Exploring transformer reliability in clinically significant prostate cancer segmentation: A comprehensive in-depth investigation.

Despite the growing prominence of transformers in medical image segmentation, their application to clinically significant prostate cancer (csPCa) has been overlooked. Minimal attention has been paid to domain shift analysis and uncertainty assessment, critical for safely implementing computer-aided diagnosis (CAD) systems. Domain shift in medical imagery refers to differences between the data used to train a model and the data evaluated later, arising from variations in imaging equipment, protocols, patient populations, and acquisition noise.

Development of a novel computational technique to create DNA and cell geometrical models for Geant4-DNA.

Background: This study aimed to develop a novel human cell geometry for the Geant4-DNA simulation toolkit that explicitly incorporates all 23 chromosome pairs of the human cell. This approach contrasts with the existing, default human cell, geometrical model, which utilizes a continuous Hilbert curve.

Methods: A Python-based tool named "complexDNA" was developed to facilitate the design of both simple and complex DNA geometries.

Cross-modal tumor segmentation using generative blending augmentation and self-training.

Objectives: Data scarcity and domain shifts lead to biased training sets that do not accurately represent deployment conditions. A related practical problem is cross-modal image segmentation, where the objective is to segment unlabelled images using previously labelled datasets from other imaging modalities.

Methods: We propose a cross-modal segmentation method based on conventional image synthesis boosted by a new data augmentation technique called Generative Blending Augmentation (GBA).

CT respiratory motion synthesis using joint supervised and adversarial learning.

Four-dimensional computed tomography (4DCT) imaging consists in reconstructing a CT acquisition into multiple phases to track internal organ and tumor motion. It is commonly used in radiotherapy treatment planning to establish planning target volumes. However, 4DCT increases protocol complexity, may not align with patient breathing during treatment, and lead to higher radiation delivery.

Development of engineered endolysins with in vitro intracellular activity against streptococcal bovine mastitis-causing pathogens.

Bacteriophage-derived endolysins are a novel class of antimicrobials known to rapidly kill bacteria, including antibiotic-resistant strains. We here engineered endolysins against the bovine mastitis pathogens Streptococcus uberis, Streptococcus agalactiae and Streptococcus dysgalactiae, also targeting intracellular survival and biofilm formation. For this purpose, high-throughput DNA assembly was used to create a library with >80,000 theoretical endolysin variants for screening of their bacteriolytic activity against Gram-positive isolates from (sub)clinically affected cows.

Monte Carlo Dosimetry Validation for X-Ray Guided Endovascular Procedures.

Background: Endovascular treatment is continuously gaining ground in vascular surgery procedures. However, current patient radiation dose estimation does not take into account the exact patient morphology and organs' composition. Monte Carlo (MC) simulation can accurately estimate the dose by recreating the irradiation process generated during X-ray-guided interventions.

Fast dose calculation in x-ray guided interventions by using deep learning.

Patient dose estimation in x-ray-guided interventions is essential to prevent radiation-induced biological side effects. Current dose monitoring systems estimate the skin dose based in dose metrics such as the reference air kerma. However, these approximations do not take into account the exact patient morphology and organs composition.

Contour-guided deep learning based deformable image registration for dose monitoring during CBCT-guided radiotherapy of prostate cancer.

Purpose: To evaluate deep learning (DL)-based deformable image registration (DIR) for dose accumulation during radiotherapy of prostate cancer patients.

Methods And Materials: Data including 341 CBCTs (209 daily, 132 weekly) and 23 planning CTs from 23 patients was retrospectively analyzed. Anatomical deformation during treatment was estimated using free-form deformation (FFD) method from Elastix and DL-based VoxelMorph approaches.

Optimisation and validation of a co-manipulated robot for brachytherapy procedure.

Background: Low-dose rate brachytherapy is the referent treatment for early-stage prostate cancer and consists in manually inserting radioactive seeds within the organ to destroy tumorous cells. This treatment is inaccurate leading to side effects. Researchers developed robots to improve this technique.

Deep learning-based segmentation in prostate radiation therapy using Monte Carlo simulated cone-beam computed tomography.

Purpose: Segmenting organs in cone-beam CT (CBCT) images would allow to adapt the radiotherapy based on the organ deformations that may occur between treatment fractions. However, this is a difficult task because of the relative lack of contrast in CBCT images, leading to high inter-observer variability. Deformable image registration (DIR) and deep-learning based automatic segmentation approaches have shown interesting results for this task in the past years.

VMAT-Based Planning Allows Sparing of a Spatial Dose Pattern Associated with Radiation Pneumonitis in Patients Treated with Radiotherapy for a Locally Advanced Lung Cancer.

Introduction: In patients treated with radiotherapy for locally advanced lung cancer, respect for dose constraints to organs at risk (OAR) insufficiently protects patients from acute pulmonary toxicity (APT), such toxicities being associated with a potential impact on the treatment’s completion and the patient’s quality of life. Dosimetric planning does not take into account regional lung functionality. An APT prediction model combining usual dosimetry features with the mean dose (DMeanPmap) received by a voxel-based volume (Pmap) localized in the posterior right lung has been previously developed.

Organ-based estimation and minimization of clinician's X-ray dose.

Purpose: Hybrid surgeries, allowing real-time visualization of patient inner anatomy, are possible through the use of intraoperative X-ray imaging. However, the intensive use of X-rays can have undesired consequences for the clinicians or the patient in the operating room (OR).

Methods: In this paper, we provide a tool to visualize the X-rays and to optimally place protective shields in the hybrid operating room to reduce the clinician's dose according to their most sensitive body parts.

SARS-CoV-2 Envelope (E) Protein Binds and Activates TLR2 Pathway: A Novel Molecular Target for COVID-19 Interventions.

This paper presents a molecular characterization of the interaction between the SARS-CoV-2 envelope (E) protein and TLR2. We demonstrated that the E protein, both as a recombinant soluble protein and as a native membrane protein associated with SARS-CoV-2 viral particles, interacts physically with the TLR2 receptor in a specific and dose-dependent manner. Furthermore, we showed that the specific interaction with the TLR2 pathway activates the NF-κB transcription factor and stimulates the production of the CXCL8 inflammatory chemokine.

Nonsurgical Management of Osteoarthritis of the Knee: What You Should Be Injecting Now and in the Future.

Osteoarthritis of the knee affects many Americans. With the aging of the population and increasing comorbidities (eg, obesity, diabetes, hypertension, heart disease), the use of oral or topical NSAIDs is often contraindicated. Injectable treatment options are advantageous because of the ability to decrease or avoid the unwanted systematic adverse effects of NSAIDs.

Standardization and Validation of Brachytherapy Seeds' Modelling Using GATE and GGEMS Monte Carlo Toolkits.

This study aims to validate GATE and GGEMS simulation toolkits for brachytherapy applications and to provide accurate models for six commercial brachytherapy seeds, which will be freely available for research purposes. The AAPM TG-43 guidelines were used for the validation of two Low Dose Rate (LDR), three High Dose Rate (HDR), and one Pulsed Dose Rate (PDR) brachytherapy seeds. Each seed was represented as a 3D model and then simulated in GATE to produce one single Phase-Space (PHSP) per seed.

Technical note: Surface imaging for real-time patient positioning in external radiation therapy.

Purpose: In the last few years, there has been a growing interest in surface imaging for patient positioning in external radiation therapy. The aim of this study is to evaluate the accuracy of daily patient positioning using the Azure Kinect surface imaging.

Methods: A total of 50 fractions in 10 patients including lung, pelvic, and head and neck tumors were analyzed in real time.

Development and prospective validation of a spatial dose pattern based model predicting acute pulmonary toxicity in patients treated with volumetric arc-therapy for locally advanced lung cancer.

Introduction: (Chemo)-radiotherapy is the standard treatment for patients with locally advanced lung cancer (LALC) not accessible to surgery. Despite strict application of dose constraints, acute toxicities such as acute pulmonary toxicity (APT) remain frequent, and may impact treatment's compliance and patients' quality of life. Previously, on a population treated with intensity-modulated photon therapy or passive scattering proton therapy, spatial dose patterns associated with APT were identified in the lower lungs, especially in the posterior right lung.

Radiomics Analysis of 3D Dose Distributions to Predict Toxicity of Radiotherapy for Cervical Cancer.

Standard treatment for locally advanced cervical cancer (LACC) is chemoradiotherapy followed by brachytherapy. Despite radiation therapy advances, the toxicity rate remains significant. In this study, we compared the prediction of toxicity events after radiotherapy for locally advanced cervical cancer (LACC), based on either dose-volume histogram (DVH) parameters or the use of a radiomics approach applied to dose maps at the voxel level.

Dosimetric Validation of a GAN-Based Pseudo-CT Generation for MRI-Only Stereotactic Brain Radiotherapy.

Purpose: Stereotactic radiotherapy (SRT) has become widely accepted as a treatment of choice for patients with a small number of brain metastases that are of an acceptable size, allowing for better target dose conformity, resulting in high local control rates and better sparing of organs at risk. An MRI-only workflow could reduce the risk of misalignment between magnetic resonance imaging (MRI) brain studies and computed tomography (CT) scanning for SRT planning, while shortening delays in planning. Given the absence of a calibrated electronic density in MRI, we aimed to assess the equivalence of synthetic CTs generated by a generative adversarial network (GAN) for planning in the brain SRT setting.

Advanced Monte Carlo simulations of emission tomography imaging systems with GATE.

Built on top of the Geant4 toolkit, GATE is collaboratively developed for more than 15 years to design Monte Carlo simulations of nuclear-based imaging systems. It is, in particular, used by researchers and industrials to design, optimize, understand and create innovative emission tomography systems. In this paper, we reviewed the recent developments that have been proposed to simulate modern detectors and provide a comprehensive report on imaging systems that have been simulated and evaluated in GATE.

Micro-coil probes for magnetic intracortical neural stimulation: Trade-offs in materials and design.

Neural probes for intracortical neuromodulation in the brain have advanced with the developments in micro- and nanofabrication technologies. Most of these technologies for the intracortical stimulation have relied on the direct electrical stimulation via electrodes or arrays of electrodes. Generating electric fields using time-varying magnetic fields is a more recent neuromodulation technique that has proven to be more specifically effective for the intracortical stimulation.

Radiomics analysis of 3D dose distributions to predict toxicity of radiotherapy for lung cancer.

Purpose: (Chemo)-radiotherapy (RT) is the gold standard treatment for patients with locally advanced lung cancer non accessible for surgery. However, current toxicity prediction models rely on clinical and dose volume histograms (DVHs) and remain unsufficient. The goal of this work is to investigate the added predictive value of the radiomics approach applied to dose maps regarding acute and late toxicities in both the lungs and esophagus.

Surface imaging for real-time patient respiratory function assessment in intensive care.

Purpose: Monitoring of physiological parameters is a major concern in Intensive Care Units (ICU) given their role in the assessment of vital organ function. Within this context, one issue is the lack of efficient noncontact techniques for respiratory monitoring. In this paper, we present a novel noncontact solution for real-time respiratory monitoring and function assessment of ICU patients.