IMRT: a review and preview

Feasibility of proton dosimetry overriding planning CT with daily CBCT elaborated through generative artificial intelligence tools

Radiotherapy commonly utilizes cone beam computed tomography (CBCT) for patient positioning and treatment monitoring. CBCT is deemed to be secure for patients, making it suitable for the delivery of fractional doses. However, limitations such as a narrow field of view, beam hardening, scattered radiation artifacts, and variability in pixel intensity hinder the direct use of raw CBCT for dose recalculation during treatment. To address this issue, reliable correction techniques are necessary to remove artifacts and remap pixel intensity into Hounsfield Units (HU) values. This study proposes a deep-learning framework for calibrating CBCT images acquired with narrow field of view (FOV) systems and demonstrates its potential use in proton treatment planning updates. Cycle-consistent generative adversarial networks (cGAN) processes raw CBCT to reduce scatter and remap HU. Monte Carlo simulation is used to generate CBCT scans, enabling the possibility to focus solely on the algorithm's ability to reduce artifacts and cupping effects without considering intra-patient longitudinal variability and producing a fair comparison between planning CT (pCT) and calibrated CBCT dosimetry. To showcase the viability of the approach using real-world data, experiments were also conducted using real CBCT. Tests were performed on a publicly available dataset of 40 patients who received ablative radiation therapy for pancreatic cancer. The simulated CBCT calibration led to a difference in proton dosimetry of less than 2%, compared to the planning CT. The potential toxicity effect on the organs at risk decreased from about 50% (uncalibrated) up the 2% (calibrated). The gamma pass rate at 3%/2 mm produced an improvement of about 37% in replicating the prescribed dose before and after calibration (53.78% vs 90.26%). Real data also confirmed this with slightly inferior performances for the same criteria (65.36% vs 87.20%). These results may confirm that generative artificial intelligence brings the use of narrow FOV CBCT scans incrementally closer to clinical translation in proton therapy planning updates.

Sintilimab Plus Lenvatinib with or Without Radiotherapy for Advanced Hepatocellular Carcinoma with Pulmonary Metastasis

Background

Hepatocellular carcinoma (HCC) with pulmonary metastasis (PM) significantly worsens prognosis, and current treatment options remain limited.

Methods

A retrospective study was conducted on HCC patients treated with sintilimab combined with lenvatinib at three hospitals in China between 2020 and 2021. Progression-free survival (PFS), overall survival (OS), and tumor response based on RECIST 1.1 were compared. Treatment safety was assessed by analyzing treatment-related adverse events (TRAEs).

Results

Among 144 patients, 105 received sintilimab combined with lenvatinib (S+L), while 39 were treated with radiotherapy combined with sintilimab and lenvatinib (RT+S+L). The RT+S+L group showed superior outcomes in OS (25 months vs 16 months, HR = 0.58, 95% CI = 0.35-0.94, P=0.025) and PFS (14 months vs 6 months, HR = 0.61, 95% CI = 0.40-0.94, P=0.022) compared to the S+L group. Similarly, the RT+S+L group exhibited significantly higher objective response rate (ORR) and disease control rate (DCR) compared to the S+L group (61.5% vs 27.6%, P<0.001; 94.9% vs 76.2%, P=0.011). The most common grade 3/4 TRAEs in the RT+S+L group were hypertension, decreased platelet count, elevated total bilirubin, and proteinuria.

Conclusion

Radiotherapy combined with sintilimab and lenvatinib is an effective strategy for treating HCC with pulmonary metastasis. These findings highlight the critical role of radiotherapy in the management of HCC.

Quantum Computing in Medicine

Quantum computing (QC) represents a paradigm shift in computational power, offering unique capabilities for addressing complex problems that are infeasible for classical computers. This review paper provides a detailed account of the current state of QC, with a particular focus on its applications within medicine. It explores fundamental concepts such as qubits, superposition, and entanglement, as well as the evolution of QC from theoretical foundations to practical advancements. The paper covers significant milestones where QC has intersected with medical research, including breakthroughs in drug discovery, molecular modeling, genomics, and medical diagnostics. Additionally, key quantum techniques such as quantum algorithms, quantum machine learning (QML), and quantum-enhanced imaging are explained, highlighting their relevance in healthcare. The paper also addresses challenges in the field, including hardware limitations, scalability, and integration within clinical environments. Looking forward, the paper discusses the potential for quantum-classical hybrid systems and emerging innovations in quantum hardware, suggesting how these advancements may accelerate the adoption of QC in medical research and clinical practice. By synthesizing reliable knowledge and presenting it through a comprehensive lens, this paper serves as a valuable reference for researchers interested in the transformative potential of QC in medicine.

Generating 3D images of VMAT plans for predictive models and activation maps associated with plan deliverability

BACKGROUND

Intensity modulation with dynamic multi-leaf collimator (MLC) and monitor unit (MU) changes across control points (CPs) characterizes volumetric modulated arc therapy (VMAT). The increased uncertainty in plan deliverability required patient-specific quality assurance (PSQA), which remained inefficient upon Quality Assurance (QA) failure. To prevent waste before QA, plan complexity metrics (PCMs) and machine learning models with the metrics were generated, which were lack of providing CP-specific information upon QA failures.

PURPOSE

By generating 3D images from digital imaging and comminications in medicine in radiation therapy (DICOM RT) plan, we proposed a predictive model that can estimate the deliverability of VMAT plans and visualize CP-specific regions associated with plan deliverability.

METHODS

The patient cohort consisted of 259 and 190 cases for left- and right-breast VMAT treatments, which were split into 235 and 166 cases for training and 24 cases from each treatment for testing the networks. Three-channel 3D images generated from DICOM RT plans were fed into a DenseNet-based deep learning network. To reflect VMAT plan complexity as an image, the first two channels described MLC and MU variations between two consecutive CPs, while the last channel assigned the beam field size. The network output was defined as binary classified PSQA results, indicating deliverability. The predictive performance was assessed by accuracy, sensitivity, specificity, F1-score, and area under the curve (AUC). The gradient-weighted class activation map (Grad-CAM) highlighted the regions of CPs in VMAT plans associated with deliverability, compared against PCMs by Spearman correlation.

RESULTS

The DenseNet-based predictive model yielded AUCs of 92.2% and 93.8%, F1-scores of 97.0% and 93.8% and accuracies of 95.8% and 91.7% for the left- and right-breast VMAT cases. Additionally, the specificity of 87.5% for both cases indicated that the predictive model accurately detected QA failing cases. The activation maps significantly differentiated QA failing-labeled from passing-labeled classes for the non-deliverable cases. The PCM with the highest correlation to the Grad-CAM varied from patient cases, implying that plan deliverability would be considered patient-specific.

CONCLUSION

This work demonstrated that the deep learning-based network based on visualization of dynamic VMAT plan information successfully predicted plan deliverability, which also provided control-point specific planning parameter information associated with plan deliverability in a patient-specific manner.

Optimizing spot intensity with lower bound constraints for IMPT: Exposing shortcomings and introducing an enhanced strategy

BACKGROUND

Intensity Modulated Proton Therapy (IMPT) is a sophisticated radiation treatment allowing for precise dose distributions. However, conventional spot selection strategies in IMPT face challenges, particularly with minimum monitor unit (MU) constraints, affecting planning quality and efficiency.

PURPOSE

This study introduces an innovative Two-Stage Mixed Integer Linear Programming (MILP) method to optimize spot intensity in IMPT with Lower Bound (LB) constraints. This method seeks to improve treatment planning efficiency and precision, overcoming limitations of existing strategies.

METHODS

Our approach evaluates prevalent IMPT spot selection strategies, identifying their limitations, especially concerning MU constraints. We integrated LB constraints into a MILP framework, using a novel three-phase strategy for spot pool selection, to enhance performance over traditional heuristic methods and L1 + L∞ strategies. The method's efficacy was tested in eight study cases, using Dose-Volume Histograms (DVHs), spot selection efficiency, and computation time analysis for benchmarking against established methods.

RESULTS

The proposed method showed superior performance in DVH quality, adhering to LB constraints while maintaining high-quality treatment plans. It outperformed existing techniques in spot selection, reducing unnecessary spots and balancing precision with efficiency. Cases studies confirmed the method's effectiveness in producing clinically feasible plans with enhanced dose distributions and reduced hotspots, especially in cases with elevated LB constraints.

CONCLUSIONS

Our Two-Stage MILP strategy signifies a significant advancement in IMPT treatment planning. By incorporating LB constraints directly into the optimization process, it achieves superior plan quality and deliverability compared to current methods. This approach is particularly advantageous in clinical settings requiring minimum spot number and high MU LB constraints, offering the potential for improved patient outcomes through more precise and efficient radiation therapy plans.

Use of a pressure sensor array for multifunctional patient monitoring in radiotherapy: A feasibility study

BACKGROUND

Modern radiotherapeutic techniques, such as intensity-modulated radiation therapy or stereotactic body radiotherapy, require high-dose delivery precision. However, the precise localization of tumors during patient respiration remains a challenge. Therefore, it is essential to investigate effective methods for monitoring respiration to minimize potential complications. Despite several systems currently in clinical use, there are drawbacks, including the complexity of the setup, the discomfort to the patient, and the high cost.

PURPOSE

This study investigated the feasibility of using a novel pressure sensor array (PSA) as a tool to monitor respiration during radiotherapy treatments. The PSA was positioned between the treatment couch and the back of the patient lying on it and was intended to overcome some limitations of current methods. The main objectives included assessing the PSA's capability in monitoring respiratory behavior and to investigate prospective applications that extend beyond respiratory monitoring.

METHODS

A PSA with 31 pressure-sensing elements was used in 12 volunteers. The participants were instructed to breathe naturally while lying on a couch without any audio or visual guidance. The performance of the PSA was compared to that of a camera-based respiratory monitoring system (RPM, Varian, USA), which served as a reference. Several metrics, including pressure distribution, weight sensitivity, and correlations between PSA and RPM signals, were analyzed. The PSA's capacity to provide information on potential applications related to patient stability was also investigated.

RESULTS

The linear relationship between the weight applied to the PSA and its output was demonstrated in this study, confirming its sensitivity to pressure changes. A comparison of PSA and RPM curves revealed a high correlation coefficient of 0.9391 on average, indicating consistent respiratory cycles. The PSA also effectively measured the weight distribution at the volunteer's back in real-time, which allows for monitoring the patient's movements during the radiotherapy.

CONCLUSION

PSA is a promising candidate for effective respiratory monitoring during radiotherapy treatments. Its performance is comparable to the established RPM system, and its additional capabilities suggest its multifaceted utility. This paper shows the potential use of PSA for patient monitoring in radiotherapy and suggests possibilities for further research, including performance comparisons with other existing systems and real-patient applications with respiratory training.

Use of Zernike moments to characterize dose conformity for radiotherapy treatment plans

Dose conformity is an essential parameter used in radiotherapy and radiosurgery that measures the correspondence of the dose distribution derived from a Treatment Planning System (TPS) with the actual volume to be treated, the Planning Treatment Volume (PTV). The present work uses a method based on the expansion of dose distributions and PTVs by three-dimensional Zernike polynomials and further comparison of their moments to define a general criterion of dose conformity. To carry on this study, data coming from 20 patients comprising 80 datasets exported from the TPS, which included imaging data (PTVs) and dose distributions corresponding to different treatment modalities: three-dimensional conformal radiotherapy, intensity-modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT), were used. The expansions in Zernike polynomials were obtained up to order 6 and reconstructed dose distributions and PTVs were obtained and compared, and several definitions for a general dose conformity index were proposed. Results indicate agreement between the proposed dose conformity index and the Conformation Number CN. The proposed method allows for a systematic approach to the analysis of dose distributions with further extensions in AI applications.

Novel multileaf collimator designs with tongues and grooves in leaf end and Monte Carlo simulations

In this study, we proposed 2 new multileaf collimator leaf designs to eliminate leaf gaps for closed leaf pairs so that radiation leakage can be avoided. In the new designs, multi tongues and grooves were added to the conventional multileaf collimators leaf ends. Thus, when a pair of leaves closed, tongues of a leaf can enter grooves of its opposing leaf. Consequently, there would be no radiation leakage through closed leaves. One design was named finger-shaped MLC, and another design with doubled leaf end thickness was named hand-shaped MLC. Monte Carlo simulations were performed to simulate dosimetric characteristics of the new MLC designs and comparison to conventional MLCs was performed. The simulations show that for the closed field, the new designs reduce leakage dramatically. And for the open field, the finger-shaped MLC has a larger penumbra width than conventional MLC, while the penumbra for the hand-shaped MLC is comparable to that of conventional MLC. With the application of new MLC designs, it is expected to eliminate leaf gaps for MLC usage and protect normal tissues better.

Brain diffusion MRI biomarkers after oncology treatments

In addition to providing a measurement of the tumor's size and dimensions, magnetic resonance imaging (MRI) provides excellent noninvasive radiographic detection of tumor location. The MRI technique is an important modality that has been shown to be useful in the prognosis, diagnosis, treatment planning, and evaluation of response and recurrence in solid cancers. Diffusion-weighted imaging (DWI) is an imaging technique that quantifies water mobility. This imaging approach is good for identifying sub-voxel microstructure of tissues, correlates with tumor cellularity, and has been proven to be valuable in the early assessment of cytotoxic treatment for a variety of malignancies. Diffusion tensor imaging (DTI) is an MRI method that assesses the preferred amount of water transport inside tissues. This enables precise measurements of water diffusion, which changes according to the direction of white matter fibers, their density, and myelination. This measurement corresponds to some related variables: fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD), and others. DTI biomarkers can detect subtle changes in white matter microstructure and integrity following radiation therapy (RT) or chemoradiotherapy, which may have implications for cognitive function and quality of life. In our study, these indices were evaluated after brain chemoradiotherapy.

Deep learning based uncertainty prediction of deformable image registration for contour propagation and dose accumulation in online adaptive radiotherapy

Objective.Online adaptive radiotherapy aims to fully leverage the advantages of highly conformal therapy by reducing anatomical and set-up uncertainty, thereby alleviating the need for robust treatments. This requires extensive automation, among which is the use of deformable image registration (DIR) for contour propagation and dose accumulation. However, inconsistencies in DIR solutions between different algorithms have caused distrust, hampering its direct clinical use. This work aims to enable the clinical use of DIR by developing deep learning methods to predict DIR uncertainty and propagating it into clinically usable metrics.Approach.Supervised and unsupervised neural networks were trained to predict the Gaussian uncertainty of a given deformable vector field (DVF). Since both methods rely on different assumptions, their predictions differ and were further merged into a combined model. The resulting normally distributed DVFs can be directly sampled to propagate the uncertainty into contour and accumulated dose uncertainty.Main results.The unsupervised and combined models can accurately predict the uncertainty in the manually annotated landmarks on the DIRLAB dataset. Furthermore, for 5 patients with lung cancer, the propagation of the predicted DVF uncertainty into contour uncertainty yielded for both methods anexpected calibration errorof less than 3%. Additionally, theprobabilisticly accumulated dose volume histograms(DVH) encompass well the accumulated proton therapy doses using 5 different DIR algorithms. It was additionally shown that the unsupervised model can be used for different DIR algorithms without the need for retraining.Significance.Our work presents first-of-a-kind deep learning methods to predict the uncertainty of the DIR process. The methods are fast, yield high-quality uncertainty estimates and are useable for different algorithms and applications. This allows clinics to use DIR uncertainty in their workflows without the need to change their DIR implementation.

Quality assurance for mixed electron-photon beam radiation therapy using treatment log files and MapCHECK

BACKGROUND

Mixed photon-electron beam radiotherapy (MBRT) is a technique that combines the use of both photons and electrons in one single treatment plan to exploit their advantageous and complimentary characteristics. Compared to other photon treatment modalities, it has been shown that the MBRT technique contributes to better target coverage and organ-at-risk (OAR) sparing. However, the use of combined photons and electrons in one delivery makes the technique more complex and a well-established quality assurance (QA) protocol for MBRT is essential.

PURPOSE

To investigate the feasibility of using MapCHECK and log file-dose reconstruction for MBRT plan verification and to recommend a patient-specific quality assurance (PSQA) protocol for MBRT.

METHODS

MBRT plans were robustly optimized for five soft-tissue sarcoma (STS) patients. Each plan comprised step-and-shoot deliveries of a six MV photon beam and a combination of five electron beam energies at an SAD of 100 cm. The plans were delivered to the MapCHECK device with collapsed gantry angle and the 2D dose distributions at the detector depth were measured. To simulate the expected dose distribution delivered to the MapCHECK, a MapCHECK computational phantom was modeled in EGSnrc based on vendor-supplied blueprint information. The dose to the detectors in the model was scored using the DOSXYZnrc user code. The agreement between the measured and the simulated dose distribution was evaluated using 2D gamma analysis with a gamma criterion of 3%/2 mm and a low dose threshold of 10%. One of the plans was selected and delivered with a rotating gantry angle for trajectory log file collection. To evaluate the potential interlinac and intralinac differences, the plan was delivered repeatedly on three linacs. From the collected log files, delivery parameters were retrieved to recalculate the 3D dose distributions in the patient's anatomy with DOSXYZnrc. The recalculated mean dose to the clinical target volume (CTV) and OARs from all deliveries were computed and compared with the planned dose in terms of percentage difference. To validate the accuracy of log file-based QA, the log file-recalculated dose was also compared with film measurement.

RESULTS

The agreement of the total dose distribution between the MapCHECK measurement and simulation showed gamma passing rates of above 97% for all five MBRT plans. In the log file-dose recalculation, the difference between the recalculated and the planned dose to the CTV and OARs was below 1% for all deliveries. No significant inter- or intralinac differences were observed. The log file-dose had a gamma passing rate of 98.6% compared to film measurement.

CONCLUSION

Both the MapCHECK measurements and log file-dose recalculations showed excellent agreement with the expected dose distribution. This study demonstrates the potential of using MapCHECK and log files as MBRT QA tools.

Lattice position optimization for LATTICE therapy

BACKGROUND

LATTICE radiation therapy delivers 3D heterogenous dose of high peak-to-valley dose ratio (PVDR) to the tumor target, with peak dose at lattice vertices inside the target and valley dose for the rest of the target. Although the lattice vertex positions can impact PVDR inside the target and sparing of organs-at-risk (OAR), they are fixed as constants and not optimized during treatment planning in current clinical practice.

PURPOSE

This work proposes a new LATTICE plan optimization method that can optimize lattice vertex positions during LATTICE treatment planning, which is the first lattice position optimization study to the best of our knowledge.

METHODS

The new LATTICE treatment planning method optimizes lattice vertex positions as well as other plan variables (e.g., photon fluences or proton spot weights), with optimization objectives for target PVDR and OAR sparing. To satisfy mathematical differentiability, the lattice vertices are approximated in sigmoid functions. For geometric feasibility, proper geometry constraints are enforced onto lattice vertex positions. The lattice position optimization problem is solved by iterative convex relaxation (ICR) method and alternating direction method of multipliers (ADMM), and lattice vertex positions and photon/proton plan variables are jointly updated via the Quasi-Newton method.

RESULTS

Both photon and proton LATTICE RT were considered, and the optimal lattice vertex positions in terms of plan objectives were found by solving all possible combinations on given discrete positions via exhaustive searching based on standard IMRT/IMPT, which served as the ground truth for validating the new LATTICE method. The results show that the new method indeed provided the optimal lattice vertex positions with the smallest optimization objective, the largest target PVDR, and the best OAR sparing.

CONCLUSIONS

A new LATTICE treatment planning method is proposed and validated that can optimize lattice vertex positions as well as other photon or proton plan variables for improving target PVDR and OAR sparing.

Implementation and evaluation of an iterative-based algorithm for automatic beam angle optimization in breast cancer treatment planning

INTRODUCTION

A beam angle optimization (BAO) algorithm was developed to evaluate its clinical feasibility and investigate the impact of varying BAO constraints on breast cancer treatment plans.

MATERIALS AND METHODS

A two-part study was designed. In part 1, we retrospectively selected 20 patients treated with radiotherapy after breast-conserving surgery. For each patient, BAO plans were designed using beam angles optimized by the BAO algorithm and the same optimization constraints as manual plans. Dosimetric indices were compared between BAO and manual plans. In part 2, fifteen patients with left breast cancer were included. For each patient, three distinct cardiac constraints (mean heart dose < 5 Gy, 3 Gy or 1 Gy) were established during the BAO process to obtain three optimized beam sets which were marked as BAO_H1, BAO_H3, BAO_H5, respectively. These sets of beams were then utilized under identical IMRT constraints for planning. Comparative analysis was conducted among the three groups of plans.

RESULTS

For part 1, no significant differences were observed between BAO plans and manual plans in all dosimetric indices, except for ipsilateral lung V5, where BAO plans performed slightly better than manual plans (35.5% ± 5.6% vs 36.9% ± 4.3%, p = 0.034). For part 2, Stricter BAO heart constraints resulted in more perpendicular beams. However, there was no significant difference between BAO_H1, BAO_H3 and BAO_H5 with the same IMRT constraint in the heart dose. Meanwhile, the left lung dose was increased while the right breast and lung doses were decreased with stricter heart constraints in BAO. When mean heart dose < 5 Gy in IMRT constraint, the mean dose to the right lung was decreased from 0.46 Gy for BAO_H5 to 0.33 Gy for BAO_H1 (p = 0.027).

CONCLUSIONS

The BAO algorithm can achieve quality plans comparable to manual plans. IMRT constraints dominate the final plan dose, while varying BAO constraints alter the trade-off among structures, providing an additional degree of freedom in planning design.

Artificial general intelligence for radiation oncology

The emergence of artificial general intelligence (AGI) is transforming radiation oncology. As prominent vanguards of AGI, large language models (LLMs) such as GPT-4 and PaLM 2 can process extensive texts and large vision models (LVMs) such as the Segment Anything Model (SAM) can process extensive imaging data to enhance the efficiency and precision of radiation therapy. This paper explores full-spectrum applications of AGI across radiation oncology including initial consultation, simulation, treatment planning, treatment delivery, treatment verification, and patient follow-up. The fusion of vision data with LLMs also creates powerful multimodal models that elucidate nuanced clinical patterns. Together, AGI promises to catalyze a shift towards data-driven, personalized radiation therapy. However, these models should complement human expertise and care. This paper provides an overview of how AGI can transform radiation oncology to elevate the standard of patient care in radiation oncology, with the key insight being AGI's ability to exploit multimodal clinical data at scale.

Technical note: A collision prediction tool using Blender

Non-coplanar radiotherapy treatment techniques on C-arm linear accelerators have the potential to reduce dose to organs-at-risk in comparison with coplanar treatment techniques. Accurately predicting possible collisions between gantry, table and patient during treatment planning is needed to ensure patient safety. We offer a freely available collision prediction tool using Blender, a free and open-source 3D computer graphics software toolset. A geometric model of a C-arm linear accelerator including a library of patient models is created inside Blender. Based on the model, collision predictions can be used both to calculate collision-free zones and to check treatment plans for collisions. The tool is validated for two setups, once with and once without a full body phantom with the same table position. For this, each gantry-table angle combination with a 2° resolution is manually checked for collision interlocks at a TrueBeam system and compared to simulated collision predictions. For the collision check of a treatment plan, the tool outputs the minimal distance between the gantry, table and patient model and a video of the movement of the gantry and table, which is demonstrated for one use case. A graphical user interface allows user-friendly input of the table and patient specification for the collision prediction tool. The validation resulted in a true positive rate of 100%, which is the rate between the number of correctly predicted collision gantry-table combinations and the number of all measured collision gantry-table combinations, and a true negative rate of 89%, which is the ratio between the number of correctly predicted collision-free combinations and the number of all measured collision-free combinations. A collision prediction tool is successfully created and able to produce maps of collision-free zones and to test treatment plans for collisions including visualisation of the gantry and table movement.

Delivery time reduction for mixed photon-electron radiotherapy by using photon MLC collimated electron arcs

Objective. Electron arcs in mixed-beam radiotherapy (Arc-MBRT) consisting of intensity-modulated electron arcs with dynamic gantry rotation potentially reduce the delivery time compared to mixed-beam radiotherapy containing electron beams with static gantry angle (Static-MBRT). This study aims to develop and investigate a treatment planning process (TPP) for photon multileaf collimator (pMLC) based Arc-MBRT.Approach. An existing TPP for Static-MBRT plans is extended to integrate electron arcs with a dynamic gantry rotation and intensity modulation using a sliding window technique. The TPP consists of a manual setup of electron arcs, and either static photon beams or photon arcs, shortening of the source-to-surface distance for the electron arcs, initial intensity modulation optimization, selection of a user-defined number of electron beam energies based on dose contribution to the target volume and finally, simultaneous photon and electron intensity modulation optimization followed by full Monte Carlo dose calculation. Arc-MBRT plans, Static-MBRT plans, and photon-only plans were created and compared for four breast cases. Dosimetric validation of two Arc-MBRT plans was performed using film measurements.Main results. The generated Arc-MBRT plans are dosimetrically similar to the Static-MBRT plans while outperforming the photon-only plans. The mean heart dose is reduced by 32% on average in the MBRT plans compared to the photon-only plans. The estimated delivery times of the Arc-MBRT plans are similar to the photon-only plans but less than half the time of the Static-MBRT plans. Measured and calculated dose distributions agree with a gamma passing rate of over 98% (3% global, 2 mm) for both delivered Arc-MBRT plans.Significance. A TPP for Arc-MBRT is successfully developed and Arc-MBRT plans showed the potential to improve the dosimetric plan quality similar as Static-MBRT while maintaining short delivery times of photon-only treatments. This further facilitates integration of pMLC-based MBRT into clinical practice.

Influence of optimisation parameters on directly deliverable Pareto fronts explored for prostate cancer

PURPOSE

In inverse radiotherapy treatment planning, the Pareto front is the set of optimal solutions to the multi-criteria problem of adequately irradiating the planning target volume (PTV) while reducing dose to organs at risk (OAR). The Pareto front depends on the chosen optimisation parameters whose influence (clinically relevant versus not clinically relevant) is investigated in this paper.

METHODS

Thirty-one prostate cancer patients treated at our clinic were randomly selected. We developed an in-house Python script that controlled the commercial treatment planning system RayStation to calculate directly deliverable Pareto fronts. We calculated reference Pareto fronts for a given set of objective functions, varying the PTV coverage and the mean dose of the primary OAR (rectum) and fixing the mean doses of the secondary OARs (bladder and femoral heads). We calculated the fronts for different sets of objective functions and different mean doses to secondary OARs. We compared all fronts using a specific metric (clinical distance measure).

RESULTS

The in-house script was validated for directly deliverable Pareto front calculations in two and three dimensions. The Pareto fronts depended on the choice of objective functions and fixed mean doses to secondary OARs, whereas the parameters most influencing the front and leading to clinically relevant differences were the dose gradient around the PTV, the weight of the PTV objective function, and the bladder mean dose.

CONCLUSIONS

Our study suggests that for multi-criteria optimisation of prostate treatments using external therapy, dose gradient around the PTV and bladder mean dose are the most influencial parameters.

Intensity-modulated radiotherapy for cushing's disease: single-center experience in 70 patients

Context

Intensity-modulated radiotherapy (IMRT) is a modern precision radiotherapy technique for the treatment of the pituitary adenoma.

Objective

Aim to investigate the efficacy and toxicity of IMRT in treating Cushing's Disease (CD).

Methods

70 of 115 patients with CD treated with IMRT at our institute from April 2012 to August 2021 were included in the study. The radiation doses were usually 45-50 Gy in 25 fractions. After IMRT, endocrine evaluations were performed every 6 months and magnetic resonance imaging (MRI) annually. Endocrine remission was defined as suppression of 1 mg dexamethasone test (DST) or normal 24-hour urinary free cortisol level (24hUFC). The outcome of endocrine remission, endocrine recurrence, tumor control and complications were retrieved from medical record.

Results

At a median follow-up time of 36.8 months, the endocrine remission rate at 1, 2, 3 and 5 years were 28.5%, 50.2%, 62.5% and 74.0%, respectively. The median time to remission was 24 months (95%CI: 14.0-34.0). Endocrine recurrence was found in 5 patients (13.5%) till the last follow-up. The recurrence-free rate at 1, 2, 3 and 5 years after endocrine remission was 98.2%, 93.9%, 88.7% and 88.7%, respectively. The tumor control rate was 98%. The overall incidence of new onset hypopituitarism was 22.9%, with hypothyroidism serving as the most common individual axis deficiency. Univariate analysis indicated that only higher Ki-67 index (P=0.044) was significant favorable factors for endocrine remission.

Conclusion

IMRT was a highly effective second-line therapy with low side effect profile for CD patients. Endocrine remission, tumor control and recurrence rates were comparable to previous reports on FRT and SRS.

Simultaneous dose distribution and fluence prediction for nasopharyngeal carcinoma IMRT

BACKGROUND

Current intensity-modulated radiation therapy (IMRT) treatment planning is still a manual and time/resource consuming task, knowledge-based planning methods with appropriate predictions have been shown to enhance the plan quality consistency and improve planning efficiency. This study aims to develop a novel prediction framework to simultaneously predict dose distribution and fluence for nasopharyngeal carcinoma treated with IMRT, the predicted dose information and fluence can be used as the dose objectives and initial solution for an automatic IMRT plan optimization scheme, respectively.

METHODS

We proposed a shared encoder network to simultaneously generate dose distribution and fluence maps. The same inputs (three-dimensional contours and CT images) were used for both dose distribution and fluence prediction. The model was trained with datasets of 340 nasopharyngeal carcinoma patients (260 cases for training, 40 cases for validation, 40 cases for testing) treated with nine-beam IMRT. The predicted fluence was then imported back to treatment planning system to generate the final deliverable plan. Predicted fluence accuracy was quantitatively evaluated within projected planning target volumes in beams-eye-view with 5 mm margin. The comparison between predicted doses, predicted fluence generated doses and ground truth doses were also conducted inside patient body.

RESULTS

The proposed network successfully predicted similar dose distribution and fluence maps compared with ground truth. The quantitative evaluation showed that the pixel-based mean absolute error between predicted fluence and ground truth fluence was 0.53% ± 0.13%. The structural similarity index also showed high fluence similarity with values of 0.96 ± 0.02. Meanwhile, the difference in the clinical dose indices for most structures between predicted dose, predicted fluence generated dose and ground truth dose were less than 1 Gy. As a comparison, the predicted dose achieved better target dose coverage and dose hot spot than predicted fluence generated dose compared with ground truth dose.

CONCLUSION

We proposed an approach to predict 3D dose distribution and fluence maps simultaneously for nasopharyngeal carcinoma patients. Hence, the proposed method can be potentially integrated in a fast automatic plan generation scheme by using predicted dose as dose objectives and predicted fluence as a warm start.

Assessment of dosimetric impact of interfractional 6D setup error in tongue cancer treated with IMRT and VMAT using daily kV-CBCT

Background

This study aimed to evaluate the dosimetric influence of 6-dimensional (6D) interfractional setup error in tongue cancer treated with intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) using daily kilovoltage cone-beam computed tomography (kV-CBCT).

Materials and methods

This retrospective study included 20 tongue cancer patients treated with IMRT (10), VMAT (10), and daily kV-CBCT image guidance. Interfraction 6D setup errors along the lateral, longitudinal, vertical, pitch, roll, and yaw axes were evaluated for 600 CBCTs. Structures in the planning CT were deformed to the CBCT using deformable registration. For each fraction, a reference CBCT structure set with no rotation error was created. The treatment plan was recalculated on the CBCTs with the rotation error (R_Error), translation error (T_Error), and translation plus rotation error (T+R_Error). For targets and organs at risk (OARs), the dosimetric impacts of R_Error, T_Error, and T+R_Error were evaluated without and with moderate correction of setup errors.

Results

The maximum dose variation ΔD (%) for D_98% in clinical target volumes (CTV): CTV-60, CTV-54, planning target volumes (PTV): PTV-60, and PTV-54 was -1.2%, -1.9%, -12.0%, and -12.3%, respectively, in the T+R_Error without setup error correction. The maximum ΔD (%) for D_98% in CTV-60, CTV-54, PTV-60, and PTV-54 was -1.0%, -1.7%, -9.2%, and -9.5%, respectively, in the T+R_Error with moderate setup error correction. The dosimetric impact of interfractional 6D setup errors was statistically significant (p < 0.05) for D_98% in CTV-60, CTV-54, PTV-60, and PTV-54.

Conclusions

The uncorrected interfractional 6D setup errors could significantly impact the delivered dose to targets and OARs in tongue cancer. That emphasized the importance of daily 6D setup error correction in IMRT and VMAT.

Beam angle optimization for proton therapy via group-sparsity based angle generation method

BACKGROUND

In treatment planning, beam angle optimization (BAO) refers to the selection of a subset with a given number of beam angles from all available angles that provides the best plan quality. BAO is a NP-hard combinatorial problem. Although exhaustive search (ES) can exactly solve BAO by exploring all possible combinations, ES is very time-consuming and practically infeasible.

PURPOSE

To the best of our knowledge, (1) no optimization method has been demonstrated that can provide the exact solution to BAO, and (2) no study has validated an optimization method for solving BAO by benchmarking with the optimal BAO solution (e.g., via ES), both of which will be addressed by this work.

METHODS

This work considers BAO for proton therapy, for example, the selection of 2-4 beam angles for IMPT. The optimal BAO solution is obtained via ES and serves as the ground truth. A new BAO algorithm, namely angle generation (AG) method, is proposed, and demonstrated to provide nearly-exact solutions for BAO in reference to the ES solution. AG iteratively optimizes the angular set via group-sparsity (GS) regularization, until the planning objective does not decrease further.

RESULTS

Since GS alone can also solve BAO, AG was validated and compared with GS for 2-angle brain, 3-angle lung, and 4-angle brain cases, in reference to the optimal BAO solutions obtained by ES: the AG solution had the rank (1/276, 1/2024, 4/10 626), while the GS solution had the rank (42/276, 279/2024, 4328/10 626).

CONCLUSIONS

A new BAO algorithm called AG is proposed and shown to provide substantially improved accuracy for BAO from current methods with nearly-exact solutions to BAO, in reference to the ground truth of optimal BAO solution via ES.

Dosimetric Comparison of Hypofractionated Regimen in Breast Cancer Using Two Different Techniques: Intensity-Modulated Radiation Therapy (IMRT) and Volumetric-Modulated Arc Therapy (VMAT)

INTRODUCTION

Breast cancer treated with adjuvant hypofractionation radiotherapy with two different techniques, i.e., volumetric-modulated arc therapy (VMAT) and intensity-modulated radiation therapy (IMRT) and their effects in terms of loco-regional control and adverse effects in terms of cutaneous, pulmonary, and cardiac outcomes are compared.

MATERIALS AND METHODS

This is a prospective non-randomized observational study. VMAT and IMRT plan for 30 breast cancer patients who were supposed to receive adjuvant radiotherapy were prepared using a hypofractionation schedule. The plans were dosimetrically evaluated.

OBJECTIVE

Dosimetric comparative analysis of IMRT and VMAT in hypofractionated radiotherapy in breast cancer is done and tested whether VMAT has a dosimetric advantage over IMRT. These patients were recruited for a clinical assessment of toxicities. They were followed up for at least three months.

RESULT

On dosimetric analysis, planning target volume (PTV) coverage (PTV_ V95) of both VMAT (96.41 ± 1.31) and IMRT (96.63 ± 1.56) were similar with significantly lower monitor units required with VMAT plans (1,084.36 ± 270.82 vs 1,181.55 ± 244.50, p = 0.043). Clinically, all patients tolerated hypofractionation through VMAT (n = 8) and IMRT (n = 8) satisfactorily in the short term. No cardiotoxicity or appreciable falls in pulmonary function test parameters were observed. Acute radiation dermatitis poses challenges similar to standard fractionation or any other delivery technique.

CONCLUSION

PVT dose, homogeneity, and conformity indices were similar in both VMAT and IMRT groups. In VMAT, there was high-dose sparing of some critical organs like the heart and lungs at the cost of the low-dose baths to these organs. Increased risk of secondary cancer will require a decade-long follow-up study to indict the VMAT technique. As we move toward precision in oncology, "one-size-fits-all" can never be an acceptable dictum. Each patient is unique and therefore we must offer, and the patient must "choose wisely."

Toward real-time automatic treatment planning (RTTP) with a one-step 3D fluence map prediction method and (nonorthogonal) convolution technique

PURPOSE

To establish and evaluate a (quasi) real-time automated treatment planning (RTTP) strategy utilizing a one-step full 3D fluence map prediction model based on a nonorthogonal convolution operation for rectal cancer radiotherapy.

METHODS

The RTTP approach directly extracts 3D projections from volumetric CT and anatomical data according to the beam incident direction. A 3D deep learning model with a nonorthogonal convolution operation was established that takes projections in cone beam space as input, extracts the features along and around the ray-trace path, and outputs a predicted fluence map (PFM) for each beam. The PFM is then converted to the MLC sequence with deliverable MUs to generate the final treatment plan. A total of 314 rectal adenocarcinoma patients with 2198 projection data samples were used in model training and validation. An extra 20 patients were used to test the feasibility of the RTTP method by comparing the plan quality, efficiency, deliverability performance, and physician blinded review results with the manual plans.

RESULTS

Overall, the RTTP plans met the clinical dose criteria for target coverage, conformity, homogeneity, and organ-at-risk dose sparing. Compared to manual plans, the RTTP plans showed increases in PTV D_1% by only 2.33% (p < 0.001) and a decrease in PTV D_99% by 0.45% (p < 0.05). The RTTP plans showed a dose increase in the bladder, with a V₅₀ of 14.01 ± 11.75% vs. 10.74 ± 8.51%, respectively, and no significant increases in the femoral head with the mean dose. The planning efficiency was improved in RTTP planning, with 39 s vs. 944 s in fluence map generation; the deliverability performance was saved by 1.91% (p < 0.001) in total MU. According to the blinded plan review by our physician, 55% of RTTP plans can be directly used in clinical radiotherapy treatment.

CONCLUSION

The quasi RTTP method improves the planning efficiency and deliverability performance while maintaining a plan quality close to that of the optimized manual plans in rectal radiotherapy.

Quality assurance assessment of intra-acquisition diffusion-weighted and T2-weighted magnetic resonance imaging registration and contour propagation for head and neck cancer radiotherapy

BACKGROUND/PURPOSE

Adequate image registration of anatomical and functional magnetic resonance imaging (MRI) scans is necessary for MR-guided head and neck cancer (HNC) adaptive radiotherapy planning. Despite the quantitative capabilities of diffusion-weighted imaging (DWI) MRI for treatment plan adaptation, geometric distortion remains a considerable limitation. Therefore, we systematically investigated various deformable image registration (DIR) methods to co-register DWI and T2-weighted (T2W) images.

MATERIALS/METHODS

We compared three commercial (ADMIRE, Velocity, Raystation) and three open-source (Elastix with default settings [Elastix Default], Elastix with parameter set 23 [Elastix 23], Demons) post-acquisition DIR methods applied to T2W and DWI MRI images acquired during the same imaging session in twenty immobilized HNC patients. In addition, we used the non-registered images (None) as a control comparator. Ground-truth segmentations of radiotherapy structures (tumour and organs at risk) were generated by a physician expert on both image sequences. For each registration approach, structures were propagated from T2W to DWI images. These propagated structures were then compared with ground-truth DWI structures using the Dice similarity coefficient and mean surface distance.

RESULTS

19 left submandibular glands, 18 right submandibular glands, 20 left parotid glands, 20 right parotid glands, 20 spinal cords, and 12 tumours were delineated. Most DIR methods took <30 s to execute per case, with the exception of Elastix 23 which took ∼458 s to execute per case. ADMIRE and Elastix 23 demonstrated improved performance over None for all metrics and structures (Bonferroni-corrected p < 0.05), while the other methods did not. Moreover, ADMIRE and Elastix 23 significantly improved performance in individual and pooled analysis compared to all other methods.

CONCLUSIONS

The ADMIRE DIR method offers improved geometric performance with reasonable execution time so should be favoured for registering T2W and DWI images acquired during the same scan session in HNC patients. These results are important to ensure the appropriate selection of registration strategies for MR-guided radiotherapy.

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

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

Efficiency enhancements of a Monte Carlo beamlet based treatment planning process: implementation and parameter study

Objective.The computational effort to perform beamlet calculation, plan optimization and final dose calculation of a treatment planning process (TPP) generating intensity modulated treatment plans is enormous, especially if Monte Carlo (MC) simulations are used for dose calculation. The goal of this work is to improve the computational efficiency of a fully MC based TPP for static and dynamic photon, electron and mixed photon-electron treatment techniques by implementing multiple methods and studying the influence of their parameters.Approach.A framework is implemented calculating MC beamlets efficiently in parallel on each available CPU core. The user can specify the desired statistical uncertainty of the beamlets, a fractional sparse dose threshold to save beamlets in a sparse format and minimal distances to the PTV surface from which 2 × 2 × 2 = 8 (medium) or even 4 × 4 × 4 = 64 (large) voxels are merged. The compromise between final plan quality and computational efficiency of beamlet calculation and optimization is studied for several parameter values to find a reasonable trade-off. For this purpose, four clinical and one academic case are considered with different treatment techniques.Main results.Setting the statistical uncertainty to 5% (photon beamlets) and 15% (electron beamlets), the fractional sparse dose threshold relative to the maximal beamlet dose to 0.1% and minimal distances for medium and large voxels to the PTV to 1 cm and 2 cm, respectively, does not lead to substantial degradation in final plan quality compared to using 2.5% (photon beamlets) and 5% (electron beamlets) statistical uncertainty and no sparse format nor voxel merging. Only OAR sparing is slightly degraded. Furthermore, computation times are reduced by about 58% (photon beamlets), 88% (electron beamlets) and 96% (optimization).Significance.Several methods are implemented improving computational efficiency of beamlet calculation and plan optimization of a fully MC based TPP without substantial degradation in final plan quality.

Feasibility of a multigroup Boltzmann-Fokker-Planck solution for electron beam dose calculations

Legacy nuclear-reactor Boltzmann solvers start clinical deployment as an alternative to Monte Carlo (MC) codes and Fermi-Eyges semiemprical models in radiation oncology treatment planning. Today's certified clinical solvers are limited to photon beams. In this paper, ELECTR, a state-of-the-art multigroup electron cross sections generation module in NJOY is presented and validated against Lockwood's calorimetric measurements, EGS-nrc and GEANT-4 for 1-20 MeV unidirectional electron beams. The nuclear-reactor DRAGON-5 solver is upgraded to access the library and solve the Boltzmann-Fokker-Planck (BFP) equation. A variety of heterogeneous radiotherapy and radiosurgery phantom configurations were used for validation purpose. Case studies include a thorax benchmark, that of a typical breast Intra-Operative Radiotherapy and a high-heterogeneity patient-like benchmark. For all beams, [Formula: see text] of the water voxels satisfied the American Association of Physicists in Medicine accuracy criterion for a BFP-MC dose error below [Formula: see text]. At least, [Formula: see text] of adipose, muscle, bone, lung, tumor and breast voxels satisfied the [Formula: see text] criterion. The average BFP-MC relative error was about [Formula: see text] for all voxels, beams and materials combined. By irradiating homogeneous slabs from [Formula: see text] (hydrogen) to [Formula: see text] (einsteinium), we reported performance and defects of the CEPXS mode [US. Sandia National Lab., SAND-89-1685] in ELECTR for the entire periodic table. For all Lockwood's benchmarks, NJOY-DRAGON dose predictions are within the experimental data precision for [Formula: see text] of voxels.

Application of individualized multimodal radiotherapy combined with immunotherapy in metastatic tumors

Radiotherapy is one of the mainstays of cancer treatment. More than half of cancer patients receive radiation therapy. In addition to the well-known direct tumoricidal effect, radiotherapy has immunomodulatory properties. When combined with immunotherapy, radiotherapy, especially high-dose radiotherapy (HDRT), exert superior systemic effects on distal and unirradiated tumors, which is called abscopal effect. However, these effects are not always effective for cancer patients. Therefore, many studies have focused on exploring the optimized radiotherapy regimens to further enhance the antitumor immunity of HDRT and reduce its immunosuppressive effect. Several studies have shown that low-dose radiotherapy (LDRT) can effectively reprogram the tumor microenvironment, thereby potentially overcoming the immunosuppressive stroma induced by HDRT. However, bridging the gap between preclinical commitment and effective clinical delivery is challenging. In this review, we summarized the existing studies supporting the combined use of HDRT and LDRT to synergistically enhance antitumor immunity, and provided ideas for the individualized clinical application of multimodal radiotherapy (HDRT+LDRT) combined with immunotherapy.

Outcomes of allogeneic haematopoietic stem cell transplantation with intensity-modulated total body irradiation by helical tomotherapy: a 2-year prospective follow-up study

BACKGROUND AND OBJECTIVES

Intensity-modulated radiation therapy (IMRT) helps achieve good radiation dose conformity and precise dose evaluation. We conducted a single-centre prospective study to assess the safety and feasibility of total body irradiation with IMRT (IMRT-TBI) using helical tomotherapy in allogeneic haematopoietic stem cell transplantation (allo-HSCT).

PATIENTS AND METHODS

Thirty-nine adult patients with haematological malignancy (acute lymphoblastic leukaemia [n = 21], chronic myeloid leukaemia [n = 6], mixed phenotype acute leukaemia [n = 5], acute myeloid leukaemia [n = 4], and malignant lymphoma [n = 3]) who received 12 Gy IMRT-TBI were enrolled with a median follow-up of 934.5 (range, 617-1254) d. At the time of transplantation, 33 patients (85%) achieved complete remission. The conditioning regimen used IMRT-TBI (12 Gy in 6 fractions twice daily, for 3 d) and cyclophosphamide (60 mg/kg/d, for 2 d), seven patients were combined with cytarabine, and five with etoposide. We set dose constraints for the lungs, kidneys and lens as the organs at risk.

RESULTS

The mean doses for the lungs and kidneys were 7.50 and 9.11 Gy, respectively. The mean maximum dose for the lens (right/left) was 5.75/5.87 Gy. The 2-year overall survival (OS), disease-free survival (DFS), cumulative incidence of relapse (CIR) and non-relapse mortality (NRM) were 69, 64, 18 and 18%, respectively. Thirty-six patients developed early adverse events (AEs) (including four patients with Grade 3/4 toxicities), most of which were reversible oral mucositis and may partially have been related to IMRT-TBI. However, the incidence of toxicity was comparable to conventional TBI-based conditioning transplantation. None of the patients developed primary graft failure, or Grade III-IV acute graft-versus-host disease (GVHD). In late complications, chronic kidney disease was observed in six patients, a lower incidence compared to conventional TBI-based conditioning transplantation. No radiation pneumonitis or cataracts were observed in any of the patients.

CONCLUSIONS

IMRT-TBI is safe and feasible for haematological malignancies with acceptable clinical outcomes.KEY MESSAGESIMRT-TBI-helical tomotherapy aids in accurate dose calculation and conformity.It could be used without any considerable increase in the rate of TBI-related AEs.Allo-HSCT with IMRT-TBI may be an alternative to conventional TBI for clinical use.

Knowledge-based planning using both the predicted DVH of organ-at risk and planning target volume

PURPOSE

The purpose of this study was to evaluate the performance of a knowledge-based planning (KBP) method in nasopharyngeal cancer radiotherapy using the predicted dose-volume histogram (DVH) of organ-at risk (OAR) and planning target volume (PTV).

METHODS AND MATERIALS

A total of 85 patients previously treated for nasopharyngeal cancer using 9-field 6-MV intensity-modulated radiation therapy (IMRT) were identified for training and 30 similar patients were identified for testing. The dosimetric deposition information, individual dose-volume histograms (IDVHs) induced by a series of fields with uniform-intensity irradiation, was used to predict both OAR and PTV DVH. Two KBP methods (KBP_OAR and KBP_OAR+PTV) were established for plan generation based on the DVH prediction. The KBP_OAR method utilized the dose constraints based on the predicted OAR DVH and the PTV dose constraints obtained according to the planning experience, while the KBP_OAR+PTV method applied the dose constraints based on the predicted OAR and PTV DVH. For the plan evaluation, the PTV dose coverage was used D₉₈ and D₂, and the maximum dose, mean dose or dose-volume parameters were used for the OARs. Statistical differences of the two KBP methods were tested with the Wilcoxon signed rank test.

RESULTS

For patients with T3 tumors, there was no significant difference between the KBP_OAR and KBP_OAR+PTV methods in dosimetric results at most OARs and PTVs. Both KBP methods achieved a similar number of plans meeting the dose requirements. For patients with T4 tumors, KBP_OAR+PTV reduced the maximum dose by more than 1 Gy in the body, spinal cord, optic nerve, eye and temporal lobes and reduced the V₅₀ value by more than 3.9% in the larynx and tongue without reducing the PTV dose compared with KBP_OAR. The KBP_OAR+PTV method increased the plans by more than 14.2% in meeting the maximum dose requirements at the body, optic nerve, mandible and eye and increased the plans by more than 21.4% in meeting the V₅₀ of the larynx and V₅₀ of the tongue when compared with the KBP_OAR method.

CONCLUSIONS

For patients with T3 tumors, no significant difference was found between the KBP_OAR and KBP_OAR+PTV methods in dosimetric results at most OARs and PTVs. For patients with T4 tumors, the KBP_OAR+PTV method performs better than the KBP_OAR method in improving the quality of the plans. Compared with the KBP_OAR method, dose sparing of some OARs was achieved without reducing PTV dose coverage and helped to increase the number of plans meeting the dose requirements when the KBP_OAR+PTV method was utilized.

Intensity-modulated radiotherapy in locally advanced head-and-neck cancers in elderly patients

Introduction

Head and neck cancer is one of the most common malignancies in Indian males. Due to poor socioeconomic status, presentation is usually in advanced stage. Treatment option is limited to radiotherapy with or without chemotherapy. Intensity-modulated radiotherapy (IMRT) provides highly conformal dose distributions creating nonuniform spatial intensity using different segments in the beam. Concomitant chemoradiation is highly toxic in this age group.

Material and Methods

During 2016-2017, 44 patients with locally advanced head-and-neck cancers were treated with a curative intent with IMRT. They were in the age range of 65-75. The median age was 69 years. Thirty five were male and nine were female. Histopathologically, all had squamous cell carcinoma. Stage wise, all were T3N2 or more. The standard technique of IMRT was used with sparing of organs at risk and defining treatment volumes: gross, clinical, and planning. Patients were assessed after 4 weeks of completion of treatment for response and toxicities.

Results

Response vise, 14 patients achieved complete response, 28 patients had partial response, and 2 had stable disease. There was no treatment-related mortality. Six patients had treatment interruptions due to toxicity. Incidence of mucositis was of Grade 1-2 in all patients. No hematological toxicity was seen. Patients having dysphagia during treatment were given nasogastric feed.

Treatment plan prescreening for patient-specific quality assurance measurements using independent Monte Carlo dose calculations

PURPOSE

This study proposes a method to identify plans that failed patient-specific quality assurance (QA) and attempts to establish a criterion to prescreen treatment plans for patient-specific QA measurements with independent Monte Carlo dose calculations.

MATERIALS AND METHODS

Patient-specific QA results measured with an ArcCHECK diode array of 207 patients (head and neck: 25; thorax: 61; abdomen: 121) were retrospectively analyzed. All patients were treated with the volumetric modulated arc therapy (VMAT) technique and plans were optimized with a Pinnacle v16.2 treatment planning system using an analytical algorithm-based dose engine. Afterwards, phantom verification plans were designed and recalculated by an independent GPU-accelerated Monte Carlo (MC) dose engine, ArcherQA. Moreover, sensitivity and specificity analyzes of gamma passing rates between measurements and MC calculations were carried out to show the ability of MC to monitor failing plans (ArcCHECK 3%/3 mm,<90%), and attempt to determine the appropriate threshold and gamma passing rate criterion utilized by ArcherQA to prescreen treatment plans for ArcCHECK measurements. The receiver operator characteristic (ROC) curve was also utilized to characterize the performance of different gamma passing rate criterion used by ArcherQA.

RESULTS

The thresholds for 100% sensitivity to detect plans that failed patient-specific QA by independent calculation were 97.0%, 95.4%, and 91.0% for criterion 3%/3 mm, 3%/2 mm, and 2%/2 mm, respectively, which corresponded to specificities of 0.720, 0.528, and 0.585, respectively. It was shown that the 3%/3 mm criterion with 97% threshold for ArcherQA demonstrated perfect sensitivity and the highest specificity compared with other criteria, which may be suitable for prescreening treatment plans treated with the investigated machine to implement measurement-based patient-specific QA of patient plans. In addition, the area under the curve (AUC) calculated from ROC analysis for criterion 3%/3 mm, 3%/2 mm, and 2%/2 mm used by ArcherQA were 0.948, 0.924, and 0.929, respectively.

CONCLUSIONS

Independent dose calculation with the MC-based program ArcherQA has potential as a prescreen treatment for measurement-based patient-specific QA. AUC values (>0.9) showed excellent classification accuracy for monitoring failing plans with independent MC calculations.

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.

Dosimetric Impact on the Flattening Filter and Addition of Gold Nanoparticles in Radiotherapy: A Monte Carlo Study on Depth Dose Using the 6 and 10 MV FFF Photon Beams

PURPOSE

This phantom study investigated through Monte Carlo simulation how the dose enhancement varied with depth, when gold nanoparticles (NPs) were added using the flattening filter-free (FFF) photon beams in gold NP-enhanced radiotherapy.

METHOD

A phantom with materials varying from pure water to a mixture of water and gold NPs at different concentrations (3-40 mg/mL) were irradiated by the 6 and 10 MV flattening filter (FF) and FFF photon beams. Monte Carlo simulations were carried out to determine the depth doses along the central beam axis of the phantom up to a depth of 40 cm. The dose enhancement ratio (DER) and FFF enhancement ratio (FFFER) were calculated based on the Monte Carlo results.

RESULTS

The DER values were found decreased with an increase of depth and increase of NP concentration in the phantom. For the maximum NP concentration of 40 mg/mL, the DER values decreased 6.9, 12, 4.6 and 7.2% at a phantom depth from 2 to 40 cm, using the 6 MV FF, 6 MV FFF, 10 MV FF and 10 MV FFF photon beams, respectively. The maximum DER values for the 6 MV beams were 1.08 (FF) and 1.14 (FFF), while those for the 10 MV beams were 1.04 (FF) and 1.07 (FFF). When the FF was removed from the linear accelerator head, the FFFER showed a more significant increase of dose enhancement for the 6 MV beams (1.057) than the 10 MV (1.031).

CONCLUSION

From the DER and FFFER values based on the Monte Carlo results, it is concluded that the dose enhancement with depth was dependent on the NP and beam variables, namely, NP concentration, presence of FF in the beam and beam energy. Dose enhancement was more significant when using the lower photon beam energy (i.e., 6 MV), FFF photon beam and higher NP concentration in the study.

Comparison between Three-Dimensional Conformal Radiation Therapy (3DCRT) and Intensity-Modulated Radiation Therapy (IMRT) for Radiotherapy of Cervical Carcinoma: A Heterogeneous Phantom Study

BACKGROUND

Radiotherapy plays a major role in the treatment of the cervical cancer.

OBJECTIVE

Dosimetric comparison of intensity-modulated radiation therapy (IMRT) with three-dimensional conformal radiation therapy (3DCRT) in cervical cancer treatment was performed by modifying the beams arrangements to achieve better organ at risk (OAR) sparing.

MATERIAL AND METHODS

The analytical evaluation study was made by modifying the IMRT plan, subtracting the rectal volume from planning target volume (PTV), and applying the field-in-field technique in 3DCRT. Eight patients in various cervical cancer stages, from I‒III, were inducted for this investigation. The prescribed dose was 5000 cGy in 25 fractions. For all cases, both IMRT and 3DCRT plans were generated. For PTV and OARs, dose volume histogram (DVH) comparative analysis was carried out. For safety checks and quality control, pre-treatment verification of all the plans was performed using an indigenously developed pelvic phantom (for IMRT and 3DCRT) and gamma analysis with Delta4 phantom (for IMRT).

RESULTS

This study indicated that IMRT can treat cervical cancer more efficiently with less damage to OARs as compare to 3DCRT.

CONCLUSION

In this study, we observe that the IMRT plans with subtracting rectal volume achieve better OAR sparing.

QUANTIFICATION OF 6D INTER-FRACTION TUMOUR LOCALISATION ERRORS IN TONGUE AND PROSTATE CANCER USING DAILY KV-CBCT FOR 1000 IMRT AND VMAT TREATMENT FRACTIONS

This study aimed to evaluate the 6D inter-fraction tumour localisation errors in 20 tongue and 20 prostate cancer patients treated with intensity-modulated radiation therapy and volumetric-modulated arc therapy. The patient tumour localisation errors in lateral, longitudinal and vertical translation axes and pitch, roll and yaw rotational axes were analysed by automatic image registration of daily pretreatment kilovoltage cone-beam computed tomography (kV-CBCT) with planning CT in 1000 fractions. The overall mean error (M), systematic error (Σ), random error (σ) and planning target volume (PTV) margins were evaluated. The frequency distributions of setup errors were normally distributed about the mean except for pitch in the tongue and prostate. The overall 3D vector length ≥ 5 mm was 14.2 and 49.8% in the ca-tongue and ca-prostate, respectively. The frequency of rotational errors ≥1 degree was a maximum of 37 and 59.5%, respectively, in ca-tongue and ca-prostate. The M, Σ and σ for all translational and rotational axes decreased with increasing frequency of verification correction in ca-tongue and ca-prostate patients. Similarly, the PTV margin was reduced with no correction to alternate day correction from a maximum of 4.7 to 2.5 mm in ca-tongue and from a maximum of 8.6 to 4.7 mm in ca-prostate. The results emphasised the vital role of the higher frequency of kV-CBCT based setup correction in reducing M, Σ, σ and PTV margins in ca-tongue and ca-prostate patients.

Enabling non-isocentric dynamic trajectory radiotherapy by integration of dynamic table translations

Objective. The purpose of this study is to develop a treatment planning process (TPP) for non-isocentric dynamic trajectory radiotherapy (DTRT) using dynamic gantry rotation, collimator rotation, table rotation, longitudinal, vertical and lateral table translations and intensity modulation and to validate the dosimetric accuracy. Approach. The TPP consists of two steps. First, a path describing the dynamic gantry rotation, collimator rotation and dynamic table rotation and translations is determined. Second, an optimization of the intensity modulation along the path is performed. We demonstrate the TPP for three use cases. First, a non-isocentric DTRT plan for a brain case is compared to an isocentric DTRT plan in terms of dosimetric plan quality and delivery time. Second, a non-isocentric DTRT plan for a craniospinal irradiation (CSI) case is compared to a multi-isocentric intensity modulated radiotherapy (IMRT) plan. Third, a non-isocentric DTRT plan for a bilateral breast case is compared to a multi-isocentric volumetric modulated arc therapy (VMAT) plan. The non-isocentric DTRT plans are delivered on a TrueBeam in developer mode and their dosimetric accuracy is validated using radiochromic films. Main results. The non-isocentric DTRT plan for the brain case is similar in dosimetric plan quality and delivery time to the isocentric DTRT plan but is expected to reduce the risk of collisions. The DTRT plan for the CSI case shows similar dosimetric plan quality while reducing the delivery time by 45% in comparison with the IMRT plan. The DTRT plan for the breast case showed better treatment plan quality in comparison with the VMAT plan. The gamma passing rates between the measured and calculated dose distributions are higher than 95% for all three plans. Significance. The versatile benefits of non-isocentric DTRT are demonstrated with three use cases, namely reduction of collision risk, reduced setup and delivery time and improved dosimetric plan quality.

On the value of a multistage optimization approach for intensity-modulated radiation therapy planning*

Objective. Intensity-modulated radiation therapy (IMRT) aims to distribute a prescribed dose of radiation to cancerous tumors while sparing the surrounding healthy tissue. A typical approach to IMRT planning uniformly divides and allocates the same dose prescription (DP) across several successive treatment sessions. A more flexible fractionation scheme would lend the capability to vary DPs and utilize updated CT scans and future predictions to adjust treatment delivery. Therefore, our objective is to develop optimization-based models and methodologies that take advantage of adapting treatment decisions across fractions by utilizing predictions of tumor evolution. Approach. We introduce a nonuniform generalization of the uniform allocation scheme that does not automatically assume equal DPs for all sessions. We develop new deterministic and stochastic multistage optimization-based models for such a generalization. Our models allow us to simultaneously identify optimal DPs and fluence maps for individual sessions. We conduct extensive numerical experiments to compare these models using multiple metrics and dose-volume histograms. Main results. Our numerical results in both deterministic and stochastic settings reveal the restrictive nature of the uniform allocation scheme. The results also demonstrate the value of nonuniform multistage models across multiple performance metrics. The improvements can be maintained even when restricting the underlying fractionation scheme to small degrees of nonuniformity. Significance. Our models and computational results support multistage stochastic programming (SP) methodology to derive ideal allocation schemes and fluence maps simultaneously. With technological and computational advancements, we expect the multistage SP methodologies to continue to serve as innovative optimization tools for radiation therapy planning applications.

Dosimetric effect of modelling non-homogeneous LINAC couch using cone-beam computed tomography on quality assurance (QA) results

Aim:

To evaluate the dosimetric effect of modelling a non-homogeneous couch on patients’ quality assurance (QA) gamma pass rates for intensity-modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) techniques.

Materials and Methods:

A non-homogeneous treatment couch (TxT 550 TTM, CIVCO, USA) was imaged using the LINAC mounted cone-beam computer tomography (CBCT) system. Modelling this couch in different situations, including incomplete (homogeneous model), correct model and not defined situations in the treatment planning system (TPS), was performed based on the geometrical and material densities data extracted from the CBCT images. Calculated gamma pass rates between TPS dose calculations and the measurements in a phantom for different couch models were obtained and compared at two gamma criteria (2%-2 mm and 3%-3 mm).

Results:

Comparing TPS calculations for the correct modelled couch and the measurements showed high gamma pass rates for both the IMRT and VMAT techniques (96·5 ±  0·9%, 99·2 ±  0·5% for IMRT in 2%-2 mm and 3%-3 mm criteria; 97·5 ±  0·8%, 99·4 ±  0·5% for VMAT). The overall gamma pass rate of the IMRT plan QAs was reduced by about 2% and 3% on average for incomplete and no couch modelling, respectively. These reductions for VMAT techniques were 2·5% and 4·3%, respectively.

Conclusions:

Non-homogeneous couches have different parts with different attenuations, which can be correctly defined using LINAC CBCT. Modelling of treatment couch has a significant effect on patient QA results for VMAT and IMRT plans, especially in radiation fields/subfield transmitting from the couch. We suggest using LINAC CBCTs as an appropriate device for couch modelling in modulated radiotherapy techniques.

PET/CT for Target Delineation of Lung Cancer Before Radiation Therapy

In clinical routine of patients suffering from lung cancer, radiotherapy/radiation oncology represents one of the therapeutic hallmarks in the multimodal treatment besides or in combination with other local treatments such as surgery, but also systemic treatments such as chemotherapy, tyrosine kinase, and immune check-point inhibitors. Conventional morphological imagings such as CT or MR are commonly used for staging, response assessment, but also for radiotherapy planning. However, advanced imaging techniques such as PET do continuously get increasing access to clinical routine overcoming limitations of standard imaging techniques by visualizing and quantifying molecular processes such as glucose metabolism, which is also of relevance for radiotherapy planning. This review article summarizes the current place of radiotherapy within the treatment regimens of patients with lung cancer and elucidates current concepts of standard morphological imaging for staging and radiotherapy planning. Moreover, the place of PET-based radiotherapy planning in a clinical context is presented and current methodological/technical advances that do comprise a potential role for radiotherapy planning in lung cancer patients are discussed.

The development of a deep reinforcement learning network for dose-volume-constrained treatment planning in prostate cancer intensity modulated radiotherapy

Although commercial treatment planning systems (TPSs) can automatically solve the optimization problem for treatment planning, human planners need to define and adjust the planning objectives/constraints to obtain clinically acceptable plans. Such a process is labor-intensive and time-consuming. In this work, we show an end-to-end study to train a deep reinforcement learning (DRL) based virtual treatment planner (VTP) that can behave like a human to operate a dose-volume constrained treatment plan optimization engine following the parameters used in Eclipse TPS for high-quality treatment planning. We considered the prostate cancer IMRT treatment plan as the testbed. The VTP took the dose-volume histogram (DVH) of a plan as input and predicted the optimal strategy for constraint adjustment to improve the plan quality. The training of VTP followed the state-of-the-art Q-learning framework. Experience replay was implemented with epsilon-greedy search to explore the impacts of taking different actions on a large number of automatically generated plans, from which an optimal policy can be learned. Since a major computational cost in training was to solve the plan optimization problem repeatedly, we implemented a graphical processing unit (GPU)-based technique to improve the efficiency by 2-fold. Upon the completion of training, the established VTP was deployed to plan for an independent set of 50 testing patient cases. Connecting the established VTP with the Eclipse workstation via the application programming interface, we tested the performance the VTP in operating Eclipse TPS for automatic treatment planning with another two independent patient cases. Like a human planner, VTP kept adjusting the planning objectives/constraints to improve plan quality until the plan was acceptable or the maximum number of adjustment steps was reached under both scenarios. The generated plans were evaluated using the ProKnow scoring system. The mean plan score (± standard deviation) of the 50 testing cases were improved from 6.18 ± 1.75 to 8.14 ± 1.27 by the VTP, with 9 being the maximal score. As for the two cases under Eclipse dose optimization, the plan scores were improved from 8 to 8.4 and 8.7 respectively by the VTP. These results indicated that the proposed DRL-based VTP was able to operate the in-house dose-volume constrained TPS and Eclipse TPS to automatically generate high-quality treatment plans for prostate cancer IMRT.

Efficient method for whole-breast irradiation therapy using Halcyon linear accelerators

Background

The Halcyon is a linear accelerator‐based treatment machine designed for a high‐throughput simplified workflow. The machine features a compact jawless design, dual‐layer multileaf collimators, and a single 6‐MV flattening filter‐free (FFF) beam. However, the machine's 6‐MV FFF beam may restrict its applicability to conventional techniques, such as field‐in‐field (FiF) radiotherapy, for breast cancer treatment. This study developed a practical and efficient hybrid method for imaging, planning, and irradiation procedures for whole‐breast irradiation using Halcyon linear accelerators.

Materials and methods

The proposed method involves five major steps: (1) field arrangement, (2) planning target volume (PTV) generation and evaluation, (3) basal plan generation, (4) inverse planning intensity–modulated radiation therapy plan generation, and (5) plan evaluation and irradiation. The PTV is generated using isodose curves plotted on the basis of tangential fields, which are applied to create a basal plan. Subsequently, a basal‐dose‐compensation approach is applied to further optimize the treatment plan. This efficient workflow necessitates executing only one onboard cone‐beam computed tomography procedure. This study included 10 patients with early‐stage breast cancer who were treated at our center. The performance of the proposed method was evaluated by comparing its corresponding irradiation time and dose statistics with those derived for a dynamically flattened beam‐based FiF (DFB‐FiF) method.

Results

All plans were normalized to ensure that 98% of the prescribed dose covered 95% of the PTV. On average, the global maximum doses in the proposed and DFB‐FiF methods were lower than 106%. The homogeneity index for right‐sided (left‐sided) breast cancer was 0.053 (0.056) in the proposed method and 0.073 (0.076) in the DFB‐FiF method. The dose statistics of normal tissues, including the contralateral breast, heart, and lungs, were comparable between the methods. However, the irradiation time per monitor unit in the proposed method was approximately five times faster than that in the DFB‐FiF method, but the planning time and complexity were similar between the methods.

Conclusions

This study developed and evaluated an efficient and practical hybrid method for whole‐breast irradiation using the Halcyon. This method can significantly reduce the irradiation time, while providing comparable dose statistics to the DFB‐FiF method.

Advances of Patient-Derived Organoids in Personalized Radiotherapy

Patient-derived organoids (PDO), based on the advanced three-dimensional (3D) culture technology, can provide more relevant physiological and pathological cancer models, which is especially beneficial for developing and optimizing cancer therapeutic strategies. Radiotherapy (RT) is a cornerstone of curative and palliative cancer treatment, which can be performed alone or integrated with surgery, chemotherapy, immunotherapy, or targeted therapy in clinical care. Among all cancer therapies, RT has great local control, safety and effectiveness, and is also cost-effective per life-year gained for patients. It has been reported that combing RT with chemotherapy or immunotherapy or radiosensitizer drugs may enhance treatment efficacy at faster rates and lower cost. However, very few FDA-approved combinations of RT with drugs or radiosensitizers exist due to the lack of accurate and relevant preclinical models. Meanwhile, radiation dose escalation may increase treatment efficacy and induce more toxicity of normal tissue as well, which has been studied by conducting various clinical trials, very expensive and time-consuming, often burdensome on patients and sometimes with controversial results. The surged PDO technology may help with the preclinical test of RT combination and radiation dose escalation to promote precision radiation oncology, where PDO can recapitulate individual patient’ tumor heterogeneity, retain characteristics of the original tumor, and predict treatment response. This review aims to introduce recent advances in the PDO technology and personalized radiotherapy, highlight the strengths and weaknesses of the PDO cancer models, and finally examine the existing RT-related PDO trials or applications to harness personalized and precision radiotherapy.

A hybrid column generation and simulated annealing algorithm for direct aperture optimization

The purpose of this work was to develop a hybrid column generation (CG) and simulated annealing (SA) algorithm for direct aperture optimization (H-DAO) and to show its effectiveness in generating high quality treatment plans for intensity modulated radiation therapy (IMRT) and mixed photon-electron beam radiotherapy (MBRT). The H-DAO overcomes limitations of the CG-DAO with two features improving aperture selection (branch-feature) and enabling aperture shape changes during optimization (SA-feature). The H-DAO algorithm iteratively adds apertures to the plan. At each iteration, a branch is created for each field provided. First, each branch determines the most promising aperture of its assigned field and adds it to a copy of the current apertures. Afterwards, the apertures of each branch undergo an MU-weight optimization followed by an SA-based simultaneous shape and MU-weight optimization and a second MU-weight optimization. The next H-DAO iteration continues the branch with the lowest objective function value. IMRT and MBRT treatment plans for an academic, a brain and a head and neck case generated using the CG-DAO and H-DAO were compared. For every investigated case and both IMRT and MBRT, the H-DAO leads to a faster convergence of the objective function value with number of apertures compared to the CG-DAO. In particular, the H-DAO needs about half the apertures to reach the same objective function value as the CG-DAO. The average aperture areas are 27% smaller for H-DAO than for CG-DAO leading to a slightly larger discrepancy between optimized and final dose. However, a dosimetric benefit remains. The H-DAO was successfully developed and applied to IMRT and MBRT. The faster convergence with number of apertures of the H-DAO compared to the CG-DAO allows to select a better compromise between plan quality and number of apertures.

Evaluation of kV-CBCT based 3D dose calculation accuracy and its validation using delivery fluence derived dose metrics in Head and Neck Cancer

PURPOSE

The purpose of this study is to evaluate the dosimetric impact of Hounsfield unit (HU) variations in kilovoltage cone-beam computed tomography (kV-CBCT) based 3D dose calculation accuracy in the treatment planning system and its validation using measured treatment delivery dose (MTDD) derived dose metrics for Volumetric Modulated Arc Therapy (VMAT) and Intensity Modulated Radiotherapy (IMRT) plans in Head and Neck (HN) Cancer.

METHODS

CBCT dose calculation accuracy was evaluated for 8 VMAT plans on inhomogeneous phantom and 40 VMAT and IMRT plans of HN Cancer patients and validated using ArcCHECK diode array MTDD derived 3D dose metric on CT and CBCT.

RESULTS

The mean percentage dose difference between CBCT and CT in TPS (ΔD(CBCT-CT)_TPS) and 3DVH (ΔD(CBCT-CT)_3DVH) were compared for the corresponding evaluation dose metrics (D_98%, D_95%, D_50%, D_2%, D_max, D_1cc, D_0.03cc, D_mean) of all PTVs and OARs in phantom and patients. ΔD(CBCT-CT)_TPS and ΔD(CBCT-CT)_3DVH for all evaluation dose points of all PTVs and OARs were less than 2.55% in phantom and 2.4% in HN patients. The Pearson correlation coefficient (r) between ΔD(CBCT-CT)_TPS and ΔD(CBCT-CT)_3DVH for all dose points in all PTVs and OARs showed a strong to moderate correlation in phantom and patients with p < 0.001.

CONCLUSIONS

This study evaluated and validated the potential feasibility of kV-CBCT for treatment plan 3D dose reconstruction in clinical decision making for Adaptive radiotherapy on CT in Head and Neck cancer.

A dose volume constraint (DVC) projection-based algorithm for IMPT inverse planning optimization

PURPOSE

Provide a projection-based algorithm to solve the class of optimization problems encountered in intensity modulated proton therapy (IMPT). The algorithm can handle percentage dose-volume constraints that are usually found in such problems.

METHODS

To seek a feasible solution, the automatic relaxation method was used to project the spot weight vector onto the interval defined by lower and upper bound target dose constraints. The obtained solution was optimized separately based on the objective of each OAR in addition to maximizing the minimum target dose using the bisection search method using a stopping criterion of 10 cGy. The combined weight was used in the CQ algorithm to solve the split feasibility problem but with a special projection technique due to the non-convexity of dose volume constraints. The algorithm was applied to four clinical IMPT cases (meningioma, prostate, tongue, and oropharynx) and compared to the corresponding treatment plans optimized in Eclipse.

RESULTS

The treatment plans obtained, for the four cases, using the BCQ-ARM algorithm have dosimetric endpoints that are similar to their counterparts generated from Eclipse. The algorithm worked equally well with all cases, including the complex head and neck ones. The stopping criterion of 10 cGy results in making the generated plans slightly less optimal (ε-optimal) rather than optimal, but with the advantage of the possibility of generating a database of plans.

CONCLUSIONS

The application of the BCQ-ARM algorithm to different cases of IMPT plans with dose volume constraints was demonstrated. The algorithm is successful in generating plans that are dosimetrically equivalent to their corresponding Eclipse plans. Thus, it is suitable to generate optimized treatment plans in a clinically reasonable time frame. This article is protected by copyright. All rights reserved.

Automated and Clinically Optimal Treatment Planning for Cancer Radiotherapy

Each year, approximately 18 million new cancer cases are diagnosed worldwide, and about half must be treated with radiotherapy. A successful treatment requires treatment planning with the customization of penetrating radiation beams to sterilize cancerous cells without harming nearby normal organs and tissues. This process currently involves extensive manual tuning of parameters by an expert planner, making it a time-consuming and labor-intensive process, with quality and immediacy of critical care dependent on the planner's expertise. To improve the speed, quality, and availability of this highly specialized care, Memorial Sloan Kettering Cancer Center developed and applied advanced optimization tools to this problem (e.g., using hierarchical constrained optimization, convex approximations, and Lagrangian methods). This resulted in both a greatly improved radiotherapy treatment planning process and the generation of reliable and consistent high-quality plans that reflect clinical priorities. These improved techniques have been the foundation of high-quality treatments and have positively impacted over 4,000 patients to date, including numerous patients in severe pain and in urgent need of treatment who might have otherwise required longer hospital stays or undergone unnecessary surgery to control the progression of their disease. We expect that the wide distribution of the system we developed will ultimately impact patient care more broadly, including in resource-constrained countries.