Status of LGBTQ+ Inclusion: Multi-Institution Assessment of US Radiology Residencies.

Introduction: An inclusive residency program is crucial to the recruitment and retention of competitive and diverse applicants. The radiology lesbian, gay, bisexual, transgender, queer or questioning, or another diverse gender identity (LGBTQ+) inclusion audit was published in 2022, which provided a road map for assessing the inclusivity of a program's policies, facilities, culture, and engagement. In this multi-institutional trial, we detail the results of the LGBTQ+ inclusion audit for nine US radiology residency programs.

Characterizing continuous positive airway pressure (CPAP) Belly Syndrome in preterm infants in the neonatal intensive care unit (NICU).

Objective: Reproducibly define CPAP Belly Syndrome (CBS) in preterm infants and describe associated demographics, mechanical factors, and outcomes.

Study Design: A retrospective case-control study was conducted in infants <32 weeks gestation in the Stanford Children's NICU from January 1, 2020 to December 31, 2021. CBS was radiographically defined by a pediatric radiologist.

The Influence of Extracurricular Activities on Radiology Resident Selection Decisions.

Purpose: Extracurricular activities (EAs) listed on radiology residency applications can signal traits and characteristics desired in holistic reviews. The authors conducted an objective analysis to determine the influence of EAs on resident selection decisions.

Methods: A discrete-choice experiment was designed to model radiology resident selection and determine the relative weights of EAs among academic and demographic application factors.

Brain Tumor Radiogenomic Classification of O-Methylguanine-DNA Methyltransferase Promoter Methylation in Malignant Gliomas-Based Transfer Learning.

Artificial Intelligence (AI) is the subject of a challenge and attention in the field of oncology and raises many promises for preventive diagnosis, but also fears, some of which are based on highly speculative visions for the classification and detection of tumors. A brain tumor that is malignant is a life-threatening disorder. Glioblastoma is the most prevalent kind of adult brain cancer and the 1 with the poorest prognosis, with a median survival time of less than a year.

Improved Detection of Bone Metastases in Children and Young Adults with Ferumoxytol-enhanced MRI.

Purpose To evaluate if ferumoxytol can improve the detection of bone marrow metastases at diffusion-weighted (DW) MRI in pediatric and young adult patients with cancer. Materials and Methods In this secondary analysis of a prospective institutional review board-approved study (ClinicalTrials.gov identifier NCT01542879), 26 children and young adults (age range: 2-25 years; 18 males) underwent unenhanced or ferumoxytol-enhanced whole-body DW MRI between 2015 and 2020.

Imaging of pediatric testicular tumors: A COG Diagnostic Imaging Committee/SPR Oncology Committee White Paper.

Primary intratesticular tumors are uncommon in children, but incidence and risk of malignancy both sharply increase during adolescence. Ultrasound is the mainstay for imaging the primary lesion, and cross-sectional modalities are often required for evaluation of regional or distant disease. However, variations to this approach are dictated by additional clinical and imaging nuances.

Imaging of pediatric ovarian tumors: A COG Diagnostic Imaging Committee/SPR Oncology Committee White Paper.

Ovarian tumors in children are uncommon. Like those arising in the adult population, they may be broadly divided into germ cell, sex cord, and surface epithelium subtypes; however, germ cell tumors comprise the majority of lesions in children, whereas tumors of surface epithelial origin predominate in adults. Diagnostic workup, including the use of imaging, requires an approach that often differs from that required in an adult.

Artificial Intelligence Algorithm Improves Radiologist Performance in Skeletal Age Assessment: A Prospective Multicenter Randomized Controlled Trial.

Background Previous studies suggest that use of artificial intelligence (AI) algorithms as diagnostic aids may improve the quality of skeletal age assessment, though these studies lack evidence from clinical practice. Purpose To compare the accuracy and interpretation time of skeletal age assessment on hand radiograph examinations with and without the use of an AI algorithm as a diagnostic aid. Materials and Methods In this prospective randomized controlled trial, the accuracy of skeletal age assessment on hand radiograph examinations was performed with ( = 792) and without ( = 739) the AI algorithm as a diagnostic aid.

Machine learning approach to differentiation of peripheral schwannomas and neurofibromas: A multi-center study.

Background: Non-invasive differentiation between schwannomas and neurofibromas is important for appropriate management, preoperative counseling, and surgical planning, but has proven difficult using conventional imaging. The objective of this study was to develop and evaluate machine learning approaches for differentiating peripheral schwannomas from neurofibromas.

Methods: We assembled a cohort of schwannomas and neurofibromas from 3 independent institutions and extracted high-dimensional radiomic features from gadolinium-enhanced, T1-weighted MRI using the PyRadiomics package on Quantitative Imaging Feature Pipeline.

Machine-Learning Approach to Differentiation of Benign and Malignant Peripheral Nerve Sheath Tumors: A Multicenter Study.

Background: Clinicoradiologic differentiation between benign and malignant peripheral nerve sheath tumors (PNSTs) has important management implications.

Objective: To develop and evaluate machine-learning approaches to differentiate benign from malignant PNSTs.

Methods: We identified PNSTs treated at 3 institutions and extracted high-dimensional radiomics features from gadolinium-enhanced, T1-weighted magnetic resonance imaging (MRI) sequences.

Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT.

The Coronavirus disease 2019 (COVID-19) presents open questions in how we clinically diagnose and assess disease course. Recently, chest computed tomography (CT) has shown utility for COVID-19 diagnosis. In this study, we developed Deep COVID DeteCT (DCD), a deep learning convolutional neural network (CNN) that uses the entire chest CT volume to automatically predict COVID-19 (COVID+) from non-COVID-19 (COVID-) pneumonia and normal controls.

Differentiation of benign and malignant lymph nodes in pediatric patients on ferumoxytol-enhanced PET/MRI.

The composition of lymph nodes in pediatric patients is different from that in adults. Most notably, normal lymph nodes in children contain less macrophages. Therefore, previously described biodistributions of iron oxide nanoparticles in benign and malignant lymph nodes of adult patients may not apply to children.

Erratum: Author Correction: Human-machine partnership with artificial intelligence for chest radiograph diagnosis.

[This corrects the article DOI: 10.1038/s41746-019-0189-7.].

Human-machine partnership with artificial intelligence for chest radiograph diagnosis.

Human-in-the-loop (HITL) AI may enable an ideal symbiosis of human experts and AI models, harnessing the advantages of both while at the same time overcoming their respective limitations. The purpose of this study was to investigate a novel collective intelligence technology designed to amplify the diagnostic accuracy of networked human groups by forming real-time systems modeled on biological swarms. Using small groups of radiologists, the swarm-based technology was applied to the diagnosis of pneumonia on chest radiographs and compared against human experts alone, as well as two state-of-the-art deep learning AI models.

Deep learning to automate Brasfield chest radiographic scoring for cystic fibrosis.

Background: The aim of this study was to evaluate the hypothesis that a deep convolutional neural network (DCNN) model could facilitate automated Brasfield scoring of chest radiographs (CXRs) for patients with cystic fibrosis (CF), performing similarly to a pediatric radiologist.

Methods: All frontal/lateral chest radiographs (2058 exams) performed in CF patients at a single institution from January 2008-2018 were retrospectively identified, and ground-truth Brasfield scoring performed by a board-certified pediatric radiologist. 1858 exams (90.

Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists.

Background: Chest radiograph interpretation is critical for the detection of thoracic diseases, including tuberculosis and lung cancer, which affect millions of people worldwide each year. This time-consuming task typically requires expert radiologists to read the images, leading to fatigue-based diagnostic error and lack of diagnostic expertise in areas of the world where radiologists are not available. Recently, deep learning approaches have been able to achieve expert-level performance in medical image interpretation tasks, powered by large network architectures and fueled by the emergence of large labeled datasets.

Utility of total lower extremity radiography investigation of nonweight bearing in the young child.

Objectives: The purpose of this work was to determine the utility of total lower extremity radiographs versus dedicated tibia radiographs in the evaluation of the young child presenting with nonweight bearing without localizing signs.

Methods: This was an institutional review board-approved retrospective review of 263 consecutive patients between the ages of 9 months and 4 years who were referred for total lower extremity radiography between September 29, 2001, and November 7, 2006. Among these, a total of 133 study subjects met inclusion criteria of presentation with nonweight bearing without localizing signs or history of previous trauma.