac. The performance assessment utilized the conventional Feldkamp-Davis-Kress (FDK) algorithm and a novel Non-Local Means denoising and adaptive scattering correction (NLM-ASC) algorithm. Image quality metrics, including spatial resolution, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR), were evaluated using a Catphan 604 phantom. Radiation doses for low-dose and standard protocols were measured using a computed tomography dose index (CTDI) phantom, with comparative measurements from the Halcyon Linac's iterative CBCT (iCBCT). The NLM-ASC algorithm significantly improved image quality, achieving a 300%-1000% increase in CNR and SNR over the FDK-only images and it also showed a 100%-200% improvement over the iCBCT images from Halcyon's head protocol. The optimized low-dose protocols yielded higher image quality than the standard FDK protocols, indicating potential for reduced radiation exposure. Clinical implementation confirmed the TAICHI system's utility for precise and adaptive radiotherapy. The kV-IGRT system on the TAICHI Linac, with its novel post-processing algorithm, demonstrated superior image quality suitable for routine clinical use, effectively reducing image noise without compromising other quality metrics.

A patient-specific auto-planning method for MRI-guided adaptive radiotherapy in prostate cancer

Background and purposeFast and automated generation of treatment plans is desirable for magnetic resonance imaging (MRI)-guided adaptive radiotherapy (MRIgART). This study proposed a novel patient-specific auto-planning method and validated its feasibility in improving the existing online planning workflow. Materials and methodsData from 40 patients with prostate cancer were collected retrospectively. A patient-specific auto-planning method was proposed to generate adaptive treatment plans. First, a population dose-prediction model (M0) was trained using data from previous patients. Second, a patient-specific model (Mps) was created for each new patient by fine-tuning M0 with the patient’s data. Finally, an auto plan was optimized using the parameters derived from the predicted dose distribution by Mps. The auto plans were compared with manual plans in terms of plan quality, efficiency, dosimetric verification, and clinical evaluation. ResultsThe auto plans improved target coverage, reduced irradiation to the rectum, and provided comparable protection to other organs-at-risk. Target coverage for the planning target volume (+0.61 %, P = 0.023) and clinical target volume 4000 (+1.60 %, P < 0.001) increased. V2900cGy (−1.06 %, P = 0.004) and V1810cGy (−2.49 %, P < 0.001) to the rectal wall and V1810cGy (−2.82 %, P = 0.012) to the rectum were significantly reduced. The auto plans required less planning time (−3.92 min, P = 0.001), monitor units (−46.48, P = 0.003), and delivery time (−0.26 min, P = 0.004), and their gamma pass rates (3 %/2 mm) were higher (+0.47 %, P = 0.014). ConclusionThe proposed patient-specific auto-planning method demonstrated a robust level of automation and was able to generate high-quality treatment plans in less time for MRIgART in prostate cancer.

Mitigating Risks in Cone Beam Computed Tomography Guided Online Adaptive Radiation Therapy: A Preventative Reference Planning Review Approach

PurposeOnline adaptive radiotherapy (oART) treatment planning requires evaluating the temporal robustness of reference plans, anticipating the potential changes during treatment courses that may even lead to risks unique to the adaptive workflow. This study conducted a risk analysis of the CBCT-guided adaptive workflow and is the first to assess an adaptive specific reference planning review that mitigates risk in the planning process to prevent events and treatment deficiencies during adaptation. Methods and MaterialsA quality management team of medical physicists, residents, physicians, and radiation therapists performed a fault tree analysis, and failure mode and effects analysis (FMEA). Fault trees were created for under/overdosing targets, treatment deficiencies, as well as assisted in identifying failure modes for the FMEA. Treatment deficiency was defined to include a non-ideal oART plan resulting in treatment with a lower quality plan (either oART or scheduled plan), treatment delay, or cancelling treatment for the day. A reference planning checklist was created to catch failure modes before reaching the patient. Risk priority numbers (RPN=Severity*Detectability*Occurrence) were scored with and without the reference planning checklist to quantify risk mitigation. A root cause analysis was conducted for an event where an adaptive plan failed to generate. ResultsThe reference planning checklist (with items covering patient background, contouring/planning robustness for anatomy variability, and machine limitations) reduced the RPN for all failure modes. Only 1 failure mode with a risk priority number >150 occurred with the reference planning checklist compared to 29 failure modes without, including 14 adaptive specific failure modes. Contouring, planning, setup, scheduling, and documentation errors were identified during the fault tree analysis. 29/70 errors were adaptive specific. The reference planning checklist could address 23/33 for over/under dosing and 28/37 errors for treatment deficiency. The root cause analysis highlighted the need for checking setup prior to adaptive plan delivery, and time-out checklist. ConclusionsThe reference planning checklist improved detection of the failure modes and improves the quality and robustness of the plans produced for oART. It is ideally performed before physician plan review to prevent last minute re-plan (before or after first adaptive treatment) and delay of patient start. The checklist presented can be modified based on failures specific to individual clinics and utilized at various planning steps based on available resources.

Dose tracking assessment for magnetic resonance guided adaptive radiotherapy of rectal cancers

BackgroundMagnetic resonance-guided adaptive radiotherapy (MRgART) at MR-Linac allows for plan optimisation on the MR-based synthetic CT (sCT) images, adjusting the target and organs at risk according to the patient’s daily anatomy. Conversely, conventional linac image-guided radiotherapy (IGRT) involves rigid realignment of regions of interest to the daily anatomy, followed by the delivery of the reference computed tomography (CT) plan. This study aims to evaluate the effectiveness of MRgART versus IGRT for rectal cancer patients undergoing short-course radiotherapy, while also assessing the dose accumulation process to support the findings and determine its usefulness in enhancing treatment accuracy.MethodsNineteen rectal cancer patients treated with a 1.5 Tesla MR-Linac with a prescription dose of 25 Gy (5 Gy x 5) and undergoing daily adapted radiotherapy by plan optimization based on online MR-based sCT images, were included in this retrospective study. For each adapted plan (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\ ext{T}\ ext{P}}_{\ ext{a}\ ext{d}\ ext{a}\ ext{p}}$$\\end{document}), a second plan (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\ ext{T}\ ext{P}}_{\ ext{I}\ ext{G}\ ext{R}\ ext{T}}$$\\end{document}) was generated by recalculating the reference CT plan on the daily MR-based sCT images after rigid registration with the reference CT images to simulate the IGRT workflow. Dosimetry of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\ ext{T}\ ext{P}}_{\ ext{a}\ ext{d}\ ext{a}\ ext{p}}$$\\end{document} and\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:\\:{\ ext{T}\ ext{P}}_{\ ext{I}\ ext{G}\ ext{R}\ ext{T}}$$\\end{document}was compared for each fraction. Cumulative doses on the first and last fractions were evaluated for both workflows. The dosimetry per single fraction and the cumulative doses were compared using dose-volume histogram parameters.ResultsNinety-five fractions delivered with MRgART were compared to corresponding simulated IGRT fractions. All MRgART fractions fulfilled the target clinical requirements. IGRT treatments did not meet the expected target coverage for 63 out of 94 fractions (67.0%), with 13 fractions showing a V95 median point percentage decrease of 2.78% (range, 1.65-4.16%), and 55 fractions exceeding the V107% threshold with a median value of 15.4 cc (range, 6.0-43.8 cc). For the bladder, the median \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\ ext{D}}_{15\ ext{c}\ ext{c}}$$\\end{document} values were 18.18 Gy for the adaptive fractions and 19.60 Gy for the IGRT fractions. Similarly the median \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\ ext{D}}_{5\ ext{c}\ ext{c}}$$\\end{document} values for the small bowel were 23.40 Gy and 25.69 Gy, respectively. No statistically significant differences were observed in the doses accumulated on the first or last fraction for the adaptive workflow, with results consistent with the single adaptive fractions. In contrast, accumulated doses in the IGRT workflow showed significant variations mitigating the high dose constraint, nevertheless, more than half of the patients still did not meet clinical requirements.ConclusionsMRgART for short-course rectal cancer treatments ensures that the dose delivered matches each fraction of the planned dose and the results are confirmed by the dose accumulation process, which therefore seems redundant. In contrast, IGRT may lead to target dose discrepancies and non-compliance with organs at risk constraints and dose accumulation can still highlight notable dosimetric differences.

The Current use of Adaptive Strategies for External Beam Radiotherapy in Cervical Cancer: A Systematic Review

AimsVariability in the target and organs at risk (OARs) in cervical cancer treatment presents challenges for precise radiotherapy. Adaptive radiotherapy (ART) offers the potential to enhance treatment precision and outcomes. However, the increased workload and a lack of consensus on the most suitable ART approach hinder its clinical adoption. This systematic review aims to assess the current use of adaptive strategies for cervical cancer and define the optimal approach. Materials and methodsA systematic review of current literature published between January 2012 and May 2023 was conducted. Searches used PubMed/Medline, Cochrane Library, and Web of Science databases, supplemented with the University of Manchester, Google Scholar, and papers retrieved from reference lists. The review assessed workflows, compared dosimetric benefits, and examined resources for each identified strategy. Excluded were abstracts, conference abstracts, reviews, articles unrelated to ART management, proton therapy, brachytherapy, or qualitative studies. A narrative synthesis involved data tabulation, summarizing selected studies detailing workflow for cervical cancer and dosimetric outcomes for targets and OARs. ResultsSixteen articles met the inclusion criteria; these were mostly retrospective simulation planning studies, except four studies that had been clinically implemented. We identified five approaches for ART radiotherapy for cervical cancer: reactive and scheduled adaptation, internal target volume (ITV)-based approach using library of plans (LOP), fixed-margin approach using LOP, and real-time adaptation, with each approach reducing irradiated volumes without compromising target coverage compared to the non-ART approach. The LOP-based ITV approach is the most used and clinically assessed. ConclusionIdentifying the optimal strategy is challenging due to dosimetric assessment limitations. Implementing cervical cancer ART necessitates strategic optimization of clinical benefits and resources through research, including studies to identify the optimal frequency, and prospective evaluations of toxicity.