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Historically, several classification systems have been used for brachytherapy, and they were based on the type of clinical purpose, type of implant and timing of the implant, dose-rate, and type of loading for treatment delivery. However, over the last decades, there have been some major technological advancements, including the introduction of image-guidance and possibility to modulate the dose delivered, which have led several authors (in order to highlight the differences between old technique and new approach) to label it in a different way by replacing “brachytherapy” with “interventional radiotherapy”. Modern interventional procedures involve several key aspects, which contribute to the complexity of implant phase, such as implant type, imaging used during the procedure, and role of multi-disciplinary team in operating room. By assigning scores to these procedural elements, it is possible to classify the procedure’s complexity using a COMIRI classification (COMplexity Index of interventional Radiotherapy Implants). The aim of the COMIRI classification system is to appropriately highlight the need for suitable resources based on the complexity level of different procedures in terms of personnel expertise, equipment availability, and multi-disciplinary teamwork.

This review explores the integration of artificial intelligence (AI) in interventional radiotherapy (IRT), emphasizing its potential to streamline workflows and enhance patient care. Through a systematic analysis of 78 relevant papers spanning from 2002 to 2024, we identified significant advancements in contouring, treatment planning, outcome prediction, and quality assurance. AI-driven approaches offer promise in reducing procedural times, personalizing treatments, and improving treatment outcomes for oncological patients. However, challenges such as clinical validation and quality assurance protocols persist. Nonetheless, AI presents a transformative opportunity to optimize IRT and meet evolving patient needs.

Interventional radiotherapy (IRT, brachytherapy) is a highly effective treatment method for non-melanoma skin cancer (NMSC). Traditionally, the maximum depth of NMSC lesions considered eligible for contact IRT was 5 mm; however, following several national surveys and recent recommendations, such cut-off, lesions thicker than 5 mm may be treated by contact IRT. The use of image guidance in defining the actual depth in treating NMSC to correctly identify clinical target volume (CTV) and prevent unnecessary toxicity is of paramount importance. The aim of the paper was to describe a multilayer arrangement of catheters to treat NMSC lesions thicker than 5 mm, thus proposing an example of dynamic intensity modulated IRT, using different catheter-to-skin distance of sources to reach the best CTV coverage and maximally reduce the excess of dose to the skin.

Objective The goal of this work is to show how to implement a mixed reality application (app) for neurosurgery planning based on neuroimaging data, highlighting the strengths and weaknesses of its design. Methods Our workflow explains how to handle neuroimaging data, including how to load morphological, functional and diffusion tensor imaging data into a mixed reality environment, thus creating a first guide of this kind. Brain magnetic resonance imaging data from a paediatric patient were acquired using a 3 T Siemens Magnetom Skyra scanner. Initially, this raw data underwent specific software pre-processing and were subsequently transformed to ensure seamless integration with the mixed reality app. After that, we created three-dimensional models of brain structures and the mixed reality environment using Unity™ engine together with Microsoft® HoloLens 2™ device. To get an evaluation of the app we submitted a questionnaire to four neurosurgeons. To collect data concerning the performance of a user session we used Unity Performance Profiler. Results The use of the interactive features, such as rotating, scaling and moving models and browsing through menus, provided by the app had high scores in the questionnaire, and their use can still be improved as suggested by the performance data collected. The questionnaire's average scores were high, so the overall experiences of using our mixed reality app were positive. Conclusion We have successfully created a valuable and easy-to-use neuroimaging data mixed reality app, laying the foundation for more future clinical uses, as more models and data derived from various biomedical images can be imported.

Uveal melanoma (UM) is the most common intra-ocular tumor in Caucasian adults and may derive from melanocytes. Historically, the first therapeutic approach to treat UM has been surgical removal of the eye, with obvious consequences in terms of function, cosmesis, and psychological impact on patients. Collaborative Ocular Melanoma Study (COMS) introduced the first uniform approach to perform interventional radiotherapy (IRT – brachytherapy) procedure in a standardized way that allowed to demonstrate equivalence of IRT with enucleation in terms of overall survival. Since this milestone, several international guidelines have been issued with regard to several technical aspects of this procedure, which has become the mainstream therapy worldwide. However, nowadays, there is still a lack of consensus about the criteria to definitively assess UM response after IRT. We present a collection of paradigmatic cases treated in our institution, and then discuss in detail the latest available evidence on the topic.

Neurite Orientation Dispersion and Density Imaging (NODDI) and Bingham-NODDI diffusion MRI models are nowadays very well-known models in the field of diffusion MRI as they represent powerful tools for the estimation of brain microstructure. In order to efficiently translate NODDI imaging findings into the diagnostic clinical practice, a test-retest approach would be useful to assess reproducibility and reliability of NODDI biomarkers, thus providing validation on precision of different fitting toolboxes. In this context, we conducted a test-retest study with the aim to assess the effects of different factors (i.e. fitting algorithms, multiband acceleration, shell configuration, age of subject and hemispheric side) on diffusion models reliability, assessed in terms of Intra-class Correlation Coefficient (ICC) and Variation Factor (VF). To this purpose, data from pediatric and adult subjects were acquired with Simultaneous-MultiSlice (SMS) imaging method with two different acceleration factor (AF) and four b-values, subsequently combined in seven shell configurations. Data were then fitted with two different GPU-based algorithms to speed up the analysis. Results show that each factor investigated had a significant effect on reliability of several diffusion parameters. Particularly, both datasets reveal very good ICC values for higher AF, suggesting that faster acquisitions do not jeopardize the reliability and are useful to decrease motion artifacts. Although very small reliability differences appear when comparing shell configurations, more extensive diffusion parameters variability results when considering shell configuration with lower b-values, especially for simple model like NODDI. Also fitting tools have a significant effect on reliability, but their difference occurs in both datasets and AF, so it appears to be independent from either misalignment and motion artifacts, or noise and SNR. The main achievement of the present study is to show how 10 min multi-shell diffusion MRI acquisition for NODDI acquisition can have reliable results in WM. More complex models do not appear to be more prone to less data acquisition as well as noisier data thus stressing the idea of Bingham-NODDI having greater sensitivity to true subject variability.

PurposeTo evaluate the performance of eleven Knowledge-Based (KB) models for planning optimization (RapidPlantm (RP), Varian) of Volumetric Modulated Arc Therapy (VMAT) applied to whole breast comprehensive of nodal stations, internal mammary and/or supraclavicular regions.Methods and materialsSix RP models have been generated and trained based on 120 VMAT plans data set with different criteria. Two extra-structures were delineated: a PTV for the optimization and a ring structure. Five more models, twins of the previous models, have been created without the need of these structures.ResultsAll models were successfully validated on an independent cohort of 40 patients, 30 from the same institute that provided the training patients and 10 from an additional institute, with the resulting plans being of equal or better quality compared with the clinical plans. The internal validation shows that the models reduce the heart maximum dose of about 2 Gy, the mean dose of about 1 Gy and the V20Gy of 1.5 Gy on average. Model R and L together with model B without optimization structures ensured the best outcomes in the 20% of the values compared to other models. The external validation observed an average improvement of at least 16% for the V5Gy of lungs in RP plans. The mean heart dose and for the V20Gy for lung IPSI were almost halved. The models reduce the maximum dose for the spinal canal of more than 2 Gy on average.ConclusionsAll KB models allow a homogeneous plan quality and some dosimetric gains, as we saw in both internal and external validation. Sub-KB models, developed by splitting right and left breast cases or including only whole breast with locoregional lymph nodes, have shown good performances, comparable but slightly worse than the general model. Finally, models generated without the optimization structures, performed better than the original ones.

Introduction: Aim of this paper is to investigate the plan quality of a tri-Co-60 MRI-Hybrid system for intensity-modulated radiation therapy (IMRT) in patients affected by locally advanced rectal cancer (LARC) undergoing neo-adjuvant radiotherapy. Materials and methods: Ten consecutive LARC patients were selected. Tri-Co-60 step and shoot IMRT plans were generated simulating the presence of the magnetic field (Bon) or not (Boff) with the dedicated treatment planning system (TPS).The total planned dose was 45 Gy in 25 fractions to the mesorectum and the pelvic nodes (planning target volume 2, PTV2) and 55 Gy to the tumor and correspondent mesorectum (PTV1) through simultaneous integrated boost (SIB). Tri-Co-60 IMRT plans were compared with Volumetric Modulated Arc Therapy (VMAT) and IMRT plans for Linear Accelerator (Linac). Results: Bon and Boff tri-Co-60 IMRT plans showed no relevant differences. Mean values of PTV1 and PTV2 receiving at least 95% of the Dp (V95%) were higher than 95% in all treatment plans. All plans met the V105% constraint for the PTV1. Mean values of V105% for the PTV2 were 14.8, 5.0, and 7.3% respectively for tri-Co-60, VMAT and IMRT. Mean Wu’s HI values were similar in all plans (7.4–7.8%). All plans met the V45Gy constraint for small bowel, but mean V45Gy value was higher with tri-Co-60.Bladder irradiation was comparable and always lower than the chosen D max 65 Gy constraint.Mean values of V5Gy and V20Gy to the body and median skin doses were higher with tri-Co-60 plans. Discussion: Treatment plans with Tri-Co-60 step and shoot IMRT met the dose-volume objectives in patients with LARC. Nevertheless, a larger volume of normal tissue received low-moderate doses when compared with Linac based VMAT and IMRT.