A marginal structural model for normal tissue complication probability.

The goal of radiation therapy for cancer is to deliver prescribed radiation dose to the tumor while minimizing dose to the surrounding healthy tissues. To evaluate treatment plans, the dose distribution to healthy organs is commonly summarized as dose-volume histograms (DVHs). Normal tissue complication probability (NTCP) modeling has centered around making patient-level risk predictions with features extracted from the DVHs, but few have considered adapting a causal framework to evaluate the safety of alternative treatment plans. We propose causal estimands for NTCP based on deterministic and stochastic interventions, as well as propose estimators based on marginal structural models that impose bivariable monotonicity between dose, volume, and toxicity risk. The properties of these estimators are studied through simulations, and their use is illustrated in the context of radiotherapy treatment of anal canal cancer patients.

Geometrical accuracy of magnetic resonance imaging for ocular proton therapy planning.

Background & purpose: Magnetic resonance imaging (MRI) is increasingly used in treatment preparation of ocular proton therapy, but its spatial accuracy might be limited by geometric distortions due to susceptibility artefacts. A correct geometry of the MR images is paramount since it defines where the dose will be delivered. In this study, we assessed the geometrical accuracy of ocular MRI. Materials & methods: A dedicated ocular 3 T MRI protocol, with localized shimming and increased gradients, was compared to computed tomography (CT) and X-ray images in a phantom and in 15 uveal melanoma patients. The MRI protocol contained three-dimensional T2-weighted and T1-weighted sequences with an isotropic reconstruction resolution of 0.3-0.4 mm. Tantalum clips were identified by three observers and clip-clip distances were compared between T2-weighted and T1-weighted MRI, CT and X-ray images for the phantom and between MRI and X-ray images for the patients. Results: Interobserver variability was below 0.35 mm for the phantom and 0.30(T1)/0.61(T2) mm in patients. Mean absolute differences between MRI and reference were below 0.27 ± 0.16 mm and 0.32 ± 0.23 mm for the phantom and in patients, respectively. In patients, clip-clip distances were slightly larger on MRI than on X-ray images (mean difference T1: 0.11 ± 0.38 mm, T2: 0.10 ± 0.44 mm). Differences did not increase at larger distances and did not correlate to interobserver variability. Conclusions: A dedicated ocular MRI protocol can produce images of the eye with a geometrical accuracy below half the MRI acquisition voxel (<0.4 mm). Therefore, these images can be used for ocular proton therapy planning, both in the current model-based workflow and in proposed three-dimensional MR-based workflows.

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

Head and neck cancer patients experience systematic anatomical changes 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. A patient specific (SM) and population average (AM) model are presented which are able to capture the systematic anatomical changes of some head and neck cancer patients over the course of radiotherapy treatment. 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 SMs and AMs. The models were evaluated geometrically and dosimetrically. A leave-one-out method was used to compare the training and testing accuracy of the models. 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 2mm, 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. The large patient variability highlights the need for more complex, capable population models.

The Evolving Role of Artificial Intelligence in Radiotherapy Treatment Planning-A Literature Review.

This paper examines the integration of artificial intelligence (AI) in radiotherapy for cancer treatment. The importance of radiotherapy in cancer management and its time-intensive planning process make AI adoption appealing especially with the escalating demand for radiotherapy. This review highlights the efficacy of AI across medical domains, where it surpasses human capabilities in areas such as cardiology and dermatology. Focusing on radiotherapy, the paper details AI's applications in target segmentation, dose optimization, and outcome prediction. It discusses adaptive radiotherapy's benefits and AI's potential to enhance patient outcomes with much improved treatment accuracy. The paper explores ethical concerns, including data privacy and bias, stressing the need for robust guidelines. Educating healthcare professionals and patients about AI's role is crucial as it acknowledges potential job-role changes and concerns about patients' trust in the use of AI. Overall, the integration of AI in radiotherapy holds transformative potential in streamlining processes, improving outcomes, and reducing costs. AI's potential to reduce healthcare costs underscores its significance with impactful change globally. However, successful implementation hinges on addressing ethical and logistical challenges and fostering collaboration among healthcare professionals and patient population data sets for its optimal utilization. Rigorous education, collaborative efforts, and global data sharing will be the compass guiding its' success in radiotherapy and healthcare.

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.

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.

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.

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.

Treatment Planning Strategies for Interstitial Ultrasound Ablation of Prostate Cancer.

PURPOSE: To develop patient-specific 3D models using Finite-Difference Time-Domain (FDTD) simulations and pre-treatment planning tools for the selective thermal ablation of prostate cancer with interstitial ultrasound. This involves the integration with a FDA 510(k) cleared catheter-based ultrasound interstitial applicators and delivery system. METHODS: A 3D generalized "prostate" model was developed to generate temperature and thermal dose profiles for different applicator operating parameters and anticipated perfusion ranges. A priori planning, based upon these pre-calculated lethal thermal dose and iso-temperature clouds, was devised for iterative device selection and positioning. Full 3D patient-specific anatomic modeling of actual placement of single or multiple applicators to conformally ablate target regions can be applied, with optional integrated pilot-point temperature-based feedback control and urethral/rectum cooling. These numerical models were verified against previously reported ex-vivo experimental results obtained in soft tissues. RESULTS: For generic prostate tissue, 360 treatment schemes were simulated based on the number of transducers (1-4), applied power (8-20 W/cm2), heating time (5, 7.5, 10 min), and blood perfusion (0, 2.5, 5 kg/m3/s) using forward treatment modelling. Selectable ablation zones ranged from 0.8-3.0 cm and 0.8-5.3 cm in radial and axial directions, respectively. 3D patient-specific thermal treatment modeling for 12 Cases of T2/T3 prostate disease demonstrate applicability of workflow and technique for focal, quadrant and hemi-gland ablation. A temperature threshold (e.g., Tthres = 52 °C) at the treatment margin, emulating placement of invasive temperature sensing, can be applied for pilot-point feedback control to improve conformality of thermal ablation. Also, binary power control (e.g., Treg = 45 °C) can be applied which will regulate the applied power level to maintain the surrounding temperature to a safe limit or maximum threshold until the set heating time. CONCLUSIONS: Prostate-specific simulations of interstitial ultrasound applicators were used to generate a library of thermal-dose distributions to visually optimize and set applicator positioning and directivity during a priori treatment planning pre-procedure. Anatomic 3D forward treatment planning in patient-specific models, along with optional temperature-based feedback control, demonstrated single and multi-applicator implant strategies to effectively ablate focal disease while affording protection of normal tissues.

From Detection to Decision: How STIR Sequence MRI Influences Treatment Strategies for Osteoporotic Vertebral Fractures.

Background/Objectives: Osteoporotic vertebral fractures (OVFs) significantly impair quality of life. This study evaluates the impact of STIR sequence MR imaging on clinical decision-making for treating OVFs, mainly focusing on how MRI findings influence treatment modifications compared to those based solely on CT scans. Methods: This retrospective analysis reviewed cases from the Manninger Jeno National Traumatology Institute over ten years, where patients with suspected OVFs underwent CT and STIR sequence MR imaging. The study examined changes in treatment plans initiated by MRI findings. The diagnostic effectiveness of MRI was compared against CT in terms of sensitivity, specificity, and the ability to influence clinical treatment paths. Results: MRI detected 1.65 times more fractures than CT scans. MRI influenced treatment adjustments in 67% of cases, leading to significant changes from conservative-conservative, conservative-surgery, and surgery-surgery based on fracture characterizations provided by MRI. Conclusions: This study demonstrates that integrating STIR sequence MR imaging into the diagnostic pathway for OVFs significantly enhances the accuracy of fracture detection and profoundly impacts treatment decisions. The ability of MRI to reveal specific fracture features that are not detectable by CT scans supports its importance in the clinical evaluation of OVFs, suggesting that MRI should be incorporated more into diagnostic protocols to improve patient management and outcomes. The findings advocate for further research to establish STIR MRI as a standard osteoporosis management tool and explore its long-term benefits in preventing secondary fractures.

The Role of MRI in Breast Cancer and Breast Conservation Therapy.

Contrast-enhanced breast MRI has an established role in aiding in the detection, evaluation, and management of breast cancer. This article discusses MRI sequences, the clinical utility of MRI, and how MRI has been evaluated for use in breast radiotherapy treatment planning. We highlight the contribution of MRI in the decision-making regarding selecting appropriate candidates for breast conservation therapy and review the emerging role of MRI-guided breast radiotherapy.

Robustness evaluation of pencil beam scanning proton therapy treatment planning: A systematic review.

Compared to conventional radiotherapy using X-rays, proton therapy, in principle, allows better conformity of the dose distribution to target volumes, at the cost of greater sensitivity to physical, anatomical, and positioning uncertainties. Robust planning, both in terms of plan optimization and evaluation, has gained high visibility in publications on the subject and is part of clinical practice in many centers. However, there is currently no consensus on the methods and parameters to be used for robust optimization or robustness evaluation. We propose to overcome this deficiency by following the modified Delphi consensus method. This method first requires a systematic review of the literature. We performed this review using the PubMed and Web Of Science databases, via two different experts. Potential conflicts were resolved by a third expert. We then explored the different methods before focusing on clinical studies that evaluate robustness on a significant number of patients. Many robustness assessment methods are proposed in the literature. Some are more successful than others and their implementation varies between centers. Moreover, they are not all statistically or mathematically equivalent. The most sophisticated and rigorous methods have seen more limited application due to the difficulty of their implementation and their lack of widespread availability.

What is the optimal isodose line for stereotactic radiotherapy for single brain metastases using HyperArc?

PURPOSE: The study aimed to investigate the optimal isodose line (IDL) in linear accelerator-based stereotactic radiotherapy for single brain metastasis, using HyperArc. We compared the dosimetric parameters for target and normal brain tissue among six plans with different IDLs. METHODS: This study included 30 patients with single brain metastasis. We retrospectively generated six plans for each tumor with different IDLs (80%, 70%, 60%, 50%, 40%, and 33%) using HyperArc. All treatment plans were normalized to the prescription dose of 35 Gy in five fractions which was covered by 95% of the planning target volume (PTV), defined by adding a 1.0 mm margin to the gross tumor volume (GTV). The dosimetric parameters were compared among the six plans. RESULTS: For GTV > 0.1 cm3, the ratio of brain-GTV volumes receiving 25 Gy to PTV (V25Gy/PTV) was significantly lower at IDL 40%-70% than at IDL 80% and 33% (p < 0.01, retrospectively). For GTV < 0.1 cm3, V25Gy/PTV decreased continuously as IDL decreased. The values of D99% and D80% for GTV increased with decreasing IDL. An IDL of 50% or less was required to achieve D99% of greater than 43 Gy and D80% of greater than 50 Gy. The mean values of D99% and D80% for IDL 50% were 44.3 and 51.9 Gy. CONCLUSION: The optimal IDL is 40%-50% for GTV > 0.1 cm3. These lower IDLs could increase D99% and D80% of GTV while lowering V25Gy of normal brain tissue, which may help reduce the risk of radiation necrosis and improve local control.

A randomized controlled trial of shared decision-making treatment planning process to enhance shared decision-making in patients with MBC.

PURPOSE: Opportunities exist for patients with metastatic breast cancer (MBC) to engage in shared decision-making (SDM). Presenting patient-reported data, including patient treatment preferences, to oncologists before or during a treatment plan decision may improve patient engagement in treatment decisions. METHODS: This randomized controlled trial evaluated the standard-of-care treatment planning process vs. a novel treatment planning process focused on SDM, which included oncologist review of patient-reported treatment preferences, prior to or during treatment decisions among women with MBC. The primary outcome was patient perception of shared decision-making. Secondary outcomes included patient activation, treatment satisfaction, physician perception of treatment decision-making, and use of treatment plans. RESULTS: Among the 109 evaluable patients from December 2018 to June 2022, 28% were Black and 12% lived in a highly disadvantaged neighborhood. Although not reaching statistical significance, patients in the intervention arm perceived SDM more often than patients in the control arm (63% vs. 59%; Cramer's V = 0.05; OR 1.19; 95% CI 0.55-2.57). Among patients in the intervention arm, 31% were at the highest level of patient activation compared to 19% of those in the control arm (V = 0.18). In 82% of decisions, the oncologist agreed that the patient-reported data helped them engage in SDM. In 45% of decision, they reported changing management due to patient-reported data. CONCLUSIONS: Oncologist engagement in the treatment planning process, with oncologist review of patient-reported data, is a promising approach to improve patient participation in treatment decisions which should be tested in larger studies. TRIAL REGISTRATION: NCT03806738.

A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy.

BACKGROUND AND PURPOSE: Artificial Intelligence (AI) models in radiation therapy are being developed with increasing pace. Despite this, the radiation therapy community has not widely adopted these models in clinical practice. A cohesive guideline on how to develop, report and clinically validate AI algorithms might help bridge this gap. METHODS AND MATERIALS: A Delphi process with all co-authors was followed to determine which topics should be addressed in this comprehensive guideline. Separate sections of the guideline, including Statements, were written by subgroups of the authors and discussed with the whole group at several meetings. Statements were formulated and scored as highly recommended or recommended. RESULTS: The following topics were found most relevant: Decision making, image analysis, volume segmentation, treatment planning, patient specific quality assurance of treatment delivery, adaptive treatment, outcome prediction, training, validation and testing of AI model parameters, model availability for others to verify, model quality assurance/updates and upgrades, ethics. Key references were given together with an outlook on current hurdles and possibilities to overcome these. 19 Statements were formulated. CONCLUSION: A cohesive guideline has been written which addresses main topics regarding AI in radiation therapy. It will help to guide development, as well as transparent and consistent reporting and validation of new AI tools and facilitate adoption.

Quality assurance of an established online adaptive radiotherapy program: patch and software upgrade.

Introduction: The ability to dynamically adjust target contours, derived Boolean structures, and ultimately, the optimized fluence is the end goal of online adaptive radiotherapy (ART). The purpose of this work is to describe the necessary tests to perform after a software patch installation and/or upgrade for an established online ART program. Methods: A patch upgrade on a low-field MR Linac system was evaluated for post-software upgrade quality assurance (QA) with current infrastructure of ART workflow on (1) the treatment planning system (TPS) during the initial planning stage and (2) the treatment delivery system (TDS), which is a TPS integrated into the delivery console for online ART planning. Online ART QA procedures recommended for post-software upgrade include: (1) user interface (UI) configuration; (2) TPS beam model consistency; (3) segmentation consistency; (4) dose calculation consistency; (5) optimizer robustness consistency; (6) CT density table consistency; and (7) end-to-end absolute ART dose and predicted dose measured including interruption testing. Differences of calculated doses were evaluated through DVH and/or 3D gamma comparisons. The measured dose was assessed using an MR-compatible A26 ionization chamber in a motion phantom. Segmentation differences were assessed through absolute volume and visual inspection. Results: (1) No UI configuration discrepancies were observed. (2) Dose differences on TPS pre-/post-software upgrade were within 1% for DVH metrics. (3) Differences in segmentation when observed were small in general, with the largest change noted for small-volume regions of interest (ROIs) due to partial volume impact. (4) Agreement between TPS and TDS calculated doses was 99.9% using a 2%/2-mm gamma criteria. (5) Comparison between TPS and online ART plans for a given patient plan showed agreement within 2% for targets and 0.6 cc for organs at risk. (6) Relative electron densities demonstrated comparable agreement between TPS and TDS. (7) ART absolute and predicted measured end-to-end doses were within 1% of calculated TDS. Discussion: An online ART QA program for post-software upgrade has been developed and implemented on an MR Linac system. Testing mechanics and their respective baselines may vary across institutions, but all necessary components for a post-software upgrade QA have been outlined and detailed. These outlined tests were demonstrated feasible for a low-field MR Linac system; however, the scope of this work may be applied and adapted more broadly to other online ART platforms.

Deep learning-based voxel sampling for particle therapy treatment planning.

OBJECTIVE: Scanned particle therapy often requires complex treatment plans, robust optimization, as well as treatment adaptation. Plan optimization is especially complicated for heavy ions due to the variable relative biological effectiveness. We present a novel deep-learning model to select a subset of voxels in the planning process thus reducing the planning problem size for improved computational efficiency. Approach: Using only a subset of the voxels in target and organs at risk (OARs) we produced high-quality treatment plans, but heuristic selection strategies require manual input. We designed a deep-learning model based on P-Net to obtain an optimal voxel sampling without relying on patient-specific user input. A cohort of 70 head and neck patients that received carbon ion therapy was used for model training (50), validation (10) and testing (10). For training, a total of 12,500 carbon ion plans were optimized, using a highly efficient artificial intelligence (AI) infrastructure implemented into a research treatment planning platform. A custom loss function increased sampling density in underdosed regions, while aiming to reduce the total number of voxels. Main results: On the test dataset, the number of voxels in the optimization could be reduced by 84.8% (median) at <1% median loss in plan quality. When the model was trained to reduce sampling in the target only while keeping all voxels in OARs, a median reduction up to 71.6% was achieved, with 0.5% loss in the plan quality. The optimization time was reduced by a factor of 7.5 for the total AI selection model and a factor of 3.7 for the model with only target selection. Significance: The novel deep-learning voxel sampling technique achieves a significant reduction in computational time with a negligible loss in the plan quality. The reduction in optimization time can be especially useful for future real-time adaptation strategies. .

Dosimetric evaluation and treatment planning considerations for GammaTile permanent brain implants - a pilot, institutional experience.

PURPOSE: GammaTile® (GT) is a brachytherapy platform that received Federal Drug Administration (FDA) approval as brain tumor therapy in late 2018. Here, we reviewed our institutional experience with GT as treatment for recurrent glioblastomas and characterized dosimetric parameter and associated clinical outcome. METHODS AND MATERIALS: A total of 20 consecutive patients with 21 (n = 21) diagnosis of recurrent glioblastoma underwent resection followed by intraoperative GT implant between 01/2019 and 12/2020. Data on gross tumor volume (GTV), number of GT units implanted, dose coverage for the high-risk clinical target volume (HR-CTV), measured by D90 or dose received by 90% of the HR-CTV, dose to organs at risk, and six months local control were collected. RESULTS: The median D90 to HR-CTV was 56.0 Gy (31.7-98.7 Gy). The brainstem, optic chiasm, ipsilateral optic nerve, and ipsilateral hippocampus median Dmax were 11.2, 5.4, 6.4, and 10.0 Gy, respectively. None of the patients in this study cohort suffered from radiation necrosis or adverse events attributable to the GT. Correlation was found between pre-op GTV, the volume of the resection cavity, and the number of GT units implanted. Of the resection cavities, 7/21 (33%) of the cavity experienced shrinkage, 3/21 (14%) remained stable, and 11/21 (52%) of the cavities expanded on the 3-months post-resection/GT implant MRIs. D90 to HR-CTV was found to be associated with local recurrence at 6-month post GT implant, suggesting a dose response relationship (p = 0.026). The median local recurrence-free survival was 366.5 days (64-1,098 days), and a trend towards improved local recurrence-free survival was seen in patients with D90 to HR-CTV ≥ 56 Gy (p = 0.048). CONCLUSIONS: Our pilot, institutional experience provides clinical outcome, dosimetric considerations, and offer technical guidance in the clinical implementation of GT brachytherapy.

FET PET-based target volume delineation for the radiotherapy of glioblastoma: A pictorial guide to help overcome methodological pitfalls.

PET is increasingly used for target volume definition in the radiotherapy of glioblastoma, as endorsed by the 2023 ESTRO-EANO guidelines. In view of its growing adoption into clinical practice and upcoming PET-based multi-center trials, this paper aims to assist in overcoming common pitfalls of FET PET-based target delineation in glioblastoma.

Treatment planning for MR-guided SBRT of pancreatic tumors on a 1.5 T MR-Linac: A global consensus protocol.

Background and purpose: Treatment planning for MR-guided stereotactic body radiotherapy (SBRT) for pancreatic tumors can be challenging, leading to a wide variation of protocols and practices. This study aimed to harmonize treatment planning by developing a consensus planning protocol for MR-guided pancreas SBRT on a 1.5 T MR-Linac. Materials and methods: A consortium was founded of thirteen centers that treat pancreatic tumors on a 1.5 T MR-Linac. A phased planning exercise was conducted in which centers iteratively created treatment plans for two cases of pancreatic cancer. Each phase was followed by a meeting where the instructions for the next phase were determined. After three phases, a consensus protocol was reached. Results: In the benchmarking phase (phase I), substantial variation between the SBRT protocols became apparent (for example, the gross tumor volume (GTV) D99% ranged between 36.8 - 53.7 Gy for case 1, 22.6 - 35.5 Gy for case 2). The next phase involved planning according to the same basic dosimetric objectives, constraints, and planning margins (phase II), which led to a large degree of harmonization (GTV D99% range: 47.9-53.6 Gy for case 1, 33.9-36.6 Gy for case 2). In phase III, the final consensus protocol was formulated in a treatment planning system template and again used for treatment planning. This not only resulted in further dosimetric harmonization (GTV D99% range: 48.2-50.9 Gy for case 1, 33.5-36.0 Gy for case 2) but also in less variation of estimated treatment delivery times. Conclusion: A global consensus protocol has been developed for treatment planning for MR-guided pancreatic SBRT on a 1.5 T MR-Linac. Aside from harmonizing the large variation in the current clinical practice, this protocol can provide a starting point for centers that are planning to treat pancreatic tumors on MR-Linac systems.