Explore the vast range of titles available.

Background Synovial Sarcomas (SS) are rare tumors occurring predominantly in adolescent and young adults with a dismal prognosis in advanced phases. We report a first-in-human phase I of monoclonal antibody (OTSA-101) targeting FZD10 , overexpressed in most SS but not present in normal tissues, labelled with radioisotopes and used as a molecular vehicle to specifically deliver radiation to FZD10 expressing SS lesions. Methods Patients with progressive advanced SS were included. In the first step of this trial, OTSA-101 in vivo bio-distribution and lesions uptake were evaluated by repeated whole body planar and SPECT-CT scintigraphies from H1 till H144 after IV injection of 187 MBq of 111 In-OTSA-101. A 2D dosimetry study also evaluated the liver absorbed dose when using 90 Y-OTSA-101. In the second step, those patients with significant tumor uptake were randomized between 370 MBq (Arm A) and 1110 MBq (Arm B) of 90 Y-OTSA-101 for radionuclide therapy. Results From January 2012 to June 2015, 20 pts. (median age 43 years [21–67]) with advanced SS were enrolled. Even though 111 In-OTSA-101 liver uptake appeared to be intense, estimated absorbed liver dose was less than 20 Gy for each patient. Tracer intensity was greater than mediastinum in 10 patients consistent with sufficient tumor uptake to proceed to treatment with 90 Y-OTSA-101: 8 were randomized (Arm A: 3 patients and Arm B: 5 patients) and 2 were not randomized due to worsening PS. The most common Grade ≥ 3 AEs were reversible hematological disorders, which were more frequent in Arm B. No objective response was observed. Best response was stable disease in 3/8 patients lasting up to 21 weeks for 1 patient. Conclusions Radioimmunotherapy targeting FZD10 is feasible in SS patients as all patients presented at least one lesion with 111 In-OTSA-101 uptake. Tumor uptake was heterogeneous but sufficient to select 50% of pts. for 90 Y-OTSA-101 treatment. The recommended activity for further clinical investigations is 1110 MBq of 90 Y-OTSA-101. However, because of hematological toxicity, less energetic particle emitter radioisopotes such as Lutetium 177 may be a better option to wider the therapeutic index. Trial registration The study was registered on the NCT01469975 website with a registration code NCT01469975 on November the third, 2011.

Accurately modeling the radiobiological mechanisms responsible for the induction of DNA damage remains a major scientific challenge, particularly for understanding the effects of low doses of ionizing radiation on living beings, such as the induction of carcinogenesis. A computational approach based on the Monte Carlo technique to simulate track structures in a biological medium is currently the most reliable method for calculating the early effects induced by ionizing radiation on DNA, the primary cellular target of such effects. The Geant4-DNA Monte Carlo toolkit can simulate not only the physical, but also the physico-chemical and chemical stages of water radiolysis. These stages can be combined with simplified geometric models of biological targets, such as DNA, to assess direct and indirect early DNA damage. In this study, DNA damage induced in a human fibroblast cell was evaluated using Geant4-DNA as a function of incident particle type (gammas, protons, and alphas) and energy. The resulting double-strand break yields as a function of linear energy transfer closely reproduced recent experimental data. Other quantities, such as fragment length distribution, scavengeable damage fraction, and time evolution of damage within an analytical repair model also supported the plausibility of predicting DNA damage using Geant4-DNA.The complete simulation chain application “molecularDNA”, an example for users of Geant4-DNA, will soon be distributed through Geant4.

Nanoparticles are defined as solid colloidal particles ranging in size from 10 to 1000 nm. Nanoparticles offer many benefits to larger particles such as increased surface-to-volume ratio and increased magnetic properties. Over the last few years, there has been a steadily growing interest in using nanoparticles in different biomedical applications such as targeted drug delivery, hyperthermia, photoablation therapy, bioimaging and biosensors. Iron oxide nanoparticles have dominated applications, such as drug delivery, hyperthermia, bioimaging, cell labelling and gene delivery, because of their excellent properties such as chemical stability, non-toxicity, biocompatibility, high saturation magnetisation and high magnetic susceptibility. In this review, nanoparticles will be classified into four different nanosystems metallic nanoparticles, bimetallic or alloy nanoparticles, metal oxide nanoparticles and magnetic nanoparticles. This review investigates the use of nanosystems other than iron oxide nanoparticles such as metallic nanoparticles like gold (Au) and silver (Ag), bimetallic nanoparticles like iron cobalt (Fe-Co) and iron platinum (Fe-Pt) and metal oxides including titanium dioxide (TiO 2 ) cerium dioxide (CeO 2 ), silica (SiO 2 ) and zinc oxide (ZnO) with a focus on the lesser studied nanoparticles such as silver (Ag), iron-platinum (Fe-Pt) and titanium dioxide (TiO 2 ) and how their unique properties allow for their potential use in various biomedical applications.

Although conventional radiotherapy promotes the migration/invasion of cancer stem cells (CSCs) under normoxia, carbon ion (C-ion) irradiation actually decreases these processes. Unraveling the mechanisms of this discrepancy, particularly under the hypoxic conditions that pertain in niches where CSCs are preferentially localized, would provide a better understanding of the origins of metastases. Invasion/migration, proteins involved in epithelial-to-mesenchymal transition (EMT), and expression of MMP-2 and HIF-1α were quantified in the CSC subpopulations of two head-and-neck squamous cell carcinoma (HNSCC) cell lines irradiated with X-rays or C-ions. X-rays triggered HNSCC-CSC migration/invasion under normoxia, however this effect was significantly attenuated under hypoxia. C-ions induced fewer of these processes in both oxygenation conditions. The differential response to C-ions was associated with a lack of HIF-1α stabilization, MMP-2 expression, or activation of kinases of the main EMT signaling pathways. Furthermore, we demonstrated a major role of reactive oxygen species (ROS) in the triggering of invasion/migration in response to X-rays. Monte-Carlo simulations demonstrated that HO● radicals are quantitatively higher after C-ions than after X-rays, however they are very differently distributed within cells. We postulate that the uniform distribution of ROS after X-rays induces the mechanisms leading to invasion/migration, which ROS concentrated in C-ion tracks are unable to trigger.

Convolutional neural network (CNN), a class of artificial neural networks that has become dominant in various computer vision tasks, is attracting interest across a variety of domains, including radiology. CNN is designed to automatically and adaptively learn spatial hierarchies of features through backpropagation by using multiple building blocks, such as convolution layers, pooling layers, and fully connected layers. This review article offers a perspective on the basic concepts of CNN and its application to various radiological tasks, and discusses its challenges and future directions in the field of radiology. Two challenges in applying CNN to radiological tasks, small dataset and overfitting, will also be covered in this article, as well as techniques to minimize them. Being familiar with the concepts and advantages, as well as limitations, of CNN is essential to leverage its potential in diagnostic radiology, with the goal of augmenting the performance of radiologists and improving patient care.Key Points• Convolutional neural network is a class of deep learning methods which has become dominant in various computer vision tasks and is attracting interest across a variety of domains, including radiology.• Convolutional neural network is composed of multiple building blocks, such as convolution layers, pooling layers, and fully connected layers, and is designed to automatically and adaptively learn spatial hierarchies of features through a backpropagation algorithm.• Familiarity with the concepts and advantages, as well as limitations, of convolutional neural network is essential to leverage its potential to improve radiologist performance and, eventually, patient care.

The integration of magnetic resonance imaging (MRI) for guidance in external beam radiotherapy has faced significant research and development efforts in recent years. The current availability of linear accelerators with an embedded MRI unit, providing volumetric imaging at excellent soft tissue contrast, is expected to provide novel possibilities in the implementation of image-guided adaptive radiotherapy (IGART) protocols. This study reviews open medical physics issues in MR-guided radiotherapy (MRgRT) implementation, with a focus on current approaches and on the potential for innovation in IGART. Daily imaging in MRgRT provides the ability to visualize the static anatomy, to capture internal tumor motion and to extract quantitative image features for treatment verification and monitoring. Those capabilities enable the use of treatment adaptation, with potential benefits in terms of personalized medicine. The use of online MRI requires dedicated efforts to perform accurate dose measurements and calculations, due to the presence of magnetic fields. Likewise, MRgRT requires dedicated quality assurance (QA) protocols for safe clinical implementation. Reaction to anatomical changes in MRgRT, as visualized on daily images, demands for treatment adaptation concepts, with stringent requirements in terms of fast and accurate validation before the treatment fraction can be delivered. This entails specific challenges in terms of treatment workflow optimization, QA, and verification of the expected delivered dose while the patient is in treatment position. Those challenges require specialized medical physics developments towards the aim of fully exploiting MRI capabilities. Conversely, the use of MRgRT allows for higher confidence in tumor targeting and organs-at-risk (OAR) sparing. The systematic use of MRgRT brings the possibility of leveraging IGART methods for the optimization of tumor targeting and quantitative treatment verification. Although several challenges exist, the intrinsic benefits of MRgRT will provide a deeper understanding of dose delivery effects on an individual basis, with the potential for further treatment personalization.

The quality factor (Q) is formally linked to the stochastic (e.g., carcinogenic) risk of diverse ionizing radiations at low doses and/or low dose rates. Q can be a function of the non-stochastic physical quantity Linear Energy Transfer (LET) or the microdosimetric parameter lineal energy (y). These two physical quantities can be calculated either by Monte Carlo (MC) track-structure simulations or by analytic models. In this work, various generalized analytical models were utilized and combined to determine the proton lineal energy spectra in liquid water spheres of various sizes (i.e., 10–3000 nm diameter) over the proton energy range of 1–250 MeV. The calculated spectra were subsequently used within the Theory of Dual Radiation Action (TDRA) and the ICRU Report 40 microdosimetric methodologies to determine the variation of Q¯ with proton energy. The results revealed that the LET-based Q values underestimated the microdosimetric-based Q¯ values for protons with energy below ~100 MeV. At energies relevant to the Bragg peak region (<20–30 MeV), the differences were larger than 20–50%, while reaching 200–500% at ~5 MeV. It was further shown that the microdosimetric-based Q¯ values for protons below ~100 MeV were sensitive to the sphere size. Finally, condensed-phase effects had a very small (<5%) influence on the calculated microdosimetric-based Q¯ over the proton energy range considered here.

Pancreatic ductal adenocarcinoma (PDAC) is a highly devastating disease with poor prognosis and rising incidence. Late detection and a particularly aggressive biology are the major challenges which determine therapeutic failure. In this review, we present the current status and the recent advances in PDAC treatment together with the biological and immunological hallmarks of this cancer entity. On this basis, we discuss new concepts combining distinct treatment modalities in order to improve therapeutic efficacy and clinical outcome – with a specific focus on protocols involving radio(chemo)therapeutic approaches.

Background The targeting accuracy of proton therapy (PT) for moving soft-tissue tumours is expected to greatly improve by real-time magnetic resonance imaging (MRI) guidance. The integration of MRI and PT at the treatment isocenter would offer the opportunity of combining the unparalleled soft-tissue contrast and real-time imaging capabilities of MRI with the most conformal dose distribution and best dose steering capability provided by modern PT. However, hybrid systems for MR-integrated PT (MRiPT) have not been realized so far due to a number of hitherto open technological challenges. In recent years, various research groups have started addressing these challenges and exploring the technical feasibility and clinical potential of MRiPT. The aim of this contribution is to review the different aspects of MRiPT, to report on the status quo and to identify important future research topics. Methods Four aspects currently under study and their future directions are discussed: modelling and experimental investigations of electromagnetic interactions between the MRI and PT systems, integration of MRiPT workflows in clinical facilities, proton dose calculation algorithms in magnetic fields, and MRI-only based proton treatment planning approaches. Conclusions Although MRiPT is still in its infancy, significant progress on all four aspects has been made, showing promising results that justify further efforts for research and development to be undertaken. First non-clinical research solutions have recently been realized and are being thoroughly characterized. The prospect that first prototype MRiPT systems for clinical use will likely exist within the next 5 to 10 years seems realistic, but requires significant work to be performed by collaborative efforts of research groups and industrial partners.