Treatment planning and delivery practice of lung SBRT: Results of the 2022 ESTRO physics survey.

BACKGROUND AND PURPOSE: The use of Stereotactic Body Radiation Therapy (SBRT) in lung cancer is increasing. However, there is no consensus on the most appropriate treatment planning and delivery practice for lung SBRT. To gauge the range of practice, quantify its variability and identify where consensus might be achieved, ESTRO surveyed the medical physics community. MATERIALS AND METHODS: An online survey was distributed to ESTRO's physicist membership in 2022, covering experience, dose and fractionation, target delineation, dose calculation and planning practice, imaging protocols, and quality assurance. RESULTS: Two-hundred and forty-four unique answers were collected after data cleaning. Most respondents were from Europe the majority of which had more than 5 years' experience in SBRT. The large majority of respondents deliver lung SBRT with the VMAT technique on C-arm Linear Accelerators (Linacs) employing daily pre-treatment CBCT imaging. A broad spectrum of fractionation schemes were reported, alongside an equally wide range of dose prescription protocols. A clear preference was noted for prescribing to 95% or greater of the PTV. Several issues emerged regarding the dose calculation algorithm: 22% did not state it while 24% neglected to specify the conditions under which the dose was calculated. Contouring was usually performed on Maximum or Average Intensity Projection images while dose was mainly computed on the latter. No clear indications emerged for plan homogeneity, complexity, and conformity assessment. Approximately 40% of the responders participated in inter-centre credentialing of SBRT in the last five years. Substantial differences emerged between high and low experience centres, with the latter employing less accurate algorithms and older equipment. CONCLUSION: The survey revealed an evident heterogeneity in numerous aspects of the clinical implementation of lung SBRT treatments. International guidelines and codes of practice might promote harmonisation.

Deep learning-based optimization of field geometry for total marrow irradiation delivered with volumetric modulated arc therapy.

BACKGROUND: Total marrow (lymphoid) irradiation (TMI/TMLI) is a radiotherapy treatment used to selectively target the bone marrow and lymph nodes in conditioning regimens for allogeneic hematopoietic stem cell transplantation. A complex field geometry is needed to cover the large planning target volume (PTV) of TMI/TMLI with volumetric modulated arc therapy (VMAT). Five isocenters and ten overlapping fields are needed for the upper body, while, for patients with large anatomical conformation, two specific isocenters are placed on the arms. The creation of a field geometry is clinically challenging and is performed by a medical physicist (MP) specialized in TMI/TMLI. PURPOSE: To develop convolutional neural networks (CNNs) for automatically generating the field geometry of TMI/TMLI. METHODS: The dataset comprised 117 patients treated with TMI/TMLI between 2011 and 2023 at our Institute. The CNN input image consisted of three channels, obtained by projecting along the sagittal plane: (1) average CT pixel intensity within the PTV; (2) PTV mask; (3) brain, lungs, liver, bowel, and bladder masks. This "averaged" frontal view combined the information analyzed by the MP when setting the field geometry in the treatment planning system (TPS). Two CNNs were trained to predict the isocenters coordinates and jaws apertures for patients with (CNN-1) and without (CNN-2) isocenters on the arms. Local optimization methods were used to refine the models output based on the anatomy of the patient. Model evaluation was performed on a test set of 15 patients in two ways: (1) by computing the root mean squared error (RMSE) between the CNN output and ground truth; (2) with a qualitative assessment of manual and generated field geometries-scale: 1 = not adequate, 4 = adequate-carried out in blind mode by three MPs with different expertise in TMI/TMLI. The Wilcoxon signed-rank test was used to evaluate the independence of the given scores between manual and generated configurations (p < 0.05 significant). RESULTS: The average and standard deviation values of RMSE for CNN-1 and CNN-2 before/after local optimization were 15 ± 2/13 ± 3 mm and 16 ± 2/18 ± 4 mm, respectively. The CNNs were integrated into a planning automation software for TMI/TMLI such that the MPs could analyze in detail the proposed field geometries directly in the TPS. The selection of the CNN model to create the field geometry was based on the PTV width to approximate the decision process of an experienced MP and provide a single option of field configuration. We found no significant differences between the manual and generated field geometries for any MP, with median values of 4 versus 4 (p = 0.92), 3 versus 3 (p = 0.78), 4 versus 3 (p = 0.48), respectively. Starting from October 2023, the generated field geometry has been introduced in our clinical practice for prospective patients. CONCLUSIONS: The generated field geometries were clinically acceptable and adequate, even for an MP with high level of expertise in TMI/TMLI. Incorporating the knowledge of the MPs into the development cycle was crucial for optimizing the models, especially in this scenario with limited data.

Deep learning and atlas-based models to streamline the segmentation workflow of total marrow and lymphoid irradiation.

PURPOSE: To improve the workflow of total marrow and lymphoid irradiation (TMLI) by enhancing the delineation of organs at risk (OARs) and clinical target volume (CTV) using deep learning (DL) and atlas-based (AB) segmentation models. MATERIALS AND METHODS: Ninety-five TMLI plans optimized in our institute were analyzed. Two commercial DL software were tested for segmenting 18 OARs. An AB model for lymph node CTV (CTV_LN) delineation was built using 20 TMLI patients. The AB model was evaluated on 20 independent patients, and a semiautomatic approach was tested by correcting the automatic contours. The generated OARs and CTV_LN contours were compared to manual contours in terms of topological agreement, dose statistics, and time workload. A clinical decision tree was developed to define a specific contouring strategy for each OAR. RESULTS: The two DL models achieved a median [interquartile range] dice similarity coefficient (DSC) of 0.84 [0.71;0.93] and 0.85 [0.70;0.93] across the OARs. The absolute median Dmean difference between manual and the two DL models was 2.0 [0.7;6.6]% and 2.4 [0.9;7.1]%. The AB model achieved a median DSC of 0.70 [0.66;0.74] for CTV_LN delineation, increasing to 0.94 [0.94;0.95] after manual revision, with minimal Dmean differences. Since September 2022, our institution has implemented DL and AB models for all TMLI patients, reducing from 5 to 2 h the time required to complete the entire segmentation process. CONCLUSION: DL models can streamline the TMLI contouring process of OARs. Manual revision is still necessary for lymph node delineation using AB models.

Recommendation on the technical and dosimetric data to be included in stereotactic body radiation therapy clinical trial publications based on a systematic review.

The results of phase II and III trials on Stereotactic Body Radiation Therapy (SBRT) increased adoption of SBRT worldwide. The ability to replicate clinical trial outcomes in routine practice depends on the capability to reproduce technical and dosimetric procedures used in the clinical trial. In this systematic review, we evaluated if peer-reviewed publications of clinical trials in SBRT reported sufficient technical data to ensure safe and robust implementation in real world clinics. Twenty papers were selected for inclusion, and data was extracted by a working group of medical physicists created following the ESTRO 2021 physics workshop. A large variability in technical and dosimetric data were observed, with frequent lack of required information for reproducing trial procedures. None of the evaluated studies were judged completely reproducible from a technical perspective. A list of recommendations has been provided by the group, based on the analysis and consensus process, to ensure an adequate reproducibility of technical parameters in primary SBRT clinical trials. Future publications should consider these recommendations to assist transferability of the clinical trial in real world practice.

Considerations for intensity modulated total body or total marrow and lymphoid irradiation.

We compiled a sampling of the treatment techniques of intensity-modulated total body irradiation, total marrow irradiation and total marrow and lymphoid irradiation utilized by several centers across North America and Europe. This manuscript does not serve as a consensus guideline, but rather is meant to serve as a convenient reference for centers that are considering starting an intensity-modulated program.

Patient specific quality assurance in SBRT: a systematic review of measurement-based methods.

This topical review focuses on Patient-Specific Quality Assurance (PSQA) approaches to stereotactic body radiation therapy (SBRT). SBRT requires stricter accuracy than standard radiation therapy due to the high dose per fraction and the limited number of fractions. The review considered various PSQA methods reported in 36 articles between 01/2010 and 07/2022 for SBRT treatment. In particular comparison among devices and devices designed for SBRT, sensitivity and resolution, verification methodology, gamma analysis were specifically considered. The review identified a list of essential data needed to reproduce the results in other clinics, highlighted the partial miss of data reported in scientific papers, and formulated recommendations for successful implementation of a PSQA protocol.

Twenty Years of Advancements in a Radiotherapy Facility: Clinical Protocols, Technology, and Management.

BACKGROUND: Hypo-fractionation can be an effective strategy to lower costs and save time, increasing patient access to advanced radiation therapy. To demonstrate this potential in practice within the context of temporal evolution, a twenty-year analysis of a representative radiation therapy facility from 2003 to 2022 was conducted. This analysis utilized comprehensive data to quantitatively evaluate the connections between advanced clinical protocols and technological improvements. The findings provide valuable insights to the management team, helping them ensure the delivery of high-quality treatments in a sustainable manner. METHODS: Several parameters related to treatment technique, patient positioning, dose prescription, fractionation, equipment technology content, machine workload and throughput, therapy times and patients access counts were extracted from departmental database and analyzed on a yearly basis by means of linear regression. RESULTS: Patients increased by 121 ± 6 new per year (NPY). Since 2010, the incidence of hypo-fractionation protocols grew thanks to increasing Linac technology. In seven years, both the average number of fractions and daily machine workload decreased by -0.84 ± 0.12 fractions/year and -1.61 ± 0.35 patients/year, respectively. The implementation of advanced dose delivery techniques, image guidance and high dose rate beams for high fraction doses, currently systematically used, has increased the complexity and reduced daily treatment throughput since 2010 from 40 to 32 patients per 8 h work shift (WS8). Thanks to hypo-fractionation, such an efficiency drop did not affect NPY, estimating 693 ± 28 NPY/WS8, regardless of the evaluation time. Each newly installed machine was shown to add 540 NPY, while absorbing 0.78 ± 0.04 WS8. The COVID-19 pandemic brought an overall reduction of 3.7% of patients and a reduction of 0.8 fractions/patient, to mitigate patient crowding in the department. CONCLUSIONS: The evolution of therapy protocols towards hypo-fractionation was supported by the use of proper technology. The characteristics of this process were quantified considering time progression and organizational aspects. This strategy optimized resources while enabling broader access to advanced radiation therapy. To truly value the benefit of hypo-fractionation, a reimbursement policy should focus on the patient rather than individual treatment fractionation.

Total tumor volume as a predictor of survival in patients with multiple oligometastases treated with stereotactic ablative radiotherapy (SABR).

BACKGROUND: Delivering stereotactic ablative radiotherapy (SABR) in patients with multiple oligometastases represents a challenge for clinical and technical reasons. We aimed to evaluate the outcome of patients affected by multiple oligometastases treated with SABR and the impact of tumor volume on survival. MATERIALS AND METHODS: We included all the patients treated with single course SABR for 3 to 5 extracranial oligometastases. All patients were treated with the volumetric modulated arc therapy (VMAT) technique with ablative intent. End-points of the analysis were overall survival (OS), progression free survival (PFS), local control (LC) and toxicity. RESULTS: 136 patients were treated from 2012 to 2020 on 451 oligometastases. Most common primary tumor was colorectal cancer (44.1%) followed by lung cancer (11.8%). A total of 3, 4 and 5 lesions were simultaneously treated in 102 (75.0%), 26 (19.1%), and 8 (5.9%) patients, respectively. Median total tumor volume (TTV) was 19.1 cc (range 0.6-245.1). With a median follow-up of 25.0 months, OS at 1 and 3 years was 88.4% and 50.2%, respectively. Increasing TTV was independent predictive factor of worse OS (HR 2.37, 95% CI 1.18-4.78, p = 0.014) and PFS (HR 1.63, 95% CI 1.05-2.54; p = 0.028). Median OS was 80.6 months if tumor volume was ≤ 10 cc (1 and 3 years OS rate 93.6% and 77.5%, respectively), and 31.1 months if TTV was higher than 10 cc (1 and 3 years OS rate 86.7% and 42.3%, respectively). Rates of LC at 1 and 3 years were 89.3% and 76.5%. In terms of toxicity, no grade 3 or higher toxicity was reported both in the acute and late settings. CONCLUSION: We demonstrated the impact of tumor volume on survival and disease control of patients affected by multiple oligometastases treated with single course SABR.

Enhancing Radiotherapy Workflow for Head and Neck Cancer with Artificial Intelligence: A Systematic Review.

BACKGROUND: Head and neck cancer (HNC) is characterized by complex-shaped tumors and numerous organs at risk (OARs), inducing challenging radiotherapy (RT) planning, optimization, and delivery. In this review, we provided a thorough description of the applications of artificial intelligence (AI) tools in the HNC RT process. METHODS: The PubMed database was queried, and a total of 168 articles (2016-2022) were screened by a group of experts in radiation oncology. The group selected 62 articles, which were subdivided into three categories, representing the whole RT workflow: (i) target and OAR contouring, (ii) planning, and (iii) delivery. RESULTS: The majority of the selected studies focused on the OARs segmentation process. Overall, the performance of AI models was evaluated using standard metrics, while limited research was found on how the introduction of AI could impact clinical outcomes. Additionally, papers usually lacked information about the confidence level associated with the predictions made by the AI models. CONCLUSIONS: AI represents a promising tool to automate the RT workflow for the complex field of HNC treatment. To ensure that the development of AI technologies in RT is effectively aligned with clinical needs, we suggest conducting future studies within interdisciplinary groups, including clinicians and computer scientists.

The role of EPID in vivo dosimetry in the risk management of stereotactic lung treatments.

BACKGROUND AND OBJECTIVE: In this work we report our experience with the use of in vivo dosimetry (IVD) in the risk management of stereotactic lung treatments. METHODS: A commercial software based on the electronic portal imaging device (EPID) signal was used to reconstruct the actual planning target volume (PTV) dose of stereotactic lung treatments. The study was designed in two phases: i) in the observational phase, the IVD results of 41 consecutive patients were reviewed and out-of-tolerance cases were studied for root cause analysis; ii) in the active phase, the IVD results of 52 patients were analyzed and corrective actions were taken when needed. Moreover, proactive preventions were further introduced to reduce the risk of future failures. The error occurrence rate was analyzed to evaluate the effectiveness of proactive actions. RESULTS: A total of 330 fractions were analyzed. In the first phase, 13 errors were identified. In the active phase, 12 errors were detected, 5 of which needed corrective actions; in 4 patients the actions taken corrected the error. Several preventions and barriers were introduced to reduce the risk of future failures: the planning checklist was updated, the procedure for vacuum pillows was improved, and use of the respiratory compression belt was optimized. A decrease in the failure rate was observed, showing the effectiveness of procedural adjustment. CONCLUSION: The use of IVD allowed the quality of lung stereotactic body radiation therapy (SBRT) treatments to be improved. Patient-specific and procedural corrective actions were successfully taken as part of risk management, leading to an overall improvement in the dosimetric accuracy.

Impact of the Extremities Positioning on the Set-Up Reproducibility for the Total Marrow Irradiation Treatment.

Total marrow (lymph node) irradiation (TMI/TMLI) delivery requires more time than standard radiotherapy treatments. The patient's extremities, through the joints, can experience large movements. The reproducibility of TMI/TMLI patients' extremities was evaluated to find the best positioning and reduce unwanted movements. Eighty TMI/TMLI patients were selected (2013-2022). During treatment, a cone-beam computed tomography (CBCT) was performed for each isocenter to reposition the patient. CBCT-CT pairs were evaluated considering: (i) online vector shift (OVS) that matched the two series; (ii) residual vector shift (RVS) to reposition the patient's extremities; (iii) qualitative agreement (range 1-5). Patients were subdivided into (i) arms either leaning on the frame or above the body; (ii) with or without a personal cushion for foot positioning. The Mann-Whitney test was considered (p < 0.05 significant). Six-hundred-twenty-nine CBCTs were analyzed. The median OVS was 4.0 mm, with only 1.6% of cases ranked < 3, and 24% of RVS > 10 mm. Arms leaning on the frame had significantly smaller RVS than above the body (median: 8.0 mm/6.0 mm, p < 0.05). Using a personal cushion for the feet significantly improved the RVS than without cushions (median: 8.5 mm/1.8 mm, p < 0.01). The role and experience of the radiotherapy team are fundamental to optimizing the TMI/TMLI patient setup.

Multicentric evaluation of a machine learning model to streamline the radiotherapy patient specific quality assurance process.

PURPOSE: Patient-specific quality assurance (PSQA) is performed to ensure that modulated treatment plans can be delivered as intended, but constitutes a substantial workload that could slow down the radiotherapy process and delay the start of clinical treatments. In this study, we investigated a machine learning (ML) tree-based ensemble model to predict the gamma passing rate (GPR) for volumetric modulated arc therapy (VMAT) plans. MATERIALS AND METHODS: 5622 VMAT plans from multiple treatment sites were selected from a database of Institution 1 and the ML model trained using 19 metrics. PSQA analyses were performed automatically using criteria 3%/1 mm (global normalization, absolute dose, 10% threshold) and 95% action limit. Model's performance was evaluated on an out-of-sample test set of Institution 1 and on two independent sets of measurements collected at Institution 2 and Institution 3. Mean absolute error (MAE), as well as the model's sensitivity and specificity, were computed. RESULTS: The model obtained a MAE of 2.33%, 2.54% and 3.91% for the three Institutions, with a specificity of 0.90, 0.90 and 0.68, and a sensitivity of 0.61, 0.25, and 0.55, respectively. Small positive median values of the residuals (i.e., the difference between measurements and predictions) were observed for each Institution (0.95%, 1.66%, and 3.42%). Thus, the model's predictions were, on average, close to the real values and provided a conservative estimation of the GPR. CONCLUSIONS: ML models can be integrated into clinical practice to streamline the radiotherapy workflow, but they should be center-specific or thoroughly verified within centers before clinical use.

Evaluation of plan complexity and dosimetric plan quality of total marrow and lymphoid irradiation using volumetric modulated arc therapy.

PURPOSE: To assess the impact of the planner's experience and optimization algorithm on the plan quality and complexity of total marrow and lymphoid irradiation (TMLI) delivered by means of volumetric modulated arc therapy (VMAT) over 2010-2022 at our institute. METHODS: Eighty-two consecutive TMLI plans were considered. Three complexity indices were computed to characterize the plans in terms of leaf gap size, irregularity of beam apertures, and modulation complexity. Dosimetric points of the target volume (D2%) and organs at risk (OAR) (Dmean) were automatically extracted to combine them with plan complexity and obtain a global quality score (GQS). The analysis was stratified based on the different optimization algorithms used over the years, including a knowledge-based (KB) model. Patient-specific quality assurance (QA) using Portal Dosimetry was performed retrospectively, and the gamma agreement index (GAI) was investigated in conjunction with plan complexity. RESULTS: Plan complexity significantly reduced over the years (r = -0.50, p < 0.01). Significant differences in plan complexity and plan dosimetric quality among the different algorithms were observed. Moreover, the KB model allowed to achieve significantly better dosimetric results to the OARs. The plan quality remained similar or even improved during the years and when moving to a newer algorithm, with GQS increasing from 0.019 ± 0.002 to 0.025 ± 0.003 (p < 0.01). The significant correlation between GQS and time (r = 0.33, p = 0.01) indicated that the planner's experience was relevant to improve the plan quality of TMLI plans. Significant correlations between the GAI and the complexity metrics (r = -0.71, p < 0.01) were also found. CONCLUSION: Both the planner's experience and algorithm version are crucial to achieve an optimal plan quality in TMLI plans. Thus, the impact of the optimization algorithm should be carefully evaluated when a new algorithm is introduced and in system upgrades. Knowledge-based strategies can be useful to increase standardization and improve plan quality of TMLI treatments.

Internal Guidelines for Reducing Lymph Node Contour Variability in Total Marrow and Lymph Node Irradiation.

BACKGROUND: The total marrow and lymph node irradiation (TMLI) target includes the bones, spleen, and lymph node chains, with the latter being the most challenging structures to contour. We evaluated the impact of introducing internal contour guidelines to reduce the inter- and intraobserver lymph node delineation variability in TMLI treatments. METHODS: A total of 10 patients were randomly selected from our database of 104 TMLI patients so as to evaluate the guidelines' efficacy. The lymph node clinical target volume (CTV_LN) was recontoured according to the guidelines (CTV_LN_GL_RO1) and compared to the historical guidelines (CTV_LN_Old). Both topological (i.e., Dice similarity coefficient (DSC)) and dosimetric (i.e., V95 (the volume receiving 95% of the prescription dose) metrics were calculated for all paired contours. RESULTS: The mean DSCs were 0.82 ± 0.09, 0.97 ± 0.01, and 0.98 ± 0.02, respectively, for CTV_LN_Old vs. CTV_LN_GL_RO1, and between the inter- and intraobserver contours following the guidelines. Correspondingly, the mean CTV_LN-V95 dose differences were 4.8 ± 4.7%, 0.03 ± 0.5%, and 0.1 ± 0.1%. CONCLUSIONS: The guidelines reduced the CTV_LN contour variability. The high target coverage agreement revealed that historical CTV-to-planning-target-volume margins were safe, even if a relatively low DSC was observed.

Adaptive Volumetric-Modulated Arc Radiation Therapy for Head and Neck Cancer: Evaluation of Benefit on Target Coverage and Sparing of Organs at Risk.

BACKGROUND: Radiotherapy is essential in the management of head-neck cancer. During the course of radiotherapy, patients may develop significant anatomical changes. Re-planning with adaptive radiotherapy may ensure adequate dose coverage and sparing of organs at risk. We investigated the consequences of adaptive radiotherapy on head-neck cancer patients treated with volumetric-modulated arc radiation therapy compared to simulated non-adaptive plans: Materials and methods: We included in this retrospective dosimetric analysis 56 patients treated with adaptive radiotherapy. The primary aim of the study was to analyze the dosimetric differences with and without an adaptive approach for targets and organs at risk, particularly the spinal cord, parotid glands, oral cavity and larynx. The original plan (OPLAN) was compared to the adaptive plan (APLAN) and to a simulated non-adaptive dosimetric plan (DPLAN). RESULTS: The non-adaptive DPLAN, when compared to OPLAN, showed an increased dose to all organs at risk. Spinal cord D2 increased from 27.91 (21.06-31.76) Gy to 31.39 (27.66-38.79) Gy (p = 0.00). V15, V30 and V45 of the DPLAN vs. the OPLAN increased by 20.6% (p = 0.00), 14.78% (p = 0.00) and 15.55% (p = 0.00) for right parotid; and 16.25% (p = 0.00), 18.7% (p = 0.00) and 20.19% (p = 0.00) for left parotid. A difference of 36.95% was observed in the oral cavity V40 (p = 0.00). Dose coverage was significantly reduced for both CTV (97.90% vs. 99.96%; p = 0.00) and PTV (94.70% vs. 98.72%; p = 0.00). The APLAN compared to the OPLAN had similar values for all organs at risk. CONCLUSIONS: The adaptive strategy with re-planning is able to avoid an increase in dose to organs at risk and better target coverage in head-neck cancer patients, with potential benefits in terms of side effects and disease control.

Predictive value of clinical and radiomic features for radiation therapy response in patients with lymph node-positive head and neck cancer.

BACKGROUND: Prediction of survival and radiation therapy response is challenging in head and neck cancer with metastatic lymph nodes (LNs). Here we developed novel radiomics- and clinical-based predictive models. METHODS: Volumes of interest of LNs were employed for radiomic features extraction. Radiomic and clinical features were investigated for their predictive value relatively to locoregional failure (LRF), progression-free survival (PFS), and overall survival (OS) and used to build multivariate models. RESULTS: Hundred and six subjects were suitable for final analysis. Univariate analysis identified two radiomic features significantly predictive for LRF, and five radiomic features plus two clinical features significantly predictive for both PFS and OS. The area under the curve of receiver operating characteristic curve combining clinical and radiomic predictors for PFS and OS resulted 0.71 (95%CI: 0.60-0.83) and 0.77 (95%CI: 0.64-0.89). CONCLUSIONS: Radiomic and clinical features resulted to be independent predictive factors, but external independent validation is mandatory to support these findings.

Automatic planning of the lower extremities for total marrow irradiation using volumetric modulated arc therapy.

PURPOSE: Total marrow (and lymphoid) irradiation (TMI-TMLI) is limited by the couch travel range of modern linacs, which forces the treatment delivery to be split into two plans with opposite orientations: a head-first supine upper-body plan, and a feet-first supine lower extremities plan. A specific field junction is thus needed to obtain adequate target coverage in the overlap region of the two plans. In this study, an automatic procedure was developed for field junction creation and lower extremities plan optimization. METHODS: Ten patients treated with TMI-TMLI at our institution were selected retrospectively. The planning of the lower extremities was performed automatically. Target volume parameters (CTV_J­V98% > 98%) at the junction region and several dose statistics (D98%, Dmean, and D2%) were compared between automatic and manual plans. The modulation complexity score (MCS) was used to assess plan complexity. RESULTS: The automatic procedure required 60-90 min, depending on the case. All automatic plans achieved clinically acceptable dosimetric results (CTV_J­V98% > 98%), with significant differences found at the junction region, where Dmean and D2% increased on average by 2.4% (p < 0.03) and 3.0% (p < 0.02), respectively. Similar plan complexity was observed (median MCS = 0.12). Since March 2022, the automatic procedure has been introduced in our clinic, reducing the TMI-TMLI simulation-to-delivery schedule by 2 days. CONCLUSION: The developed procedure allowed treatment planning of TMI-TMLI to be streamlined, increasing efficiency and standardization, preventing human errors, while maintaining the dosimetric plan quality and complexity of manual plans. Automated strategies can simplify the future adoption and clinical implementation of TMI-TMLI treatments in new centers.

Applications of artificial intelligence in stereotactic body radiation therapy.

This topical review focuses on the applications of artificial intelligence (AI) tools to stereotactic body radiation therapy (SBRT). The high dose per fraction and the limited number of fractions in SBRT require stricter accuracy than standard radiation therapy. The intent of this review is to describe the development and evaluate the possible benefit of AI tools integration into the radiation oncology workflow for SBRT automation. The selected papers were subdivided into four sections, representative of the whole radiotherapy process: 'AI in SBRT target and organs at risk contouring', 'AI in SBRT planning', 'AI during the SBRT delivery', and 'AI for outcome prediction after SBRT'. Each section summarises the challenges, as well as limits and needs for improvement to achieve better integration of AI tools in the clinical workflow.

The influence of basic plan parameters on calculated small field output factors - A multicenter study.

PURPOSE: The influence of basic plan parameters such as slice thickness, grid resolution, algorithm type and field size on calculated small field output factors (OFs) was evaluated in a multicentric study. METHODS AND MATERIALS: Three computational homogeneous water phantoms with slice thicknesses (ST) 1, 2 and 3 mm were shared among twenty-one centers to calculate OFs for 1x1, 2x2 and 3x3 cm2 field sizes (FSs) (normalized to 10x10 cm2 FS), with their own treatment planning system (TPS) and the energy clinically used for stereotactic body radiation therapy delivery. OFs were calculated for each combination of grid resolution (GR) (1, 2 and 3 mm) and ST and finally compared with the OFs measured for the TPS commissioning. A multivariate analysis was performed to test the effect of basic plan parameters on calculated OFs. RESULTS: A total of 509 data points were collected. Calculated OFs are slightly higher than measured ones. The multivariate analysis showed that Center, GR, algorithm type, and FS are predictive variables of the difference between calculated and measured OFs (p < 0.001). As FS decreases, the spread in the difference between calculated and measured OFs became larger when increasing the GR. Monte Carlo and Analytical Anisotropic Algorithms, presented a dependence on GR (p < 0.01), while Collapsed Cone Convolution and Acuros did not. The effect of the ST was found to be negligible. CONCLUSIONS: Modern TPSs slightly overestimate the calculated small field OFs compared with measured ones. Grid resolution, algorithm, center number and field size influence the calculation of small field OFs.