Cerebellar microhemorrhages have not been previously documented in methylmalonic acidemia with homocystinuria (MMA-HC), a rare inherited metabolic disorder. Herein, we reported an 18-year-old female presented with acute gait instability and dysarthria post-febrile illness. Biochemical testing revealed severe hyperhomocysteinemia. Brain MRI demonstrated bilateral cerebellar DWI/FLAIR hyperintensities. Whole-exome sequencing confirmed compound heterozygous MMACHC mutations, establishing cblC-type MMA-HC diagnosis. Symptoms resolved after one month of vitamin-based therapy. Follow-up 3.0 T MRI and 7.0 T MRI susceptibility-weighted imaging (SWI) uncovered multiple punctate cerebellar vermian microhemorrhages-a previously unreported finding. This case highlights an unusual adult-onset presentation of MMA-HC and represents the first report of SWI-detectable cerebellar vermis microhemorrhages with this condition, visualized. This finding suggests that cerebellar microhemorrhages may be an under-recognized feature in MMA-HC, particularly detectable using high-field SWI during acute exacerbations, and contributes to a more comprehensive understanding of the neurological complications in this metabolic disorder.
Introduction: This study investigates the application of a deep learning model, YOLOv8-Seg, for the automated classification of osteoporotic vertebral fractures (OVFs) from computed tomography (CT) images.
Methods: A dataset of 673 CT images from patients admitted between March 2013 and May 2023 was collected and classified according to the European Vertebral Osteoporosis Study Group (EVOSG) system. Of these, 643 images were used for training and validation, while a separate set of 30 images was reserved for testing.
Results: The model achieved a mean Average Precision (mAP50-95) of 85.9% in classifying fractures into crush, anterior wedge, and biconcave types.
Discussion: The results demonstrate the high proficiency of the YOLOv8-Seg model in identifying OVFs, indicating its potential as a decision-support tool to streamline the current manual diagnostic process. This work underscores the significant potential of deep learning to assist medical professionals in achieving early and precise diagnoses, thereby improving patient outcomes.
Background: Large language models (LLMs) appear to be capable of performing a variety of tasks, including answering questions, but there are few studies evaluating them in direct comparison with clinicians. This study aims to compare the performance of artificial intelligence (AI) models and clinical specialists in informing patients about varicocele embolization. Additionally, we aim to establish an evidence base for future hybrid informational systems that integrate both AI and clinical expertise.
Methods: In this prospective, double-blind, randomized controlled trial, 25 frequently asked questions about varicocele embolization (collected via Google Search trends, patient forums, and clinical experience) were answered by three AI models (ChatGPT-4o, Gemini Pro, and Microsoft Copilot) and one interventional radiologist. Responses were randomized and evaluated by two independent interventional radiologists using a valid 5-point Likert scale for academic accuracy and empathy.
Results: Gemini achieved the highest mean scores for both academic accuracy (4.09 ± 0.50, 95% CI: 3.95-4.23) and higher expert-rated scores for empathetic communication (3.54 ± 0.59, 95% CI: 3.38-3.70), followed by Copilot (academic: 4.07 ± 0.46, 95% CI: 3.94-4.20; empathy: 3.48 ± 0.53, 95% CI: 3.33-3.63), ChatGPT (academic: 3.83 ± 0.58, 95% CI: 3.67-3.99; empathy: 2.92 ± 0.78, 95% CI: 2.70-3.14), and the comparator physician (academic: 3.75 ± 0.41, 95% CI: 3.64-3.86; empathy: 3.12 ± 0.82, 95% CI: 2.89-3.35). ANOVA revealed statistically significant differences across groups for both academic accuracy (F = 6.181, p < 0.001, η 2 = 0.086) and empathy (F = 9.106, p < 0.001, η 2 = 0.122). Effect sizes were medium for academic accuracy and large for empathy.
Conclusions: AI models, particularly Gemini, received higher ratings from expert evaluators compared to the comparator physician in patient education regarding varicocele embolization, excelling in both academic accuracy and empathetic communication style. These preliminary findings suggest that AI models hold significant potential to complement patient education systems in interventional radiology practice and provide compelling evidence for the development of hybrid patient education models.
Background: According to the 5th revision of World Health Organization (WHO) of central nervous system tumors classification, gliosarcoma is a malignant tumor grade 4 and is the rarest and aggressive subtype of isocitrate dehydrogenase (IDH) wild-type glioblastoma. The special histopathological feature of the tumor is its biphasic differentiation including both the glial and the sarcomatous (mesenchymal) components of the tumor. The characteristics mentioned above create difficulties in radiological and histological diagnoses. Because of its rarity, gliosarcoma is typically not even considered in the differential diagnosis.
Case presentation: This clinical case study describes a 55-year-old man exhibiting acute right-sided hemiparesis and disorientation for 12 h with loss of consciousness. A brain МRI of the patient revealed an intracerebral mass in the left frontoparietal area with close relationship with the dura mater, ring-like enhancement, severe perifocal edema, restricted diffusion of the solid component, internal vascular shunts, microhemorrhages, and elevated perfusion values. At the preoperative stage, the differential diagnosis included glioblastoma, solitary metastasis, and the possibility of an anaplastic meningioma. Tumor microsurgical resection was performed. According to the results of histological and immunohistochemical studies, gliosarcoma was diagnosed.
Discussion: The only characteristic gliosarcoma feature was the phenomenon of solid node heterogeneity detected on the conventional T2-weighted sequence: a combination of hypo- and hyperintense parts. While multiparametric magnetic resonance imaging (MRI) aids in differentiating high-grade gliomas, metastases, and meningiomas, gliosarcoma remains underrecognized because of overlapping features. The observed T2 heterogeneity may serve as a potential radiological marker for gliosarcoma. Accurate and timely identification of brain tumor type is required to establish the appropriate extent of resection in surgical planning.
Conclusion: This case publication does not intend to ignore the data of conventional sequences and instead considers them to be included in the structure of the multiparametric MRI protocol. However, larger studies are needed to validate the findings of this case study and refine diagnostic criteria for this rare tumor.
Background: The anatomical definition of fenestration in the posterior communicating artery (PCoA) has long been contentious. Previously reported cases exhibiting "dual-origin" characteristics more closely align with partial duplication, resulting in a lack of definitive clinical evidence for true fenestrations. This study presents the first globally reported case of a PCoA fenestration confirmed by multimodal imaging and co-occurring with an aneurysm at the same site, providing critical evidence for establishing imaging diagnostic criteria for fenestrations.
Case presentation: A 65-year-old woman presented with persistent dizziness. Digital subtraction angiography (DSA) revealed a localized fenestration at the origin of the left PCoA, with a saccular aneurysm arising proximal to the fenestrated segment. Intraoperative 3D rotational angiography definitively characterized the fenestration as an interruption in a single vessel wall without parallel vascular structures (excluding partial duplication). The aneurysm was successfully treated via endovascular coil embolization, achieving Raymond-Roy Class I occlusion. No recurrence was observed at 12-month follow-up (mRS score 0).
Conclusion: This study establishes the first imaging diagnostic criteria for PCoA fenestration, demonstrating that it can be distinguished from partial duplication by the key radiological feature of "single-vessel-wall interruption." Embryologically, PCoA fenestration likely results from abnormal fusion of primitive embryonic vascular plexuses, with hemodynamic disturbance at the fenestration site identified as a critical mechanism for aneurysm formation. This case suggests the potential safety and efficacy of endovascular intervention proved safe and effective for managing intracranial aneurysms associated with arterial fenestration at the same location.
Background: Iodinated contrast media-acute adverse reactions (ICM-AARs) are frequent and clinically significant complications associated with radiological imaging. Despite investigation of their risk factors, there is no consensus, and no comprehensive synthesis has been conducted. This systematic review and meta-analysis aimed to investigate the factors influencing ICM-AARs.
Methods: A systematic search for studies published in Chinese or English up to 22 July 2024 in the PubMed, Web of Science, Cochrane Library, Embase, CNKI, WanFang, CQVIP, and SinoMed databases was conducted. Studies on patients undergolng contrast-enhanced CT examinations with nonionic ICM were selected. The primary outcome measures were risk factors associated with ICM-AARs. The studies were analyzed for heterogeneity using the Q-test and I2 statistic, while publication bias was assessed using funnel plots, Egger's test, and Begg's test. Stata 17 software was used for the meta-analysis.
Results: Seventeen studies were included, encompassing 2,576,446 CT-enhanced examinations. Of these, 11,621 acute adverse reactions were reported, with a mean incidence of 0.45% and a quality score of ≥7. The meta-analysis showed that female sex (OR = 1.27, 95% CI = 1.13, 1.41), age <35 years (OR = 1.77, 95% CI = 1.19, 2.64), high body mass index (OR = 1.06, 95% CI = 1.01, 1.10), type of medical visit (outpatient) (OR = 2.23, 95% CI = 1.01, 4.93), history of adverse ICM reactions (OR = 11.03, 95% CI = 2.25, 53.97), history of other allergies (OR = 3.16, 95% CI = 1.27, 7.84), history of asthma (OR = 1.75, 95% CI = 1.19, 2.57), hyperthyroldism (OR = 4.59, 95% CI = 1.65, 12.82), and type of ICM (OR = 2.27, 95% CI = 1.68, 3.06) were risk factors for ICM-AARs. Age >60 years (OR = 0.71, 95% CI = 0.53, 0.95), pre-injection medication (OR = 0.56, 95% CI = 0.39, 0.79), and hypertensive disorders (OR = 0.78, 95% CI = 0.65, 0.94) were identified as protective against ICM-AARs.
Conclusions: The incidence of ICM-AARs is influenced by a variety of clinical and demographic factors. Healthcare professionals may benefit from dynamically assessing patient-specific risk factors and considering targeted preventive measures for high-risk groups, particularly in populations similar to those studied.
Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/, PROSPERO (CRD42024571470).
Objectives: To investigate the evaluation of the effectiveness of contrast-enhanced ultrasound (CEUS) in the diagnosis of small hepatocellular carcinoma (HCC).
Methods: A thorough search was conducted for pertinent literature using PubMed, SCOPUS, Web of Science, Science Direct, and Wiley Library. Rayyan QRCI was used throughout this extensive procedure.
Results: Our results included thirteen studies with a total of 2016 patients, and 1672 (82.9%) were males. The follow-up duration ranged from 3 months to 24 months. CEUS was useful in anticipating the early recurrence of HCC, predicting the early recurrence of solitary lesion HCC patients, and differentiating between HCC and intrahepatic cholangiocarcinoma <3 Cm, distinguishing HCC from dysplastic nodules from tiny liver nodules, CEUS in cirrhotic patients. When paired with CEUS, conventional ultrasonography can detect minor HCC and assist in patient monitoring for those who receive an early diagnosis of HCC. CEUS showed high concordance with CECT for diagnosing lesions 2.1-3.0 cm in size. Notable limitations included heterogeneity in protocols and predominance of Asian populations (12/13 studies).
Conclusion: CEUS offers significant clinical value as a noninvasive diagnostic tool, particularly for 1-3 cm lesions in cirrhotic patients and cases where CT is contraindicated, though protocol standardization and Western population validation remain needed.
Background: Intervertebral disc anomalies, such as degeneration and herniation, are common causes of spinal disorders, often leading to chronic pain and disability. Accurate diagnosis and classification of these anomalies are critical for determining appropriate treatment strategies. Traditional methods, such as manual image analysis, are prone to subjectivity and time-consuming. With the advancements in deep learning, automated and precise classification of intervertebral disc anomalies has become a promising alternative.
Objective: This study aims to propose a deep learning-based method for classifying intervertebral disc abnormalities, with the goal of improving diagnostic accuracy and clinical efficiency in spinal health management.
Methods: From August 2021 to March 2024, a dataset consisting of 574 CT images of intervertebral discs was collected and labeled into four clinically relevant categories: normal intervertebral discs, Schmorl's nodes, disc bulges, and disc protrusions. The dataset was divided into 500 images for model training, and 74 images for validation. A YOLOv8-seg network was employed for classification, with multiple preprocessing techniques applied to ensure data consistency and enhance model performance.
Results: The IDAICS demonstrated high accuracy in classifying various intervertebral disc anomalies, including disc degeneration, herniation, and bulging, with a classification accuracy of over 93.2%, with a kappa coefficient of 0.905 (P < 0.001).
Conclusion: This deep learning-based classification approach provides an efficient and reliable alternative to manual assessment, enabling automated diagnosis of intervertebral disc abnormalities. It offers significant potential to enhance clinical decision-making and improve spinal health management outcomes.
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