Deep learning on CT scans to predict checkpoint inhibitor treatment outcomes in advanced melanoma.

Immune checkpoint inhibitor (ICI) treatment has proven successful for advanced melanoma, but is associated with potentially severe toxicity and high costs. Accurate biomarkers for response are lacking. The present work is the first to investigate the value of deep learning on CT imaging of metastatic lesions for predicting ICI treatment outcomes in advanced melanoma.

[How well does artificial intelligence detect fractures in the cervical spine on CT?].

Objective: To compare diagnostic accuracy of artificial intelligence (AI) for cervical spine (C-spine) fracture detection on CT with attending radiologists.

Design: Retrospective, diagnostic accuracy study.

Methods: AI analyzed 2368 scans from patients screened for C-spine fracture with CT (2007-2014, fracture prevalence 9.

AERIFY-1/2: two phase 3, randomised, controlled trials of itepekimab in former smokers with moderate-to-severe COPD.

Background: Accumulating data implicate interleukin (IL)-33, a proinflammatory cytokine released locally upon epithelial cell damage, in the pathogenesis of COPD. In a phase 2 study, itepekimab, a human monoclonal antibody against IL-33, reduced exacerbations and improved lung function in a subgroup analysis of former smokers with COPD with an acceptable safety profile.

Methods: The study designs of AERIFY-1 and AERIFY-2 are described in this article.

Advancements in supervised deep learning for metal artifact reduction in computed tomography: A systematic review.

Background: Metallic artefacts caused by metal implants, are a common problem in computed tomography (CT) imaging, degrading image quality and diagnostic accuracy. With advancements in artificial intelligence, novel deep learning (DL)-based metal artefact reduction (MAR) algorithms are entering clinical practice.

Objective: This systematic review provides an overview of the performance of the current supervised DL-based MAR algorithms for CT, focusing on three different domains: sinogram, image, and dual domain.

Methodological considerations on diffusion MRI tractography in infants aged 0-2 years: a scoping review.

Diffusion MRI (dMRI) enables studying the complex architectural organization of the brain's white matter (WM) through virtual reconstruction of WM fiber tracts (tractography). Despite the anticipated clinical importance of applying tractography to study structural connectivity and tract development during the critical period of rapid infant brain maturation, detailed descriptions on how to approach tractography in young infants are limited. Over the past two decades, tractography from infant dMRI has mainly been applied in research settings and focused on diffusion tensor imaging (DTI).

Development and validation of a deep learning-based method for automatic measurement of uterus, fibroid, and ablated volume in MRI after MR-HIFU treatment of uterine fibroids.

Introduction: The non-perfused volume divided by total fibroid load (NPV/TFL) is a predictive outcome parameter for MRI-guided high-intensity focused ultrasound (MR-HIFU) treatments of uterine fibroids, which is related to long-term symptom relief. In current clinical practice, the MR-HIFU outcome parameters are typically determined by visual inspection, so an automated computer-aided method could facilitate objective outcome quantification. The objective of this study was to develop and evaluate a deep learning-based segmentation algorithm for volume measurements of the uterus, uterine fibroids, and NPVs in MRI in order to automatically quantify the NPV/TFL.

Intravoxel incoherent motion (IVIM)-derived perfusion fraction mapping for the visual evaluation of MR-guided high intensity focused ultrasound (MR-HIFU) ablation of uterine fibroids.

Background: A method for periprocedural contrast agent-free visualization of uterine fibroid perfusion could potentially shorten magnetic resonance-guided high intensity focused ultrasound (MR-HIFU) treatment times and improve outcomes. Our goal was to test feasibility of perfusion fraction mapping by intravoxel incoherent motion (IVIM) modeling using diffusion-weighted MRI as method for visual evaluation of MR-HIFU treatment progression.

Methods: Conventional and T2-corrected IVIM-derived perfusion fraction maps were retrospectively calculated by applying two fitting methods to diffusion-weighted MRI data ( = 0, 50, 100, 200, 400, 600 and 800 s/mm at 1.

Effectiveness and complication rates of high intensity focused ultrasound treatment for abdominal wall endometriosis: A systematic review.

In this review, a systematic literature search on the effectiveness and complication rates of ultrasound-guided and magnetic resonance-guided high-intensity focused ultrasound (USg-/MRgHIFU) for abdominal wall endometriosis (AWE) was conducted in six databases in May/June 2023. Original articles of (non)randomized trials, cohort studies, case-control studies and case series published in peer-reviewed journals were included. Of the included studies the level of evidence (LoE) and methodological quality using the ROBINS-I and IHE-QAT was assessed.

Waste analysis and energy use estimation during MR-HIFU treatment: first steps towards calculating total environmental impact.

Objectives: To assess the environmental impact of the non-invasive Magnetic Resonance image-guided High-Intensity Focused Ultrasound (MR-HIFU) treatment of uterine fibroids, we aimed to perform a full Life Cycle Assessment (LCA). However, as a full LCA was not feasible at this time, we evaluated the CO (carbon dioxide) emission from the MRI scanner, MR-HIFU device, and the medication used, and analyzed solid waste produced during treatment.

Methods: Our functional unit was one uterine fibroid MR-HIFU treatment.

Image quality and metal artifact reduction in total hip arthroplasty CT: deep learning-based algorithm versus virtual monoenergetic imaging and orthopedic metal artifact reduction.

Background: To compare image quality, metal artifacts, and diagnostic confidence of conventional computed tomography (CT) images of unilateral total hip arthroplasty patients (THA) with deep learning-based metal artifact reduction (DL-MAR) to conventional CT and 130-keV monoenergetic images with and without orthopedic metal artifact reduction (O-MAR).

Methods: Conventional CT and 130-keV monoenergetic images with and without O-MAR and DL-MAR images of 28 unilateral THA patients were reconstructed. Image quality, metal artifacts, and diagnostic confidence in bone, pelvic organs, and soft tissue adjacent to the prosthesis were jointly scored by two experienced musculoskeletal radiologists.

Bone microarchitecture and strength assessment in adults with osteogenesis imperfecta using HR-pQCT: normative comparison and challenges.

Data on bone microarchitecture in osteogenesis imperfecta (OI) are scarce. The aim of this cross-sectional study was to assess bone microarchitecture and strength in a large cohort of adults with OI using high-resolution peripheral quantitative computed tomography (HR-pQCT) and to evaluate challenges of using HR-pQCT in this cohort. Second-generation HR-pQCT scans were obtained at the distal radius and tibia in 118 men and women with Sillence OI type I, III, or IV using an extremity-length-dependent scan protocol.

External validation of the RSNA 2020 pulmonary embolism detection challenge winning deep learning algorithm.

Purpose: To evaluate the diagnostic performance and generalizability of the winning DL algorithm of the RSNA 2020 PE detection challenge to a local population using CTPA data from two hospitals.

Materials And Methods: Consecutive CTPA images from patients referred for suspected PE were retrospectively analysed. The winning RSNA 2020 DL algorithm was retrained on the RSNA-STR Pulmonary Embolism CT (RSPECT) dataset.

Adjuvant dendritic cell therapy in stage IIIB/C melanoma: the MIND-DC randomized phase III trial.

Autologous natural dendritic cells (nDCs) treatment can induce tumor-specific immune responses and clinical responses in cancer patients. In this phase III clinical trial (NCT02993315), 148 patients with resected stage IIIB/C melanoma were randomized to adjuvant treatment with nDCs (n = 99) or placebo (n = 49). Active treatment consisted of intranodally injected autologous CD1c+ conventional and plasmacytoid DCs loaded with tumor antigens.

Image-guided point-shear-wave elastography: a valid and reliable technique for liver fibrotic staging.

Purpose: Despite progressive implementation of image-guided point-shear wave elastography (pSWE) in guidelines as an alternative to transient elastography for the staging of fibrotic liver disease, pSWE is not widely adopted in clinical workflow. More information on reliability and validity of pSWE systems is needed. Therefore, we performed a phantom study to evaluate the validity and reliability of pSWE with ultrasound systems.

Five-Year Follow-Up of a Slowly Resorbable Biosynthetic P4HB Mesh (Phasix) in VHWG Grade 3 Incisional Hernia Repair.

Objective: To assess the 5-year recurrence rate of incisional hernia repair in Ventral Hernia Working Group (VHWG) 3 hernia with a slowly resorbable mesh.

Summary Background Data: Incisional hernia recurs frequently after initial repair. In potentially contaminated hernia, recurrences rise to 40%.

Advances in metal artifact reduction in CT images: A review of traditional and novel metal artifact reduction techniques.

Metal artifacts degrade CT image quality, hampering clinical assessment. Numerous metal artifact reduction methods are available to improve the image quality of CT images with metal implants. In this review, an overview of traditional methods is provided including the modification of acquisition and reconstruction parameters, projection-based metal artifact reduction techniques (MAR), dual energy CT (DECT) and the combination of these techniques.

No Difference in Revision Rates and High Survival Rates in Large-head Metal-on-metal THA Versus Metal-on-polyethylene THA: Long-term Results of a Randomized Controlled Trial.

Background: Pseudotumor formation is a well-known complication in metal-on-metal (MoM) THA. Pseudotumors combined with elevated serum ion levels and complaints from patients can lead to high revision rates. Long-term (> 10 years) results obtained from randomized trials comparing large-head MoM THA and conventional metal-on-polyethylene (MoP) THA are lacking regarding revision and survival rates, pseudotumor formation, functional outcomes, and serum ion levels.

An artificial intelligence algorithm for pulmonary embolism detection on polychromatic computed tomography: performance on virtual monochromatic images.

Objectives: Virtual monochromatic images (VMI) are increasingly used in clinical practice as they improve contrast-to-noise ratio. However, due to their different appearances, the performance of artificial intelligence (AI) trained on conventional CT images may worsen. The goal of this study was to assess the performance of an established AI algorithm trained on conventional polychromatic computed tomography (CT) images (CPI) to detect pulmonary embolism (PE) on VMI.