Current Medical Imaging Reviews最新文献

Introduction: Struma ovarii (SO) is a rare ovarian teratoma composed predominantly of thyroid tissue, often misdiagnosed due to its non-specific clinical manifestations and low prevalence.

Methods: The ultrasound and clinical features of 16 histologically confirmed cases of SO (mean age 45 ± 10 years) were analyzed. Key ultrasound parameters evaluated included tumor size, internal echo patterns, calcification, blood flow (Adler grading), and pelvic effusion.

Results: Half of patients with SO have been found to be postmenopausal women over 50 years of age, and that most tumors are discovered incidentally during routine examination. The large cystic components with regular margins, accompanied by calcified and vascularized solid elements, are ultrasound characteristics of SO. In particular, the presence of calcification and distinct vascular patterns on Doppler imaging (as per Adler classification) has been identified as a critical marker distinguishing SO from other adnexal masses.

Discussion: Compared to existing SO research, this study has found the ultrasound characteristics of SO to mostly manifest as a large cystic echo, regular boundaries, and calcification. At the same time, compared to the existing imaging techniques, such as CT and MRI, characteristic ultrasonography has been found to be a good complement to the diagnosis of SO.

Conclusion: When an adnexal tumor is classified as O-RADS 3-5 and exhibits features, such as a large cystic echo, regular boundaries, and calcification, SO should be considered in the differential diagnosis. These findings can enhance the accuracy of preoperative assessment, facilitate individualized surgical planning, and contribute to improved clinical management by reducing the likelihood of misdiagnosis.

Introduction: This study aimed to evaluate the predictive value of radiomic features derived from 18F-FluoroDeoxyGlucose (FDG) PET/CT for Epidermal Growth Factor Receptor (EGFR) gene mutations in patients with lung adenocarcinoma.

Methods: A retrospective analysis was conducted on 93 patients diagnosed with solitary lung adenocarcinoma who underwent 18F-FDG PET/ CT imaging and EGFR mutation results. The patients were divided into training (46 cases) and testing (47 cases) cohorts. Radiomic features were extracted from the primary tumor sites' PET and CT images. Feature selection was performed using the Mann-Whitney U test and least absolute shrinkage and selection operator (LASSO) regression. A radiomics score (Rad-score) was constructed, and combined models incorporating clinical factors and metabolic parameters were developed. Predictive performance was evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC), accuracy, and decision curve analysis (DCA).

Results: The radiomics model achieved AUCs of 0.865 (95% CI: 0.747-0.983) and 0.737 (95% CI: 0.572-0.901) in the training and testing sets, respectively, with corresponding accuracies of 80.9% and 78.3%. The clinical model alone demonstrated inferior performance, with AUCs of 0.637 and 0.645. The combined model showed slightly improved AUCs (0.885 and 0.714) but did not significantly outperform the radiomics-only model (P > 0.05). DCA indicated greater clinical utility for the radiomics model across a wide range of threshold probabilities.

Discussion: PET/CT-based radiomics research has also achieved good efficacy in predicting EGFR gene mutations. Compared with morphological imaging techniques, such as X-ray, ultrasound, and CT, 18F-FDG PET/CT imaging has the significant advantage of providing functional and metabolic information of lesions. Both radiomics and composite models could predict EGFR mutation status in lung adenocarcinoma patients, but the radiomics model showed slightly better clinical predictive efficacy than the composite model.

Conclusion: The radiomics model and the combined model integrating Rad-score with clinical factors demonstrated comparable abilities in effectively predicting EGFR mutation status in patients with lung adenocarcinoma. These models could offer a non-invasive approach for identifying EGFR mutations.

Background: Meningiomas (MGM) are common intracranial tumors, while complete situs inversus totalis (SIT) is an uncommon congenital anomaly. However, there are few documented cases of complete situs inversus coexisting with brain tumors, and particularly, there have been no reports on the relationship between surgically treated MGM and complete situs inversus.

Case presentation: A 52-year-old female, presenting with a 7-month headache history, worsening over the past 10 days, with new-onset left lower limb weakness. She reported difficulty lifting the left leg, dragging during ambulation, and a "stepping on cotton" sensation. No significant past medical history.

Conclusion: This case highlights that the surgical approach must be determined based on the precise tumor-to-brain anatomy provided by 3D printing technology, while also accounting for the patient's complete situs inversus and dominant hand.

Introduction: Kidney abnormalities such as cysts, stones, tumors, and other structural disorders pose significant health risks and can lead to chronic kidney disease if not diagnosed in time.

Materials and methods: This study proposes a deep learning-based diagnostic framework that introduces an enhanced feature extraction strategy through a novel model known as Kidney Transformer Network (KTNET). The system is designed to automatically detect and classify multiple kidney conditions by effectively extracting disease-specific features from CT scan images. By leveraging transformer-based architecture, KTNET improves feature representation and enables highly accurate discrimination between Normal, Cyst, Tumor, and Stone cases.

Results: Experimental results demonstrate that the proposed model achieves outstanding diagnostic performance, recording 99.7% accuracy, 99.4% precision, 99.3% recall, and a 99.6% F1-score, surpassing traditional image processing methods and several existing deep learning models.

Discussion: The model's adaptability and efficiency with diverse CT scan images highlight its potential for practical integration in clinical workflows.

Conclusion: This research advances medical imaging by providing an intelligent, reliable, and accurate framework for the early detection and classification of kidney abnormalities, ultimately enhancing patient diagnosis and clinical decision-making.

Background: Although Mucoepidermoid Carcinoma (MEC) most commonly arises in the salivary glands, its precise etiological factors and pathogenic mechanisms remain elusive. Pancreatic involvement is an extremely uncommon manifestation, with only 15 documented cases in the medical literature to date. Owing to the absence of typical imaging features and tumor markers, the diagnosis of pancreatic MEC still relies on pathological examination.

Case presentation: This report presents the case of a 57-year-old female patient with a five-year history of abdominal discomfort. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) demonstrated a mass in the tail of the pancreas, which showed progressive ring-like delayed enhancement. The diagnosis of pancreatic mucoepidermoid carcinoma was confirmed by pathology following a laparoscopic distal pancreatectomy.

Conclusion: Pancreatic MEC is exceedingly rare. In this article, the authors summarize the imaging features of this tumor and systematically review the literature to provide a better understanding of this disease.

Introduction: Behçet's disease (BD), a chronic multisystem inflammatory disorder, rarely involves the heart. Aortic regurgitation (AR) is the predominant valvular lesion. When AR precedes characteristic mucocutaneous symptoms, misdiagnosis and treatment delays often occur.

Case presentation: We, herein, report the case of a 37-year-old male presenting with isolated aortic regurgitation (AR) as the initial manifestation of Behçet's disease (BD). Initial echocardiography revealed severe eccentric AR with left coronary cusp prolapse and a vegetation-like lesion, raising suspicion of infective endocarditis; however, relapsed oral ulcers developed postoperatively, ultimately confirming BD diagnosis. Despite successful aortic valve replacement, delayed diagnosis due to absent early mucocutaneous symptoms contributed to catastrophic prosthetic valve dehiscence with severe paravalvular leak. The patient underwent an emergency Bentall procedure with venoarterial extracorporeal membrane oxygenation (VAECMO) support but succumbed to cardiogenic shock and multiorgan failure. Pathological analysis demonstrated tissue necrosis with minimal inflammation.

Conclusion: Isolated AR may be BD's initial manifestation, preceding classic symptoms by months. Echocardiographic features, including valve prolapse and perivalvular lesions, despite their non-specificity, should prompt screening for BD. Inherent tissue fragility in BD significantly elevates postoperative risks of paravalvular leak and prosthetic valve dehiscence. Early identification, optimal surgical procedure, and timely immunosuppressive therapy are essential to improve the prognosis of cardiac BD.

Shoulder pain is among the most frequent musculoskeletal complaints and remains a significant therapeutic challenge in clinical practice. A wide spectrum of conditions may contribute to this symptom, including rotator cuff tendinosis or tears, calcific tendinopathy, labral or capsuloligamentous injuries and degenerative changes of the glenohumeral joint. Accurate diagnosis requires an integrated approach that combines clinical history, physical examination, and imaging. However, variability in examination technique and interpretation often limits the reliability of clinical assessment alone. Diagnostic imaging plays a crucial role in evaluating the shoulder joint and its surrounding soft-tissue structures. Magnetic resonance imaging has become the gold standard for shoulder evaluation due to its high resolution and superior soft-tissue contrast, allowing for a detailed assessment of tendons, muscles, cartilage, and bone marrow. Magnetic resonance arthrography further enhances sensitivity for labroligamentous and cartilage injuries, and remains essential in many clinical scenarios. Recent technological advancements, such as radial imaging, kinematic or cine-MRI, 3D acquisition and reconstruction, dynamic contrast-enhanced sequences, ultrashort time-to-echo imaging, T2 mapping, and fat quantification, are expanding the diagnostic capabilities of MRI and promoting a shift from qualitative to quantitative evaluation of tissue integrity. Additionally, demand for faster imaging has driven the development of accelerated acquisition techniques that retain diagnostic image quality with shorter acquisition times. Emerging artificial intelligence-driven tools are beginning to influence every stage of imaging, from protocol optimization to automated segmentation and the extraction of quantitative biomarkers. These innovations promise to improve diagnostic accuracy, streamline workflows, and usher in a new era of patient-specific care in shoulder pain imaging.

Objective: This study aimed to develop and validate a deep learning radiomics (DLR) model based on superb microvascular imaging (SMI) for the noninvasive assessment of the severity of arteriolosclerosis in patients with chronic kidney disease (CKD).

Materials and methods: From June 2022 to December 2024, we prospectively recruited 326 CKD patients who underwent kidney biopsy across two medical centers. The enrolled patients were randomly allocated to the training or testing set in a 7:3 ratio. Deep learning (DL) features and radiomics features from SMI images were extracted, and after dimensionality reduction, they were used to establish deep learning radiomics (DLR) models. The performance of the proposed models was assessed through receiver operating characteristic (ROC) analysis and decision curve analysis (DCA).

Results: Among the 326 CKD patients, 165 were positive for arteriolosclerosis and 161 were negative. In the training group, the area under the curve (AUC) values for the CDUS model,clinical model, radiomics model, DL model, and DLR model were 0.621 (0.547-0.695), 0.68 (0.611-0.749), 0.763 (0.703-0.823), 0.820 (0.767-0.874), and 0.840 (0.790-0.890), respectively. In the testing group, the AUCs were 0.677 (0.571-0.783), 0.776 (0.684-0.869), 0.727 (0.626-0.829), 0.779 (0.687-0.872), and 0.819 (0.735-0.903), respectively. The DLR model outperformed standalone radiomics, DL models, and the CDUS-based clinical model. The DCA validated the clinical utility of the DLR model.

Conclusion: The DLR model utilizing SMI imaging can precisely and non-invasively assess the severity of arteriolosclerosis in CKD patients, which can assist physicians in formulating more favorable treatment plans for patients.

Objective: This study aimed to investigate the clinical and MR imaging differences between dual-phenotype hepatocellular carcinoma (DPHCC) and conventional hepatocellular carcinoma (HCC).

Methods: A retrospective analysis was conducted on the clinical data and MRI findings of 29 patients with DPHCC and 29 propensity score-matched patients with conventional HCC, confirmed by surgical pathology, from January 2019 to January 2022 at Fudan University Zhongshan Hospital. Clinical characteristics, lesion location, morphology, size, signal intensity, enhancement patterns, vascular invasion, and lymph node metastasis were analyzed for both groups.

Results: Between the DPHCC group and the HCC group, statistically significant differences were found in cirrhosis, pathological grade, lesion morphology, enhancement patterns, delayed capsular enhancement, and lymph node metastasis. There were no statistically significant differences between the two groups in terms of age, gender, hepatitis B infection, AFP, CA199, microvascular invasion (MVI), capsular invasion, lesion size, location, vascular invasion, ADC values, and T1WI and T2WI signals.

Conclusion: Compared to HCC, DPHCC has a higher pathological grade, more irregular lesion morphology, and a higher incidence of both fast-in and slow-out and slow-in and slow-out enhancement patterns, as well as higher rates of lymph node metastasis. The findings have provided valuable insights for the accurate diagnosis of DPHCC.

Introduction: This study introduces a novel fusion of 3D ResNet classification and Grad-CAM visualization to predict vaginal invasion in early-stage cervical cancer using T2WI-MRI, enhancing diagnostic accuracy while enabling anatomical localization of invasive lesions.

Methods: This retrospective study analyzed sagittal T2WI from 160 patients with pathologically confirmed stage IB-IIA cervical cancer to predict vaginal invasion. Following an 8:2 training-test split, radiomic features were extracted from manually delineated intratumoral regions and four concentrically expanded peritumoral regions (1-4mm). Features selection by Pearson correlation and LASSO regression. Random forest models incorporating intratumoral and peritumoral (0-4mm) features were constructed, with ROC analysis identifying the optimal model. Subsequently, a 3D-ResNet architecture, enhanced with anisotropic convolutional layers and sophisticated data augmentation, was developed and optimized using the optimal ROI configuration. Model interpretability was facilitated using Grad-CAM, with performance assessed by AUC, sensitivity, specificity, accuracy, and precision.

Results: The AIC-enhanced 3D ResNet-18 model, integrating intratumoral and 3mm peritumoral regions, showed superior test performance (AUC: 0.784, Sensitivity: 0.650, Specificity: 0.765, Accuracy: 0.611, Precision: 0.686) versus the baseline (AUC: 0.742), representing a 6% AUC improvement. Grad-CAM heatmaps identified diagnostically relevant regions within the tumor microenvironment, enhancing biological plausibility and model interpretability.

Discussion: This attention-integrated 3D ResNet-18 framework (AUC=0.784) facilitates non-invasive vaginal invasion detection for fertility-sparing decisions, validated through Grad-CAM tumor localization; however, derivation from a single-center cohort warrants external validation and prospective studies before clinical translation.

Conclusion: This preliminary study demonstrates promising deep learning performance (3D ResNet-18+Grad-CAM+AIC) for vaginal invasion assessment, despite moderate n; however, a single-center retrospective design limits generalizability.