Hydrogenated Amorphous Silicon Charge-Selective Contact Devices on a Polyimide Flexible Substrate for Dosimetry and Beam Flux Measurements.

Hydrogenated amorphous silicon (a-Si:H) devices on flexible substrates are currently being studied for application in dosimetry and beam flux measurements. The necessity of in vivo dosimetry requires thin devices with maximal transparency and flexibility. For this reason, a thin (<10 µm) a-Si:H device deposited on a thin polyimide sheet is a very valid option for this application.

Robust machine learning challenge: An AIFM multicentric competition to spread knowledge, identify common pitfalls and recommend best practice.

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).

Mobility Gaps of Hydrogenated Amorphous Silicon Related to Hydrogen Concentration and Its Influence on Electrical Performance.

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.

Advancing patient setup: A comprehensive scoping review of tattoo-less techniques in radiation therapy.

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.

Developing an ensemble machine learning study: Insights from a multi-center proof-of-concept study.

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.

Dosimetry of microbeam radiotherapy by flexible hydrogenated amorphous silicon detectors.

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.Two detectors are explored: an n-type/intrinsic/p-type planar diode (NIP) and an NIP with an additional charge selective layer (NIP + CSC).

Radiomic- and dosiomic-based clustering development for radio-induced neurotoxicity in pediatric medulloblastoma.

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.

Characterization of a flexible a-Si:H detector for in vivo dosimetry in therapeutic x-ray beams.

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.

The application of virtual environment radiotherapy for RTT training: A scoping review.

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.

Fully automated volumetric modulated arc therapy technique for radiation therapy of locally advanced breast cancer.

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).

Hydrogenated amorphous silicon high flux x-ray detectors for synchrotron microbeam radiation therapy.

. 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.

Characterization of Mediastinal Bulky Lymphomas with FDG-PET-Based Radiomics and Machine Learning Techniques.

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.

A multicenter evaluation of a deep learning software (LungQuant) for lung parenchyma characterization in COVID-19 pneumonia.

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.

Deriving quantitative information from multiparametric MRI via Radiomics: Evaluation of the robustness and predictive value of radiomic features in the discrimination of low-grade versus high-grade gliomas with machine learning.

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.

Radiosensitizing Effect of Trabectedin on Human Soft Tissue Sarcoma Cells.

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.

High-Resolution Photoemission Study of Neutron-Induced Defects in Amorphous Hydrogenated Silicon Devices.

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.

Performance of 3D diamond detectors in small field dosimetry: The impact of pixel size.

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.

Applications of artificial intelligence in stereotactic body radiation therapy.

This topical review focuses on the applications of artificial intelligence (AI) tools to stereotactic body radiation therapy (SBRT). The high dose per fraction and the limited number of fractions in SBRT require stricter accuracy than standard radiation therapy. The intent of this review is to describe the development and evaluate the possible benefit of AI tools integration into the radiation oncology workflow for SBRT automation.

A semi-automatic planning technique for whole breast irradiation with tangential IMRT fields.

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 (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.

Magnetron Sputtered CsPbCl Perovskite Detectors as Real-Time Dosimeters for Clinical Radiotherapy.

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, 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.

Enhancing the impact of Artificial Intelligence in Medicine: A joint AIFM-INFN Italian initiative for a dedicated cloud-based computing infrastructure.

Artificial Intelligence (AI) techniques have been implemented in the field of Medical Imaging for more than forty years. Medical Physicists, Clinicians and Computer Scientists have been collaborating since the beginning to realize software solutions to enhance the informative content of medical images, including AI-based support systems for image interpretation. Despite the recent massive progress in this field due to the current emphasis on Radiomics, Machine Learning and Deep Learning, there are still some barriers to overcome before these tools are fully integrated into the clinical workflows to finally enable a precision medicine approach to patients' care.

Quantification of pulmonary involvement in COVID-19 pneumonia by means of a cascade of two U-nets: training and assessment on multiple datasets using different annotation criteria.

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.

Automatic VMAT technique to treat glioblastoma: A two years' experience.

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.