Radiologist

To assess the impact of low-dose contrast media (CM) injection protocol with deep learning image reconstruction (DLIR) algorithm on image quality in coronary CT angiography (CCTA). In this prospective study, patients underwent CCTA were prospectively and randomly assigned to three groups with different contrast volume protocols (at 320mgI/mL concentration and constant flow rate of 5ml/s). After pairing basic information, 210 patients were enrolled in this study: Group A, 0.7mL/kg (n = 70); Group B, 0...

This article presents a multidisciplinary approach adopted in the Sicily mummy project, highlighting unique challenges and major ethical concerns inherent to the scientific study, conservation, and presentation of these mummies. Recognizing mummies as a distinct category of human remains, this paper argues for the development and application of specialized guidelines that address the intricate balance between scientific inquiry and respect for the cultural, religious, and mortuary practices that characterize the cultural context, in this case of Sicily...

Cancer is the second most common cause of death worldwide. Bowel emergencies in patients with cancer are becoming increasingly more prevalent due to advances in cancer therapy and longer overall patient survival. When these patients present acutely, they are often frail and may have pre-existing co-morbidities. This article discusses the imaging features of bowel emergencies commonly encountered in oncological patients in clinical practice. These include chemotherapy related colitis, neutropenia enterocolitis and typhlitis, toxic megacolon, bowel perforation, malignant bowel obstruction and gastrointestinal haemorrhage...

INTRODUCTION: Contrast-enhanced computed tomography (CECT) is an emergent diagnostic imaging modality to identify the bleeding site and survey the abdominal cavity. The diagnostic utility of CECT for ectopic pregnancy (EP) has not been well-investigated. The objective of this study was to evaluate the characteristics of CECT findings in patients with EP and extract specific findings that could contribute to the identification of implantation sites. METHOD: We conducted a retrospective study, reviewing suspected EP cases between April 2015 and March 2018 in our hospital...

BACKGROUND: Due to the variations in surgical approaches and prognosis between intraspinal schwannomas and meningiomas, it is crucial to accurately differentiate between the two prior to surgery. Currently, there is limited research exploring the implementation of machine learning (ML) methods for distinguishing between these two types of tumors. This study aimed to establish a classification and regression tree (CART) model and a random forest (RF) model for distinguishing schwannomas from meningiomas...

BACKGROUND: Thyroid nodules are commonly identified through ultrasound imaging, which plays a crucial role in the early detection of malignancy. The diagnostic accuracy, however, is significantly influenced by the expertise of radiologists, the quality of equipment, and image acquisition techniques. This variability underscores the critical need for computational tools that support diagnosis. METHODS: This retrospective study evaluates an artificial intelligence (AI)-driven system for thyroid nodule assessment, integrating clinical practices from multiple prominent Thai medical centers...

BACKGROUND: Accurate preoperative diagnosis of endometrial cancer (EC) with deep myometrial invasion (DMI) is critical to deciding whether to perform lymphadenectomy. However, the presence of adenomyosis makes distinguishing DMI from superficial myometrial invasion (SMI) on magnetic resonance imaging (MRI) challenging. We aimed to evaluate the accuracy of multiparametric MRI (mpMRI) in diagnosing DMI in EC coexisting with adenomyosis (EC-A) compared with EC without coexisting adenomyosis and to evaluate the effect of different adenomyosis subtypes on myometrial invasion (MI) depth in EC...

BACKGROUND: Anterior cruciate ligament (ACL) injuries are closely associated with knee osteoarthritis (OA). However, diagnosing ACL injuries based on knee magnetic resonance imaging (MRI) has been subjective and time-consuming for clinical doctors. Therefore, we aimed to devise a deep learning (DL) model leveraging MRI to enable a comprehensive and automated approach for the detection of ACL injuries. METHODS: A retrospective study was performed extracting data from the Osteoarthritis Initiative (OAI)...

OBJECTIVE: To explore the application of multiparametric MRI-based radiomic nomogram for assessing HER-2 2+ status of breast cancer (BC). METHODS: Patients with pathology-proven HER-2 2+ invasive BC, who underwent preoperative MRI were divided into training (72 patients, 21 HER-2-positive and 51 HER-2-negative) and validation (32 patients, 9 HER-2-positive and 23 HER-2-negative) sets by randomization. All were classified as HER-2 2+ FISH-positive (HER-2-positive) or -negative (HER-2-negative) according to IHC and FISH...

OBJECTIVE: This study evaluated the performance of attentional fusion model-based multiscale features in classifying intracerebral hemorrhage and the localization of bleeding focus based on weakly supervised target localization. METHODS: A publicly available dataset provided by the American College of Neuroradiology (ASNR) was used, consisting of 750,000 computed tomography (CT) scans of the brain, manually marked by radiologists for intracranial hemorrhage and five hemorrhage subtypes...

BACKGROUND: Low nuclear grade ductal carcinoma in situ (DCIS) patients can adopt proactive management strategies to avoid unnecessary surgical resection. Different personalized treatment modalities may be selected based on the expression status of molecular markers, which is also predictive of different outcomes and risks of recurrence. DCIS ultrasound findings are mostly non mass lesions, making it difficult to determine boundaries. Currently, studies have shown that models based on deep learning radiomics (DLR) have advantages in automatic recognition of tumor contours...

BACKGROUND AND AIMS: Charcot neuroosteoarthropathy (CN) is considered a rare complication of diabetic neuropathy. Due to its insidious mode of presentation, CN may be difficult to diagnose timely and a high index of suspicion is required from both, the diabetic patient (especially those with neuropathy) and their physicians for the early diagnosis and treatment to prevent major complications. METHODS: We planned a narrative review and searched MEDLINE database to identify evidence regarding CN incidence, treatment options, and recent guidelines...

BACKGROUND: This study systematically compares the impact of innovative deep learning image reconstruction (DLIR, TrueFidelity) to conventionally used iterative reconstruction (IR) on nodule volumetry and subjective image quality (IQ) at highly reduced radiation doses. This is essential in the context of low-dose CT lung cancer screening where accurate volumetry and characterization of pulmonary nodules in repeated CT scanning are indispensable. MATERIALS AND METHODS: A standardized CT dataset was established using an anthropomorphic chest phantom (Lungman, Kyoto Kaguku Inc...

BACKGROUND: Patients with Turner syndrome (TS) face an increased risk of developing aortic dilatation (AD), but diagnosing AD in children presents greater complexity compared to adults. This study aimed to investigate the application of various assessment indicators of AD in Chinese children and adolescents with TS. METHODS: This study included TS patients admitted to Shenzhen Children's Hospital from 2017 to 2022. Cardiovascular lesions were diagnosed by experienced radiologists...

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An ongoing challenge in academic radiology is balancing the need to read the scans and generate relative value units (RVUs) with the need to ensure academic leadership and the consistent production of impactful publications. Indeed, the tripartite mission of academic radiology (i.e. clinical care, research, and teaching) does not lend itself to obvious answers in an era when institutions and departments are increasingly focused on RVU generation. Even the minority of radiologists who are interested in pursuing the academic mission and accept academic jobs are likely to find their time increasingly squeezed by massive volumes of scans to read and the priority placed on RVU generation...

BACKGROUND AND PURPOSE: Intracranial steno-occlusive lesions are responsible for acute ischemic stroke. However, the clinical benefits of artificial intelligence-based methods for detecting pathologic lesions in intracranial arteries have not been evaluated. We aimed to validate the clinical utility of an artificial intelligence model for detecting steno-occlusive lesions in the intracranial arteries. MATERIALS AND METHODS: Overall, 138 TOF-MRA images were collected from two institutions, which served as internal (n = 62) and external (n = 76) test sets, respectively...

BACKGROUND: Prostate cancer ranks among the most prevalent cancers affecting men globally. While conventional MRI serves as a diagnostic tool, its extended acquisition time, associated costs, and strain on healthcare systems, underscore the necessity for more efficient methods. The emergence of AI-acceleration in prostate MRI offers promise to mitigate these challenges. METHODS: A systematic review of studies looking at AI-accelerated prostate MRI was conducted, with a focus on acquisition time along with various qualitative and quantitative measurements...

The integration of Computer-Aided Diagnosis (CAD) with Large Language Models (LLMs) presents a promising frontier in clinical applications, notably in automating diagnostic processes akin to those performed by radiologists and providing consultations similar to a virtual family doctor. Despite the promising potential of this integration, current works face at least two limitations: (1) From the perspective of a radiologist, existing studies typically have a restricted scope of applicable imaging domains, failing to meet the diagnostic needs of different patients...

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence . 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. Purpose To assess the performance of a local open-source large language model (LLM) on various information extraction tasks from real-life emergency brain MRI reports...