Applying mixed-integer linear programming to the non-coplanar beam angle optimization of intensity-modulated radiotherapy for liver cancer.

Background: Currently, intensity-modulated radiation therapy (IMRT) is commonly used in radiotherapy clinics. However, designing a treatment plan with multiple beam angles depends on the experience of human planners, and is mostly achieved using a trial-and-error approach. It is preferrable but challenging to solve this issue automatically and mathematically using an optimization approach. The goal of this study is to develop a mixed-integer linear programming (MILP) approach for the beam angle optimization (BAO) of non-coplanar IMRT for liver cancer. Methods: MILP models for the BAO of both coplanar and non-coplanar IMRT treatment plans were developed. The beam angles of the IMRT plans were first selected by the MILP model built using mathematical optimization software. Next, the IMRT plans with the selected beam angles was created in a commercial treatment planning system. Finally, the fluence map and dose distribution of the IMRT plans were generated under pre-defined dose-volume constraints. The IMRT plans of 10 liver cancer patients previously treated at our institute were used to assessed the proposed MILP models. For each patient, both coplanar and non-coplanar IMRT plans with beam angles optimized by the MILP models were compared with the IMRT plan clinically approved by physicians. Results: The MILP model-guided IMRT plans showed reduced doses for most of the organs at risk (OARs). Compared with the IMRT plans clinically approved by physicians, the doses for the spinal cord (28.5 vs. 36.1, P=0.001<0.05) and liver (27.6 vs. 29.1, P=0.005<0.05) decreased significantly in the IMRT plans with non-coplanar beams selected by the MILP models. Conclusions: The MILP model is an effective tool for the BAO in coplanar and non-coplanar IMRT treatment planning. It facilitates the automation of IMRT treatment planning for current high-precision radiotherapy.

A mini review of plan quality and secondary cancer risk in CyberKnife M6 radiosurgery for benign intracranial tumors.

With advancements in medical technology, stereotactic radiosurgery (SRS) has become an essential option for treating benign intracranial tumors. Due to its minimal side effects and high local control rate, SRS is widely applied. This paper evaluates the plan quality and secondary cancer risk (SCR) in patients with benign intracranial tumors treated with the CyberKnife M6 system. The CyberKnife M6 robotic radiosurgery system features both multileaf collimator (MLC) and IRIS variable aperture collimator systems, providing different treatment options. The study included 15 patients treated with the CyberKnife M6 system, examining the differences in plan quality and SCR between MLC and IRIS systems. Results showed that MLC and IRIS plans had equal PTV (planning target volume) coverage (98.57% vs. 98.75%). However, MLC plans demonstrated better dose falloff and conformity index (CI: 1.81 ± 0.26 vs. 1.92 ± 0.27, P = 0.025). SCR assessment indicated that MLC plans had lower cancer risk estimates, with IRIS plans having average LAR (lifetime attributable risk) and EAR (excess absolute risk) values approximately 25% higher for cancer induction and 15% higher for sarcoma induction compared to MLC plans. The study showed that increasing tumor volume increases SCR probability, but there was no significant difference between different plans in PTV and brainstem analyses.

A planning study of proton therapy dose escalation for non-small cell lung cancer.

In non-small-cell lung cancer (NSCLC), improving local control through radiotherapy dose escalation might improve survival. However, a photon-based RCT showed increased organ at risk dose exposure and worse overall survival in the dose escalation arm. In this study, intensity-modulated proton therapy plans with dose escalation to the primary tumour were created for 20 NSCLC patients. The mediastinal envelope was delineated to spare structures around the heart. It was possible to increase primary tumour dose up to 74.0 Gy without a significant increase in organ at risk doses and predicted toxicity.

Development and validation of 3D printed anthropomorphic head phantom with eccentric holes for medical LINAC quality assurance testing in stereotactic radiosurgery.

Stereotactic Radiosurgery (SRS) for brain tumors using Medical Linear Accelerator (LINAC) demands high precision and accuracy. A specific Quality Assurance (QA) is essential for every patient undergoing SRS to protect nearby non-cancerous cells by ensuring that the X-ray beams are targeted according to tumor position. In this work, a water-filled generic anthropomorphic head phantom consisting of two removable parts with eccentric holes was developed using Additive Manufacturing (AM) process for performing QA in SRS. In the patient specific QA, the planned radiation dose using Treatment Planning System (TPS) was compared with the dose measured in the phantom. Also, the energy consistency of radiation beams was tested at 200 MU for different energy beams at the central and eccentric holes of the phantom using an ionization chamber. Experimentally examined results show that planned doses in TPS are reaching the target within a 5% deviation. The ratio of the dose delivered in the eccentric hole to the dose delivered to the central hole shows variations of less than 2% for the energy consistency test. The designed, low-cost water-filled anthropomorphic phantom is observed to improve positioning verification and accurate dosimetry of patient-specific QA in SRS treatment.

Deep reinforcement learning in radiation therapy planning optimization: A comprehensive review.

PURPOSE: The formulation and optimization of radiation therapy plans are complex and time-consuming processes that heavily rely on the expertise of medical physicists. Consequently, there is an urgent need for automated optimization methods. Recent advancements in reinforcement learning, particularly deep reinforcement learning (DRL), show great promise for automating radiotherapy planning. This review summarizes the current state of DRL applications in this field, evaluates their effectiveness, and identifies challenges and future directions. METHODS: A systematic search was conducted in Google Scholar, PubMed, IEEE Xplore, and Scopus using keywords such as "deep reinforcement learning", "radiation therapy", and "treatment planning". The extracted data were synthesized for an overview and critical analysis. RESULTS: The application of deep reinforcement learning in radiation therapy plan optimization can generally be divided into three categories: optimizing treatment planning parameters, directly optimizing machine parameters, and adaptive radiotherapy. From the perspective of disease sites, DRL has been applied to cervical cancer, prostate cancer, vestibular schwannoma, and lung cancer. Regarding types of radiation therapy, it has been used in HDRBT, IMRT, SBRT, VMAT, GK, and Cyberknife. CONCLUSIONS: Deep reinforcement learning technology has played a significant role in advancing the automated optimization of radiation therapy plans. However, there is still a considerable gap before it can be widely applied in clinical settings due to three main reasons: inefficiency, limited methods for quality assessment, and poor interpretability. To address these challenges, significant research opportunities exist in the future, such as constructing evaluators, parallelized training, and exploring continuous action spaces.

Hybrid-resolution facial and dental scanning: a cost-effective approach to utilizing 3D facial data in anterior dental implant restoration.

As digital technology becomes more prevalent in the practice of dental medicine, methods to fully replace 2-dimensional photography and analog devices such as the facebow are still in their infancy. As more practices adopt 3-dimensional (3D) intraoral scanners, effective digital communication of the relationships between the teeth and the face becomes essential. With the high cost of intraoral scanners, the additional expense of a face scanner is not a feasible investment for many practices. This article explores a technique for meshing (lower resolution) facial data obtained from a smartphone-based scanner with high-resolution intraoral scan data. In this approach, the data from a free 3D scanning application on a smartphone and a traditional intraoral scanner are meshed so that high-resolution data are available for intraoral features and lower resolution data are used to capture the gross contours of the face. In this way, a hybrid-resolution composite scan that incorporates all of the data needed to simulate the face and accurately reproduce the teeth is generated without the cost of additional scanning equipment. This article defines a new term, the facial registration scan, for use alongside the familiar digital bite registration obtained with an intraoral scanner. To illustrate the clinical use of the hybrid-resolution scan concept, this article presents a case in which this method was used for the restoration of maxillary anterior implants.

Deep learning applied to dose prediction in external radiation therapy: A narrative review.

Over the last decades, the use of artificial intelligence, machine learning and deep learning in medical fields has skyrocketed. Well known for their results in segmentation, motion management and posttreatment outcome tasks, investigations of machine learning and deep learning models as fast dose calculation or quality assurance tools have been present since 2000. The main motivation for this increasing research and interest in artificial intelligence, machine learning and deep learning is the enhancement of treatment workflows, specifically dosimetry and quality assurance accuracy and time points, which remain important time-consuming aspects of clinical patient management. Since 2014, the evolution of models and architectures for dose calculation has been related to innovations and interest in the theory of information research with pronounced improvements in architecture design. The use of knowledge-based approaches to patient-specific methods has also considerably improved the accuracy of dose predictions. This paper covers the state of all known deep learning architectures and models applied to external radiotherapy with a description of each architecture, followed by a discussion on the performance and future of deep learning predictive models in external radiotherapy.

Evaluation of calculation accuracy and computation time in a commercial treatment planning system for accelerator-based boron neutron capture therapy.

This study aims to evaluate the feasibility of using a commercially available boron neutron capture therapy (BNCT) dose calculation program (NeuCure® Dose Engine) in terms of calculation accuracy and computation time. Treatment planning was simulated under the following calculation parameters: 1.5-5.0 mm grid sizes and 1-10% statistical uncertainties. The calculated monitor units (MUs) and computation times were evaluated. The MUs calculated on grid sizes larger than 2 mm were overestimated by 2% compared with the result of 1.5 mm grid. We established the two-step method for the routine administration of BNCT: multiple calculations involved in beam optimization should be done at a 5 mm grid and a 10% statistical uncertainty (the shortest computation time: 10.3 ± 2.1 min) in the first-step, and final dose calculations should be performed at a 2 mm grid and a 10% statistical uncertainty (satisfied clinical accuracy: 6.9 ± 0.3 h) in the second-step.

Refining Treatment Planning in STereotactic Arrhythmia Radioablation (STAR): Benchmark Results and Consensus Statement from the STOPSTORM.eu Consortium.

BACKGROUND AND PURPOSE: STereotactic Arrhythmia Radioablation (STAR) showed promising results in patients with refractory ventricular tachycardia (VT). However, clinical data is scarce and heterogeneous. The STOPSTORM.eu consortium was established to investigate and harmonize STAR in Europe. The primary goal of this benchmark study was to investigate current treatment planning practice within the STOPSTORM project as a baseline for future harmonization. METHODS: Planning target volumes (PTV) overlapping extra-cardiac organs-at-risk and/or cardiac substructures were generated for three STAR cases. Participating centers were asked to create single fraction treatment plans with 25 Gy dose prescription based on in-house clinical practice. All treatment plans were reviewed by an expert panel and quantitative crowd knowledge-based analysis was performed with independent software using descriptive statistics for ICRU report 91 relevant parameters and crowd dose-volume-histograms. Thereafter, treatment planning consensus statements were established using a dual-stage voting process. RESULTS: Twenty centers submitted 67 treatment plans for this study. In most plans (75%) Intensity Modulated Arc Therapy (IMAT) with 6 MV flattening-filter-free beams was used. Dose prescription was mainly based on PTV D95% (49%) or D96-100% (19%). Many participants preferred to spare close extra-cardiac organs-at-risk (75%) and cardiac substructures (50%) by PTV coverage reduction. PTV D0.035cm3 ranged 25.5-34.6 Gy, demonstrating a large variety of dose inhomogeneity. Estimated treatment times without motion compensation or setup ranged 2-80 minutes. For the consensus statements, strong agreement was reached for beam technique planning, dose calculation, prescription methods and trade-offs between target and extra-cardiac critical structures. No agreement was reached on cardiac substructure dose limitations and on desired dose inhomogeneity in the target. CONCLUSION: This STOPSTORM multi-center treatment planning benchmark study showed strong agreement on several aspects of STAR treatment planning, but also revealed disagreement on others. To standardize and harmonize STAR in the future, consensus statements were established, however clinical data is urgently needed for actionable guidelines for treatment planning.

Upper airway dimensions and craniofacial morphology: A correlation study using cone beam computed tomography.

Objective: To determine the correlation between dentoskeletal parameters related to craniofacial morphology and the upper airway (UA) volume. Methods: Cone-beam computed tomography images of 106 randomly selected orthodontic patients were analyzed using NemoFab Ortho software. The dentoskeletal variables assessed were anterior facial height (AFH), posterior facial height (PFH), PFH/AFH ratio, hyoid position, maxillary width (MW), and palatal depth. The UA volume (evaluation in anatomical regions and as a whole) was also assessed using the same software. We also evaluated potential differences in UA variables between age and sex groups. The correlation between the dentoskeletal parameters and UA volume was calculated using the Pearson correlation coefficient (R). Analysis of variance and Student's t test were performed to assess differences between age and sex for UA variables. Statistical analyses were performed using SPSS software (version 26 for Windows). Results: This study found that PFH, AFH, and MW were the dentoskeletal parameters most strongly correlated with UA volume. However, the ANB angle did not show any significant correlation with UA volume. Additionally, differences in UA volumes were observed between age groups. Sex differences were found in both the "8-12" and "≥ 16" age groups for oropharyngeal and pharyngeal volumes. Conclusions: In conclusion, our findings indicate a significant correlation between UA volume and dentoskeletal parameters, particularly those related to facial height and MW.

Doctor AI? A pilot study examining responses of artificial intelligence to common questions asked by geriatric patients.

Introduction: AI technologies have the potential to transform patient care. AI has been used to aid in differential diagnosis and treatment planning for psychiatric disorders, administer therapeutic protocols, assist with interpretation of cognitive testing, and patient treatment planning. Despite advancements, AI has notable limitations and remains understudied and further research on its strengths and limitations in patient care is required. This study explored the responses of AI (Chat-GPT 3.5) and trained clinicians to commonly asked patient questions. Methods: Three clinicians and AI provided responses to five dementia/geriatric healthcare-related questions. Responses were analyzed by a fourth, blinded clinician for clarity, accuracy, relevance, depth, and ease of understanding and to determine which response was AI generated. Results: AI responses were rated highest in ease of understanding and depth across all responses and tied for first for clarity, accuracy, and relevance. The rating for AI generated responses was 4.6/5 (SD = 0.26); the clinician s' responses were 4.3 (SD = 0.67), 4.2 (SD = 0.52), and 3.9 (SD = 0.59), respectively. The AI generated answers were identified in 4/5 instances. Conclusions: AI responses were rated more highly and consistently on each question individually and overall than clinician answers demonstrating that AI could produce good responses to potential patient questions. However, AI responses were easily distinguishable from those of clinicians. Although AI has the potential to positively impact healthcare, concerns are raised regarding difficulties discerning AI from human generated material, the increased potential for proliferation of misinformation, data security concerns, and more.

Total body irradiation: A transition from a Co-60 treatment unit to an IMRT lateral-field extended-SAD technique.

PURPOSE: The purpose of this work was to detail our center's experience in transitioning from a Co-60 treatment technique to an intensity modulated radiation therapy (IMRT) based lateral-field extended source-to-axis distance (e-SAD) technique for total body irradiation (TBI). MATERIALS AND METHODS: An existing beam model in RayStation v.10A was validated for the use of e-SAD TBI treatments. Data were acquired with an Elekta Synergy linear accelerator (LINAC) at an extended source-to-surface distance of 365 cm with an 18 MV beam. Beam model validation measurements included percentage depth dose (PDD), profile data, surface dose, build-up region and transmission measurements. End-to-end testing was carried out using an anthropomorphic phantom. Treatments were performed in a supine position in a whole-body Vac-Lok at an e-SAD of 400 cm with a beam spoiler 10 cm from the couch. Planning was achieved using IMRT, where multi-leaf collimators were used to modulate the beam and shield the organs at risk. Beam's eye view projection images were used for in-room patient positioning and in-vivo dosimetry was performed for every treatment. RESULTS: The percent difference between the measured and calculated PDD and profiles was less than 2% at all locations. Surface dose was 83.8% of the maximum dose with the beam spoiler at a 10 cm distance from the phantom. The largest percent difference between the treatment planning system (TPS) and measured data within the anthropomorphic phantom was approximately 2%. In-vivo dosimetry measurements yielded results within the 5% institutional threshold. CONCLUSION: In 2022, 17 patients were successfully treated using the new IMRT-based lateral-field e-SAD TBI technique. The resulting clinical plans respected the institutional standard. The commissioning process, as well as the treatment planning and delivery aspects were described in this work with the intention of supporting other clinics in implementing this treatment method.

The Diagnostic Impact of Contrast-Enhanced Computed Tomography (CECT) in Evaluating Lymph Node Involvement in Colorectal Cancer: A Comprehensive Review.

Colorectal cancer (CRC) remains a significant global health burden, necessitating accurate staging and treatment planning for optimal patient outcomes. Lymph node involvement is a critical determinant of prognosis in CRC, emphasizing the importance of reliable imaging techniques for its evaluation. Contrast-enhanced computed tomography (CECT) has emerged as a cornerstone in CRC imaging, offering high-resolution anatomical detail and vascular assessment. This comprehensive review synthesizes the existing literature to evaluate the diagnostic impact of CECT in assessing lymph node involvement in CRC. Key findings highlight CECT's high sensitivity and specificity in detecting lymph node metastases, facilitating accurate staging and treatment selection. However, challenges such as limited resolution for small lymph nodes and potential false-positives call for a cautious interpretation. Recommendations for clinical practice suggest the integration of CECT into multidisciplinary treatment algorithms, optimizing imaging protocols and enhancing collaboration between radiologists and clinicians. Future research directions include refining imaging protocols, comparative effectiveness studies with emerging modalities, and prospective validation of CECT's prognostic value. Overall, this review stresses the pivotal role of CECT in CRC management and identifies avenues for further advancements in imaging-guided oncology care.

Efficient radiation treatment planning based on voxel importance.

Objective.Radiation treatment planning (RTP) involves optimization over a large number of voxels, many of which carry limited information about the clinical problem. We propose an approach to reduce the large optimization problem by only using a representative subset of informative voxels. This way, we drastically improve planning efficiency while maintaining the plan quality.Approach.Within an initial probing step, we pre-solve an easier optimization problem involving a simplified objective from which we derive an importance score per voxel. This importance score is then turned into a sampling distribution, which allows us to subsample a small set of informative voxels using importance sampling. By solving a-now reduced-version of the original optimization problem using this subset, we effectively reduce the problem's size and computational demands while accounting for regions where satisfactory dose deliveries are challenging.Main results.In contrast to other stochastic (sub-)sampling methods, our technique only requires a single probing and sampling step to define a reduced optimization problem. This problem can be efficiently solved using established solvers without the need of modifying or adapting them. Empirical experiments on open benchmark data highlight substantially reduced optimization times, up to 50 times faster than the original ones, for intensity-modulated radiation therapy, all while upholding plan quality comparable to traditional methods.Significance.Our novel approach has the potential to significantly accelerate RTP by addressing its inherent computational challenges. We reduce the treatment planning time by reducing the size of the optimization problem rather than modifying and improving the optimization method. Our efforts are thus complementary to many previous developments.

Modelling systematic anatomical uncertainties of head and neck cancer patients during fractionated radiotherapy treatment.

Objective.Head and neck cancer patients experience systematic as well as random day to day anatomical changes during fractionated radiotherapy treatment. Modelling the expected systematic anatomical changes could aid in creating treatment plans which are more robust against such changes.Approach.Inter- patient correspondence aligned all patients to a model space. Intra- patient correspondence between each planning CT scan and on treatment cone beam CT scans was obtained using diffeomorphic deformable image registration. The stationary velocity fields were then used to develop B-Spline based patient specific (SM) and population average (AM) models. The models were evaluated geometrically and dosimetrically. A leave-one-out method was used to compare the training and testing accuracy of the models.Main results.Both SMs and AMs were able to capture systematic changes. The average surface distance between the registration propagated contours and the contours generated by the SM was less than 2 mm, showing that the SM are able to capture the anatomical changes which a patient experiences during the course of radiotherapy. The testing accuracy was lower than the training accuracy of the SM, suggesting that the model overfits to the limited data available and therefore, also captures some of the random day to day changes. For most patients the AMs were a better estimate of the anatomical changes than assuming there were no changes, but the AMs could not capture the variability in the anatomical changes seen in all patients. No difference was seen in the training and testing accuracy of the AMs. These observations were highlighted in both the geometric and dosimetric evaluations and comparisons.Significance.In this work, a SM and AM are presented which are able to capture the systematic anatomical changes of some head and neck cancer patients over the course of radiotherapy treatment. The AM is able to capture the overall trend of the population, but there is large patient variability which highlights the need for more complex, capable population models.

RBE-based dose planning, and calculation of TCP and NTCP with gold nanoparticles for intermediate photon energy in pancreatic cancer.

Objective.This study simulated the potential of gold nanoparticles (GNPs) to improve the effectiveness of radiation therapy in pancreatic cancer cases. The purpose of this study was to assess the impact of GNPs on tumor control probability (TCP) and normal tissue complication probability (NTCP) in pancreatic cancer cases undergoing radiation therapy. The work aimed to compare treatment plans generated with a novel 2.5 MV beam using GNPs to conventional 6 MV plans and evaluate the dose-volume histogram (DVH), TCP, and NTCP.Approach.Treatment planning for five pancreatic computed tomography (CT) images was performed using the open-source MATLAB-based treatment planning program matRad. MATLAB codes were developed to calculate the relative biological effectiveness (RBE) of GNPs and apply the corresponding dose and RBE values to each voxel. TCP and NTCP were calculated based on the applied RBE values.Main results.Adding GNPs to the 2.5 MV treatment plan resulted in a significant increase in TCP, from around 59% to 93.5%, indicating that the inclusion of GNPs improved the effectiveness of the radiation treatment. The range in NTCP without GNPs was relatively larger compared to that with GNPs.Significance.The results indicated that the addition of GNPs to a 2.5 MV plan can increase TCP while maintaining a relatively low NTCP value (<1%). The use of GNPs may also reduce NTCP values by decreasing the dose to normal tissues while maintaining the same prescribed dose to the tumor. Hence, the addition of GNPs can improve the balance between TCP and NTCP.

An interface tool to parametrize treatment plans for the TrueBeam radiotherapy system into TOPAS parameter control files for Monte Carlo simulation.

PURPOSE: The Monte Carlo (MC) method, the gold standard method for radiotherapy dose calculations, is underused in clinical research applications mainly due to computational speed limitations. Another reason is the time-consuming and error prone conversion of treatment plan specifications into MC parameters. To address this issue, we developed an interface tool that creates a set of TOPAS parameter control files (PCF) from information exported from a clinical treatment planning system (TPS) for plans delivered by the TrueBeam radiotherapy system. METHODS: The interface allows the user to input DICOM-RT files, exported from a TPS and containing the plan parameters, and choose different multileaf-collimator models, variance reduction technique parameters, scoring quantities and simulation output formats. Radiation sources are precomputed phase space files obtained from Varian. Based on this information, ready-to-run TOPAS PCF that incorporate the position and angular rotation of the TrueBeam dynamic collimation devices, gantry, couch, and patient according to treatment plan specifications are created. RESULTS: Dose distributions computed using these PCF were compared against predictions from commercial TPS for different clinical treatment plans and techniques (3D-CRT, IMRT step-and-shoot and VMAT) to evaluate the performance of the interface. The agreement between dose distributions from TOPAS and TPS (>98 % pass ratio in the gamma test) confirmed the correct parametrization of treatment plan specifications into MC PCF. CONCLUSIONS: This interface tool is expected to widen the use of MC methods in the clinical medical physics field by facilitating the straightforward transfer of treatment plan parameters from commercial TPS into MC PCF.

Surface dose measurement by optically stimulated luminescent dosimeter: A phantom study.

INTRODUCTION: Radiotherapy is the standard treatment for breast cancer patients after surgery. However, radiotherapy can cause side effects such as dry and moist desquamation of the patient's skin. The dose calculation from a treatment planning system (TPS) might also be inaccurate. The purpose of this study is to measure the surface dose on the CIRS thorax phantom by an optically stimulated luminescent dosimeter (OSLD). METHODS: The characteristics of OSLD were studied in terms of dose linearity, reproducibility, and angulation dependence on the solid water phantom. To determine the surface dose, OSLD (Landauer lnc., USA) was placed on 5 positions at the CIRS phantom (Tissue Simulation and Phantom Technology, USA). The five positions were at the tip, medial, lateral, tip-medial, and tip-lateral. Then, the doses from OSLD and TPS were compared. RESULTS: The dosimeter's characteristic test was good. The maximum dose at a depth of 15 mm was 514.46 cGy, which was at 100%. The minimum dose at the surface was 174.91 cGy, which was at 34%. The results revealed that the surface dose from TPS was less than the measurement. The percent dose difference was -2.17 ± 6.34, -12.08 ± 3.85, and -48.71 ± 1.29 at the tip, medial, and lateral positions, respectively. The surface dose from TPS at tip-medial and tip-lateral was higher than the measurement, which was 12.56 ± 5.55 and 10.45 ± 1.76 percent dose different, respectively. CONCLUSION: The percent dose difference is within the acceptable limit, except for the lateral position because of the body curvature. However, OSLD is convenient to assess the radiation dose, and further study is to measure in vivo. IMPLICATION FOR PRACTICE: The OSL NanoDot dosimeter can be used for dose validation with a constant setup location. The measurement dose is higher than the dose from TPS, except for some tilt angles.

A novel fast robust optimization algorithm for intensity-modulated proton therapy with minimum monitor unit constraint.

BACKGROUND: Intensity-modulated proton therapy (IMPT) optimizes spot intensities and position, providing better conformability. However, the successful application of IMPT is dependent upon addressing the challenges posed by range and setup uncertainties. In order to address the uncertainties in IMPT, robust optimization is essential. PURPOSE: This study aims to develop a novel fast algorithm for robust optimization of IMPT with minimum monitor unit (MU) constraint. METHODS AND MATERIALS: The study formulates a robust optimization problem and proposes a novel, fast algorithm based on the alternating direction method of multipliers (ADMM) framework. This algorithm enables distributed computation and parallel processing. Ten clinical cases were used as test scenarios to evaluate the performance of the proposed approach. The robust optimization method (RBO-NEW) was compared with plans that only consider nominal optimization using CTV (NMO-CTV) without handling uncertainties and PTV (NMO-PTV) to handle the uncertainties, as well as with conventional robust-optimized plans (RBO-CONV). Dosimetric metrics, including D95, homogeneity index, and Dmean, were used to evaluate the dose distribution quality. The area under the root-mean-square dose (RMSD)-volume histogram curves (AUC) and dose-volume histogram (DVH) bands were used to evaluate the robustness of the treatment plan. Optimization time cost was also assessed to measure computational efficiency. RESULTS: The results demonstrated that the RBO plans exhibited better plan quality and robustness than the NMO plans, with RBO-NEW showing superior computational efficiency and plan quality compared to RBO-CONV. Specifically, statistical analysis results indicated that RBO-NEW was able to reduce the computational time from 389.70 ± 207.40 $389.70\pm 207.40$ to 228.60 ± 123.67 $228.60\pm 123.67$ s ( p < 0.01 $p<0.01$ ) and reduce the mean organ-at-risk (OAR) dose from 9.38 ± 12.80 $9.38\pm 12.80$ % of the prescription dose to 9.07 ± 12.39 $9.07\pm 12.39$ % of the prescription dose ( p < 0.05 $p<0.05$ ) compared to RBO-CONV. CONCLUSION: This study introduces a novel fast robust optimization algorithm for IMPT treatment planning with minimum MU constraint. Such an algorithm is not only able to enhance the plan's robustness and computational efficiency without compromising OAR sparing but also able to improve treatment plan quality and reliability.