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Many new X-Ray treatment machines using small and/or non-standard radiation fields, e.g., Tomotherapy, Cyber-knife, and linear accelerators equipped with high-resolution multi-leaf collimators and on-board imaging system, have been introduced in the radiotherapy clinical routine within the last few years. The introduction of these new treatment modalities has led to the development of high conformal radiotherapy treatment techniques like Intensity Modulated photon Radiation Therapy, Volumetric Modulated Arc Therapy, and stereotactic radiotherapy. When using these treatment techniques, patients are exposed to non-uniform radiation fields, high dose gradients, time and space variation of dose rates, and beam energy spectrum. This makes reaching the required degree of accuracy in clinical dosimetry even more demanding. Continuing to use standard field procedures and detectors in fields smaller than 3 × 3 cm2, will generate a reduced accuracy of clinical dosimetry, running the risk to overshadowing the progress made so far in radiotherapy applications. These dosimetric issues represent a new challenge for medical physicists. To choose the most appropriate detector for small field dosimetry, different features must be considered. Short- and long-term stability, linear response to the absorbed dose and dose rate, no energy and angular dependence, are all needed but not sufficient. The two most sought-after attributes for small field dosimetry are water equivalence and small highly sensitive (high sensitivity) volumes. Both these requirements aim at minimizing perturbations of charged particle fluence approaching the Charged Particle Equilibrium condition as much as possible, while maintaining high spatial resolution by reducing the averaging effect for non-uniform radiation fields. A compromise between different features is necessary because no dosimeter currently fulfills all requirements, but diamond properties seem promising and could lead to a marked improvement. Diamonds have long been used as materials for dosimeters, but natural diamonds were only first used for medical applications in the 80 s. The availability of reproducible synthetic diamonds at a lower cost compared to natural ones made the diffusion of diamonds in dosimetry possible. This paper aims to review the use of synthetic poly and single-crystal diamond dosimeters in radiotherapy, focusing on their performance under MegaVoltage photon beams. Both commercial and prototype diamond dosimeters behaviour are described and analyzed. Moreover, this paper will report the main related results in literature, considering diamond development issues like growth modalities, electrical contacts, packaging, readout electronics, and how do they affect all the dosimetric parameters of interest such as signal linearity, energy dependence, dose-rate dependence, reproducibility, rise and decay times.

Purpose: A novel and unconventional approach to a machine learning challenge was designed to spread knowledge, identify robust methods and highlight potential pitfalls about machine learning within the Medical Physics community., Methods: A public dataset comprising 41 radiomic features and 535 patients was employed to assess the potential of radiomics in distinguishing between primary lung tumors and metastases. Each participant developed two classification models using: (i) all features (base model); (ii) only robust features (robust model). Both models were validated with cross-validation and on unseen data. The population stability index (PSI) was used as diagnostic metric for implementation issues. Performance was compared to reference. Base and robust models were compared in terms of performance and stability (coefficient of variation (CoV) of prediction probabilities)., Results: PSI detected potential implementation errors in 70 % of models. The dataset exhibited strong imbalance. The average Gmean (i.e. an appropriate metric for imbalance) among all participants was 0.67 ± 0.01, significantly higher than reference Gmean of 0.50 ± 0.04. Robust models performances were slightly worse than base models (p < 0.05). Regarding stability, robust models exhibited lower median CoV on training set only., Conclusion: AI4MP-Challenge models overperformed the reference, significantly improving the Gmean. Exclusion of less-robust features did not improve model robustness and it should be avoided when confounding effects are absent. Other methods, like harmonization or data augmentation, should be evaluated. This study demonstrated how the collaborative effort to foster knowledge on machine learning among medical physicists, through interactive sessions and exchange of information among participants, can result in improved models., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.)

This paper presents a comprehensive study of hydrogenated amorphous silicon (a-Si)-based detectors, utilizing electrical characterization, Raman spectroscopy, photoemission, and inverse photoemission techniques. The unique properties of a-Si have sparked interest in its application for radiation detection in both physics and medicine. Although amorphous silicon (a-Si) is inherently a highly defective material, hydrogenation significantly reduces defect density, enabling its use in radiation detector devices. Spectroscopic measurements provide insights into the intricate relationship between the structure and electronic properties of a-Si, enhancing our understanding of how specific configurations, such as the choice of substrate, can markedly influence detector performance. In this study, we compare the performance of a-Si detectors deposited on two different substrates: crystalline silicon (c-Si) and flexible Kapton. Our findings suggest that detectors deposited on Kapton exhibit reduced sensitivity, despite having comparable noise and leakage current levels to those on crystalline silicon. We hypothesize that this discrepancy may be attributed to the substrate material, differences in film morphology, and/or the alignment of energy levels. Further measurements are planned to substantiate these hypotheses.

In recent years, alternative methods to dark ink tattoos for patient positioning in radiotherapy have been explored. This review aims to analyse the evidence for alternative strategies to traditional dark tattoos. An electronic search was conducted in PubMed, EMBASE, Cochrane Library, Web of Sciences and SCOPUS. Twenty-one articles out of 383 titles fulfilled the selection criteria and were included in the review. These studies were categorized into tattoo-less methods (n=14), UV ink tattoos (n=4) and other techniques (n=3). In most of the selected articles (n=13) focusing on tattoo-less treatments, SGRT is used for patient positioning. These three alternative techniques to dark tattoos are used in different anatomical regions and treatment modalities, with breast cancer being the most prevalent. Tattoo-less techniques are a promising alternative to traditional tattoo-based methods for patient positioning. They have the potential to improve the patient experience and represent an area of ongoing innovation and improvement., Competing Interests: Declaration of Competing Interest Icro Meattini declares occasional small fees received for advisory boards supported by Eli Lilly, Novartis, Astra Zeneca, Daiichi Sankyo, Gilead, SeaGen, Pfizer, Menarini StemLine. No other conflicts of interest are declared for the remaining authors., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: To address the numerous unmeet clinical needs, in recent years several Machine Learning models applied to medical images and clinical data have been introduced and developed. Even when they achieve encouraging results, they lack evolutionary progression, thus perpetuating their status as autonomous entities. We postulated that different algorithms which have been proposed in the literature to address the same diagnostic task, can be aggregated to enhance classification performance. We suggested a proof of concept to define an ensemble approach useful for integrating different algorithms proposed to solve the same clinical task., Methods: The proposed approach was developed starting from a public database consisting of radiomic features extracted from CT images relating to 535 patients suffering from lung cancer. Seven algorithms were trained independently by participants in the AI4MP working group on Artificial Intelligence of the Italian Association of Physics in Medicine to discriminate metastatic from non-metastatic patients. The classification scores generated by these algorithms are used to train SVM classifier. The Explainable Artificial Intelligence approach is applied to the final model. The ensemble model was validated following an 80-20 hold-out and leave-one-out scheme on the training set., Results: Compared to individual algorithms, a more accurate result was achieved. On the independent test the ensemble model achieved an accuracy of 0.78, a F1-score of 0.57 and a log-loss of 0.49. Shapley values representing the contribution of each algorithm to the final classification result of the ensemble model were calculated. This information represents an added value for the end user useful for evaluating the appropriateness of the classification result on a particular case. It also allows us to evaluate on a global level which methodological approaches of the individual algorithms are likely to have the most impact., Conclusion: Our proposal represents an innovative approach useful for integrating different algorithms that populate the literature and which lays the foundations for future evaluations in broader application scenarios., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Fanizzi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Texture analysis extracts many quantitative image features, offering a valuable, cost-effective, and non-invasive approach for individual medicine. Furthermore, multimodal machine learning could have a large impact for precision medicine, as texture biomarkers can underlie tissue microstructure. This study aims to investigate imaging-based biomarkers of radio-induced neurotoxicity in pediatric patients with metastatic medulloblastoma, using radiomic and dosiomic analysis., Methods: This single-center study retrospectively enrolled children diagnosed with metastatic medulloblastoma (MB) and treated with hyperfractionated craniospinal irradiation (CSI). Histological confirmation of medulloblastoma and baseline follow-up magnetic resonance imaging (MRI) were mandatory. Treatment involved helical tomotherapy (HT) delivering a dose of 39 Gray (Gy) to brain and spinal axis and a posterior fossa boost up to 60 Gy. Clinical outcomes, such as local and distant brain control and neurotoxicity, were recorded. Radiomic and dosiomic features were extracted from tumor regions on T1, T2, FLAIR (fluid-attenuated inversion recovery) MRI-maps, and radiotherapy dose distribution. Different machine learning feature selection and reduction approaches were performed for supervised and unsupervised clustering., Results: Forty-eight metastatic medulloblastoma patients (29 males and 19 females) with a mean age of 12 ± 6 years were enrolled. For each patient, 332 features were extracted. Greater level of abstraction of input data by combining selection of most performing features and dimensionality reduction returns the best performance. The resulting one-component radiomic signature yielded an accuracy of 0.73 with sensitivity, specificity, and precision of 0.83, 0.64, and 0.68, respectively., Conclusions: Machine learning radiomic-dosiomic approach effectively stratified pediatric medulloblastoma patients who experienced radio-induced neurotoxicity. Strategy needs further validation in external dataset for its potential clinical use in ab initio management paradigms of medulloblastoma., (© 2024. The Author(s).)

Objective. Detectors that can provide accurate dosimetry for microbeam radiation therapy (MRT) must possess intrinsic radiation hardness, a high dynamic range, and a micron-scale spatial resolution. In this work we characterize hydrogenated amorphous silicon detectors for MRT dosimetry, presenting a novel combination of flexible, ultra-thin and radiation-hard features. Approach. Two detectors are explored: an n-type/intrinsic/p-type planar diode (NIP) and an NIP with an additional charge selective layer (NIP + CSC). Results. The sensitivity of the NIP + CSC detector was greater than the NIP detector for all measurement conditions. At 1 V and 0 kGy under the 3T Cu-Cu synchrotron broadbeam, the NIP + CSC detector sensitivity of (7.76 ± 0.01) pC cGy -1 outperformed the NIP detector sensitivity of (3.55 ± 0.23) pC cGy -1 by 219%. The energy dependence of both detectors matches closely to the attenuation coefficient ratio of silicon against water. Radiation damage measurements of both detectors out to 40 kGy revealed a higher radiation tolerance in the NIP detector compared to the NIP + CSC (17.2% and 33.5% degradations, respectively). Percentage depth dose profiles matched the PTW microDiamond detector's performance to within ±6% for all beam filtrations except in 3T Al-Al due to energy dependence. The 3T Cu-Cu microbeam field profile was reconstructed and returned microbeam width and peak-to-peak values of (51 ± 1) μ m and (405 ± 5) μ m, respectively. The peak-to-valley dose ratio was measured as a function of depth and agrees within error to the values obtained with the PTW microDiamond. X-ray beam induced charge mapping of the detector revealed minimal dose perturbations from extra-cameral materials. Significance. The detectors are comparable to commercially available dosimeters for quality assurance in MRT. With added benefits of being micron-sized and possessing a flexible water-equivalent substrate, these detectors are attractive candidates for quality assurance, in-vivo dosimetry and in-line beam monitoring for MRT and FLASH therapy., (Creative Commons Attribution license.)

Background: Virtual Environment Radiotherapy Training (VERT) is a virtual tool used in radiotherapy with a dual purpose: patient education and student training. This scoping review aims to identify the applications of VERT to acquire new skills in specific activities of Radiation Therapists (RTTs) clinical practice and education as reported in the literature. This scoping review will identify any gaps in this field and provide suggestions for future research., Methods: In accordance with Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) extension for scoping reviews and Arskey and O'Malley framework, an electronic search was conducted to retrieve complete original studies, reporting the use and implementation of VERT for teaching skills to RTTs. Studies were searched in PubMed, EMBASE, and SCOPUS databases and included retrieved articles if they investigated the use of VERT for RTTs training., Results: Of 251 titles, 16 articles fulfilled the selection criteria and most of the studies were qualitative evaluation studies (n=5) and pilot studies (n=4). The specific use of VERT for RTTs training was grouped into four categories (Planning CT, Set-up, IGRT, and TPS)., Conclusion: The use of VERT was described for each category by examining the interaction of the students or trainee RTTs in performing each phase within the virtual environment and describing their perceptions. This system Virtual Reality (VR) enables the development of specific motor skills without interfering and pressurising clinical resources by using clinical equipment in a risk-free offline environment, improving the clinical confidence of students or trainee RTTs. However, even if VR can be integrated into the RTTs training with a great advantage, VERT has still not been embraced. This mainly due to the presence of significant issues and limitations, such as inadequate coverage within the current literature, software and hardware costs., (Copyright © 2024. Published by Elsevier Inc.)

Background: The increasing use of complex and high dose-rate treatments in radiation therapy necessitates advanced detectors to provide accurate dosimetry. Rather than relying on pre-treatment quality assurance (QA) measurements alone, many countries are now mandating the use of in vivo dosimetry, whereby a dosimeter is placed on the surface of the patient during treatment. Ideally, in vivo detectors should be flexible to conform to a patient's irregular surfaces., Purpose: This study aims to characterize a novel hydrogenated amorphous silicon (a-Si:H) radiation detector for the dosimetry of therapeutic x-ray beams. The detectors are flexible as they are fabricated directly on a flexible polyimide (Kapton) substrate., Methods: The potential of this technology for application as a real-time flexible detector is investigated through a combined dosimetric and flexibility study. Measurements of fundamental dosimetric quantities were obtained including output factor (OF), dose rate dependence (DPP), energy dependence, percentage depth dose (PDD), and angular dependence. The response of the a-Si:H detectors investigated in this study are benchmarked directly against commercially available ionization chambers and solid-state diodes currently employed for QA practices., Results: The a-Si:H detectors exhibit remarkable dose linearities in the direct detection of kV and MV therapeutic x-rays, with calibrated sensitivities ranging from (0.580 ± 0.002) pC/cGy to (19.36 ± 0.10) pC/cGy as a function of detector thickness, area, and applied bias. Regarding dosimetry, the a-Si:H detectors accurately obtained OF measurements that parallel commercially available detector solutions. The PDD response closely matched the expected profile as predicted via Geant4 simulations, a PTW Farmer ionization chamber and a PTW ROOS chamber. The most significant variation in the PDD performance was 5.67%, observed at a depth of 3 mm for detectors operated unbiased. With an external bias, the discrepancy in PDD response from reference data was confined to ± 2.92% for all depths (surface to 250 mm) in water-equivalent plastic. Very little angular dependence is displayed between irradiations at angles of 0° and 180°, with the most significant variation being a 7.71% decrease in collected charge at a 110° relative angle of incidence. Energy dependence and dose per pulse dependence are also reported, with results in agreement with the literature. Most notably, the flexibility of a-Si:H detectors was quantified for sample bending up to a radius of curvature of 7.98 mm, where the recorded photosensitivity degraded by (-4.9 ± 0.6)% of the initial device response when flat. It is essential to mention that this small bending radius is unlikely during in vivo patient dosimetry. In a more realistic scenario, with a bending radius of 15-20 mm, the variation in detector response remained within ± 4%. After substantial bending, the detector's photosensitivity when returned to a flat condition was (99.1 ± 0.5)% of the original response., Conclusions: This work successfully characterizes a flexible detector based on thin-film a-Si:H deposited on a Kapton substrate for applications in therapeutic x-ray dosimetry. The detectors exhibit dosimetric performances that parallel commercially available dosimeters, while also demonstrating excellent flexibility results., (© 2024 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Background: This study aimed to evaluate an a-priori multicriteria plan optimization algorithm (mCycle) for locally advanced breast cancer radiation therapy (RT) by comparing automatically generated VMAT (Volumetric Modulated Arc Therapy) plans (AP-VMAT) with manual clinical Helical Tomotherapy (HT) plans., Methods: The study included 25 patients who received postoperative RT using HT. The patient cohort had diverse target selections, including both left and right breast/chest wall (CW) and III-IV node, with or without internal mammary node (IMN) and Simultaneous Integrated Boost (SIB). The Planning Target Volume (PTV) was obtained by applying a 5 mm isotropic expansion to the CTV (Clinical Target Volume), with a 5 mm clip from the skin. Comparisons of dosimetric parameters and delivery/planning times were conducted. Dosimetric verification of the AP-VMAT plans was performed., Results: The study showed statistically significant improvements in AP-VMAT plans compared to HT for OARs (Organs At Risk) mean dose, except for the heart and ipsilateral lung. No significant differences in V 95% were observed for PTV breast/CW and PTV III-IV, while increased coverage (higher V 95% ) was seen for PTV IMN in AP-VMAT plans. HT plans exhibited smaller values of PTV V 105% for breast/CW and III-IV, with no differences in PTV IMN and boost. HT had an average (± standard deviation) delivery time of (17 ± 8) minutes, while AP-VMAT took (3 ± 1) minutes. The average γ passing rate for AP-VMAT plans was 97%±1%. Planning times reduced from an average of 6 h for HT to about 2 min for AP-VMAT., Conclusions: Comparing AP-VMAT plans with clinical HT plans showed similar or improved quality. The implementation of mCycle demonstrated successful automation of the planning process for VMAT treatment of locally advanced breast cancer, significantly reducing workload., (© 2023. The Author(s).)

Objective . Microbeam radiation therapy (MRT) is an alternative emerging radiotherapy treatment modality which has demonstrated effective radioresistant tumour control while sparing surrounding healthy tissue in preclinical trials. This apparent selectivity is achieved through MRT combining ultra-high dose rates with micron-scale spatial fractionation of the delivered x-ray treatment field. Quality assurance dosimetry for MRT must therefore overcome a significant challenge, as detectors require both a high dynamic range and a high spatial resolution to perform accurately. Approach . In this work, a series of radiation hard a-Si:H diodes, with different thicknesses and carrier selective contact configurations, have been characterised for x-ray dosimetry and real-time beam monitoring applications in extremely high flux beamlines utilised for MRT at the Australian Synchrotron. Results . These devices displayed superior radiation hardness under constant high dose-rate irradiations on the order of 6000 Gy s -1 , with a variation in response of 10% over a delivered dose range of approximately 600 kGy. Dose linearity of each detector to x-rays with a peak energy of 117 keV is reported, with sensitivities ranging from (2.74 ± 0.02) nC/Gy to (4.96 ± 0.02) nC/Gy. For detectors with 0.8 μ m thick active a-Si:H layer, their operation in an edge-on orientation allows for the reconstruction of micron-size beam profiles (microbeams). The microbeams, with a nominal full-width-half-max of 50 μ m and a peak-to-peak separation of 400 μ m, were reconstructed with extreme accuracy. The full-width-half-max was observed as 55 ± 1 μ m. Evaluation of the peak-to-valley dose ratio and dose-rate dependence of the devices, as well as an x-ray induced charge (XBIC) map of a single pixel is also reported. Significance . These devices based on novel a-Si:H technology possess a unique combination of accurate dosimetric performance and radiation resistance, making them an ideal candidate for x-ray dosimetry in high dose-rate environments such as FLASH and MRT., (Creative Commons Attribution license.)

Background: The role of computed tomography (CT) in the diagnosis and characterization of coronavirus disease 2019 (COVID-19) pneumonia has been widely recognized. We evaluated the performance of a software for quantitative analysis of chest CT, the LungQuant system, by comparing its results with independent visual evaluations by a group of 14 clinical experts. The aim of this work is to evaluate the ability of the automated tool to extract quantitative information from lung CT, relevant for the design of a diagnosis support model., Methods: LungQuant segments both the lungs and lesions associated with COVID-19 pneumonia (ground-glass opacities and consolidations) and computes derived quantities corresponding to qualitative characteristics used to clinically assess COVID-19 lesions. The comparison was carried out on 120 publicly available CT scans of patients affected by COVID-19 pneumonia. Scans were scored for four qualitative metrics: percentage of lung involvement, type of lesion, and two disease distribution scores. We evaluated the agreement between the LungQuant output and the visual assessments through receiver operating characteristics area under the curve (AUC) analysis and by fitting a nonlinear regression model., Results: Despite the rather large heterogeneity in the qualitative labels assigned by the clinical experts for each metric, we found good agreement on the metrics compared to the LungQuant output. The AUC values obtained for the four qualitative metrics were 0.98, 0.85, 0.90, and 0.81., Conclusions: Visual clinical evaluation could be complemented and supported by computer-aided quantification, whose values match the average evaluation of several independent clinical experts., Key Points: We conducted a multicenter evaluation of the deep learning-based LungQuant automated software. We translated qualitative assessments into quantifiable metrics to characterize coronavirus disease 2019 (COVID-19) pneumonia lesions. Comparing the software output to the clinical evaluations, results were satisfactory despite heterogeneity of the clinical evaluations. An automatic quantification tool may contribute to improve the clinical workflow of COVID-19 pneumonia., (© 2023. The Author(s).)

Background: This study tested the diagnostic value of 18F-FDG PET/CT (FDG-PET) volumetric and texture parameters in the histological differentiation of mediastinal bulky disease due to classical Hodgkin lymphoma (cHL), primary mediastinal B-cell lymphoma (PMBCL) and grey zone lymphoma (GZL), using machine learning techniques., Methods: We reviewed 80 cHL, 29 PMBCL and 8 GZL adult patients with mediastinal bulky disease and histopathological diagnoses who underwent FDG-PET pre-treatment. Volumetric and radiomic parameters were measured using FDG-PET both for bulky lesions (BL) and for all lesions (AL) using LIFEx software (threshold SUV ≥ 2.5). Binary and multiclass classifications were performed with various machine learning techniques fed by a relevant subset of radiomic features., Results: The analysis showed significant differences between the lymphoma groups in terms of SUVmax, SUVmean, MTV, TLG and several textural features of both first- and second-order grey level. Among machine learning classifiers, the tree-based ensembles achieved the best performance both for binary and multiclass classifications in histological differentiation., Conclusions: Our results support the value of metabolic heterogeneity as an imaging biomarker, and the use of radiomic features for early characterization of mediastinal bulky lymphoma.

Purpose: Analysis pipelines based on the computation of radiomic features on medical images are widely used exploration tools across a large variety of image modalities. This study aims to define a robust processing pipeline based on Radiomics and Machine Learning (ML) to analyze multiparametric Magnetic Resonance Imaging (MRI) data to discriminate between high-grade (HGG) and low-grade (LGG) gliomas., Methods: The dataset consists of 158 multiparametric MRI of patients with brain tumor publicly available on The Cancer Imaging Archive, preprocessed by the BraTS organization committee. Three different types of image intensity normalization algorithms were applied and 107 features were extracted for each tumor region, setting the intensity values according to different discretization levels. The predictive power of radiomic features in the LGG versus HGG categorization was evaluated by using random forest classifiers. The impact of the normalization techniques and of the different settings in the image discretization was studied in terms of the classification performances. A set of MRI-reliable features was defined selecting the features extracted according to the most appropriate normalization and discretization settings., Results: The results show that using MRI-reliable features improves the performance in glioma grade classification (AUC=0.93±0.05) with respect to the use of raw (AUC=0.88±0.08) and robust features (AUC=0.83±0.08), defined as those not depending on image normalization and intensity discretization., Conclusions: These results confirm that image normalization and intensity discretization strongly impact the performance of ML classifiers based on radiomic features. Thus, special attention should be provided in the image preprocessing step before typical radiomic and ML analysis are carried out., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.)

Purpose: Set up a lung SBRT end-to-end (e2e) test and perform a multicentre validation., Material and Methods: A group of medical physicists from four hospitals and the Italian Institute of Ionizing Radiation Metrology designed the present e2e test. One sub-group set up the test, while another tested its feasibility and ease of use. A satisfaction questionnaire was used to collect user feedback. Each participating centre (PC) received the ADAM breathing phantom, a microDiamond detector and radiochromic films. Following the e2e protocol, each PC performed its standard internal procedure for simulating, planning, and irradiating the phantom. Each PC uploaded its planning and treatment delivery data in a shared Google Drive. A single centre analyzed all the data., Results: The e2e test was successfully performed by all PCs. Participants' comments indicated that ADAM was well suited to the purpose and the protocol well described. All PCs performed the test in static and dynamic modes. The ratio between measured and planned point dose obtained by PC1, PC2, PC3, PC4 was: 0.99, 0.96, 1.01 and 1.01 (static track) and 0.99, 1.02, 1.01 and 0.94 (dynamic track). The gamma passing rates (3 % global, 3 mm) between planned and measured dose maps were 98.5 %, 94.0 %, 99.1 % and 94.0 % (static track) and 99.5 %, 96.5 %, 86.0 % and 94.5 % (dynamic track) for PC1, PC2, PC3 and PC4, respectively., Conclusions: An e2e test for lung SBRT has been proposed and tested in a multicentre framework. The results and user feedback prove the validity of the proposed e2e test., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.)

Trabectedin is used for the treatment of advanced soft tissue sarcomas (STSs). In this study, we evaluated if trabectedin could enhance the efficacy of irradiation (IR) by increasing the intrinsic cell radiosensitivity and modulating tumor micro-environment in fibrosarcoma (HS 93.T), leiomyosarcoma (HS5.T), liposarcoma (SW872), and rhabdomyosarcoma (RD) cell lines. A significant reduction in cell surviving fraction (SF) following trabectedin + IR compared to IR alone was observed in liposarcoma and leiomyosarcoma (enhancement ratio at 50%, ER50: 1.45 and 2.35, respectively), whereas an additive effect was shown in rhabdomyosarcoma and fibrosarcoma. Invasive cells' fraction significantly decreased following trabectedin ± IR compared to IR alone. Differences in cell cycle distribution were observed in leiomyosarcoma and rhabdomyosarcoma treated with trabectedin + IR. In all STS lines, trabectedin + IR resulted in a significantly higher number of γ-H2AX (histone H2AX) foci 30 min compared to the control, trabectedin, or IR alone. Expression of ATM, RAD50, Ang-2, VEGF, and PD-L1 was not significantly altered following trabectedin + IR. In conclusion, trabectedin radiosensitizes STS cells by affecting SF (particularly in leiomyosarcoma and liposarcoma), invasiveness, cell cycle distribution, and γ-H2AX foci formation. Conversely, no synergistic effect was observed on DNA damage repair, neoangiogenesis, and immune system.

The aim of this study is to investigate the feasibility of manufacturing thin real-time relative dosimeters for clinical radiotherapy (RT) with potential applications for transmission monitoring in vivo dosimetry and pre-treatment dose verifications. Thin (≈1μm) layers of a high sensitivity, wide bandgap semiconductor, the inorganic perovskite CsPbCl 3 , have been grown for the first time by magnetron sputtering on plastic substrates equipped with electrode arrays. Prototype devices have been tested in real-time configuration to evaluate the dose delivered by a 6MV photon beam from a linear accelerator. Linearity of the charge with the dose has been verified over three order of magnitudes, linearity of the current signal with the dose rate has been also successfully tested in the range 0.5-4.3Gy/min. The combination of high sensitivity per unit volume and wide bandgap provides high signal-to-noise ratios, up to 70, even at moderate applied voltages. The Schottky diode configuration allows the detector to operate without bias voltage (null bias).The blocking-barrier structure allows to confine the active volume within sub-millimetric sizes, a quite attractive feature in view to increase granularity and achieve the high spatial resolutions required in modern RT techniques. All the above-mentioned features indeed pave the way to a novel generation of flexible, transmission, real time dosimeters for clinical radiotherapy., (Copyright © 2022. Published by Elsevier GmbH.)

In this paper, by means of high-resolution photoemission, soft X-ray absorption and atomic force microscopy, we investigate, for the first time, the mechanisms of damaging, induced by neutron source, and recovering (after annealing) of p-i-n detector devices based on hydrogenated amorphous silicon (a-Si:H). This investigation will be performed by mean of high-resolution photoemission, soft X-Ray absorption and atomic force microscopy. Due to dangling bonds, the amorphous silicon is a highly defective material. However, by hydrogenation it is possible to reduce the density of the defect by several orders of magnitude, using hydrogenation and this will allow its usage in radiation detector devices. The investigation of the damage induced by exposure to high energy irradiation and its microscopic origin is fundamental since the amount of defects determine the electronic properties of the a-Si:H. The comparison of the spectroscopic results on bare and irradiated samples shows an increased degree of disorder and a strong reduction of the Si-H bonds after irradiation. After annealing we observe a partial recovering of the Si-H bonds, reducing the disorder in the Si (possibly due to the lowering of the radiation-induced dangling bonds). Moreover, effects in the uppermost coating are also observed by spectroscopies.

Purpose: Small photon beams used in radiotherapy techniques have inherent characteristics of charge particle disequilibrium and high-dose gradient making accurate dosimetry for such fields very challenging. By means of a 3D manufacturing technique, it is possible to create arrays of pixels with a very small sensitive volume for radiotherapy dosimetry. We investigate the impact of 3D pixels size on absorbed dose sensitivity, linearity of response with dose rate, reproducibility and beam profile measurements., Methods: Diamond detectors with different pixel sizes have been produced in the 3DOSE experiment framework. To investigate the pixels size impact, they were tested using an Elekta Synergy LINAC. Dose rate dependence, absorbed dose sensitivity, reproducibility and beam profile measurement accuracy have been investigated and compared with PTW 60019 and IBA SFD reference dosimeters., Results: All of the 3D pixels had a linear and reproducible response to the dose rate. The sensitivity of a pixel decreases with its size, although even the smallest pixel has a high absorbed dose sensitivity (15 nC/Gy). The penumbra width measured with the smallest pixel size was consistent with the PTW microDiamond and differed by 0.2 mm from the IBA SFD diode., Conclusions: The study demonstrates that variation in pixel size do not affect the linearity of response with dose rate and the reproducibility of response. Due to the 3D geometry, the absorbed dose sensitivity of the detector remains high even for the smallest pixel, furthermore the pixel size was demonstrated to be of fundamental importance in the measurement of beam profiles., (Copyright © 2022 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.)

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., (© 2022 Institute of Physics and Engineering in Medicine.)

Purpose: To implement a semi-automatic planning technique for whole breast irradiation with two tangential IMRT fields and to test the produced dose distribution against clinical 3DCRT plans, for introducing the technique in clinical practice., Methods: The Auto-Planning module of the Pinnacle 3 (Philips) treatment planning system was used for generating a Treatment Technique on left-sided breast cancer patients treated in free breathing or in deep inspiration breath hold (DIBH) and to right-sided breast cancer patients. The technique was evaluated against 3DCRT clinical plans in terms of dosimetric plan parameters. Plan robustness toward patient displacements was assessed on a subset of patients by inducing shifts to the isocenter., Results: A statistically significant improvement in target coverage and dose homogeneity was observed for autoIMRT. No statistically significant differences were observed for ipsilateral organs, except for the ipsilateral lung in left DIBH, where slightly lower D mean and V 18% are registered for autoIMRT. Slightly higher D mean doses (although far below the constraints) to contralateral organs were observed for autoIMRT plans. AutoIMRT plans were shown to be as robust as 3DCRT plans toward isocenter shifts, with a maximum decrease in CTV coverage of -2.2% and -2.1% for autoIMRT and 3DCRT, respectively. Average planning times were 40 min for 3DCRT and 6 min for IMRT plans., Conclusions: The developed autoIMRT technique was proven to be advantageous for target coverage and homogeneity and sufficiently robust towards isocenter displacements. The use of automated planning consistently reduces the planning workload with improvements in plan quality., (Copyright © 2022 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.)

Purpose: This study aims at exploiting artificial intelligence (AI) for the identification, segmentation and quantification of COVID-19 pulmonary lesions. The limited data availability and the annotation quality are relevant factors in training AI-methods. We investigated the effects of using multiple datasets, heterogeneously populated and annotated according to different criteria., Methods: We developed an automated analysis pipeline, the LungQuant system, based on a cascade of two U-nets. The first one (U-net[Formula: see text]) is devoted to the identification of the lung parenchyma; the second one (U-net[Formula: see text]) acts on a bounding box enclosing the segmented lungs to identify the areas affected by COVID-19 lesions. Different public datasets were used to train the U-nets and to evaluate their segmentation performances, which have been quantified in terms of the Dice Similarity Coefficients. The accuracy in predicting the CT-Severity Score (CT-SS) of the LungQuant system has been also evaluated., Results: Both the volumetric DSC (vDSC) and the accuracy showed a dependency on the annotation quality of the released data samples. On an independent dataset (COVID-19-CT-Seg), both the vDSC and the surface DSC (sDSC) were measured between the masks predicted by LungQuant system and the reference ones. The vDSC (sDSC) values of 0.95±0.01 and 0.66±0.13 (0.95±0.02 and 0.76±0.18, with 5 mm tolerance) were obtained for the segmentation of lungs and COVID-19 lesions, respectively. The system achieved an accuracy of 90% in CT-SS identification on this benchmark dataset., Conclusion: We analysed the impact of using data samples with different annotation criteria in training an AI-based quantification system for pulmonary involvement in COVID-19 pneumonia. In terms of vDSC measures, the U-net segmentation strongly depends on the quality of the lesion annotations. Nevertheless, the CT-SS can be accurately predicted on independent test sets, demonstrating the satisfactory generalization ability of the LungQuant., (© 2021. The Author(s).)

Purpose: The present work aims to guide the physicist in order to start automated planning for the VMAT treatment of glioblastoma multiforme (GBM) by giving a recipe that was set up and tested during a long-term (two years) evaluation., Methods: An automatic technique in AutoPlanning module of the Pinnacle3 (Philips Medical Systems, Fitchburg, WI) treatment planning system was created and validated by comparing dose distributions of automatic plans (APs) and manual plans (MPs) and by performing a blind AP-MP comparison on a cohort of 20 patients. Automatic technique was then applied to 145 patients and failures were recorded i.e. the number of times for which dose distributions produced by the automatic module were not suitable for treatment., Results: Each of the 20 APs considered in the validation step was clinically acceptable and proved to be better (15 cases) or equal (5 cases) respect to MPs. A statistically significant improvement in brain stem, optic pathways, cochleae, pituitary gland and scalp sparing was observed for APs, while no statistically significant differences were recorded in target coverage or plan parameters. For only 5 cases out of the 145 plans the operator intervention was needed in order to obtain a clinical acceptable plan, while for the remaining 140 plans the automatic created solution was suitable., Conclusions: A straightforward automatic procedure has been created and tested in our clinic. The AutoPlanning technique proposed represents a reliable tool to improve treatment planning efficiency and the recipe, here presented, could be simply imported to every radiotherapy center., (Copyright © 2021 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)

Purpose: To determine the targeting accuracy of brain radiosurgery when planning procedures employing different MRI and MRI + CT combinations are adopted., Materials and Method: A new phantom, the BrainTool, has been designed and realized to test image co-registration and targeting accuracy in a realistic anatomical situation. The phantom was created with a 3D printer and materials that mimic realistic brain MRI and CT contrast using a model extracted from a synthetic MRI study of a human brain. Eight markers distributed within the BrainTool provide for assessment of the accuracy of image registrations while two cavities that host an ionization chamber are used to perform targeting accuracy measurements with an iterative cross-scan method. Two procedures employing 1.5 T MRI-only or a combination of MRI (taken with 1.5 T or 3 T scanners) and CT to carry out Gamma Knife treatments were investigated. As distortions can impact targeting accuracy, MR images were preliminary evaluated to assess image deformation extent using GammaTool phantom., Results: MR images taken with both scanners showed average and maximum distortion of 0.3 mm and 1 mm respectively. The marker distances in co-registered images resulted below 0.5 mm for both MRI scans. The targeting mismatches obtained were 0.8 mm, 1.0 mm and 1.2 mm for MRI-only and MRI + CT (1,5T and 3 T), respectively., Conclusions: Procedures using a combination of MR and CT images provide targeting accuracies comparable to those of MRI-only procedures. The BrainTool proved to be a suitable tool for carrying out co-registration and targeting accuracy of Gamma Knife brain radiosurgery treatments., (Copyright © 2021 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)

Purpose: Patient-specific quality assurance (QA) is very important in radiotherapy, especially for patients with highly conformed treatment plans like VMAT plans. Traditional QA protocols for these plans are time-consuming reducing considerably the time available for patient treatments. In this work, a new MC-based secondary dose check software (SciMoCa) is evaluated and benchmarked against well-established TPS (Monaco and Pinnacle 3 ) by means of treatment plans and dose measurements., Methods: Fifty VMAT plans have been computed using same calculation parameters with SciMoCa and the two primary TPSs. Plans were validated with measurements performed with a 3D diode detector (ArcCHECK) by translating patient plans to phantom geometry. Calculation accuracy was assessed by measuring point dose differences and gamma passing rates (GPR) from a 3D gamma analysis with 3%-2 mm criteria. Comparison between SciMoCa and primary TPS calculations was made using the same estimators and using both patient and phantom geometry plans., Results: TPS and SciMoCa calculations were found to be in very good agreement with validation measurements with average point dose differences of 0.7 ± 1.7% and -0.2 ± 1.6% for SciMoCa and two TPSs, respectively. Comparison between SciMoCa calculations and the two primary TPS plans did not show any statistically significant difference with average point dose differences compatible with zero within error for both patient and phantom geometry plans and GPR (98.0 ± 3.0% and 99.0 ± 3.0% respectively) well in excess of the typical 95 % clinical tolerance threshold., Conclusion: This work presents results obtained with a significantly larger sample than other similar analyses and, to the authors' knowledge, compares SciMoCa with a MC-based TPS for the first time. Results show that a MC-based secondary patient-specific QA is a clinically viable, reliable, and promising technique, that potentially allows significant time saving that can be used for patient treatment and a per-plan basis QA that effectively complements traditional commissioning and calibration protocols., (© 2021 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Objectives: This multicentric study was carried out to investigate the impact of small field output factors (OFs) inaccuracies on the calculated dose in volumetric arctherapy (VMAT) radiosurgery brain plans., Methods: Nine centres, realised the same five VMAT plans with common planning rules and their specific clinical equipment Linac/treatment planning system commissioned with their OFs measured values (OFbaseline). In order to simulate OFs errors, two new OFs sets were generated for each centre by changing only the OFs values of the smallest field sizes (from 3.2 × 3.2 cm 2 to 1 × 1 cm 2 ) with well-defined amounts (positive and negative). Consequently, two virtual machines for each centre were recommissioned using the new OFs and the percentage dose differences ΔD (%) between the baseline plans and the same plans recalculated using the incremented (OFup) and decremented (OFdown) values were evaluated. The ΔD (%) were analysed in terms of planning target volume (PTV) coverage and organs at risk (OARs) sparing at selected dose/volume points., Results: The plans recalculated with OFdown sets resulted in higher variation of doses than baseline within 1.6 and 3.4% to PTVs and OARs respectively; while the plans with OFup sets resulted in lower variation within 1.3% to both PTVs and OARs. Our analysis highlights that OFs variations affect calculated dose depending on the algorithm and on the delivery mode (field jaw/MLC-defined). The Monte Carlo (MC) algorithm resulted significantly more sensitive to OFs variations than all of the other algorithms., Conclusion: The aim of our study was to evaluate how small fields OFs inaccuracies can affect the dose calculation in VMAT brain radiosurgery treatments plans. It was observed that simulated OFs errors, return dosimetric calculation accuracies within the 3% between concurrent plans analysed in terms of percentage dose differences at selected dose/volume points of the PTV coverage and OARs sparing., Advances in Knowledge: First multicentre study involving different Planning/Linacs about undetectable errors in commissioning output factor for small fields.

Stereotactic body radiation therapy (SBRT) has been recognized as a standard treatment option for many anatomical sites. Sophisticated radiation therapy techniques have been developed for carrying out these treatments and new quality assurance (QA) programs are therefore required to guarantee high geometrical and dosimetric accuracy. This paper focuses on recent advances on in-vivo measurements methods (IVM) for SBRT treatment. More specifically, all of the online QA methods for estimating the effective dose delivered to patients were compared. Determining the optimal IVM for performing SBRT treatments would reduce the risk of errors that could jeopardize treatment outcome. A total of 89 papers were included. The papers were subdivided into the following topics: point dosimeters (PD), transmission detectors (TD), log file analysis (LFA), electronic portal imaging device dosimetry (EPID), dose accumulation methods (DAM). The detectability capability of the main IVM detectors/devices were evaluated. All of the systems have some limitations: PD has no spatial data, EPID has limited sensitivity towards set-up errors and intra-fraction motion in some anatomical sites, TD is insensitive towards patient related errors, LFA is not an independent measure, DAMs are not always based on measures. In order to minimize errors in SBRT dose delivery, we recommend using synergic combinations of two or more of the systems described in our review: on-line tumor position and patient information should be combined with MLC position and linac output detection accuracy. In this way the effects of SBRT dose delivery errors will be reduced., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Purpose: To develop and validate a robust template for VMAT SBRT of lung lesions, using the multicriterial optimization (MCO) of a commercial treatment planning system., Methods: The template was established and refined on 10 lung SBRT patients planned for 55 Gy/5 fr. To improve gradient and conformity a ring structure around the planning target volume (PTV) was set in the list of objectives. Ideal fluence optimization was conducted giving priority to organs at risk (OARs) and using the MCO, which further pushes OARs doses. Segmentation was conducted giving priority to PTV coverage. Two different templates were produced with different degrees of modulation, by setting the Fluence Smoothing parameter to Medium (MFS) and High (HFS). Each template was applied on 20 further patients. Automatic and manual plans were compared in terms of dosimetric parameters, delivery time, and complexity. Statistical significance of differences was evaluated using paired two-sided Wilcoxon signed-rank test., Results: No statistically significant differences in PTV coverage and maximum dose were observed, while an improvement was observed in gradient and conformity. A general improvement in dose to OARs was seen, which resulted to be significant for chest wall V 30 Gy , total lung V 20 Gy , and spinal cord D 0.1 cc . MFS plans are characterized by a higher modulation and longer delivery time than manual plans. HFS plans have a modulation and a delivery time comparable to manual plans, but still present an advantage in terms of gradient., Conclusion: The automation of the planning process for lung SBRT using robust templates and MCO was demonstrated to be feasible and more efficient., (© 2020 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)

Purpose: To determine dose constraints that correlate with alopecia in patients treated with photon-based Volumetric Modulated Arc Therapy (VMAT) for primary brain tumors. Methods: During the treatment planning process, the scalp was drawn as a region of interest. Dose received by 0.1 cc (D 0.1cc ), mean dose (D mean ), absolute volumes receiving different doses (V 16Gy , V 20Gy , V 25Gy , V 30Gy , V 35Gy , V 40Gy , and V 43Gy ) were registered for the scalp. Alopecia was assessed according to Common Terminology Criteria for Adverse Events (CTCAE) v4.0. Receiver operating characteristics (ROC) curve analysis was used to identify parameters associated with hair-loss. Results: One-hundred and one patients were included in this observational study. At the end of radiotherapy (RT), 5 patients did not develop alopecia (D mean scalp 3.1 Gy). The scalp of the patients with G1 ( n = 11) and G2 ( n = 85) alopecia received D mean of 10.6 Gy and 11.8 Gy, respectively. At ROC analysis, V 16Gy20Gy ≥ 5.2 cc were the strongest predictors of acute alopecia risk. Chronic hair-loss assessment was available for 74 patients: median time to recovery from G2 alopecia was 5, 9 months. The actuarial rate of hair regrowth was 98.1% at 18 months after the end of RT. At ROC analysis, V 40Gy43Gy ≥2.2 cc were the strongest predictors of chronic G2-alopecia risk. V 20Gy , V 40Gy , and D 0,1cc were shown to be independent variables according to correlation coefficient r. Conclusions: V 20Gy and V 40Gy were the strongest predictors for acute and chronic G2 hair-loss, respectively. The low-dose bath typical of VMAT corresponds to large areas of acute but transient alopecia. However, the steep dose gradient of VMAT allows to reduce the areas of the scalp that receive higher doses, minimizing the risk of permanent alopecia. The application of our dosimetric findings for the scalp may help in reducing the alopecia risk and also in estimating the probability of hair-loss during patient counseling before starting radiotherapy., (Copyright © 2020 Scoccianti, Simontacchi, Greto, Perna, Terziani, Talamonti, Teriaca, Caramia, Lo Russo, Olmetto, Delli Paoli, Grassi, Carfora, Saieva, Bonomo, Detti, Mangoni, Desideri, Francolini, Di Cataldo, Marrazzo, Pallotta and Livi.)

Background: We investigated whether the pattern of intensity-modulated radiotherapy (IMRT) dose distribution to the skin can be correlated with the development of G3/G4 radiation dermatitis (RD)., Methods: A frequency-matched cohort analysis was perfomed on patients treated with IMRT and concurrent cisplatin or cetuximab. Risk ratios were obtained by fitting Poisson regression models., Results: The incidence of G3/G4 RD was 41.1% in 90 patients included (50% vs 36.6% in the cetuximab and cisplatin cohorts, respectively). In multivariate analysis, PS ≥ 1 and weight loss at RT completion >10 kg were the only factors that retained significance. The best dosimetric predictive accuracy was provided by 19.9 cc and 5.8 cc of skin ring 2 mm V50 and V60, respectively (AUC: 0.61 for both)., Conclusion: Along with clinical factors, the pattern of dose distribution to a ring structure localized 2 mm below the patient's surface may help predict the development of severe RD., (© 2019 Wiley Periodicals, Inc.)

Radiotherapy treatment of moving lesions is a challenging task in which different strategies can be used. The clinical implementation of the variety of complex technical solutions pursued to treat cancer, while sparing healthy tissues, requires accurate quality assurance tests and adequate phantoms. The aim of this work is to report on the anthropomorphic dynamic breathing model (ADAM) and on its applications to test image quality and dose delivery for four-dimensional (4D) techniques used to compensate for breathing-induced tumor motion. ADAM reproduces a male torso containing realistic ribs and spinal cord. Lungs move along circular or elliptical paths lying on sagittal planes, while the anterior chest surface moves independently up and down. Lungs host radiochromic films, a diamond detector and objects of known shape and dimensions to test 4D scanners. Markers are embedded around a target and a specific tool, hosting orthogonal radiochromic films, has been provided to perform end-to-end tests of tracking systems. To demonstrate ADAM's performance in testing techniques and methods used to image and treat moving lesions we report the results of three experiments performed to evaluate the accuracy of 4D computed tomography reconstructed volumes, the repeatability of measured dose in volumetric modulated arc treatments and tracking performances of a robotic system. The results obtained show that the percentage differences of reconstructed volumes, with respect to the known volume, depend on the breathing pattern and the pitch size (maximum 5% for breathing rate  >  10 breaths per minute). Repeatability of measured dose maps obtained comparing radiochromic films was demonstrated (GI3%/2 mm  >  99%). Differences between planned and delivered point dose measurements were  <1%. Tracking errors were all below the tolerance level (range 0.6-0.9 mm). The results of this study demonstrate that ADAM is suitable to test techniques and methods used to image and treat moving lesions.

A quantitative evaluation of the performances of the deformable image registration (DIR) algorithm implemented in MIM-Maestro was performed using multiple similarity indices. Two phantoms, capable of mimicking different anatomical bending and tumor shrinking were built and computed tomography (CT) studies were acquired after applying different deformations. Three different contrast levels between internal structures were artificially created modifying the original CT values of one dataset. DIR algorithm was applied between datasets with increasing deformations and different contrast levels and manually refined with the Reg Refine tool. DIR algorithm ability in reproducing positions, volumes, and shapes of deformed structures was evaluated using similarity indices such as: landmark distances, Dice coefficients, Hausdorff distances, and maximum diameter differences between segmented structures. Similarity indices values worsen with increasing bending and volume difference between reference and target image sets. Registrations between images with low contrast (40 HU) obtain scores lower than those between images with high contrast (970 HU). The use of Reg Refine tool leads generally to an improvement of similarity parameters values, but the advantage is generally less evident for images with low contrast or when structures with large volume differences are involved. The dependence of DIR algorithm on image deformation extent and different contrast levels is well characterized through the combined use of multiple similarity indices., (© 2019 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)