Multi-Energy Evaluation of Image Quality in Spectral CT Pulmonary Angiography Using Different Strength Deep Learning Spectral Reconstructions.

Rationale And Objectives: To evaluate and compare image quality of different energy levels of virtual monochromatic images (VMIs) using standard versus strong deep learning spectral reconstruction (DLSR) on dual-energy CT pulmonary angiogram (DECT-PA).

Materials And Methods: A retrospective study was performed on 70 patients who underwent DECT-PA (15 PE present; 55 PE absent) scans. VMIs were reconstructed at different energy levels ranging from 35 to 200 keV using standard and strong levels with deep learning spectral reconstruction.

Machine learning prediction of brain metastasis invasion pattern on brain magnetic resonance imaging scans.

Background: Brain metastasis invasion pattern (BMIP) is an emerging biomarker associated with recurrence-free and overall survival in patients, and differential response to therapy in preclinical models. Currently, BMIP can only be determined from the histopathological examination of surgical specimens, precluding its use as a biomarker prior to therapy initiation. The aim of this study was to investigate the potential of machine learning (ML) approaches to develop a noninvasive magnetic resonance imaging (MRI)-based biomarker for BMIP determination.

Surgeon versus radiologist: an inter-rater reliability analysis of the CLOSE checklist for preoperative CT sinus assessment.

Purpose: To assess the inter-rater agreement of the Cribriform plate, Lamina papyracea, Onodi cell, Sphenoid sinus pneumatization, and Ethmoidal artery (CLOSE) checklist results among rhinology & skull-base surgeons and a head and neck-neuroradiology specialist for pre-operative computed tomography (CT) sinus assessment.

Methods: This retrospective cross-sectional study reviewed 50 patients who underwent endoscopic sinus surgery (ESS) in the period between January 2013 and March 2014 at the Royal Victoria Hospital in Montreal, Canada. According to the CLOSE checklist, the CT scans were evaluated independently by one surgeon and one radiologist using the InteleRadiology Picture Archiving and Communication System (IntelePACS).

RIDGE: Reproducibility, Integrity, Dependability, Generalizability, and Efficiency Assessment of Medical Image Segmentation Models.

Deep learning techniques hold immense promise for advancing medical image analysis, particularly in tasks like image segmentation, where precise annotation of regions or volumes of interest within medical images is crucial but manually laborious and prone to interobserver and intraobserver biases. As such, deep learning approaches could provide automated solutions for such applications. However, the potential of these techniques is often undermined by challenges in reproducibility and generalizability, which are key barriers to their clinical adoption.

Automated Segmentation of Lymph Nodes on Neck CT Scans Using Deep Learning.

Early and accurate detection of cervical lymph nodes is essential for the optimal management and staging of patients with head and neck malignancies. Pilot studies have demonstrated the potential for radiomic and artificial intelligence (AI) approaches in increasing diagnostic accuracy for the detection and classification of lymph nodes, but implementation of many of these approaches in real-world clinical settings would necessitate an automated lymph node segmentation pipeline as a first step. In this study, we aim to develop a non-invasive deep learning (DL) algorithm for detecting and automatically segmenting cervical lymph nodes in 25,119 CT slices from 221 normal neck contrast-enhanced CT scans from patients without head and neck cancer.

A Descriptive Study of Quality of Life Following Neoadjuvant Chemotherapy and Transoral Robotic Surgery for Human Papillomavirus-Associated Oropharyngeal Squamous Cell Carcinoma.

Background: Patients with oropharyngeal squamous cell carcinoma (OPSCC) treated with radiation-based therapy suffer from short- and long-term toxicities that affect quality of life (QOL). Transoral robotic surgery (TORS) has an established role in the management of early OPSCC but adjuvant treatment is often indicated postoperatively due to the high incidence of nodal metastasis associated with advanced human papillomavirus (HPV)-related OPSCC. To overcome the need for adjuvant radiation therapy (RT), neoadjuvant chemotherapy followed by TORS and neck dissection (ND) is proposed.

Multiple Sclerosis: Clinical Update and Clinically-Oriented Radiologic Reporting.

Multiple sclerosis (MS) is a chronic inflammatory disease of the nervous system. MR imaging findings play an integral part in establishing diagnostic hallmarks of the disease during initial diagnosis and evaluating disease status. Multiple iterations of diagnostic criteria and consensus guidelines are put forth by various expert groups incorporating imaging of the brain and spine, and efforts have been made to standardize imaging protocols for MS.

Periodic injections of Relaxin 2, its pharmacokinetics and remodeling of rat hearts.

Relaxin-2 (RLX), a critical hormone in pregnancy, has been investigated as a therapy for heart failure. In most studies, the peptide was delivered continuously, subcutaneously for 2 weeks in animals or intravenously for 2-days in human subjects, for stable circulating [RLX]. However, pulsatile hormone levels may better uncover the normal physiology.

Medical imaging and multimodal artificial intelligence models for streamlining and enhancing cancer care: opportunities and challenges.

Introduction: Artificial intelligence (AI) has the potential to transform oncologic care. There have been significant developments in AI applications in medical imaging and increasing interest in multimodal models. These are likely to enable improved oncologic care through more precise diagnosis, increasingly in a more personalized and less invasive manner.

Natural Language Processing for the Identification of Incidental Lung Nodules in Computed Tomography Reports: A Quality Control Tool.

Purpose: To evaluate the diagnostic performance of a natural language processing (NLP) model in detecting incidental lung nodules (ILNs) in unstructured chest computed tomography (CT) reports.

Methods: All unstructured consecutive reports of chest CT scans performed at a tertiary hospital between 2020 and 2021 were retrospectively reviewed (n = 21,542) to train the NLP tool. Internal validation was performed using reference readings by two radiologists of both CT scans and reports, using a different external cohort of 300 chest CT scans.

Promotion of higher rates of early fusion using activated titanium versus polyetheretherketone cages in adults undergoing 1- and 2-level transforaminal lumbar interbody fusion procedures: a randomized controlled trial.

Objective: There is ongoing debate on the relative benefits and drawbacks of polyetheretherketone (PEEK) versus titanium (Ti) in generating a bone-to-implant surface microenvironment conducive to osseointegration. Micro- and nanoscale internal and topographic cage modifications have recently been posited to facilitate osseointegration and fusion, but human in vivo confirmation remains lacking. The authors of this study sought to directly compare early radiological outcomes in adults undergoing 1- and 2-level transforaminal lumbar interbody fusion (TLIF) procedures using either PEEK or nano-etched Ti interbody cages with an incorporated microlattice structure.

A new view of axon growth and guidance grounded in the stochastic dynamics of actin networks.

The mechanism of axon growth and guidance is a core, unsolved problem in neuroscience and cell biology. For nearly three decades, our view of this process has largely been based on deterministic models of motility derived from studies of neurons cultured on rigid substrates. Here, we suggest a fundamentally different, inherently probabilistic model of axon growth, one that is grounded in the stochastic dynamics of actin networks.

Enabled primarily controls filopodial morphology, not actin organization, in the TSM1 growth cone in .

Ena/VASP proteins are processive actin polymerases that are required throughout animal phylogeny for many morphogenetic processes, including axon growth and guidance. Here we use in vivo live imaging of morphology and actin distribution to determine the role of Ena in promoting the growth of the TSM1 axon of the wing. Altering Ena activity causes stalling and misrouting of TSM1.

A Practical Guide for AI Algorithm Selection for the Radiology Department.

There is a steadily increasing number of artificial intelligence (AI) tools available and cleared for use in clinical radiological practice. Radiologists will increasingly be faced with options provided by other radiologist colleagues, clinician colleagues, vendors, or other professionals for obtaining and deploying AI algorithms in clinical practice. It is important that radiologists are familiar with basic and practical aspects that need to be considered when assessing an AI tool for use in their practice, so that resources are properly allocated and there is an appropriate return on investment through enhancements in patient quality of care, safety, and/or process efficiency.

Artificial Intelligence for Cardiothoracic Imaging: Overview of Current and Emerging Applications.

Artificial intelligence algorithms can learn by assimilating information from large datasets in order to decipher complex associations, identify previously undiscovered pathophysiological states, and construct prediction models. There has been tremendous interest and increased incorporation of artificial intelligence into various industries, including healthcare. As a result, there has been an exponential rise in the number of research articles and industry participants producing models intended for a variety of applications in medical imaging, which can be challenging to navigate for radiologists.

Applications of Artificial Intelligence in the Radiology Roundtrip: Process Streamlining, Workflow Optimization, and Beyond.

There are many impactful applications of artificial intelligence (AI) in the electronic radiology roundtrip and the patient's journey through the healthcare system that go beyond diagnostic applications. These tools have the potential to improve quality and safety, optimize workflow, increase efficiency, and increase patient satisfaction. In this article, we review the role of AI for process improvement and workflow enhancement which includes applications beginning from the time of order entry, scan acquisition, applications supporting the image interpretation task, and applications supporting tasks after image interpretation such as result communication.

Introduction to Radiomics and Artificial Intelligence: A Primer for Radiologists.

Health informatics and artificial intelligence (AI) are expected to transform the healthcare enterprise and the future practice of radiology. There is an increasing body of literature on radiomics and deep learning/AI applications in medical imaging. There are also a steadily increasing number of FDA cleared AI applications in radiology.