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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 V95% were observed for PTV breast/CW and PTV III-IV, while increased coverage (higher V95%) was seen for PTV IMN in AP-VMAT plans. HT plans exhibited smaller values of PTV V105% 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. [ABSTRACT FROM AUTHOR]

Simple Summary: This manuscript aims to address the diagnostic challenges of mediastinal bulky lymphomas with the baseline value of 18F-FDG PET/CT metabolic, volumetric and texture parameters, also relying on machine learning techniques, in patients with grey zone lymphoma, primary diffuse large B-cell lymphoma of the mediastinum and classical Hodgkin lymphoma. Different types of histology demonstrated several baseline 18F-FDG PET/CT radiomics parameters that were significantly different from one another, suggesting the possibility of identifying potential histological heterogeneity and aggressive transformation. Moreover, using radiomics-based imaging biomarkers, machine learning techniques offer a solution for separating not completely disjoint histological types. To date, the gold standard for diagnosis is biopsy, but machine learning methods could be combined with radiomics to build a histological representation of mediastinal bulky masses that is able to successfully identify different types of lymphomas. Finally, this preliminary study supports the potential of metabolic texture analyses as future imaging biomarkers, with a growing role in clinical diagnosis. 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 1 ) is devoted to the identification of the lung parenchyma; the second one (U-net 2 ) 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. [ABSTRACT FROM AUTHOR]

Featured Application: Transit dosimetry, with Electronic Portal Imaging Device (EPID), is a powerful tool for in vivo dosimetry (IVD). Despite its wide availability and its simplicity of use, the clinical implementation of such a dosimetric technique is still challenging. One reason for the difficulty of IVD implementation is the lack of guidelines around the validation and acceptance of EPID IVD algorithms. In this paper we propose an evaluation method for assessing the accuracy of a novel 3D EPID back-projection algorithm for IVD. To the best of our knowledge, the EPID back-projection algorithms have never been validated in anthropomorphic phantom against direct two-dimensional measurements, and the new algorithm, introduced in version 5.8 of the Dosimetry Check software for dealing with tissue inhomogeneity, has never been evaluated before. The aim of this study was to develop and apply an evaluation method for assessing the accuracy of a novel 3D EPID back-projection algorithm for in vivo dosimetry. The novel algorithm of Dosimetry Check (DC) 5.8 was evaluated. A slab phantom homogeneously filled, or with air and bone inserts, was used for fluence reconstruction of different squared fields. VMAT plans in different anatomical sites were delivered on an anthropomorphic phantom. Dose distributions were measured with radiochromic films. The 2D Gamma Agreement Index (GAI) between the DC and the film dose distributions (3%, 3 mm) was computed for assessing the accuracy of the algorithm. GAIs between films and TPS and between DC and TPS were also computed. The fluence reconstruction accuracy was within 2% for all squared fields in the three slabs' configurations. The GAI between the DC and the film was 92.7% in the prostate, 92.9% in the lung, 96.6% in the head and the neck, and 94.6% in the brain. An evaluation method for assessing the accuracy of a novel EPID algorithm was developed. The DC algorithm was shown to be able to accurately reconstruct doses in all anatomic sites, including the lung. The methodology described in the present study can be applied to any EPID back-projection in vivo algorithm. [ABSTRACT FROM AUTHOR]

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 Pinnacle3) 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. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is a branch of computer science dedicated to giving machines or computers the ability to perform human-like cognitive functions, such as learning, problem-solving, and decision making. Since it is showing superior performance than well-trained human beings in many areas, such as image classification, object detection, speech recognition, and decision-making, AI is expected to change profoundly every area of science, including healthcare and the clinical application of physics to healthcare, referred to as medical physics. As a result, the Italian Association of Medical Physics (AIFM) has created the "AI for Medical Physics" (AI4MP) group with the aims of coordinating the efforts, facilitating the communication, and sharing of the knowledge on AI of the medical physicists (MPs) in Italy. The purpose of this review is to summarize the main applications of AI in medical physics, describe the skills of the MPs in research and clinical applications of AI, and define the major challenges of AI in healthcare. [ABSTRACT FROM AUTHOR]

We evaluate the combined usage of two systems, the Integral Quality Monitor (IQM) transmission detector and SoftDiso software, for in vivo dose monitoring by simultaneous detection of delivery and patient setup errors in whole breast irradiation. An Alderson RANDO phantom was adapted with silicon breast prostheses to mimic the female anatomy. Plans with simulated delivery errors were created from a reference left breast plan, and patient setup errors were simulated by moving the phantom. Deviations from reference values recorded by both monitoring systems were measured for all plans and phantom positions. A 2D global gamma analysis was performed in SoftDiso for all phantom displacements. Both IQM signals and SoftDiso R-values are sensitive to small MU variations. However, only IQM is sensitive to jaw position variations. Conversely, IQM is unable to detect patient positioning errors, and the R-value has good sensitivity to phantom displacements. A gamma comparison analysis allows one to determine alert thresholds to detect phantom shifts or relatively large rotations. The combined use of the IQM and SoftDiso allows for fast identification of both delivery and setup errors and substantially reduces the impact of error identification and correction on the treatment workflow. [ABSTRACT FROM AUTHOR]

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 V30 Gy, total lung V20 Gy, and spinal cord D0.1 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. [ABSTRACT FROM AUTHOR]

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 (D0.1cc), mean dose (Dmean), absolute volumes receiving different doses (V16Gy, V20Gy, V25Gy, V30Gy, V35Gy, V40Gy, and V43Gy) 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 (Dmean scalp 3.1 Gy). The scalp of the patients with G1 (n = 11) and G2 (n = 85) alopecia received Dmean of 10.6 Gy and 11.8 Gy, respectively. At ROC analysis, V16Gy20Gy ≥ 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, V40Gy43Gy ≥2.2 cc were the strongest predictors of chronic G2-alopecia risk. V20Gy, V40Gy, and D0,1cc were shown to be independent variables according to correlation coefficient r. Conclusions: V20Gy and V40Gy 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Inorganic CsPbBr3 perovskite devices have been manufactured and tested as dosimeters under both conventional and Intensity Modulated Radiotherapy (IMRT) X-ray beams. Samples showed a very good linear dependence of the collected charge/current on dose/dose rates in the range of 0.1–5.0 Gy/0.1–4.0 Gy/min of interest for clinical applications. A device sensitivity of about 70 nC/Gy mm3 compares favorably with other solid-state dosimeters. The first verification of an IMRT dose profile of a prostate cancer treatment, performed by moving the perovskite device on a 10 cm-long profile with a 0.5 mm pitch, showed agreement within 5% with the dose distribution required by the treatment planning system. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: Tumor resistance to apoptosis is one the main causes of anticancer treatment failure. Previous studies showed that LMW‐PTP overexpression enhances resistance of cancer cells to traditional anticancer drugs. Today, the role of LMW‐PTP in inducing resistance to apoptosis in melanoma cells remains to be elucidated. Experimental setting include MTT assay, Annexin V/Pi method, and colony assay to assess whether silencing of LMW‐PTP improves the sensitivity of A375 to dacarbazine, 5‐FU, and radiotherapy. Pharmacological targeting of LMW‐PTP was obtained using Morin, a LMW‐PTP inhibitor. The ability of Morin to improve the effectiveness of anticancer drugs and radiotherapy was also studied. Moreover, PC3 cells were used as an alternative cellular model to confirm the data obtained with melanoma cells. We found that LMW‐PTP silencing improves the effectiveness of dacarbazine, 5‐FU, and radiotherapy. Identical results were obtained in vivo when Morin was used to target LMW‐PTP. We demonstrated that Morin synergizes with dacarbazine, improving its cytotoxic activity. However, we showed that the combined treatment, Morin‐anticancer drug, does not affect the viability of noncancerous cells. Knockdown of LMW‐PTP sensitizes also PC3 cells to docetaxel and radiotherapy. In conclusion, we showed that LMW‐PTP targeting improves effectiveness of anticancer drugs used for treatment of melanoma. Moreover, our results suggest that Morin could be used as adjuvant to improve the outcome of patients affected by metastatic melanoma. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to test the sensitivity of a transmission detector for online dose monitoring of intensity-modulated radiation therapy (IMRT) for detecting small delivery errors. Furthermore, the correlation of changes in detector output induced by small delivery errors with other metrics commonly employed to quantify the deviations between calculated and delivered dose distributions was investigated.Methods: Transmission detector measurements were performed at three institutions. Seven types of errors were induced in nine clinical step-and-shoot (S&S) IMRT plans by modifying the number of monitor units (MU) and introducing small deviations in leaf positions. Signal reproducibility was investigated for short- and long-term stability. Calculated dose distributions were compared in terms of γ passing rates and dose-volume histogram (DVH) metrics (e.g., Dmean, Dx%, Vx%). The correlation between detector signal variations, γ passing rates, and DVH parameters was investigated.Results: Both short- and long-term reproducibility was within 1%. Dose variations down to 1 MU (∆signal 1.1 ± 0.4%) as well as changes in field size and positions down to 1 mm (∆signal 2.6 ± 1.0%) were detected, thus indicating high error-detection sensitivity. A moderate correlation of detector signal was observed with γ passing rates (R2 = 0.57-0.70), while a good correlation was observed with DVH metrics (R2 = 0.75-0.98).Conclusion: The detector is capable of detecting small delivery errors in MU and leaf positions, and is thus a highly sensitive dose monitoring device for S&S IMRT for clinical practice. The results of this study indicate a good correlation of detector signal with DVH metrics; therefore, clinical action levels can be defined based on the presented data. [ABSTRACT FROM AUTHOR]

Purpose: Radiosurgery (RS) is a well-established treatment in selected patients with brain metastasis. The aim of this study is to compare the differences between CyberKnife (CK) and TomoTherapy (HT) treatment plans of RS of single brain metastasis (BM) to define when HT should be used in cases beyond Cyberknife-when both systems are readily available for the radiation oncologist. Methods and materials: Nineteen patients with single brain metastasis treated with CK were re-planned for radiosurgery using TomoTherapy Hi-ART system. Two planning approaches have been used for TomoTherapy plans: the classical one (HT) and the improved conformity (icHT) that produces dose distributions more similar to those of RS plans. PTV coverage, Conformity Index (CI), Paddick Conformity Index (nCI), Homogeneity Index (HI), Gradient Index (GI), and beam on time of CK, HT, and icHT plans were evaluated and compared. Results: A good coverage was found for CK, HT, and icHT plans. A difference between mean HI of CK and icHT plans was observed ( p = 0.007). Better dose gradients compared to both icHT and HT modalities were observed in CK plans. icHT modality showed improved mean CI respect to HT modality, similar to that obtained in CK plans. Conclusions: CK plans show higher conformity and lower GI than icHT and HT plans. TomoTherapy demonstrates the advantage of being a device capable to reach different clinical objectives depending on the different planning modality employed. CyberKnife and TomoTherapy are both optimal RS devices, the choice to use one over another has to be clinically guided. [ABSTRACT FROM AUTHOR]

Background: Soft tissue sarcomas (STS) are a group of rare, heterogeneous tumors. Scarce terapeuthic options are currently available, given the limited efficacy of systemic chemotherapy. Patients with low-burden metastatic disease may benefit from adjunction of local ablative treatments. However, because of the presumed radioresistance of STS, stereotactic body radiotherapy (SBRT) has not been steadily considered as a viable treatment option. Methods: We retrospectively reviewed STS patients treated with SBRT in a single institution,, for oligometastatic and oligoprogressive metastatic disease. Local control (LC), disease-free survival (DFS), and overall survival (OS) were assessed. We also reported the incidence of early and late adverse events and their grade according to Common Terminology Criteria for Adverse Events (CTCAE) 5.0. Univariate and multivariate statistical analysis were carried out to investigate variables correlated with outcome and toxicity. Results: Forty patients were treated between 2012 and 2019 to 77 metastases with SBRT to a median biologic effective dose (BED5, assuming an α/β=5) of 105 Gy (range 66 to 305 Gy). 63% of patients had received 2 or more chemotherapy lines at the time of SBRT. LC, DFS and OS at two years were 67%, 23% and 40% respectively. At multivariate analysis, LC only significantly improved in patients receving a BED5>150 Gy (hazard ratio [HR] 3.9. 95% confidence interval [CI], 1.6-9.7; p=0.028), while an interval>24 months between primary tumor diagnosis and metastatic disease relapse was correlated with improved DFS and OS (HR, 0.46; 95% CI, 0.22-0.96; P = 0.01 and HR, 0.48; 95% CI, 0.23- 0.99; P = 0.03, respectively). Early toxicity was observed in 4 patients, late toxicity in 1 patient. No toxicity grade>2 was observed. Conclusions: SBRT is an effective therapeutic option in metastatic STS. A BED5>150 Gy5 is required to obtain better tumor control rates. Metastatic relapse>24 months after diagnosis is correlated with improved overall survival. Adverse events are scarce and mostly transient, meaning SBRT is safe and could be an option even in heavily pretreated patients. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this work is to investigate the behavior of a single crystal diamond diode (SCDD) for volumetric modulated arc therapy (VMAT) dose verifications. This delivery technique is one of the most severe test of a dosimeter performance due to the modulation of the dose rate achieved by simultaneously changing the velocity of the gantry and the position of the collimator leaves. The performed measurements with VMAT photon beams can therefore contribute to an overall global validation of the device to be used in dose distribution verifications. Methods: The SCDD response to 6 MVRX has been tested and compared with reference ionization chambers and treatment planning system (TPS) calculations in different experiments: (a) measurements of output factors for small field sizes (square fields of side ranging between 8 mm and 104 mm) by SCDD and A1SL ionization chamber; (b) angular dependence evaluation of the entire experimental set-up by SCDD, A1SL, and Farmer ionization chambers; and (c) acquisition of dose profiles for a VMAT treatment of a pulmonary disease in latero-lateral and gantry-target directions by SCDD and A1SL ionization chamber. Results: The output factors measured by SCDD favorably compare with the ones obtained by A1SL, whose response is affected by the lack of charged particle equilibrium and by averaging effect when small fields are involved. From the experiment on angular dependence, a good agreement is observed among the diamond diode, the ion chambers, and the TPS. In VMAT profiles, the absorbed doses measured by SCDD and A1SL compare well with the TPS calculated ones. An overall better agreement is observed in the case of the diamond dosimeter, which is also showing a better accuracy in terms of distance to agreement in the high gradient regions. Conclusions: Synthetic diamond diodes, whose performance were previously studied for conformal and IMRT radiotherapy techniques, were found to be suitable detectors also for dosimetric measurements in volumetric arc therapy treatments. [ABSTRACT FROM AUTHOR]

Today, there is a steadily growing interest in the use of proton beams for tumor therapy, as they permit to tightly shape the dose delivered to the target reducing the exposure of the surrounding healthy tissues. Nonetheless, accuracy in the determination of the dose distribution in proton-therapy is up to now limited by the uncertainty in stopping powers, which are presently calculated from the photon attenuation coefficients measured by X-ray tomography. Proton computed tomography apparatus (pCT) has been proposed to directly measure the stopping power and reduce this uncertainty. Main problem with proton imaging is the blurring effect introduced by multiple Coulomb scattering: single proton tracking is a promising technique to face this difficulty. As a first step towards a pCT system, we designed a proton radiography (pCR) prototype based on a silicon microstrip tracker (to characterize particle trajectories) and a segmented YAG:Ce calorimeter (to measure their residual energy). Aim of the system is to detect protons with a ∼1 MHz particle rate of and with kinetic energy in the range 250-270 MeV, high enough to pass through human body. Design and development of the pCR prototype, as well as the characterization of its single components, are described in this paper. [ABSTRACT FROM AUTHOR]

In view of their appreciable insights, we take the opportunity to clarify some issues regarding the potential causative role of skin dose distribution for the development of radiation dermatitis in head and neck radiotherapy. It is, in our opinion, rather surprising that no skin dose-volume constraints were ever formally recognized in the intensity-modulated radiation therapy (IMRT) era, spanning from the quantitative analyses of normal tissue effects in the clinic seminal publication to more recent reviews. Analysis of skin dose distribution for the prediction of severe radiation dermatitis in head and neck squamous cell carcinoma patients treated with concurrent chemo-radiotherapy. [Extracted from the article]

Purpose: To present BrainTool, a new anthropomorphic phantom made to assess the accuracy of brain radiosurgery treatments. Materials and method: A synthetic human brain T1/ MRI study was segmented, simplified, and 3D printed. The printed objects were glued to two PMMA disks thus creating four compartments. Each compartment was filled with different substances to create realistic MRI and CT brain contrasts. A water-equivalent silicon rubber casing covers the phantom giving it a realistic head look. Eight markers were attached inside the BrainTool to test the accuracy of image registration procedures. A holder hosting an ionization chamber can be inserted into the BrainTool to assess the targeting accuracy. BrainTool can be placed on any CT, MRI or linac couch or fixed to a Lexell stereotactic frame. To exemplify a possible use of BrainTool, we measured the registration accuracy and the targeting accuracy of a GammaKnife system. Registration accuracy- CT and T1 MRI images of the BrainTool phantom were sent to GammaPlan (Elekta) system and registered with the provided tool. The distances between correspondent markers’ positions in CT and MRI registered studies were evaluated and mean and range values were calculated. Registrations were carried out in the entire image or in a smaller userdefined region of interest (ROI) inside the phantom. Targeting accuracy- It was assessed by comparing planned and measured target positions. The measured target position was obtained using an iterative cross scan approach aimed to localize the camera measuring point. Planned and measured target positions uncertainty were both assessed. Results: In Fig. 1, a picture of the BrainTool phantom, a CT slice with measured HU and axial coronal and sagittal slice of a T1 weighted MR images, are shown. Two NiCl2 solutions (1.2 mmol/l and 0.8 mmol/l) were used to fill the phantom and simulate grey and white matter T1 relaxation times (650 ms and 1200 ms). Several T1 or T2 MRI relaxation times, can be simulated by choosing different solutions or concentrations. Registration accuracy – The mean distances between correspondent markers in CT and MRI registered studies were 1.2± 0.5mm; range [1.0mm -1.4mm] and 0.3 ± 0.2mm; range [0.0mm -0.6mm] for the entire image and the selected ROI, respectively. Targeting accuracy - Targeting mismatches along x, y and z and cumulative (r) were x=-0.7±0.5 mm, y=0.3±0.4 mm z= -0.9± 0.4 mm and r=1.2±0.8 mm, respectively. Conclusions: We have developed a phantom and exemplified its use in brain radiosurgery treatments. [ABSTRACT FROM AUTHOR]

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. 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 cm2 to 1 × 1 cm2) 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. 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. 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. First multicentre study involving different Planning/Linacs about undetectable errors in commissioning output factor for small fields. [ABSTRACT FROM AUTHOR]