Adaptive Radiotherapy: Moving Into the Future

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.

Technical note: Visual, rapid, scintillation point dosimetry for in vivo MV photon beam radiotherapy treatments

BACKGROUND

While careful planning and pre-treatment checks are performed to ensure patient safety during external beam radiation therapy (EBRT), inevitable daily variations mean that in vivo dosimetry (IVD) is the only way to attain the true delivered dose. Several countries outside the US require daily IVD for quality assurance. However, elsewhere, the manual labor and time considerations of traditional in vivo dosimeters may be preventing frequent use of IVD in the clinic.

PURPOSE

This study expands upon previous research using plastic scintillator discs for optical dosimetry for electron therapy treatments. We present the characterization of scintillator discs for in vivo x-ray dosimetry and describe additional considerations due to geometric complexities.

METHODS

Plastic scintillator discs were coated with reflective white paint on all sides but the front surface. An anti-reflective, matte coating was applied to the transparent face to minimize specular reflection. A time-gated iCMOS camera imaged the discs under various irradiation conditions. In post-processing, background-subtracted images of the scintillators were fit with Gaussian-convolved ellipses to extract several parameters, including integral output, and observation angle.

RESULTS

Dose linearity and x-ray energy independence were observed, consistent with ideal characteristics for a dosimeter. Dose measurements exhibited less than 5% variation for incident beam angles between 0° and 75° at the anterior surface and 0-60∘ $^\circ $ at the posterior surface for exit beam dosimetry. Varying the angle between the disc surface and the camera lens did not impact the integral output for the same dose up to 55°. Past this point, up to 75°, there is a sharp falloff in response; however, a correction can be used based on the detected width of the disc. The reproducibility of the integral output for a single disc is 2%, and combined with variations from the gantry angle, we report the accuracy of the proposed scintillator disc dosimeters as ±5.4%.

CONCLUSIONS

Plastic scintillator discs have characteristics that are well-suited for in vivo optical dosimetry for x-ray radiotherapy treatments. Unlike typical point dosimeters, there is no inherent readout time delay, and an optical recording of the measurement is saved after treatment for future reference. While several factors influence the integral output for the same dose, they have been quantified here and may be corrected in post-processing.

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.

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.

Evaluation of knowledge-based planning models for male pelvic CBCT-based online adaptive radiotherapy on conventional linear accelerators

PURPOSE

To assess the practicality of employing a commercial knowledge-based planning tool (RapidPlan) to generate adapted intact prostate and prostate bed volumetric modulated arc therapy (VMAT) plans on iterative cone-beam computed tomography (iCBCT) datasets.

METHODS AND MATERIALS

Intact prostate and prostate bed RapidPlan models were trained utilizing planning data from 50 and 44 clinical cases, respectively. To ensure that refined models were capable of producing adequate clinical plans with a single optimization, models were tested with 50 clinical planning CT datasets by comparing dose-volume histogram (DVH) and plan quality metric (PQM) values between clinical and RapidPlan-generated plans. The RapidPlan tool was then used to retrospectively generate adapted VMAT plans on daily iCBCT images for 20 intact prostate and 15 prostate bed cases. As before, DVH and PQM metrics were utilized to dosimetrically compare scheduled (iCBCT Verify) and adapted (iCBCT RapidPlan) plans. Timing data was collected to further evaluate the feasibility of integrating this approach within an online adaptive radiotherapy workflow.

RESULTS

Model testing results confirmed the models were capable of producing VMAT plans within a single optimization that were overall improved upon or dosimetrically comparable to original clinical plans. Direct application of RapidPlan on iCBCT datasets produced satisfactory intact prostate and prostate bed plans with generally improved target volume coverage/conformality and rectal sparing relative to iCBCT Verify plans as indicated by DVH values, though bladder metrics were marginally increased on average. Average PQM values for iCBCT RapidPlans were significantly improved compared to iCBCT Verify plans. The average time required [in mm:ss] to generate adapted plans was 06:09 ± 02:06 (intact) and 07:12 ± 01:04 (bed).

CONCLUSION

This study demonstrated the feasibility of leveraging RapidPlan to expeditiously generate adapted VMAT intact prostate and prostate bed plans on iCBCT datasets. In general, adapted plans were dosimetrically improved relative to scheduled plans, emphasizing the practicality of the proposed approach.

Artificial Intelligence and the future of radiotherapy planning: The Australian radiation therapists prepare to be ready

The use of artificial intelligence (AI) solutions is rapidly changing the way radiation therapy tasks, traditionally relying on human skills, are approached by enabling fast automation. This evolution represents a paradigm shift in all aspects of the profession, particularly for treatment planning applications, opening up opportunities but also causing concerns for the future of the multidisciplinary team. In Australia, radiation therapists (RTs), largely responsible for both treatment planning and delivery, are discussing the impact of the introduction of AI and the potential developments in the future of their role. As medical physicists, who are part of the multidisciplinary team, in this editorial we reflect on the considerations of RTs, and on the implications of this transition to AI.

Addressing challenges in low-income and middle-income countries through novel radiotherapy research opportunities

Although radiotherapy continues to evolve as a mainstay of the oncological armamentarium, research and innovation in radiotherapy in low-income and middle-income countries (LMICs) faces challenges. This third Series paper examines the current state of LMIC radiotherapy research and provides new data from a 2022 survey undertaken by the International Atomic Energy Agency and new data on funding. In the context of LMIC-related challenges and impediments, we explore several developments and advances-such as deep phenotyping, real-time targeting, and artificial intelligence-to flag specific opportunities with applicability and relevance for resource-constrained settings. Given the pressing nature of cancer in LMICs, we also highlight some best practices and address the broader need to develop the research workforce of the future. This Series paper thereby serves as a resource for radiation professionals.

Dynamic CBCT imaging using prior model-free spatiotemporal implicit neural representation (PMF-STINR)

Objective. Dynamic cone-beam computed tomography (CBCT) can capture high-spatial-resolution, time-varying images for motion monitoring, patient setup, and adaptive planning of radiotherapy. However, dynamic CBCT reconstruction is an extremely ill-posed spatiotemporal inverse problem, as each CBCT volume in the dynamic sequence is only captured by one or a few x-ray projections, due to the slow gantry rotation speed and the fast anatomical motion (e.g. breathing).Approach. We developed a machine learning-based technique, prior-model-free spatiotemporal implicit neural representation (PMF-STINR), to reconstruct dynamic CBCTs from sequentially acquired x-ray projections. PMF-STINR employs a joint image reconstruction and registration approach to address the under-sampling challenge, enabling dynamic CBCT reconstruction from singular x-ray projections. Specifically, PMF-STINR uses spatial implicit neural representations to reconstruct a reference CBCT volume, and it applies temporal INR to represent the intra-scan dynamic motion of the reference CBCT to yield dynamic CBCTs. PMF-STINR couples the temporal INR with a learning-based B-spline motion model to capture time-varying deformable motion during the reconstruction. Compared with the previous methods, the spatial INR, the temporal INR, and the B-spline model of PMF-STINR are all learned on the fly during reconstruction in a one-shot fashion, without using any patient-specific prior knowledge or motion sorting/binning.Main results. PMF-STINR was evaluated via digital phantom simulations, physical phantom measurements, and a multi-institutional patient dataset featuring various imaging protocols (half-fan/full-fan, full sampling/sparse sampling, different energy and mAs settings, etc). The results showed that the one-shot learning-based PMF-STINR can accurately and robustly reconstruct dynamic CBCTs and capture highly irregular motion with high temporal (∼ 0.1 s) resolution and sub-millimeter accuracy.Significance. PMF-STINR can reconstruct dynamic CBCTs and solve the intra-scan motion from conventional 3D CBCT scans without using any prior anatomical/motion model or motion sorting/binning. It can be a promising tool for motion management by offering richer motion information than traditional 4D-CBCTs.

Image quality characterization of an ultra-high-speed kilovoltage cone-beam computed tomography imaging system on an O-ring linear accelerator

PURPOSE

The quality of on-board imaging systems, including cone-beam computed tomography (CBCT), plays a vital role in image-guided radiation therapy (IGRT) and adaptive radiotherapy. Recently, there has been an upgrade of the CBCT systems fused in the O-ring linear accelerators called HyperSight, featuring a high imaging performance. As the characterization of a new imaging system is essential, we evaluated the image quality of the HyperSight system by comparing it with Halcyon 3.0 CBCT and providing benchmark data for routine imaging quality assurance.

METHODS

The HyperSight features ultra-fast scan time, a larger kilovoltage (kV) detector, a more substantial kV tube, and an advanced reconstruction algorithm. Imaging protocols in the two modes of operation, treatment mode with IGRT and the CBCT for planning (CBCTp) mode were evaluated and compared with Halcyon 3.0 CBCT. Image quality metrics, including spatial resolution, contrast resolution, uniformity, noise, computed tomography (CT) number linearity, and calibration error, were assessed using a Catphan and an electron density phantom and analyzed with TotalQA software.

RESULTS

HyperSight demonstrated substantial improvements in contrast-to-noise ratio and noise in both IGRT and CBCTp modes compared to Halcyon 3.0 CBCT. CT number calibration error of HyperSight CBCTp mode (1.06%) closely matches that of a full CT scanner (0.72%), making it suitable for adaptive planning. In addition, the advanced hardware of HyperSight, such as ultra-fast scan time (5.9 s) or 2.5 times larger heat unit capacity, enhanced the clinical efficiency in our experience.

CONCLUSIONS

HyperSight represented a significant advancement in CBCT imaging. With its image quality, CT number accuracy, and ultra-fast scans, HyperSight has a potential to transform patient care and treatment outcomes. The enhanced scan speed and image quality of HyperSight are expected to significantly improve the quality and efficiency of treatment, particularly benefiting patients.

Adapt-On-Demand: A Novel Strategy for Personalized Adaptive Radiation Therapy for Locally Advanced Lung Cancer

PURPOSE

Real-time adaptation of thoracic radiation plans is compelling because offline adaptive experiences show that tumor volumes and lung anatomy can change during therapy. We present and analyze a novel adaptive-on-demand (AOD) workflow combining online adaptive radiation therapy (o-ART) on the ETHOS system with image guided radiation therapy delivery on a Halcyon unit for conventional fractionated radiation therapy of locally advanced lung cancer (LALC).

METHODS AND MATERIALS

We analyzed 26 patients with LALC treated with the AOD workflow, adapting weekly. We timed segments of the workflow to evaluate efficiency in a real-world clinic. Target coverage and organ at risk (OAR) doses were compared between adaptive plans (ADP) and nonadaptive scheduled plans (SCH). Planning robustness was evaluated by the frequency of preplanning goals achieved in ADP plans, stratified by tumor volume change.

RESULTS

The AOD workflow was achievable within 30 minutes for most radiation fractions. Over the course of therapy, we observed an average 26.6% ± 23.3% reduction in internal target volume (ITV). Despite these changes, with o-ART, ITV and planning target volume (PTV) coverage (V100%) was 99.2% and 93.9% for all members of the cohort, respectively. This represented a 2.9% and 6.8% improvement over nonadaptive plans (P < .05), respectively. For tumors that grew >10%, V100% was 93.1% for o-ART and 76.4% for nonadaptive plans, representing a median 17.2% improvement in the PTV coverage (P < .05). In these plans, critical OAR constraints were met 94.1% of the time, whereas in nonadaptive plans, this figure was 81.9%. This represented reductions of 1.32 Gy, 1.34 Gy, or 1.75 Gy in the heart, esophagus, and lung, respectively. The effect was larger when tumors had shrunk more than 10%. Regardless of tumor volume alterations, the PTV/ITV coverage was achieved for all adaptive plans. Exceptional cases, where dose constraints were not met, were due to large initial tumor volumes or tumor growth.

CONCLUSIONS

The AOD workflow is efficient and robust in responding to anatomic changes in LALC patients, providing dosimetric advantages over standard therapy. Weekly adaptation was adequate to keep pace with changes. This approach is a feasible alternative to conventional offline replanning workflows for managing anatomy changes in LALC radiation therapy.

Motion compensated cone-beam CT reconstruction using ana priorimotion model from CT simulation: a pilot study

Objective. To combat the motion artifacts present in traditional 4D-CBCT reconstruction, an iterative technique known as the motion-compensated simultaneous algebraic reconstruction technique (MC-SART) was previously developed. MC-SART employs a 4D-CBCT reconstruction to obtain an initial model, which suffers from a lack of sufficient projections in each bin. The purpose of this study is to demonstrate the feasibility of introducing a motion model acquired during CT simulation to MC-SART, coined model-based CBCT (MB-CBCT).Approach. For each of 5 patients, we acquired 5DCTs during simulation and pre-treatment CBCTs with a simultaneous breathing surrogate. We cross-calibrated the 5DCT and CBCT breathing waveforms by matching the diaphragms and employed the 5DCT motion model parameters for MC-SART. We introduced the Amplitude Reassignment Motion Modeling technique, which measures the ability of the model to control diaphragm sharpness by reassigning projection amplitudes with varying resolution. We evaluated the sharpness of tumors and compared them between MB-CBCT and 4D-CBCT. We quantified sharpness by fitting an error function across anatomical boundaries. Furthermore, we compared our MB-CBCT approach to the traditional MC-SART approach. We evaluated MB-CBCT's robustness over time by reconstructing multiple fractions for each patient and measuring consistency in tumor centroid locations between 4D-CBCT and MB-CBCT.Main results. We found that the diaphragm sharpness rose consistently with increasing amplitude resolution for 4/5 patients. We observed consistently high image quality across multiple fractions, and observed stable tumor centroids with an average 0.74 ± 0.31 mm difference between the 4D-CBCT and MB-CBCT. Overall, vast improvements over 3D-CBCT and 4D-CBCT were demonstrated by our MB-CBCT technique in terms of both diaphragm sharpness and overall image quality.Significance. This work is an important extension of the MC-SART technique. We demonstrated the ability ofa priori5DCT models to provide motion compensation for CBCT reconstruction. We showed improvements in image quality over both 4D-CBCT and the traditional MC-SART approach.

Functional information guided adaptive radiation therapy

Introduction

Functional informaton is introduced as the mechanism to adapt cancer therapies uniquely to individual patients based on changes defined by qualified tumor biomarkers.

Methods

To demonstrate the methodology, a tumor volume biomarker model, characterized by a tumor volume reduction rate coefficient, is used to adapt a tumor cell survival bioresponse radiotherapy model in terms of therapeutic radiation dose. Tumor volume, acquired from imaging data, serves as a surrogate measurement for tumor cell death, but the biomarker model derived from this data cannot be used to calculate the radiation dose absorbed by the target tumor. However, functional information does provide a mathematical connection between the tumor volume biomarker model and the tumor cell survival bioresponse model by quantifying both data sets in the units of information, thus creating an analytic conduit from bioresponse to biomarker.

Results

The information guided process for individualized dose adaptations using information values acquired from the tumor cell survival bioresponse model and the tumor volume biomarker model are presented in detailed form by flowchart and tabular data. Clinical data are used to generate a presentation that assists investigator application of the information guided methodology to adaptive cancer therapy research.

Conclusions

Information guided adaptation of bioresponse using surrogate data is extensible across multiple research fields because functional information mathematically connects disparate bioresponse and biomarker data sets. Thus, functional information offers adaptive cancer therapy by mathematically connecting immunotherapy, chemotherapy, and radiotherapy cancer treatment processes to implement individualized treatment plans.

MRI-Guided Radiation Therapy-An Emerging and Disruptive Process of Care: Healthcare Economic and Policy Considerations

MRI-guided radiation therapy (MRgRT) is an emerging, innovative technology that provides opportunities to transform and improve the current clinical care process in radiation oncology. As with many new technologies in radiation oncology, careful evaluation from a healthcare economic and policy perspective is required for its successful implementation. In this review article, we describe the current evidence surrounding MRgRT, framing it within the context of value within the healthcare system. Additionally, we highlight areas in which MRgRT may disrupt the current process of care, and discuss the evidence thresholds and timeline required for the widespread adoption of this promising technology.

Mitigating misalignment in MRI-to-CT synthesis for improved synthetic CT generation: an iterative refinement and knowledge distillation approach

Objective.Deep learning has shown promise in generating synthetic CT (sCT) from magnetic resonance imaging (MRI). However, the misalignment between MRIs and CTs has not been adequately addressed, leading to reduced prediction accuracy and potential harm to patients due to the generative adversarial network (GAN)hallucination phenomenon. This work proposes a novel approach to mitigate misalignment and improve sCT generation.Approach.Our approach has two stages: iterative refinement and knowledge distillation. First, we iteratively refine registration and synthesis by leveraging their complementary nature. In each iteration, we register CT to the sCT from the previous iteration, generating a more aligned deformed CT (dCT). We train a new model on the refined 〈dCT, MRI〉 pairs to enhance synthesis. Second, we distill knowledge by creating a target CT (tCT) that combines sCT and dCT images from the previous iterations. This further improves alignment beyond the individual sCT and dCT images. We train a new model with the 〈tCT, MRI〉 pairs to transfer insights from multiple models into this final knowledgeable model.Main results.Our method outperformed conditional GANs on 48 head and neck cancer patients. It reduced hallucinations and improved accuracy in geometry (3% ↑ Dice), intensity (16.7% ↓ MAE), and dosimetry (1% ↑γ3%3mm). It also achieved <1% relative dose difference for specific dose volume histogram points.Significance.This pioneering approach for addressing misalignment shows promising performance in MRI-to-CT synthesis for MRI-only planning. It could be applied to other modalities like cone beam computed tomography and tasks such as organ contouring.

Feasibility study of adaptive radiotherapy with Ethos for breast cancer

Purpose

The aim of this study was to assess the feasibility of online adaptive radiotherapy with Ethos for breast cancer.

Materials and methods

This retrospective study included 20 breast cancer patients previously treated with TrueBeam. All had undergone breast surgery for different indications (right/left, lumpectomy/mastectomy) and were evenly divided between these four cases, with five extended cone beam computed tomography (CBCT) scans per patient. The dataset was used in an Ethos emulator to test the full adaptive workflow. The contours generated by artificial intelligence (AI) for the influencers (left and right breasts and lungs, heart) and elastic or rigid propagation for the target volumes (internal mammary chain (IMC) and clavicular lymph nodes (CLNs)) were compared to the initial contours delineated by the physician using two metrics: Dice similarity coefficient (DICE) and Hausdorff 95% distance (HD95). The repeatability of influencer generation was investigated. The times taken by the emulator to generate contours, optimize plans, and calculate doses were recorded. The quality of the scheduled and adapted plans generated by Ethos was assessed using planning target volume (PTV) coverage, homogeneity indices (HIs), and doses to organs at risk (OARs) via dose-volume histogram (DVH) metrics. Quality assurance (QA) of the treatment plans was performed using an independent portal dosimetry tool (EpiQA) and gamma index.

Results

On average, the DICE for the influencers was greater than 0.9. Contours resulting from rigid propagation had a higher DICE and a lower HD95 than those resulting from elastic deformation but remained below the values obtained for the influencers: DICE values were 0.79 ± 0.11 and 0.46 ± 0.17 for the CLN and IMC, respectively. Regarding the repeatability of the influencer segmentation, the DICE was close to 1, and the mean HD95 was strictly less than 0.15 mm. The mean time was 73 ± 4 s for contour generation per AI and 80 ± 9 s for propagations. The average time was 53 ± 3 s for dose calculation and 125 ± 9 s for plan optimization. A dosimetric comparison of scheduled and adapted plans showed a significant difference in PTV coverage: dose received by 95% of the volume (D95%) values were higher and closer to the prescribed doses for adapted plans. Doses to organs at risk were similar. The average gamma index for quality assurance of adapted plans was 99.93 ± 0.38 for a 3%/3mm criterion.

Conclusion

This study comprehensively evaluated the Ethos® adaptive workflow for breast cancer and its potential technical limitations. Although the results demonstrated the high accuracy of AI segmentation and the superiority of adapted plans in terms of target volume coverage, a medical assessment is still required.

Comparison and evaluation of different deep learning models of synthetic CT generation from CBCT for nasopharynx cancer adaptive proton therapy

BACKGROUND

Cone-beam computed tomography (CBCT) scanning is used for patient setup in image-guided radiotherapy. However, its inaccurate CT numbers limit its applicability in dose calculation and treatment planning.

PURPOSE

This study compares four deep learning methods for generating synthetic CT (sCT) to determine which method is more appropriate and offers potential for further clinical exploration in adaptive proton therapy for nasopharynx cancer.

METHODS

CBCTs and deformed planning CT (dCT) from 75 patients (60/5/10 for training, validation and testing) were used to compare cycle-consistent Generative Adversarial Network (cycleGAN), Unet, Unet+cycleGAN and conditionalGenerative Adversarial Network (cGAN) for sCT generation. The sCT images generated by each method were evaluated against dCT images using mean absolute error (MAE), structural similarity (SSIM), peak signal-to-noise ratio (PSNR), spatial non-uniformity (SNU) and radial averaging in the frequency domain. In addition, dosimetric accuracy was assessed through gamma analysis, differences in water equivalent thickness (WET), and dose-volume histogram metrics.

RESULTS

The cGAN model has demonstrated optimal performance in the four models across various indicators. In terms of image quality under global condition, the average MAE has been reduced to 16.39HU, SSIM has increased to 95.24%, and PSNR has increased to 28.98. Regarding dosimetric accuracy, the gamma passing rate (2%/2 mm) has reached 99.02%, and the WET difference is only 1.28 mm. The D95 value of CTVs coverage and Dmax value of spinal cord, brainstem show no significant differences between dCT and sCT generated by cGAN model.

CONCLUSIONS

The cGAN model has been shown to be a more suitable approach for generating sCT using CBCT, considering its characteristics and concepts. The resulting sCT has the potential for application in adaptive proton therapy.

Advancing knowledge-based intensity modulated proton planning for adaptive treatment of high-risk prostate cancer

To assess the performance of a knowledge-based planning (KBP) model for generating intensity-modulated proton therapy (IMPT) treatment plans as part of an adaptive radiotherapy (ART) strategy for patients with high-risk prostate cancer. A knowledge-based planning (KBP) model for proton adaptive treatment plan generation was developed based on thirty patient treatment plans utilizing RapidPlanTM PT (Varian Medical Systems, Palo Alto, CA). The model was subsequently validated using an additional eleven patient cases. All patients in the study were administered a prescribed dose of 70.2 Gy to the prostate and seminal vesicle (CTV70.2), along with 46.8 Gy to the pelvic lymph nodes (CTV46.8) through simultaneous integrated boost (SIB) technique. To assess the quality of the validation knowledge-based proton plans (KBPPs), target coverage and organ-at-risk (OAR) dose-volume constraints were compared against those of clinically used expert plans using paired t-tests. The KBP model training statistics (R2) (mean ± SD, 0.763 ± 0.167, range, 0.406 to 0.907) and χ² values (1.162 ± 0.0867, 1.039-1.253) indicate acceptable model training quality. Moreover, the average total treatment planning optimization and calculation time for adaptive plan generation is approximately 10 minutes. The CTV70.2 D98% for the KBPPs (mean ± SD, 69.1 ± 0.08 Gy) and expert plans (69.9 ± 0.04 Gy) shows a significant difference (p < 0.05) but are both within 1.1 Gy of the prescribed dose which is clinically acceptable. While the maximum dose for some organs-at-risk (OARs) such as the bladder and rectum is generally higher in the KBPPs, the doses still fall within clinical constraints. Among all the OARs, most of them received comparable results to the expert plan, except the cauda equina Dmax, which shows statistical significance and was lower in the KBPPs than in expert plans (48.5 ± 0.06 Gy vs 49.3 ± 0.05 Gy). The generated KBPPs were clinically comparable to manually crafted plans by expert treatment planners. The adaptive plan generation process was completed within an acceptable timeframe, offering a quick same-day adaptive treatment option. Our study supports the integration of KBP as a crucial component of an ART strategy, including maintaining plan consistency, improving quality, and enhancing efficiency. This advancement in speed and adaptability promises more precise treatment in proton ART.

A multi-center survey on adaptive radiation therapy for head and neck cancer in Malaysia

The survey is to assess the current state of adaptive radiation therapy (ART) for head and neck (H&N) cases among radiotherapy centers in Malaysia and to identify any implementation limitations. An online questionnaire was sent to all radiotherapy centers in Malaysia. The 24-question questionnaire consists of general information about the center, ART practices, and limitations faced in implementing ART. 28 out of 36 radiotherapy centers responded, resulting in an overall response rate of 78%. About 52% of the responding centers rescanned and replanned less than 5% of their H&N patients. The majority (88.9%) of the respondents reported the use Cone Beam Computed Tomography alone or in combination with other modalities to trigger the ART process. The main reasons cited for adopting ART were weight loss, changes in the immobilization fitting, and anatomical variation. The adaptation process typically occurred during week 3 or week 4 of treatment. More than half of the respondents require three days or more from re-simulation to starting a new treatment plan. Both target and organ at risk delineation on new planning CT relied heavily on manual delineation by physicians and physicists, respectively. All centers perform patient-specific quality assurance for their new adaptive plans. Two main limitations in implementing ART are "limited financial resources or equipment" and "limitation on technical knowledge". There is a need for a common consensus to standardize the practice of ART and address these limitations to improve the implementation of ART in Malaysia.

Emerging technologies for cancer therapy using accelerated particles

Cancer therapy with accelerated charged particles is one of the most valuable biomedical applications of nuclear physics. The technology has vastly evolved in the past 50 years, the number of clinical centers is exponentially growing, and recent clinical results support the physics and radiobiology rationale that particles should be less toxic and more effective than conventional X-rays for many cancer patients. Charged particles are also the most mature technology for clinical translation of ultra-high dose rate (FLASH) radiotherapy. However, the fraction of patients treated with accelerated particles is still very small and the therapy is only applied to a few solid cancer indications. The growth of particle therapy strongly depends on technological innovations aiming to make the therapy cheaper, more conformal and faster. The most promising solutions to reach these goals are superconductive magnets to build compact accelerators; gantryless beam delivery; online image-guidance and adaptive therapy with the support of machine learning algorithms; and high-intensity accelerators coupled to online imaging. Large international collaborations are needed to hasten the clinical translation of the research results.

Applying a Radiation Therapy Volume Analysis Pipeline to Determine the Utility of Spectroscopic MRI-Guided Adaptive Radiation Therapy for Glioblastoma

Accurate radiation therapy (RT) targeting is crucial for glioblastoma treatment but may be challenging using clinical imaging alone due to the infiltrative nature of glioblastomas. Precise targeting by whole-brain spectroscopic MRI, which maps tumor metabolites including choline (Cho) and N-acetylaspartate (NAA), can quantify early treatment-induced molecular changes that other traditional modalities cannot measure. We developed a pipeline to determine how spectroscopic MRI changes during early RT are associated with patient outcomes to provide insight into the utility of adaptive RT planning. Data were obtained from a study (NCT03137888) where glioblastoma patients received high-dose RT guided by the pre-RT Cho/NAA twice normal (Cho/NAA ≥ 2x) volume, and received spectroscopic MRI scans pre- and mid-RT. Overlap statistics between pre- and mid-RT scans were used to quantify metabolic activity changes after two weeks of RT. Log-rank tests were used to quantify the relationship between imaging metrics and patient overall and progression-free survival (OS/PFS). Patients with lower Jaccard/Dice coefficients had longer PFS (p = 0.045 for both), and patients with lower Jaccard/Dice coefficients had higher OS trending towards significance (p = 0.060 for both). Cho/NAA ≥ 2x volumes changed significantly during early RT, putting healthy tissue at risk of irradiation, and warranting further study into using adaptive RT planning.

Contouring quality assurance methodology based on multiple geometric features against deep learning auto-segmentation

BACKGROUND

Contouring error is one of the top failure modes in radiation treatment. Multiple efforts have been made to develop tools to automatically detect segmentation errors. Deep learning-based auto-segmentation (DLAS) has been used as a baseline for flagging manual segmentation errors, but those efforts are limited to using only one or two contour comparison metrics.

PURPOSE

The purpose of this research is to develop an improved contouring quality assurance system to identify and flag manual contouring errors.

METHODS AND MATERIALS

DLAS contours were used as a reference to compare with manually segmented contours. A total of 27 geometric agreement metrics were determined from the comparisons between the two segmentation approaches. Feature selection was performed to optimize the training of a machine learning classification model to identify potential contouring errors. A public dataset with 339 cases was used to train and test the classifier. Four independent classifiers were trained using five-fold cross validation, and the predictions from each classifier were ensembled using soft voting. The trained model was validated on a held-out testing dataset. An additional independent clinical dataset with 60 cases was used to test the generalizability of the model. Model predictions were reviewed by an expert to confirm or reject the findings.

RESULTS

The proposed machine learning multiple features (ML-MF) approach outperformed traditional nonmachine-learning-based approaches that are based on only one or two geometric agreement metrics. The machine learning model achieved recall (precision) values of 0.842 (0.899), 0.762 (0.762), 0.727 (0.842), and 0.773 (0.773) for Brainstem, Parotid_L, Parotid_R, and mandible contours, respectively compared to 0.526 (0.909), 0.619 (0.765), 0.682 (0.882), 0.773 (0.568) for an approach based solely on Dice similarity coefficient values. In the external validation dataset, 66.7, 93.3, 94.1, and 58.8% of flagged cases were confirmed to have contouring errors by an expert for Brainstem, Parotid_L, Parotid_R, and mandible contours, respectively.

CONCLUSIONS

The proposed ML-MF approach, which includes multiple geometric agreement metrics to flag manual contouring errors, demonstrated superior performance in comparison to traditional methods. This method is easy to implement in clinical practice and can help to reduce the significant time and labor costs associated with manual segmentation and review.

Evaluating on-board kVCT- and MVCT-based dose calculation accuracy using a thorax phantom for helical tomotherapy treatments

Purpose.To evaluate the impact of CT number calibration and imaging parameter selection on dose calculation accuracy relative to the CT planning process in thoracic treatments for on-board helical CT imaging systems used in helical tomotherapy.Methods and Materials.Direct CT number calibrations were performed with appropriate protocols for each imaging system using an electron density phantom. Large volume and SBRT treatment plans were simulated and optimized for planning CT scans of an anthropomorphic thorax phantom and transferred to registered kVCT and MVCT scans of the phantom as appropriate. Relevant DVH metrics and dose-difference maps were used to evaluate and compare dose calculation accuracy relative to the planning CT based on a variation in imaging parameters applied for the on-board systems.Results.For helical kVCT scans of the thorax phantom, median differences in DVH parameters for the large volume treatment plan were less than ±1% with dose to the target volume either over- or underestimated depending on the imaging parameters utilized for CT number calibration and thorax phantom acquisition. For the lung SBRT plan calculated on helical kVCT scans, median dose differences were up to -2.7% with a more noticeable dependence on parameter selection. For MVCT scans, median dose differences for the large volume plan were within +2% with dose to the target overestimated regardless of the imaging protocol.Conclusion.Accurate dose calculations (median errors of <±1%) using a thorax phantom simulating realistic patient geometry and scatter conditions can be achieved with images acquired with a helical kVCT system on a helical tomotherapy unit. This accuracy is considerably improved relative to that achieved with the MV-based approach. In a clinical setting, careful consideration should be made when selecting appropriate kVCT imaging parameters for this process as dose calculation accuracy was observed to vary with both parameter selection and treatment type.

Detecting and quantifying spatial misalignment between longitudinal kilovoltage computed tomography (kVCT) scans of the head and neck by using convolutional neural networks (CNNs)

BACKGROUND

Adaptive radiotherapy (ART) aims to address anatomical modifications appearing during the treatment of patients by modifying the planning treatment according to the daily positioning image. Clinical implementation of ART relies on the quality of the deformable image registration (DIR) algorithms included in the ART workflow. To translate ART into clinical practice, automatic DIR assessment is needed.

OBJECTIVE

This article aims to estimate spatial misalignment between two head and neck kilovoltage computed tomography (kVCT) images by using two convolutional neural networks (CNNs).

METHODS

The first CNN quantifies misalignments between 0 mm and 15 mm and the second CNN detects and classifies misalignments into two classes (poor alignment and good alignment). Both networks take pairs of patches of 33x33x33 mm3 as inputs and use only the image intensity information. The training dataset was built by deforming kVCT images with basis splines (B-splines) to simulate DIR error maps. The test dataset was built using 2500 landmarks, consisting of hard and soft landmark tissues annotated by 6 clinicians at 10 locations.

RESULTS

The quantification CNN reaches a mean error of 1.26 mm (± 1.75 mm) on the landmark set which, depending on the location, has annotation errors between 1 mm and 2 mm. The errors obtained for the quantification network fit the computed interoperator error. The classification network achieves an overall accuracy of 79.32%, and although the classification network overdetects poor alignments, it performs well (i.e., it achieves a rate of 90.4%) in detecting poor alignments when given one.

CONCLUSION

The performances of the networks indicate the feasibility of using CNNs for an agnostic and generic approach to misalignment quantification and detection.

Adaptive fractionation at the MR-linac

Objective. Fractionated radiotherapy typically delivers the same dose in each fraction. Adaptive fractionation (AF) is an approach to exploit inter-fraction motion by increasing the dose on days when the distance of tumor and dose-limiting organs at risk (OAR) is large and decreasing the dose on unfavorable days. We develop an AF algorithm and evaluate the concept for patients with abdominal tumors previously treated at the MR-linac in 5 fractions.Approach. Given daily adapted treatment plans, inter-fractional changes are quantified by sparing factorsδ_tdefined as the OAR-to-tumor dose ratio. The key problem of AF is to decide on the dose to deliver in fractiont, givenδ_tand the dose delivered in previous fractions, but not knowing futureδ_ts. Optimal doses that maximize the expected biologically effective dose in the tumor (BED₁₀) while staying below a maximum OAR BED₃constraint are computed using dynamic programming, assuming a normal distribution overδwith mean and variance estimated from previously observed patient-specificδ_ts. The algorithm is evaluated for 16 MR-linac patients in whom tumor dose was compromised due to proximity of bowel, stomach, or duodenum.Main Results. In 14 out of the 16 patients, AF increased the tumor BED₁₀compared to the reference treatment that delivers the same OAR dose in each fraction. However, in 11 of these 14 patients, the increase in BED₁₀was below 1 Gy. Two patients with large sparing factor variation had a benefit of more than 10 Gy BED₁₀increase. For one patient, AF led to a 5 Gy BED₁₀decrease due to an unfavorable order of sparing factors.Significance. On average, AF provided only a small increase in tumor BED. However, AF may yield substantial benefits for individual patients with large variations in the geometry.

Evaluation of mega-voltage CT images for completed radiotherapy treatments for dogs and cats reveals uncommon but potentially consequential dose deviation in thoracic and abdominal tumors

As advanced delivery techniques such as intensity-modulated radiation therapy (IMRT) become conventional in veterinary radiotherapy, highly modulated radiation delivery helps to decrease dose to normal tissues. However, IMRT is only effective if patient setup and anatomy are accurately replicated for each treatment. Numerous techniques have been implemented to decrease patient setup error, however tumor shrinkage, variations in the patient's contour and weight loss continue to be hard to control and can result in clinically relevant dose deviation in radiotherapy plans. Adaptive radiotherapy (ART) is often the most effective means to account for gradual changes such as tumor shrinkage and weight loss, however it is often unclear when adaption is necessary. The goal of this retrospective, observational study was to review dose delivery in dogs and cats who received helical radiotherapy at University of Wisconsin, using detector dose data (D2%, D50%, D98%) and daily megavoltage computed tomography (MVCT) images, and to determine whether ART should be considered more frequently than it currently is. A total of 52 treatment plans were evaluated and included cancers of the head and neck, thorax, and abdomen. After evaluation, 6% of the radiotherapy plan delivered had clinically relevant dose deviations in dose delivery. Dose deviations were more common in thoracic and abdominal targets. While adaptation may have been considered in these cases, the decision to adapt can be complex and all factors, such as treatment delay, cost, and imaging modality, must be considered when adaptation is to be pursued.

An independent Monte Carlo-based IMRT QA tool for a 0.35 T MRI-guided linear accelerator

PURPOSE

To develop an independent log file-based intensity-modulated radiation therapy (IMRT) quality assurance (QA) tool for the 0.35 T magnetic resonance-linac (MR-linac) and investigate the ability of various IMRT plan complexity metrics to predict the QA results. Complexity metrics related to tissue heterogeneity were also introduced.

METHODS

The tool for particle simulation (TOPAS) Monte Carlo code was utilized with a previously validated linac head model. A cohort of 29 treatment plans was selected for IMRT QA using the developed QA tool and the vendor-supplied adaptive QA (AQA) tool. For 27 independent patient cases, various IMRT plan complexity metrics were calculated to assess the deliverability of these plans. A correlation between the gamma pass rates (GPRs) from the AQA results and calculated IMRT complexity metrics was determined using the Pearson correlation coefficients. Tissue heterogeneity complexity metrics were calculated based on the gradient of the Hounsfield units.

RESULTS

The median and interquartile range for the TOPAS GPRs (3%/3 mm criteria) were 97.24% and 3.75%, respectively, and were 99.54% and 0.36% for the AQA tool, respectively. The computational time for TOPAS ranged from 4 to 8 h to achieve a statistical uncertainty of <1.5%, whereas the AQA tool had an average calculation time of a few minutes. Of the 23 calculated IMRT plan complexity metrics, the AQA GPRs had correlations with 7 out of 23 of the calculated metrics. Strong correlations (|r| > 0.7) were found between the GPRs and the heterogeneity complexity metrics introduced in this work.

CONCLUSIONS

An independent MC and log file-based IMRT QA tool was successfully developed and can be clinically deployed for offline QA. The complexity metrics will supplement QA reports and provide information regarding plan complexity.

History of Technological Advancements towards MR-Linac: The Future of Image-Guided Radiotherapy

Image-guided radiotherapy (IGRT) enables optimal tumor targeting and sparing of organs-at-risk, which ultimately results in improved outcomes for patients. Magnetic resonance imaging (MRI) revolutionized diagnostic imaging with its superior soft tissue contrast, high spatiotemporal resolution, and freedom from ionizing radiation exposure. Over the past few years there has been burgeoning interest in MR-guided radiotherapy (MRgRT) to overcome current challenges in X-ray-based IGRT, including but not limited to, suboptimal soft tissue contrast, lack of efficient daily adaptation, and incremental exposure to ionizing radiation. In this review, we present an overview of the technologic advancements in IGRT that led to MRI-linear accelerator (MRL) integration. Our report is organized in three parts: (1) a historical timeline tracing the origins of radiotherapy and evolution of IGRT, (2) currently available MRL technology, and (3) future directions and aspirations for MRL applications.

Impact of Using Unedited CT-Based DIR-Propagated Autocontours on Online ART for Pancreatic SBRT

Purpose

To determine the dosimetric impact of using unedited autocontours in daily plan adaptation of patients with locally advanced pancreatic cancer (LAPC) treated with stereotactic body radiotherapy using tumor tracking.

Materials and Methods

The study included 98 daily CT scans of 35 LAPC patients. All scans were manually contoured (MAN), and included the PTV and main organs-at-risk (OAR): stomach, duodenum and bowel. Precision and MIM deformable image registration (DIR) methods followed by contour propagation were used to generate autocontour sets on the daily CT scans. Autocontours remained unedited, and were compared to MAN on the whole organs and at 3, 1 and 0.5 cm from the PTV. Manual and autocontoured OAR were used to generate daily plans using the VOLO™ optimizer, and were compared to non-adapted plans. Resulting planned doses were compared based on PTV coverage and OAR dose-constraints.

Results

Overall, both algorithms reported a high agreement between unclipped MAN and autocontours, but showed worse results when being evaluated on the clipped structures at 1 cm and 0.5 cm from the PTV. Replanning with unedited autocontours resulted in better OAR sparing than non-adapted plans for 95% and 84% plans optimized using Precision and MIM autocontours, respectively, and obeyed OAR constraints in 64% and 56% of replans.

Conclusion

For the majority of fractions, manual correction of autocontours could be avoided or be limited to the region closest to the PTV. This practice could further reduce the overall timings of adaptive radiotherapy workflows for patients with LAPC.

Image guidance for FLASH radiotherapy

FLASH radiotherapy (FLASH-RT) is an emerging ultra-high dose (>40 Gy/s) delivery that promises to improve the therapeutic potential by limiting toxicities compared to conventional RT while maintaining similar tumor eradication efficacy. Image guidance is an essential component of modern RT that should be harnessed to meet the special emerging needs of FLASH-RT and its associated high risks in planning and delivering of such ultra-high doses in short period of times. Hence, this contribution will elaborate on the imaging requirements and possible solutions in the entire chain of FLASH-RT treatment, from the planning, through the setup and delivery with online in vivo imaging and dosimetry, up to the assessment of biological mechanisms and treatment response. In patient setup and delivery, higher temporal sampling than in conventional RT should ensure that the short treatment is delivered precisely to the targeted region. Additionally, conventional imaging tools such as cone-beam computed tomography will continue to play an important role in improving patient setup prior to delivery, while techniques based on magnetic resonance imaging or positron emission tomography may be extremely valuable for either linear accelerator (Linac) or particle FLASH therapy, to monitor and track anatomical changes during delivery. In either planning or assessing outcomes, quantitative functional imaging could supplement conventional imaging for more accurate utilization of the biological window of the FLASH effect, selecting for or verifying things such as tissue oxygen and existing or transient hypoxia on the relevant timescales of FLASH-RT delivery. Perhaps most importantly at this time, these tools might help improve the understanding of the biological mechanisms of FLASH-RT response in tumor and normal tissues. The high dose deposition of FLASH provides an opportunity to utilize pulse-to-pulse imaging tools such as Cherenkov or radiation acoustic emission imaging. These could provide individual pulse mapping or assessing the 3D dose delivery superficially or at tissue depth, respectively. In summary, the most promising components of modern RT should be used for safer application of FLASH-RT, and new promising developments could be advanced to cope with its novel demands but also exploit new opportunities in connection with the unique nature of pulsed delivery at unprecedented dose rates, opening a new era of biological image guidance and ultrafast, pulse-based in vivo dosimetry.

The Promise of Magnetic Resonance Imaging in Radiation Oncology Practice in the Management of Brain, Prostate, and GI Malignancies

Magnetic resonance imaging (MRI) has a key role to play at multiple steps of the radiotherapy (RT) treatment planning and delivery process. Development of high-precision RT techniques such as intensity-modulated RT, stereotactic ablative RT, and particle beam therapy has enabled oncologists to escalate RT dose to the target while restricting doses to organs at risk (OAR). MRI plays a critical role in target volume delineation in various disease sites, thus ensuring that these high-precision techniques can be safely implemented. Accurate identification of gross disease has also enabled selective dose escalation as a means to widen the therapeutic index. Morphological and functional MRI sequences have also facilitated an understanding of temporal changes in target volumes and OAR during a course of RT, allowing for midtreatment volumetric and biological adaptation. The latest advancement in linear accelerator technology has led to the incorporation of an MRI scanner in the treatment unit. MRI-guided RT provides the opportunity for MRI-only workflow along with online adaptation for either target or OAR or both. MRI plays a key role in post-treatment response evaluation and is an important tool for guiding decision making. In this review, we briefly discuss the RT-related applications of MRI in the management of brain, prostate, and GI malignancies.

Characterization of imaging performance of a novel helical kVCT for use in image‐guided and adaptive radiotherapy

ClearRT helical kVCT imaging for the Radixact helical tomotherapy system recently received FDA approval and is available for clinical use. The system is intended to enhance image fidelity in radiation therapy treatment planning and delivery compared to the prior MV‐based onboard imaging approach. The purpose of this work was to characterize the imaging performance of this system and compare this performance with that of clinical systems used in image‐guided and/or adaptive radiotherapy (ART) or computed tomography (CT) simulation, including Radixact MVCT, TomoTherapy MVCT, Varian TrueBeam kV OBI CBCT, and the Siemens SOMATOM Definition Edge kVCT. A CT image quality phantom was scanned across clinically relevant acquisition modes for each system to evaluate image quality metrics, including noise, uniformity, contrast, spatial resolution, and CT number linearity. Similar noise levels were observed for ClearRT and Siemens Edge, whereas noise for the other systems was ∼1.5–5 times higher. Uniformity was best for Siemens Edge, whereas most scans for ClearRT exhibited a slight “cupping” or “capping” artifact. The ClearRT and Siemens Edge performed best for contrast metrics, which included low‐contrast visibility and contrast‐to‐noise ratio evaluations. Spatial resolution was best for TrueBeam and Siemens Edge, whereas the three kVCT systems exhibited similar CT number linearity. Overall, these results provide an initial indication that ClearRT image quality is adequate for image guidance in radiotherapy and sufficient for delineating anatomic structures, thus enabling its use for ART. ClearRT also showed significant improvement over MVCT, which was previously the only onboard imaging modality available on Radixact. Although the acquisition of these scans does come at the cost of additional patient dose, reported CTDI values indicate a similar or generally reduced machine output for ClearRT compared to the other systems while maintaining comparable or improved image quality overall.

Please Place Your Seat in the Full Upright Position: A Technical Framework for Landing Upright Radiation Therapy in the 21st Century

Delivering radiotherapy to patients in an upright position can allow for increased patient comfort, reduction in normal tissue irradiation, or reduction of machine size and complexity. This paper gives an overview of the requirements for the delivery of contemporary arc and modulated radiation therapy to upright patients. We explore i) patient positioning and immobilization, ii) simulation imaging, iii) treatment planning and iv) online setup and image guidance. Treatment chairs have been designed to reproducibly position seated patients for treatment and can be augmented by several existing immobilisation systems or promising emerging technologies such as soft robotics. There are few solutions for acquiring CT images for upright patients, however, cone beam computed tomography (CBCT) scans of upright patients can be produced using the imaging capabilities of standard Linacs combined with an additional patient rotation device. While these images will require corrections to make them appropriate for treatment planning, several methods indicate the viability of this approach. Treatment planning is largely unchanged apart from translating gantry rotation to patient rotation, allowing for a fixed beam with a patient rotating relative to it. Rotation can be provided by a turntable during treatment delivery. Imaging the patient with the same machinery as used in treatment could be advantageous for online plan adaption. While the current focus is using clinical linacs in existing facilities, developments in this area could also extend to lower-cost and mobile linacs and heavy ion therapy.

Clinical implementation and initial experience with a 1.5 Tesla MR-linac for MR-guided radiotherapy for gynecologic cancer: An R-IDEAL stage 1/2a first in humans/feasibility study of new technology implementation

PURPOSE

Magnetic resonance imaging-guided linear accelerator systems (MR-linacs) can facilitate the daily adaptation of radiotherapy plans. Here, we report our early clinical experience using an MR-linac for adaptive radiotherapy of gynecologic malignancies.

METHODS AND MATERIALS

Treatments were planned with an Elekta Monaco v5.4.01 and delivered by a 1.5 Tesla Elekta Unity MR-linac. The system offers a choice of daily adaptation based on either position (ATP) or shape (ATS) of the tumor and surrounding normal structures. The ATS approach has the option of manually editing the contours of tumors and surrounding normal structures before the plan is adapted. Here we documented the duration of each treatment fraction; set-up variability (assessed by isocenter shifts in each plan) between fractions; and, for quality assurance, calculated the percentage of plans meeting the γ-criterion of 3%/3-mm distance to agreement. Deformable accumulated dose calculations were used to compare accumulated versus planned dose for patient treated with exclusively ATP fractions.

RESULTS

Of the 10 patients treated with 90 fractions on the MR-linac, most received boost doses to recurrence in nodes or isolated tumors. Each treatment fraction lasted a median 32 minutes; fractions were shorter with ATP than with ATS (30 min vs 42 min, P<0.0001). The γ criterion for all fraction plans exceeded >90% (median 99.9%, range 92.4%-100%), i.e., all plans passed quality assurance testing. The average extent of isocenter shift was <0.5 cm in each axis. The accumulated dose to the gross tumor volume was within 5% of the reference plan for all ATP cases. Accumulated doses for lesions in the pelvic periphery were within <1% of the reference plan as opposed to -1.6% to -4.4% for central pelvic tumors.

CONCLUSIONS

The MR-linac is a reliable and clinically feasible tool for treating patients with gynecologic cancer.

Rapid Progress in Intelligent Radiotherapy and Future Implementation

Radiotherapy is one of the major approaches to cancer treatment. Artificial intelligence in radiotherapy (shortly, Intelligent radiotherapy) mainly involves big data, deep learning, extended reality, digital twin, radiomics, Internet plus and Internet of Things (IoT), which establish an automatic and intelligent network platform consisting of radiotherapy preparation, target volume delineation, treatment planning, radiation delivery, quality assurance (QA) and quality control (QC), prognosis judgment and post-treatment follow-up. Intelligent radiotherapy is an interdisciplinary frontier discipline in infancy. The review aims to summary the important implements of intelligent radiotherapy in various areas and put forward the future of unmanned radiotherapy center.

Proposal and Evaluation of a Physician-Free, Real-Time On-Table Adaptive Radiotherapy (PF-ROAR) Workflow for the MRIdian MR-Guided LINAC

With the implementation of MR-LINACs, real-time adaptive radiotherapy has become a possibility within the clinic. However, the process of adapting a patient’s plan is time consuming and often requires input from the entire clinical team, which translates to decreased throughput and limited patient access. In this study, the authors propose and simulate a workflow to address these inefficiencies in staffing and patient throughput. Two physicians, three radiation therapists (RTT), and a research fellow each adapted bladder and bowel contours for 20 fractions from 10 representative patient plans. Contouring ability was compared via calculation of a Dice Similarity Index (DSI). The DSI for bladder and bowel based on each potential physician–therapist pair, as well as an inter-physician comparison, exhibited good overlap amongst all comparisons (p = 0.868). Plan quality was compared through calculation of the conformity index (CI), as well as an evaluation of the plan’s dose to a ‘gold standard’ set of structures. Overall, non-physician plans passed 91.2% of the time. Of the eight non-physician plans that failed their clinical evaluation, six also failed their evaluation against the ‘gold standard’. Another two plans that passed their clinical evaluation subsequently failed in their evaluation against the ‘gold standard’. Thus, the PF-ROAR process has a success rate of 97.5%, with 78/80 plans correctly adapted to the gold standard or halted at treatment. These findings suggest that a physician-free workflow can be well tolerated provided RTTs continue to develop knowledge of MR anatomy and careful attention is given to understanding the complexity of the plan prior to treatment.

Deep learning driven predictive treatment planning for adaptive radiotherapy of lung cancer

BACKGROUND AND PURPOSE

To develop a novel deep learning algorithm of sequential analysis, Seq2Seq, for predicting weekly anatomical changes of lung tumor and esophagus during definitive radiotherapy, incorporate the potential tumor shrinkage into a predictive treatment planning paradigm, and improve the therapeutic ratio.

METHODS AND MATERIALS

Seq2Seq starts with the primary tumor and esophagus observed on the planning CT to predict their geometric evolution during radiotherapy on a weekly basis, and subsequently updates the predictions with new snapshots acquired via weekly CBCTs. Seq2Seq is equipped with convolutional long short term memory to analyze the spatial-temporal changes of longitudinal images, trained and validated using a dataset including sixty patients. Predictive plans were optimized according to each weekly prediction and made ready for weekly deployment to mitigate the clinical burden of online weekly replanning.

RESULTS

Seq2Seq tracks structural changes well: DICE between predicted and actual weekly tumor and esophagus were (0.83 ± 0.10, 0.79 ± 0.14, 0.78 ± 0.12, 0.77 ± 0.12, 0.75 ± 0.12, 0.71 ± 0.17), and (0.72 ± 0.16, 0.73 ± 0.11, 0.75 ± 0.08, 0.74 ± 0.09, 0.72 ± 0.14, 0.71 ± 0.14), respectively, while the average Hausdorff distances were within 2 mm. Evaluating dose to the actual weekly tumor and esophagus, a 4.2 Gy reduction in esophagus mean dose while maintaining 60 Gy tumor coverage was achieved with the predictive weekly plans, compared to the plan optimized using the initial tumor and esophagus alone, primarily due to noticeable tumor shrinkage during radiotherapy.

CONCLUSION

It is feasible to predict the longitudinal changes of tumor and esophagus with the Seq2Seq, which could lead to improving the efficiency and effectiveness of lung adaptive radiotherapy.

Magnetic resonance linear accelerator technology and adaptive radiation therapy: An overview for clinicians

Radiation therapy (RT) continues to play an important role in the treatment of cancer. Adaptive RT (ART) is a novel method through which RT treatments are evolving. With the ART approach, computed tomography or magnetic resonance (MR) images are obtained as part of the treatment delivery process. This enables the adaptation of the irradiated volume to account for changes in organ and/or tumor position, movement, size, or shape that may occur over the course of treatment. The advantages and challenges of ART maybe somewhat abstract to oncologists and clinicians outside of the specialty of radiation oncology. ART is positioned to affect many different types of cancer. There is a wide spectrum of hypothesized benefits, from small toxicity improvements to meaningful gains in overall survival. The use and application of this novel technology should be understood by the oncologic community at large, such that it can be appropriately contextualized within the landscape of cancer therapies. Likewise, the need to test these advances is pressing. MR-guided ART (MRgART) is an emerging, extended modality of ART that expands upon and further advances the capabilities of ART. MRgART presents unique opportunities to iteratively improve adaptive image guidance. However, although the MRgART adaptive process advances ART to previously unattained levels, it can be more expensive, time-consuming, and complex. In this review, the authors present an overview for clinicians describing the process of ART and specifically MRgART.

Improving predictive CTV segmentation on CT and CBCT for cervical cancer by diffeomorphic registration of a prior

PURPOSE

Automatic cervix-uterus segmentation of the clinical target volume (CTV) on CT and cone beam CT (CBCT) scans is challenged by the limited visibility and the non-anatomical definition of certain border regions. We study potential performance gain of convolutional neural networks by regulating the segmentation predictions as diffeomorphic deformations of a segmentation prior.

METHODS

We introduce a 3D convolutional neural network (CNN) which segments the target scan by joint voxel-wise classification and the registration of a given prior. We compare this network to two other 3D baseline models: one treating segmentation as a classification problem (segmentation-only), the other as a registration problem (deformation-only). For reference and to highlight benefits of a 3D model, these models are also benchmarked against a 2D segmentation model. Network performances are reported for CT and CBCT segmentation of the cervix-uterus CTV. We train the networks on data of 84 patients. The prior is provided by the CTV segmentation of a planning CT. Repeat CT or CBCT scans constitute the target scans to be segmented.

RESULTS

All 3D models outperformed the 2D segmentation model. For CT segmentation, combining classification and registration in the proposed joint model proved beneficial, achieving a Dice score of 0.87 and a mean squared error (MSE) of the surface distance below 1.7 mm. No such synergy was observed for CBCT segmentation, for which the joint and the deformation-only model performed similarly, achieving a Dice score of about 0.80 and a MSE surface distance of 2.5 mm. However, the segmentation-only model performed notably worse in this low contrast regime. Visual inspection revealed that this performance drop translated into geometric inconsistencies between the prior and target segmentation. Such inconsistencies where not observed for the deformation-based models.

CONCLUSION

Constraining the solution space of admissible segmentation predictions to those reachable by a diffeomorphic deformation of the prior proved beneficial as it improved geometric consistency. Especially for CBCT, with its poor soft tissue contrast, this type of regularization becomes important as shown by quantitative and qualitative evaluation. This article is protected by copyright. All rights reserved.

Utility of deformable image registration for adaptive prostate cancer treatment. Analysis and comparison of two commercially available algorithms

INTRODUCTION

This study aimed to assess and compare the capabilities of two commercially available deformable image registration algorithms implemented in Raystation 9A (A₁) and Velocity AI (A₂) for possible usage in adaptive prostate radiotherapy based on the propagation of anatomical contours from computed tomography (CT) images to cone-beam CT (CBCT) images.

MATERIAL AND METHODS

Ten patients were retrospectively selected from a group treated for localized prostate cancer. The propagation of rectum contours was analyzed in a set of CT-CBCT pairs. Two independent observers carried out qualitative analysis using the two-level descriptive scale (meet/fail). Quantitative analysis was done using landmark points distances based on implanted markers as navigation points and differently obtained contours (manually and automatically using DIR algorithms). Quantitative analysis was taken on sets preselected by qualitative analysis.

RESULTS

Qualitative analysis shows that 83.7% of the rectum contours were scored identically (meet or fail) for both algorithms, from which 53.5% and 55.8% are failed results for A₁ and A₂, respectively. For the rectum size (RWD parameter), differences between referenced and deformation-based values were 5.5 and 5.8mm, and for the rectum wall, the prostate marker distance (WMD parameter) was 4.5 and 5.5mm for A₁ and A₂, respectively. The differences between the WMD parameters were statistically significant (p=0.007).

CONCLUSIONS

In both tested algorithms, neither effectiveness nor measured uncertainties in the propagation of rectum contour process in prostate patient cases were satisfactory. Careful selection of input images followed by case/set-based verification of every deformable registration is a substantial step to avoid inappropriate results.

Synthetic CT-aided multiorgan segmentation for CBCT-guided adaptive pancreatic radiotherapy

PURPOSE

The delineation of organs at risk (OARs) is fundamental to cone-beam CT (CBCT)-based adaptive radiotherapy treatment planning, but is time consuming, labor intensive, and subject to interoperator variability. We investigated a deep learning-based rapid multiorgan delineation method for use in CBCT-guided adaptive pancreatic radiotherapy.

METHODS

To improve the accuracy of OAR delineation, two innovative solutions have been proposed in this study. First, instead of directly segmenting organs on CBCT images, a pretrained cycle-consistent generative adversarial network (cycleGAN) was applied to generating synthetic CT images given CBCT images. Second, an advanced deep learning model called mask-scoring regional convolutional neural network (MS R-CNN) was applied on those synthetic CT to detect the positions and shapes of multiple organs simultaneously for final segmentation. The OAR contours delineated by the proposed method were validated and compared with expert-drawn contours for geometric agreement using the Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95), mean surface distance (MSD), and residual mean square distance (RMS).

RESULTS

Across eight abdominal OARs including duodenum, large bowel, small bowel, left and right kidneys, liver, spinal cord, and stomach, the geometric comparisons between automated and expert contours are as follows: 0.92 (0.89-0.97) mean DSC, 2.90 mm (1.63-4.19 mm) mean HD95, 0.89 mm (0.61-1.36 mm) mean MSD, and 1.43 mm (0.90-2.10 mm) mean RMS. Compared to the competing methods, our proposed method had significant improvements (p < 0.05) in all the metrics for all the eight organs. Once the model was trained, the contours of eight OARs can be obtained on the order of seconds.

CONCLUSIONS

We demonstrated the feasibility of a synthetic CT-aided deep learning framework for automated delineation of multiple OARs on CBCT. The proposed method could be implemented in the setting of pancreatic adaptive radiotherapy to rapidly contour OARs with high accuracy.

Artificial intelligence in radiation oncology: A review of its current status and potential application for the radiotherapy workforce

OBJECTIVE

Radiation oncology is a continually evolving speciality. With the development of new imaging modalities and advanced imaging processing techniques, there is an increasing amount of data available to practitioners. In this narrative review, Artificial Intelligence (AI) is used as a reference to machine learning, and its potential, along with current problems in the field of radiation oncology, are considered from a technical position.

KEY FINDINGS

AI has the potential to harness the availability of data for improving patient outcomes, reducing toxicity, and easing clinical burdens. However, problems including the requirement of complexity of data, undefined core outcomes and limited generalisability are apparent.

CONCLUSION

This original review highlights considerations for the radiotherapy workforce, particularly therapeutic radiographers, as there will be an increasing requirement for their familiarity with AI due to their unique position as the interface between imaging technology and patients.

IMPLICATIONS FOR PRACTICE

Collaboration between AI experts and the radiotherapy workforce are required to overcome current issues before clinical adoption. The development of educational resources and standardised reporting of AI studies may help facilitate this.

The predictive value of segmentation metrics on dosimetry in organs at risk of the brain

BACKGROUND

Fully automatic medical image segmentation has been a long pursuit in radiotherapy (RT). Recent developments involving deep learning show promising results yielding consistent and time efficient contours. In order to train and validate these systems, several geometric based metrics, such as Dice Similarity Coefficient (DSC), Hausdorff, and other related metrics are currently the standard in automated medical image segmentation challenges. However, the relevance of these metrics in RT is questionable. The quality of automated segmentation results needs to reflect clinical relevant treatment outcomes, such as dosimetry and related tumor control and toxicity. In this study, we present results investigating the correlation between popular geometric segmentation metrics and dose parameters for Organs-At-Risk (OAR) in brain tumor patients, and investigate properties that might be predictive for dose changes in brain radiotherapy.

METHODS

A retrospective database of glioblastoma multiforme patients was stratified for planning difficulty, from which 12 cases were selected and reference sets of OARs and radiation targets were defined. In order to assess the relation between segmentation quality -as measured by standard segmentation assessment metrics- and quality of RT plans, clinically realistic, yet alternative contours for each OAR of the selected cases were obtained through three methods: (i) Manual contours by two additional human raters. (ii) Realistic manual manipulations of reference contours. (iii) Through deep learning based segmentation results. On the reference structure set a reference plan was generated that was re-optimized for each corresponding alternative contour set. The correlation between segmentation metrics, and dosimetric changes was obtained and analyzed for each OAR, by means of the mean dose and maximum dose to 1% of the volume (Dmax 1%). Furthermore, we conducted specific experiments to investigate the dosimetric effect of alternative OAR contours with respect to the proximity to the target, size, particular shape and relative location to the target.

RESULTS

We found a low correlation between the DSC, reflecting the alternative OAR contours, and dosimetric changes. The Pearson correlation coefficient between the mean OAR dose effect and the Dice was -0.11. For Dmax 1%, we found a correlation of -0.13. Similar low correlations were found for 22 other segmentation metrics. The organ based analysis showed that there is a better correlation for the larger OARs (i.e. brainstem and eyes) as for the smaller OARs (i.e. optic nerves and chiasm). Furthermore, we found that proximity to the target does not make contour variations more susceptible to the dose effect. However, the direction of the contour variation with respect to the relative location of the target seems to have a strong correlation with the dose effect.

CONCLUSIONS

This study shows a low correlation between segmentation metrics and dosimetric changes for OARs in brain tumor patients. Results suggest that the current metrics for image segmentation in RT, as well as deep learning systems employing such metrics, need to be revisited towards clinically oriented metrics that better reflect how segmentation quality affects dose distribution and related tumor control and toxicity.

Effect of treatment planning system parameters on beam modulation complexity for treatment plans with single-layer multi-leaf collimator and dual-layer stacked multi-leaf collimator

OBJECTIVE

High levels of beam modulation complexity (MC) and monitor units (MU) can compromise the plan deliverability of intensity-modulated radiotherapy treatments. Our study evaluates the effect of three treatment planning system (TPS) parameters on MC and MU using different multi-leaf collimator (MLC) architectures.

METHODS

192 volumetric modulated arc therapy plans were calculated using one virtual prostate phantom considering three main settings: (1) three TPS-parameters (Convergence; Aperture Shape Controller, ASC; and Dose Calculation Resolution, DCR) selected from Eclipse v15.6, (2) four levels of dose-sparing priority for organs at risk (OAR), and (3) two treatment units with same nominal conformity resolution and different MLC architectures (Halcyon-v2 dual-layer MLC, DL-MLC & TrueBeam single-layer MLC, SL-MLC). We use seven complexity metrics to evaluate the MC, including two new metrics for DL-MLC, assessed by their correlation with γ passing rate (GPR) analysis.

RESULTS

DL-MLC plans demonstrated lower dose-sparing values than SL-MLC plans (p<0.05). TPS-parameters did not change significantly the complexity metrics for either MLC architectures. However, for SL-MLC, significant variations of MU, target volume dose-homogeneity, and dose spillage were associated with ASC and DCR (p<0.05). MU were found to be correlated (highly or moderately) with all complexity metrics (p<0.05) for both MLC plans. Additionally, our new complexity metrics presented a moderate correlation with GPR (r<0.65). An important correlation was demonstrated between MC (plan deliverability) and dose-sparing priority level for DL-MLC.

CONCLUSIONS

TPS-parameters selected do not change MC for DL-MLC architecture, but they might have a potential use to control the MU, PTV homogeneity or dose spillage for SL-MLC. Our new DL-MLC complexity metrics presented important information to be considered in future pre-treatment quality assurance programs. Finally, the prominent dependence between plan deliverability and priority applied to OAR dose sparing for DL-MLC needs to be analyzed and considered as an additional predictor of GPRs in further studies.

ADVANCES IN KNOWLEDGE

Dose-sparing priority might influence in modulation complexity of DL-MLC.

Clinical implementation of artificial intelligence-driven cone-beam computed tomography-guided online adaptive radiotherapy in the pelvic region

Background and purpose

Studies have demonstrated the potential of online adaptive radiotherapy (oART). However, routine use has been limited due to resource demanding solutions. This study reports on experiences with oART in the pelvic region using a novel cone-beam computed tomography (CBCT)-based, artificial intelligence (AI)-driven solution.

Material and methods

Automated pre-treatment planning for thirty-nine pelvic cases (bladder, rectum, anal, and prostate), and one hundred oART simulations were conducted in a pre-clinical release of Ethos (Varian Medical Systems, Palo Alto, CA). Plan quality, AI-segmentation accuracy, oART feasibility and an integrated calculation-based quality assurance solution were evaluated. Experiences from the first five clinical oART patients (three bladder, one rectum and one sarcoma) are reported.

Results

Auto-generated pre-treatment plans demonstrated similar planning target volume (PTV) coverage and organs at risk doses, compared to institution reference. More than 75% of AI-segmentations during simulated oART required none or minor editing and the adapted plan was superior in 88% of cases. Limitations in AI-segmentation correlated to cases where AI model training was lacking. The five first treated patients complied well with the median adaptive procedure duration of 17.6 min (from CBCT acceptance to treatment delivery start). The treated bladder patients demonstrated a 42% median primary PTV reduction, indicating a 24%-30% reduction in V_45Gy to the bowel cavity, compared to non-ART.

Conclusions

A novel commercial oART solution was demonstrated feasible for various pelvic sites. Clinically acceptable AI-segmentation and auto-planning enabled adaptation within reasonable timeslots. Possibilities for reduced PTVs observed for bladder cancer indicated potential for toxicity reductions.

Deforming to Best Practice: Key considerations for deformable image registration in radiotherapy

Image registration is a process that underlies many new techniques in radiation oncology - from multimodal imaging and contour propagation in treatment planning to dose accumulation throughout treatment. Deformable image registration (DIR) is a subset of image registration subject to high levels of complexity in process and validation. A need for local guidance to assist in high-quality utilisation and best practice was identified within the Australian community, leading to collaborative activity and workshops. This report communicates the current limitations and best practice advice from early adopters to help guide those implementing DIR in the clinic at this early stage. They are based on the state of image registration applications in radiotherapy in Australia and New Zealand (ANZ), and consensus discussions made at the 'Deforming to Best Practice' workshops in 2018. The current status of clinical application use cases is presented, including multimodal imaging, automatic segmentation, adaptive radiotherapy, retreatment, dose accumulation and response assessment, along with uptake, accuracy and limitations. Key areas of concern and preliminary suggestions for commissioning, quality assurance, education and training, and the use of automation are also reported. Many questions remain, and the radiotherapy community will benefit from continued research in this area. However, DIR is available to clinics and this report is intended to aid departments using or about to use DIR tools now.

Evaluation of a cycle-generative adversarial network-based cone-beam CT to synthetic CT conversion algorithm for adaptive radiation therapy

PURPOSE

Image-guided radiation therapy could benefit from implementing adaptive radiation therapy (ART) techniques. A cycle-generative adversarial network (cycle-GAN)-based cone-beam computed tomography (CBCT)-to-synthetic CT (sCT) conversion algorithm was evaluated regarding image quality, image segmentation and dosimetric accuracy for head and neck (H&N), thoracic and pelvic body regions.

METHODS

Using a cycle-GAN, three body site-specific models were priorly trained with independent paired CT and CBCT datasets of a kV imaging system (XVI, Elekta). sCT were generated based on first-fraction CBCT for 15 patients of each body region. Mean errors (ME) and mean absolute errors (MAE) were analyzed for the sCT. On the sCT, manually delineated structures were compared to deformed structures from the planning CT (pCT) and evaluated with standard segmentation metrics. Treatment plans were recalculated on sCT. A comparison of clinically relevant dose-volume parameters (D₉₈, D₅₀ and D₂ of the target volume) and 3D-gamma (3%/3mm) analysis were performed.

RESULTS

The mean ME and MAE were 1.4, 29.6, 5.4 Hounsfield units (HU) and 77.2, 94.2, 41.8 HU for H&N, thoracic and pelvic region, respectively. Dice similarity coefficients varied between 66.7 ± 8.3% (seminal vesicles) and 94.9 ± 2.0% (lungs). Maximum mean surface distances were 6.3 mm (heart), followed by 3.5 mm (brainstem). The mean dosimetric differences of the target volumes did not exceed 1.7%. Mean 3D gamma pass rates greater than 97.8% were achieved in all cases.

CONCLUSIONS

The presented method generates sCT images with a quality close to pCT and yielded clinically acceptable dosimetric deviations. Thus, an important prerequisite towards clinical implementation of CBCT-based ART is fulfilled.

An international survey on the clinical use of rigid and deformable image registration in radiotherapy

OBJECTIVES

Rigid image registration (RIR) and deformable image registration (DIR) are widely used in radiotherapy. This project aims to capture current international approaches to image registration.

METHODS

A survey was designed to identify variations in use, resources, implementation, and decision-making criteria for clinical image registration. This was distributed to radiotherapy centers internationally in 2018.

RESULTS

There were 57 responses internationally, from the Americas (46%), Australia/New Zealand (32%), Europe (12%), and Asia (10%). Rigid image registration and DIR were used clinically for computed tomography (CT)-CT registration (96% and 51%, respectively), followed by CT-PET (81% and 47%), CT-CBCT (84% and 19%), CT-MR (93% and 19%), MR-MR (49% and 5%), and CT-US (9% and 0%). Respondent centers performed DIR using dedicated software (75%) and treatment planning systems (29%), with 84% having some form of DIR software. Centers have clinically implemented DIR for atlas-based segmentation (47%), multi-modality treatment planning (65%), and dose deformation (63%). The clinical use of DIR for multi-modality treatment planning and accounting for retreatments was considered to have the highest benefit-to-risk ratio (69% and 67%, respectively).

CONCLUSIONS

This survey data provides useful insights on where, when, and how image registration has been implemented in radiotherapy centers around the world. DIR is mainly in clinical use for CT-CT (51%) and CT-PET (47%) for the head and neck (43-57% over all use cases) region. The highest benefit-risk ratio for clinical use of DIR was for multi-modality treatment planning and accounting for retreatments, which also had higher clinical use than for adaptive radiotherapy and atlas-based segmentation.

Relationship between the Young's Modulus and the Crystallinity of Cross-Linked Poly(ε-caprolactone) as an Immobilization Membrane for Cancer Radiotherapy

Cancer is a leading cause of death in the world. In cancer radiotherapy, immobilization membranes composed of cross-linked poly(ε-caprolactone) (PCL) are utilized for patient positioning. A higher-dimensional stability of the membrane is urgently required to facilitate more accurate radiation dose delivery. It is extremely important to establish the relationship between the degree of crystallinity and the Young's modulus (E) because it determines the mechanical properties and can be modulated by crystallinity. When two components of the membrane with different strains are in contact, a gradient region adjacent to the interface is formed and confirmed by attenuated total reflection infrared microscopy. Atomic force microscopy (AFM) and Raman spectroscopy are used to scan the same area in the gradient region (14 µm × 14 µm) to characterize E and crystallinity (X Raman), respectively. This co-localized method ensures the accuracy of the relationship. Finally, 1764 AFM measurement data are processed and 49 pairs of E-X Raman data are obtained. The regression curve shows that E monotonically increases with X Raman. The nonlinearity of the curve may be attributed to the α-relaxation and cross-linking of PCL chains. The chemical structure of this material significantly impacts the mechanical properties, thus requiring future investigation.