Dosimetric characterization of CVD diamonds in photon, electron and proton beams

The purpose of this work is the characterization, in an on line configuration, of the dosimetric response of a commercial CVD diamond. The study shows the possibility of using CVD diamond for dosimetric purposes with clinical, high-energy electron (4-15 MeV), photon (6-15 MV) and proton (62 MeV) beams

Natural and CVD type diamond detectors as dosimeters in hadrontherapy applications

Abstract Diamond is potentially a suitable material for use as radiation dosimeter; the wide band gap results in low dark currents and low sensitivity to visible light, the high carrier mobility can give rapid response, the very high density of strong bonds in the crystal structure make diamond very resistent to radiation damage; moreover it is tissue equivalent. The more recent advances in the synthesis of polycrystalline diamond by chemical vapour deposition (CVD) techniques have allowed the synthesis of material with electronic properties suitable for dosimetric application. In this paper we will report the results obtained in the study of the response of a natural diamond dosimeter and a CVD one irradiated with 62 AMeV proton beams to demonstrate their possible application in protontherapy

Dosimetric characterization of CVD diamonds irradiated with 62 MeV proton beams

Diamond is potentially a very suitable material for use as on-line radiation dosimeter. Recent advances in the synthesis of polycrystalline diamond by chemical vapor deposition (CVD) techniques have produced material with electronic properties suitable for dosimetry applications. In this work the possibility to use a segmented commercial CVD detector in the dosimetry of proton beams has been investigated. The response as function of dose, dose rate, the priming and the rise time have been investigated thoroughly. This study shows the suitability of CVD diamond for dosimetry of clinical 62 MeV proton beams. r 2005 Elsevier B.V. All rights reserved

CATANA protontherapy facility: The state of art of clinical and dosimetric experience

After nine years of activity, about 220 patients have been treated at the CATANA Eye Protontherapy facility. A 62MeV proton beam produced by a Superconducting Cyclotron is dedicated to radiotherapy of eye lesions, as uveal melanomas. Research and development work has been done to test different dosimetry devices to be used for reference and relative dosimetry, in order to achieve dose delivering accuracy. The follow-up results demonstrated the efficacy of proton beams and encouraged us in our activity in the fight against cancer

A 62 MeV proton beam for the treatment of ocular melanoma at Laboratori Nazionali del Sud-INFN (CATANIA)

At the Istituto Nazionale di Fisica Nucleare-Laboratori Nazionali del Sud (INFN-LNS) in Catania, Italy, the first Italian protontherapy facility, named Centro di AdroTerapia e Applicazioni Nucleari Avanzate (CATANA) has been built in collaboration with the University of Catania. It is based on the use of the 62-MeV proton beam delivered by the K=800 Superconducting Cyclotron installed and working at INFN-LNS since 1995. The facility is mainly devoted to the treatment of ocular diseases like uveal melanoma. A beam treatment line in air has been assembled together with a dedicated positioning patient system. The facility has been in operation since the beginning of 2002 and 66 patients have been successfully treated up to now. The main features of CATANA together with the clinical and dosimetric features will be extensively described; particularly, the proton beam line, that has been entirely built at LNS, with all its elements, the experimental transversal and depth dose distributions of the 62-MeV proton beam obtained for a final collimator of 25-mm diameter and the experimental depth dose distributions of a modulated proton beam obtained for the same final collimator. Finally, the clinical results over 1 yr of treatments, describing the features of the treated diseases will be reported

Cellular and molecular effects of protons: apoptosis induction and potential implications for cancer therapy.

Due to their ballistic precision, apoptosis induction by protons could be a strategy to specifically eliminate neoplastic cells. To characterize the cellular and molecular effects of these hadrons, we performed dose-response and time-course experiments by exposing different cell lines (PC3, Ca301D, MCF7) to increasing doses of protons and examining them with FACS, RT-PCR, and electron spin resonance (ESR). Irradiation with a dose of 10 Gy of a 26,7 Mev proton beam altered cell structures such as membranes, caused DNA double strand breaks, and significantly increased intracellular levels of hydroxyl ions, are active oxygen species (ROS). This modified the transcriptome of irradiated cells, activated the mitochondrial (intrinsic) pathway of apoptosis, and resulted in cycle arrest at the G2/M boundary. The number of necrotic cells within the irradiated cell population did not significantly increase with respect to the controls. The effects of irradiation with 20 Gy were qualitatively as well as quantitatively similar, but exposure to 40 Gy caused massive necrosis. Similar experiments with photons demonstrated that they induce apoptosis in a significantly lower number of cells and in a temporally delayed manner. These data advance our knowledge on the cellular and molecular effects of proton irradiation and could be useful for improving current hadrontherapy protocols

Active contour algorithm with discriminant analysis for delineating tumors in positron emission tomography

In the context of cancer delineation using positron emission tomography datasets, we present an innovative approach which purpose is to tackle the real-time, three-dimensional segmentation task in a full, or at least nearly full automatized way. The approach comprises a preliminary initialization phase where the user highlights a region of interest around the cancer on just one slice of the tomographic dataset. The algorithm takes care of identifying an optimal and user-independent region of interest around the anomalous tissue and located on the slice containing the highest standardized uptake value so to start the successive segmentation task. The three-dimensional volume is then reconstructed using a slice-by-slice marching approach until a suitable automatic stop condition is met. On each slice, the segmentation is performed using an enhanced local active contour based on the minimization of a novel energy functional which combines the information provided by a machine learning component, the discriminant analysis in the present study. As a result, the whole algorithm is almost completely automatic and the output segmentation is independent from the input provided by the user. Phantom experiments comprising spheres and zeolites, and clinical cases comprising various body districts (lung, brain, and head and neck), and two different radio-tracers (18 F-fluoro-2-deoxy-D-glucose, and 11C-labeled Methionine) were used to assess the algorithm performances. Phantom experiments with spheres and with zeolites showed a dice similarity coefficient above 90% and 80%, respectively. Clinical cases showed high agreement with the gold standard (R2 = 0.98). These results indicate that the proposed method can be efficiently applied in the clinical routine with potential benefit for the treatment response assessment, and targeting in radiotherapy

A smart and operator independent system to delineate tumours in Positron Emission Tomography scans

Positron Emission Tomography (PET) imaging has an enormous potential to improve radiation therapy treatment planning offering complementary functional information with respect to other anatomical imaging approaches. The aim of this study is to develop an operator independent, reliable, and clinically feasible system for biological tumour volume delineation from PET images. Under this design hypothesis, we combine several known approaches in an original way to deploy a system with a high level of automation. The proposed system automatically identifies the optimal region of interest around the tumour and performs a slice-by-slice marching local active contour segmentation. It automatically stops when a "cancer-free" slice is identified. User intervention is limited at drawing an initial rough contour around the cancer region. By design, the algorithm performs the segmentation minimizing any dependence from the initial input, so that the final result is extremely repeatable. To assess the performances under different conditions, our system is evaluated on a dataset comprising five synthetic experiments and fifty oncological lesions located in different anatomical regions (i.e. lung, head and neck, and brain) using PET studies with 18F-fluoro-2-deoxy-d-glucose and 11C-labeled Methionine radio-tracers. Results on synthetic lesions demonstrate enhanced performances when compared against the most common PET segmentation methods. In clinical cases, the proposed system produces accurate segmentations (average dice similarity coefficient: 85.36 ± 2.94%, 85.98 ± 3.40%, 88.02 ± 2.75% in the lung, head and neck, and brain district, respectively) with high agreement with the gold standard (determination coefficient R2 = 0.98). We believe that the proposed system could be efficiently used in the everyday clinical routine as a medical decision tool, and to provide the clinicians with additional information, derived from PET, which can be of use in radiation therapy, treatment, and planning

Feasibility on the Use of Radiomics Features of 11[C]-MET PET/CT in Central Nervous System Tumours: Preliminary Results on Potential Grading Discrimination Using a Machine Learning Model

Background/Aim: Nowadays, Machine Learning (ML) algorithms have demonstrated remarkable progress in image-recognition tasks and could be useful for the new concept of precision medicine in order to help physicians in the choice of therapeutic strategies for brain tumours. Previous data suggest that, in the central nervous system (CNS) tumours, amino acid PET may more accurately demarcate the active disease than paramagnetic enhanced MRI, which is currently the standard method of evaluation in brain tumours and helps in the assessment of disease grading, as a fundamental basis for proper clinical patient management. The aim of this study is to evaluate the feasibility of ML on 11[C]-MET PET/CT scan images and to propose a radiomics workflow using a machine-learning method to create a predictive model capable of discriminating between low-grade and high-grade CNS tumours. Materials and Methods: In this retrospective study, fifty-six patients affected by a primary brain tumour who underwent 11[C]-MET PET/CT were selected from January 2016 to December 2019. Pathological examination was available in all patients to confirm the diagnosis and grading of disease. PET/CT acquisition was performed after 10 min from the administration of 11C-Methionine (401–610 MBq) for a time acquisition of 15 min. 11[C]-MET PET/CT images were acquired using two scanners (24 patients on a Siemens scan and 32 patients on a GE scan). Then, LIFEx software was used to delineate brain tumours using two different semi-automatic and user-independent segmentation approaches and to extract 44 radiomics features for each segmentation. A novel mixed descriptive-inferential sequential approach was used to identify a subset of relevant features that correlate with the grading of disease confirmed by pathological examination and clinical outcome. Finally, a machine learning model based on discriminant analysis was used in the evaluation of grading prediction (low grade CNS vs. high-grade CNS) of 11[C]-MET PET/CT. Results: The proposed machine learning model based on (i) two semi-automatic and user-independent segmentation processes, (ii) an innovative feature selection and reduction process, and (iii) the discriminant analysis, showed good performance in the prediction of tumour grade when the volumetric segmentation was used for feature extraction. In this case, the proposed model obtained an accuracy of ~85% (AUC ~79%) in the subgroup of patients who underwent Siemens tomography scans, of 80.51% (AUC 65.73%) in patients who underwent GE tomography scans, and of 70.31% (AUC 64.13%) in the whole patients’ dataset (Siemens and GE scans). Conclusions: This preliminary study on the use of an ML model demonstrated to be feasible and able to select radiomics features of 11[C]-MET PET with potential value in prediction of grading of disease. Further studies are needed to improve radiomics algorithms to personalize predictive and prognostic models and potentially support the medical decision process

The Role of [⁶⁸Ga]Ga-DOTA-SSTR PET Radiotracers in Brain Tumors: A Systematic Review of the Literature and Ongoing Clinical Trials

Background: The development of [68Ga]Ga-DOTA-SSTR PET tracers has garnered interest in neuro-oncology, to increase accuracy in diagnostic, radiation planning, and neurotheranostics protocols. We systematically reviewed the literature on the current uses of [68Ga]Ga-DOTA-SSTR PET in brain tumors. Methods: PubMed, Scopus, Web of Science, and Cochrane were searched in accordance with the PRISMA guidelines to include published studies and ongoing trials utilizing [68Ga]Ga-DOTA-SSTR PET in patients with brain tumors. Results: We included 63 published studies comprising 1030 patients with 1277 lesions, and 4 ongoing trials. [68Ga]Ga-DOTA-SSTR PET was mostly used for diagnostic purposes (62.5%), followed by treatment planning (32.7%), and neurotheranostics (4.8%). Most lesions were meningiomas (93.6%), followed by pituitary adenomas (2.8%), and the DOTATOC tracer (53.2%) was used more frequently than DOTATATE (39.1%) and DOTANOC (5.7%), except for diagnostic purposes (DOTATATE 51.1%). [68Ga]Ga-DOTA-SSTR PET studies were mostly required to confirm the diagnosis of meningiomas (owing to their high SSTR2 expression and tracer uptake) or evaluate their extent of bone invasion, and improve volume contouring for better radiotherapy planning. Some studies reported the uncommon occurrence of SSTR2-positive brain pathology challenging the diagnostic accuracy of [68Ga]Ga-DOTA-SSTR PET for meningiomas. Pre-treatment assessment of tracer uptake rates has been used to confirm patient eligibility (high somatostatin receptor-2 expression) for peptide receptor radionuclide therapy (PRRT) (i.e., neurotheranostics) for recurrent meningiomas and pituitary carcinomas. Conclusion: [68Ga]Ga-DOTA-SSTR PET studies may revolutionize the routine neuro-oncology practice, especially in meningiomas, by improving diagnostic accuracy, delineation of radiotherapy targets, and patient eligibility for radionuclide therapies