Frontiers in radiology最新文献

Verrucous Venous Malformations (VVMs) are a rare subtype of Congenital Vascular Malformations (CVMs) characterised by a hyperkeratotic, verrucous surface. We present the case of a ten-year-old male with a VVM localised to the right knee, which presented as a gradually enlarging, asymptomatic lesion since birth. A comprehensive, multi-modality diagnostic workup was performed, including thorough clinical evaluation, dermoscopy, radiologic imaging (Plain radiograph, colour Doppler ultrasonography and magnetic resonance imaging) and histopathological analysis with hematoxylin and eosin staining, along with immunohistochemical staining for CD-34. The lesion exhibited characteristic features consistent with VVM. The patient was managed by percutaneous sclerotherapy to reduce lesion size. This case highlights the importance of a multidisciplinary strategy in the diagnosis and management of VVMs to improve clinical outcomes.

Spontaneous splenic rupture (SSR) is a rare but potentially life-threatening condition, most commonly associated with underlying infectious, haematological, vascular, or neoplastic processes. Clinical presentation is often non-specific, which may lead to delayed diagnosis. Imaging, particularly contrast-enhanced computed tomography (CECT), plays a pivotal role in confirming splenic injury, identifying predisposing lesions, and guiding management. We present the case of a woman aged in her seventies with chronic atrial fibrillation on antiplatelet therapy who developed spontaneous splenic rupture secondary to an occult splenic hamartoma. Ultrasound demonstrated heterogeneous perisplenic fluid and altered splenic echotexture. CT showed a 3.5 cm laceration, moderate haemoperitoneum, and a solid lesion with delayed enhancement and no active bleeding. Follow-up CT revealed progressive organisation of haemoperitoneum and stable lesion morphology. The patient was initially managed non-operatively due to haemodynamic stability, but elective splenectomy was performed given the presence of a structural lesion and the need for chronic anticoagulation. The purpose of this article is to illustrate the diagnostic and management principles of SSR through a representative clinical case and to provide an updated review of imaging strategies, including emerging applications of radiomics and artificial intelligence (AI).

Introduction: Radiomics aims to develop image-based biomarkers by combining quantitative analysis of medical images with artificial intelligence (AI) through a robust, reproducible pipeline. Scientific societies, task groups, and consortia have published several guidelines to help researchers design robust radiomics studies. This review summarizes existing guidelines, recommendations, and regulations for designing radiomics studies that can lead to clinically adoptable biomarkers.

Methods: Relevant articles were identified through a PubMed systematic review using "radiomics" and "guideline" as keywords. Of 314 retrieved papers, after screening 99 articles were deemed relevant for extracting recommendations on developing image-based biomarkers. Additional guidelines were searched by the authors.

Results: We can synthesize the systematic review in the following high consensus recommendations divided into five major areas: a) Study Design: Carefully define the study rationale, objectives, and outcomes, ensuring the dataset is of adequate size and quality; b) Data Workflow: Use standardized protocols for image acquisition, reconstruction, preprocessing, and feature extraction-following IBSI guidelines where applicable; c) Model Development and Validation: Follow best practices for model development, including prevention of data leakage, dimensionality reduction, strategies to enhance model interpretability, and establish biological plausibility; d) Transparency and Reproducibility: Publish results with sufficient methodological details to ensure rigor and generalizability and promote open science by sharing codes and data; e) Quality and compliance: Evaluate study compliance with relevant guidelines and regulations using appropriate quality metrics.

Conclusion: Radiomics promises to offer clinically useful imaging biomarkers and can represent a significant step in personalized medicine. In the present systematic review we identified five key guidelines and regulations developed in recent years, specifically for radiomics or AI, that can guide the research community in designing and conducting radiomic studies that result in an imaging biomarker suitable for clinical practice.

This review summarizes the current advances, applications, and research prospects of computer vision in advancing medical imaging. Computer vision in healthcare has revolutionized medical practice by increasing diagnostic accuracy, improving patient care, and increasing operational efficiency. Likewise, deep learning algorithms have advanced medical image analysis, significantly improved healthcare outcomes and transforming diagnostic processes. Specifically, convolutional neural networks are crucial for modern medical image segmentation, enabling the accurate, efficient analysis of various imaging modalities and helping enhance computer-aided diagnosis and treatment planning. Computer vision algorithms have demonstrated remarkable capabilities in detecting various diseases. Artificial intelligence (AI) systems can identify lung nodules in chest computed tomography scans at a sensitivity comparable to that of experienced radiologists. Computer vision can analyze brain scans to detect problems such as aneurysms and tumors or areas affected by diseases such as Alzheimer's. In summary, computer vision in medical imaging is significantly improving diagnostic accuracy, efficiency, and patient outcomes across a range of medical specialties.

Acute myocarditis is an inflammatory condition of the myocardium, often triggered by viral infections, autoimmune diseases, or toxins. It can lead to arrhythmias, heart failure, and sudden cardiac death. Early and accurate diagnosis is crucial for timely management and preventing complications. It poses a significant diagnostic challenge in emergency departments (EDs) due to nonspecific symptoms, overlapping features with conditions like acute coronary syndrome, and limitations of conventional diagnostics. Cardiac magnetic resonance imaging (CMR) is the gold standard for noninvasive diagnosis, using the 2018 Modified Lake Louise Criteria (mLLC). However, high-field CMR (1.5-3T) faces barriers in EDs, such as longer scan times, higher cost, lack of accessibility, and contraindications in patients with implantable devices, severe kidney disease, or hemodynamic instability. Low-field MRI (<1.5T) offers advantages in portability, safety, and cost while reducing susceptibility artifacts. Recent advances in AI-driven image reconstruction (e.g., LoHiResGAN, U-net) address low signal-to-noise ratios, enabling cine imaging, strain analysis, and parametric mapping at 0.55T. Studies show that low-field CMR can detect subclinical myocarditis and predict outcomes, with ECV measurements at 0.55T strongly correlating with 1.5T (r = 0.91), demonstrating comparable reliability. By integrating low-field CMR into ED protocols, clinicians can improve early detection of occult myocarditis, guide risk stratification, and reduce long-term morbidity and healthcare costs. Standardization of imaging workflows and AI-enhanced protocols will further bridge diagnostic gaps, particularly in resource-limited settings. This review highlights low-field CMR's potential to redefine acute myocarditis management, balancing diagnostic precision with practicality in emergency care.

Low-grade endometrial stromal sarcoma (LGESS) is a rare uterine malignancy; metastasis to the inferior vena cava (IVC) and right atrium is exceptionally rare and presents significant diagnostic and therapeutic challenges. We report the case of a 37-year-old woman presenting with progressive abdominal mass enlargement, palpitations, and dyspnea. She had undergone a hysteroscopic resection for presumed uterine myoma one year prior, which was subsequently re-evaluated as LGESS. Multimodal imaging comprising ¹⁸F-FDG PET/CT, MRI, CT, and echocardiography was implemented for systemic staging and hemodynamic assessment, then revealed a solid uterine mass involving the adnexa (FIGO Stage IVB) and identified hypermetabolic tumor thrombi extending from the IVC into the right atrium and pulmonary arteries. A coordinated one-stage radical resection was performed, involving total hysterectomy and removal of intracardiac thrombi under cardiopulmonary bypass. Postoperative pathology and immunohistochemistry confirmed LGESS (CD10+, ER+, PR+) with extensive lymphovascular invasion. The patient recovered uneventfully with no residual disease on follow-up and commenced adjuvant letrozole therapy. This case highlights the necessity of multimodal imaging for accurate staging of complex vascular involvement and demonstrates that aggressive one-stage surgical management is a viable strategy to achieve locoregional control and favorable early outcomes for advanced LGESS with cardiac metastasis.

This paper introduces an Ultra-Lightweight Uncertainty-Aware Ensemble (UALE) model for large-scale multi-class medical MRI diagnosis, evaluated on the 2024 Benchmark Diagnostic MRI and Medical Imaging Dataset containing 40 classes and 33,616 images. The model integrates five specialized micro-expert networks, each designed to capture distinct MRI features, and combines them using a confidence-weighted ensemble mechanism enhanced with variance-based uncertainty quantification for robust, reliable predictions. With only 0.05M parameters and 0.18 GFLOPs, UALE achieves high efficiency and competitive performance among ultra-lightweight models with an accuracy of 69.1% and an F1 score of 68.3%. Besides lightweight models, the paper offers an extensive analysis and performance comparison with fifteen state-of-the-art models, discusses various datasets, elaborates on uncertainty estimates pertaining to the clinical trustworthiness of the models and possible clinical deployment, and highlights trade-offs and avenues for future work in economically constrained settings. The extreme compactness and reliability of the UALE affords it unique utility in scalable medical diagnostics suitable for low-resource clinical settings and portable imaging devices, such as rural hospitals.

Objective: This study aims to evaluate the diagnostic performance of diffusion-weighted imaging (DWI) with a fractional order calculus (FROC) model for differentiating breast lesions and to explore the associations between FROC/apparent diffusion coefficient (ADC)-derived diffusion metrics and prognostic biomarkers and molecular subtypes in breast cancer.

Methods: This retrospective study included 147 patients with 159 histopathology-confirmed lesions who underwent multi-b DWI using simultaneous multi-slice (SMS) readout-segmented echo-planar imaging (rs-EPI) at 3.0 T. Whole-lesion histograms were computed for mono-exponential ADC and FROC parameters (D, β, μ). The Mann-Whitney U test was used to compare the histogram metrics of each diffusion parameter between the benign and malignant groups and between groups with different prognostic biomarkers and molecular subtypes. The Kruskal-Wallis test was used to compare the histogram metrics of each DWI-derived parameter among the different molecular subtypes. The Spearman rank correlation analysis was employed to characterize correlations between diffusion parameters and prognostic biomarkers. The diagnostic performance of each DWI-derived parameter in differentiating breast lesions was assessed using receiver operating characteristic (ROC) analysis.

Results: Interobserver reproducibility was excellent (intra-class correlation coefficient 0.827-0.928). Central tendency histogram metrics (10th, 90th percentiles, mean, median) of ADC and FROC parameters were higher in benign than malignant lesions, whereas skewness (all models) and entropy/kurtosis (ADC, D, μ) were lower in benign lesions (all p < 0.05, except β-skewness). The histogram metrics of ADC-median, D_FROC-mean, and D_FROC-median showed similar diagnostic performance. The values of ADC-mean, D_FROC-10%, D_FROC-mean, D_FROC-median, β_FROC-10%, β_FROC-mean, and β_FROC-median were significantly lower in the estrogen receptor (ER)-positive group compared with those in the ER-negative group. The tumors with progesterone receptor (PR)-negative status showed significantly higher β_FROC-10%, β_FROC-mean, and β_FROC-median values than those of tumors with PR-positive status. The values of D_FROC-skewness, β_FROC-10%, and β_FROC-mean exhibited significant differences in differentiating the triple-negative and luminal subtypes.

Conclusions: FROC-based histogram analysis yields diagnostic performance comparable to ADC for benign vs. malignant classification, while providing richer associations with ER/PR status, proliferation, and nodal involvement, reflecting microstructural heterogeneity not captured by mono-exponential diffusion.

Low-dose Computed Tomography (CT) imaging minimizes radiation exposure but often results in degraded image quality, making diagnosis challenging. Image Quality Assessment (IQA) is a process of quantitatively evaluating the visual quality of images and plays a crucial role in determining whether these CT scans meet the necessary standards for accurate diagnosis. IQA methods help identify issues such as noise, blurriness, or artifacts that may compromise the diagnostic value of the scans. Traditional quality assessment measures how closely an image matches an ideal or reference image. Since obtaining a high-quality reference image is often challenging, an automated quality assessment framework (diagnosis based IQA) using No-Reference Image Quality Assessment (NRIQA) techniques is proposed, allowing quality evaluation and eliminating the need for a high-quality reference image. In this approach, various statistical and structural features are extracted from low-dose CT scans and mapped to radiologist-assigned quality scores, which are subjective evaluations given by experts to train and compare various predictive models. The framework undergoes 100-fold validation, to ensure the reliability of the proposed model. CT images with predicted quality scores of 2 and below undergo spatial domain enhancement to improve their diagnostic value. These enhanced images are then reassessed using the diagnosis based IQA (trained Support Vector Regression) model, demonstrating an improvement in predicted quality scores. In addition, the enhanced images were verified by a radiologist, confirming the effectiveness of the enhancement process. This two-stage approach, automated NRIQA-based quality prediction and selective enhancement provides a reliable, and objective method for assessing and improving low-dose CT image quality.

Objective: To evaluate the efficacy of flow diverter implantation for treating CTA-negative giant vertebral artery dissection aneurysm (VADA) and to address the challenges in lesion characterization using MRI.

Methods: A 66-year-old male patient presented with a 3-month history of left facial numbness and dysarthria. Initial MRI-T1 revealed a mixed signal intensity lesion in the CPA region. However, both CTA and digital subtraction angiography (DSA) failed to identify any significant vascular abnormalities. Subsequently, CT-perfusion and dynamic contrast-enhanced computed tomography (DCE-CT) were performed to further characterize the lesion.

Results: DCE-CT revealed a giant VADA, which was significantly larger than the lesion initially detected by MRI and was identified as the cause of hypoperfusion in the posterior circulation. Based on these findings, a flow diverter implantation procedure was performed successfully without complications. Angiographic follow-up at 8 months demonstrated no recurrence of the lesion. At the 14-month clinical follow-up, the patient exhibited complete resolution of symptoms, with a mRS score of 0, indicating an excellent functional outcome.

Conclusion: Flow diverter implantation may be an effective treatment for CTA-negative giant VADAs. The limitations of MRI in accurately characterizing lesion size underscore the necessity of advanced imaging techniques, such as DCE-CT, for precise device selection and deployment.