Background There is a pressing demand to develop an automated segmentation tool for abdominal MRI that can provide accurate and robust segmentation in more than 60 abdominal organs and structures. Purpose To develop and evaluate the accuracy and robustness of an automated multiorgan and structure segmentation tool for T1-weighted abdominal MRI. Materials and Methods In this retrospective study, a T1-weighted

Thermal ablation has started to gain acceptance as a therapeutic approach for the management of papillary thyroid cancer (PTC), mainly in the T1 stage (tumor size ≤ 2 cm in greatest dimension and limited to the thyroid). Despite its increasing popularity, a lack of uniformity in the technical details persists, and existing guidelines do not fully align with recent research advancements. To standardize

Background Portable low-field-strength (64-mT) MRI scanners show promise for increasing access to neuroimaging for clinical and research purposes; however, these devices produce lower-quality images than conventional high-field-strength scanners. Purpose To develop and evaluate a deep learning architecture to generate high-field-strength quality brain images from low-field-strength inputs using paired

Background Standardized bone tumor reporting is crucial for consistent, risk-aligned patient management. Current systems are based on expert consensus and/or lack multicenter validation. Purpose To evaluate a machine learning-based approach for differentiating between benign and malignant focal bone lesions and to propose a Bone Tumor Imaging Reporting and Data System (BTI-RADS) 2.0 for further risk

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. It is imperative that the entire medical imaging sector acts

Background Intratumoral heterogeneity (ITH) in breast cancer contributes to treatment failure and relapse. Noninvasive methods to quantify ITH are currently limited. Purpose To quantify ITH in breast cancer using pretreatment MRI, develop a nomogram to predict pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) and recurrence-free survival (RFS), and investigate biologic pathways associated

Background Current radiology report search tools are limited to keyword searches, which lack semantic understanding of underlying clinical conditions and are prone to false positives. Semantic search models address this issue, but their development requires scalable methods for generating radiology-specific training data. Purpose To develop a scalable method for training semantic search models for

Background Primary aldosteronism can arise from one or both adrenal glands. Adrenal vein sampling (AVS) is the standard of care for identifying patients with lateralized primary aldosteronism who would benefit from surgery. Variability in AVS lateralization has been primarily attributed to cosyntropin use and lateralization index thresholds. Data regarding intraprotocol variability are lacking. Purpose

This document defines the key considerations for developing and reporting an artificial intelligence (AI) interpretation model for the detection of clinically significant prostate cancer (PCa) at MRI in biopsy-naive men with a positive clinical screening status. Specific data and performance metric requirements and a checklist are provided for this use case. Data requirements emphasize the need for

Contrast-enhanced US (CEUS) allows a detailed assessment of the tissue microvasculature after intravenous injection of contrast material. The use of CEUS also allows opacification of nonvascular hollow organs and fluid compartments after intracavitary instillation of dilute contrast material. This capability is far beyond that of grayscale and Doppler US techniques. The advantages of CEUS include its

Background Limited data are available regarding the accuracy of artificial intelligence (AI) algorithms trained on bilateral mammograms for second breast cancer surveillance in patients with a personal history of breast cancer treated with unilateral mastectomy. Purpose To compare the performance of standalone AI for second breast cancer surveillance on unilateral mammograms with that of radiologists

Background Acute ischemic stroke (AIS) is a major cause of morbidity and mortality, requiring swift and precise clinical decisions based on neuroimaging. Recent advances in deep learning-based computer vision and language artificial intelligence (AI) models have demonstrated transformative performance for several stroke-related applications. Purpose To evaluate deep learning applications for imaging

Background Early identification of lymph node metastasis is crucial for microsatellite instability-high (MSI-H) colon cancer caused by deficient mismatch repair, but accuracy of CT is poor. Purpose To determine whether CT-detected lymph node distribution patterns can improve lymph node evaluation in MSI-H colon cancer. Materials and Methods This two-center retrospective study included patients with

Background The approval of amyloid-targeting therapies has made it increasingly important to differentiate Alzheimer disease (AD) from other causes of dementia. Dysfunctional glucose metabolism is a recognized pathophysiological element in AD that may be visualized with spectroscopic MRI of deuterated glucose and its metabolites, also known as deuterium metabolic imaging (DMI). Purpose To explore the

Background Ductal carcinoma in situ (DCIS) is a nonlethal, preinvasive breast cancer for which breast MRI is best suited for accurate disease extent characterization. DCIS is often overtreated, necessitating robust models for improved risk stratification. Purpose To develop logistic regression models using clinical and MRI-based radiomic features of DCIS and to evaluate the performance of such models

Background Special operations forces members often face multiple blast injuries and have a higher risk of traumatic brain injury. However, the relationship between neuroimaging markers, the cumulative severity of injury, and long-term symptoms has not previously been well-established in the literature. Purpose To determine the relationship between the frequency of blast injuries, persistent clinical

Background Metabolic dysfunction-associated steatotic liver disease (MASLD) is a growing global health challenge, with evidence indicating that hepatic inflammation and fibrosis are heterogeneous processes. Purpose To measure liver mechanical property heterogeneity using MR elastography (MRE) and evaluate its potential as a biomarker for tissue inflammation and fibrosis in patients with MASLD. Materials

Background Ovarian-Adnexal Reporting and Data System (O-RADS) for MRI helps assign malignancy risk, but radiologist adoption is inconsistent. Automatic assignment of O-RADS scores from reports could increase adoption and accuracy. Purpose To evaluate the accuracy of large language models (LLMs), after strategic optimization, for automatically calculating O-RADS scores from reports. Materials and Methods

Since the introduction of the American College of Radiology (ACR) MRI safety guidelines in 2002, the indications for use of MRI in clinical care and research have continued to expand. Similarly, MRI technologies have evolved, with multiple field strengths now available for human imaging. While several publications have updated the ACR recommendations since the first guidelines, a single source in a

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Background In patients who cannot undergo MRI, dual-energy

Background The Liver Imaging Reporting and Data System (LI-RADS) criteria have not been validated for patients with noncirrhotic chronic hepatitis C (CHC), who are at a greater risk for hepatocellular carcinoma (HCC) than the general population. Purpose To evaluate the diagnostic performance of LI-RADS category 5 (LR-5, indicating definite HCC) observations for HCC using CT and MRI in patients with

An 84-year-old female patient presented for routine CT angiography (CTA) of the arteries of the neck following right carotid endarterectomy for 75% stenosis, which had been performed without incident 2 months prior. The patient had also been diagnosed with 72% stenosis of the left carotid artery, although it was asymptomatic and thus endarterectomy was not required. The medical history included arterial

Background Alzheimer disease (AD) progression can be monitored by tracking intensity changes in PET standardized uptake value (SUV) ratios of amyloid, tau, and neurodegeneration. The spatial extent ("fill state") of these three hallmark pathologic abnormalities may serve as critical pathophysiologic information, pending further investigation. Purpose To examine the clinical utility and increase the

Background Epidural blood patching is frequently used to treat spontaneous intracranial hypotension (SIH) due to cerebrospinal fluid leaks. However, its effectiveness in sealing ventral dural tears, particularly in chronic cases with organized spinal longitudinal extradural collections (SLECs), is not well documented. Purpose To assess the efficacy of intra-SLEC patching compared with traditional patching

Background Generative artificial intelligence (AI) is anticipated to alter radiology workflows, requiring a clinical value assessment for frequent examinations like chest radiograph interpretation. Purpose To develop and evaluate the diagnostic accuracy and clinical value of a domain-specific multimodal generative AI model for providing preliminary interpretations of chest radiographs. Materials and

Background The clinical, radiologic, and prognostic implications of interstitial pneumonia with autoimmune features (IPAF) in patients with idiopathic interstitial pneumonia and pathologic usual interstitial pneumonia (UIP) have not been fully evaluated. Purpose To compare autoimmune features according to CT patterns for the diagnosis of idiopathic pulmonary fibrosis (IPF) and to assess the diagnostic

Background Steatosis is a critical health problem, creating a growing need for opportunistic screening. Early detection may allow for effective treatment and prevention of further liver complications. Purpose To evaluate photon-counting CT (PCCT) fat quantification on contrast-enhanced scans and validate the results against fat quantification via histopathologic assessment, controlled attenuation parameter

Despite the successful application of Imaging Reporting and Data Systems to improve the radiologic description and management of disease in many organs, one does not yet exist for the kidney. Instead, the radiologic approach to the kidney has focused on the Bosniak classification system, which is based on imaging characteristics for cystic renal masses, and detecting macroscopic fat within solid renal

Background During neurointerventional treatment of intracranial aneurysms (IAs), poor antiplatelet drug response increases the risk of a cerebral ischemic event (IE). Replacing clopidogrel with ticagrelor may reduce this risk. Purpose To determine whether platelet function test (PFT)-guided antiplatelet therapy reduces incidence of IEs compared with standard dual antiplatelet therapy (SDAT; daily oral

Hyperhidrosis, excessive sweating from the eccrine sweat glands, is caused by overactivity of the sympathetic nerves. Facial, axillary, and/or palmar hyperhidrosis (excessive sweating of the face, armpits, and hands) has a reported prevalence of 1%-1.6%. This condition is initially treated conservatively using a combination of topical and pharmacologic treatments. Surgical sympathectomy or percutaneous

Background The ScreenTrustCAD trial was a prospective study that evaluated the cancer detection rates for combinations of artificial intelligence (AI) computer-aided detection (CAD) and two radiologists. The results raised concerns about the tendency of radiologists to agree with AI CAD too much (when AI CAD made an erroneous flagging) or too little (when AI CAD made a correct flagging). Purpose To