BMC Medical Imaging最新文献

Background: Pituitary adenomas (PAs) are prevalent tumors that often exhibit ischemia, hypoxia, and cystic transformations, impacting their prognosis. The relationship between cystic degeneration in PAs and the expressions of hypoxia-inducible factor-1α (HIF-1α), glucose transporter 1 (GLUT1), and Ki-67 remains unclear. This study aims to analyze the correlation between MRI characteristics of cystic PA and the expression of these proteins.

Methods: This is a retrospective analysis. A total of 74 patients with cystic PA and 30 PA patients without cystic degeneration were enrolled. Their MRI signs were analyzed. According to the T2WI signs of PA, they were divided into the fluid level group (n = 26), non-fluid level group (n = 48), and non-cyst group (n = 30). Immunohistochemistry was performed to evaluate the expression levels of HIF-lα, GLUT1, and Ki-67 protein. Univariate and multinomial logistic regression analyses were used to evaluate the factors affecting MRI signs of PA. Spearman correlation was also performed.

Results: There was no significant difference in gender, age, and HIF-1α protein expression among the three groups. Significant differences were found in invasiveness (P = 0.008), GLUT1 (P < 0.001), and Ki-67 protein expression (P = 0.009) among the three groups. Pairwise comparisons revealed statistically significant differences in invasiveness, GLUT1, and Ki-67 protein expressions between the non-fluid level group and the non-cyst group. Furthermore, GLUT1 protein expression was significantly different between the non-fluid level group and the fluid level group. Notably, GLUT1 was identified as an independent factor for the non-fluid level cystic characteristics of PA. Additionally, GLUT1 was positively correlated with invasiveness and Ki-67.

Conclusion: The non-fluid level cystic PA has higher invasiveness and higher proliferation than fluid level cystic PA and non-cyst PA, which may be related to high glucose metabolism as indicated by GLUT1 expression.

Automated segmentation of retinal blood vessels in fundus images plays a key role in providing ophthalmologists with critical insights for the non-invasive diagnosis of common eye diseases. Early and precise detection of these conditions is essential for preserving vision, making vessel segmentation crucial for identifying vascular diseases that pose a threat to vision. However, accurately segmenting blood vessels in fundus images is challenging due to factors such as significant variability in vessel scale and appearance, occlusions, complex backgrounds, variations in image quality, and the intricate branching patterns of retinal vessels. To overcome these challenges, the Unified Gated Swin Transformer with Multi-Feature Full Fusion (UGS-M3F) model has been developed as a powerful deep learning framework tailored for retinal vessel segmentation. UGS-M3F leverages its Unified Multi-Context Feature Fusion (UM2F) and Gated Boundary-Aware Swin Transformer (GBS-T) modules to capture contextual information across different levels. The UM2F module enhances the extraction of detailed vessel features, while the GBS-T module emphasizes small vessel detection and ensures extensive coverage of large vessels. Extensive experimental results on publicly available datasets, including FIVES, DRIVE, STARE, and CHAS_DB1, show that UGS-M3F significantly outperforms existing state-of-the-art methods. Specifically, UGS-M3F achieves a Dice Coefficient (DC) improvement of 2.12% on FIVES, 1.94% on DRIVE, 2.52% on STARE, and 2.14% on CHAS_DB1 compared to the best-performing baseline. This improvement in segmentation accuracy has the potential to revolutionize diagnostic techniques, allowing for more precise disease identification and management across a range of ocular conditions.

Background: This study aimed to describe the color Doppler flow features of papillary thyroid carcinoma (PTC) and to further investigate the associations between these features and lymph node metastasis (LNM).

Methods: A retrospective analysis of the clinical data of 287 PTC patients confirmed by postoperative pathology at the Second Affiliated Hospital of Xi'an Jiaotong University from January 2022 to April 2023 was conducted. The Adler grading system and novel blood flow patterns were used to analyze the vascularity of the PTC lesions on color Doppler images. Univariate and multivariate logistic regression analyses were conducted to evaluate the independent effects of blood flow characteristics on LNM, and a logistic regression model was established to assess their predictive value for PTC-related LNM.

Results: In all, 287 PTC lesions were analyzed using color Doppler ultrasonography, which identified five main reference patterns: avascular (26.13%), dot-line (24.74%), branching (14.29%), garland (11.50%), and rich-disorganized (23.34%). The Adler blood flow grading was as follows: 0 (32.75%), I (18.82%), II (19.16%), and III (29.27%). A univariate analysis revealed that the Adler grade was not significantly associated with LNM (P > 0.05), whereas the garland pattern was significantly associated with LNM (P < 0.05). A multivariate analysis revealed that the garland pattern was an independent protective factor for LNM (OR [95% CI] = 0.386 [0.156-0.893]). The incorporation of the garland pattern into the model improved the predictive accuracy for LNM in PTC patients, and the AUC increased from 0.727 [95% CI: 0.669-0.786] to 0.767 [95% CI: 0.731-0.821].

Conclusions: This study classifies PTC into five types on the basis of color Doppler flow features and highlights the garland pattern as a potential predictor of LNM risk.

Background: Considering the potential association between radiation-induced hypothyroidism (RHT) and the thyroid subregions as well as the received radiation dose in each subregion, this study aims to develop a subregional prediction model for RHT.

Methods: CT images and dose images of 128 patients with nasopharyngeal carcinoma were collected retrospectively. The thyroid subregion was obtained by clustering thyroid voxels and voxel entropy. After extracting 1781 radiomics features and 1767 dosiomics features, a subregional RHT prediction model was established, and its performance was compared with that of the whole thyroid model. The phenotype and dosimetry parameters of each subregion were analyzed by AUC, T test and Delong test.

Results: Three subregions (S1, S2, S3) were identified. The subregional prediction model was constructed based on 34 radiomics and dosiomics features. According to the Delong test, the prediction performance of the subregional model was significantly superior than that of the whole thyroid model (0.813 VS 0.624, p = 0.038). Subregional analysis suggests that S1 and S3 regions may have higher radiosensitivity than S2 regions.

Conclusions: In this study, a subregional model for predicting RHT was established and the radiosensitivity of the relevant subregions was evaluated. The subregion-based RHT prediction model may help to improve radiotherapy plan design for better thyroid function protection.

Objective: To evaluate the effectiveness of the variable rate bolus tracking technique combined with third-generation dual-source CT dual-energy scanning in enhancing the quality of head and neck vascular CT angiography (CTA).

Methods: We conducted a retrospective analysis of 202 patients who underwent head and neck vascular CTA using a third-generation dual-source CT with dual-energy scanning. Patients were divided based on the contrast injection method into two groups: the variable-rate bolus tracking group (Group A, n = 100) and the fixed flow rate group (Group B, n = 102). We compared subjective image quality, venous artifacts, and objective image quality parameters between the two groups.

Results: The amount of contrast agent used in Group A was significantly lower than in Group B. Additionally, mean attenuation values of arterial segments in Group A were markedly lower than those in Group B. Compared to Group B, attenuation values of the intracranial venous sinuses, right jugular vein, superior vena cava, right subclavian vein, and left jugular vein in Group A showed significant reductions. No significant difference was observed in the subjective image quality between the two groups. However, venous artifact in the right subclavian vein was significantly diminished in Group A.

Conclusion: The application of the variable rate bolus tracking technique alongside third-generation dual-source CT dual-energy scanning in head and neck vascular CTA can achieve high-quality imaging while reducing contrast agent dosage. It enhances the attenuation contrast of intracranial arteries and veins and minimizes residual contrast and artifacts in the right subclavian vein.

Objectives: To explore the feasibility of a one-beat protocol and ultra-low tube voltage of 60 kVp in coronary CT angiography (CCTA).

Methods: This prospective study enrolled 107 patients (body mass index ≤ 26 kg/m²) undergoing CCTA examinations. Specifically, the conventional group (n = 52) underwent 100 kVp scanning with 45 ml iodine contrast agent and 4 ml/s injection rate, and the low-dose group (n = 55) underwent 60 kVp scanning with 28 ml iodine contrast agent and 2.5 ml/s injection rate. The CT value, signal-noise-ratio (SNR), contrast-noise-ratio (CNR) and subjective image quality score of two groups in aorta (AO), right coronary artery (RCA), left anterior descending (LAD) and left circumflex (LCX) are analyzed in this study. Three types of radiation doses [i.e., volume CT dose index (CTDIvol), dose length product (DLP), effective dose (ED)] of two groups are also compared.

Results: The quantitative results indicated that the low-dose group achieved higher CT values, SNR and CNR results of the AO than the conventional group (P values < 0.001). Both groups had similar CT values, SNR and CNR results in RCA, LAD, and LCX (P values > 0.05). A good agreement is noted with respect to subjective image quality scores in both groups, while the Cohen's kappa value is 0.815 in the low-dose group and 0.825 in the conventional group, respectively. In addition, the radiation dose of the low-dose group is significantly lower than the conventional group in terms of CTDIvol, DLP and ED values, and the contrast dose in the low-dose group is also significantly reduced compared to the conventional group (P values < 0.001).

Conclusions: One-beat protocol with an ultra-low tube voltage of 60 kVp could provide improved coronary image quality, reduced radiation dose and reduced iodine contrast dose.

Background: Pulmonary diseases such as COVID-19 and pneumonia, are life-threatening conditions, that require prompt and accurate diagnosis for effective treatment. Chest X-ray (CXR) has become the most common alternative method for detecting pulmonary diseases such as COVID-19, pneumonia, and lung opacity due to their availability, cost-effectiveness, and ability to facilitate comparative analysis. However, the interpretation of CXRs is a challenging task.

Methods: This study presents an automated deep learning (DL) model that outperforms multiple state-of-the-art methods in diagnosing COVID-19, Lung Opacity, and Viral Pneumonia. Using a dataset of 21,165 CXRs, the proposed framework introduces a seamless combination of the Vision Transformer (ViT) for capturing long-range dependencies, DenseNet201 for powerful feature extraction, and global average pooling (GAP) for retaining critical spatial details. This combination results in a robust classification system, achieving remarkable accuracy.

Results: The proposed methodology delivers outstanding results across all categories: achieving 99.4% accuracy and an F1-score of 98.43% for COVID-19, 96.45% accuracy and an F1-score of 93.64% for Lung Opacity, 99.63% accuracy and an F1-score of 97.05% for Viral Pneumonia, and 95.97% accuracy with an F1-score of 95.87% for Normal subjects.

Conclusion: The proposed framework achieves a remarkable overall accuracy of 97.87%, surpassing several state-of-the-art methods with reproducible and objective outcomes. To ensure robustness and minimize variability in train-test splits, our study employs five-fold cross-validation, providing reliable and consistent performance evaluation. For transparency and to facilitate future comparisons, the specific training and testing splits have been made publicly accessible. Furthermore, Grad-CAM-based visualizations are integrated to enhance the interpretability of the model, offering valuable insights into its decision-making process. This innovative framework not only boosts classification accuracy but also sets a new benchmark in CXR-based disease diagnosis.

Background: Acute-on-chronic liver failure (ACLF) is a life-threatening hepatic syndrome. Therefore, this study aimed to develop a comprehensive model combining extracellular liver volume derived from spectral CT (ECV_IC-liver) and sarcopenia, for the early prediction of short-term (90-day) disease progression in ACLF.

Materials and methods: A retrospective cohort of 126 ACLF patients who underwent hepatic spectral CT scans was included. According to the Asia-Pacific Association for the Study of the Liver (APASL) criteria, patients were divided into the progression group (n = 70) and the stable group (n = 56). ECV_IC-liver was measured on the equilibrium period (EP) images of spectral CT, and L3-SMI was measured on unenhanced CT images, with sarcopenia assessed. A comprehensive model was developed by combining independent predictors. Model performance was evaluated using receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis (DCA).

Results: In the univariate analysis, BMI, WBC, PLT, PTA, L3-SMI, IC-EP, Z-EP, K₁₄₀-EP, NIC-EP, ECV_IC-liver, and Sarcopenia demonstrated associations with disease progression status at 90 days in ACLF patients. In multivariate logistic regression, white blood cell count (WBC) (OR = 1.19, 95% CI: 1.02-1.40; P = 0.026), ECV_IC-liver (OR = 1.27, 95% CI: 1.15-1.40; P < 0.001), sarcopenia (OR = 4.15, 95% CI: 1.43-12.01; P = 0.009), MELD-Na score (OR = 1.06, 95%CI: 1.01-1.13;P = 0.042), and CLIF-SOFA score (OR = 1.37, 95%CI:1.15-1.64; P<0.001) emerged as independent risk factors for ACLF progression. The combined model exhibited superior predictive performance (AUCs = 0.910, sensitivity = 80.4%, specificity = 90.0%, PPV = 0.865, NPV = 0.851) compared to CLIF-SOFA, MELD-Na, MELD and CTP scores(both P < 0.001). Calibration curves and DCA confirmed the high clinical utility of the combined model.

Conclusions: Patients without sarcopenia and/or with a lower ECV_IC-liver have a better prognosis, and the integration of WBC, ECV_IC-liver, Sarcopenia, CLIF-SOFA and MELD-Na scores in a composite model offers a concise and effective tool for predicting disease progression in ACLF patients.

Trial registration: Not Applicable.

Purposes: This retrospective study was designed to explore the relationship between right ventricular fibroblast activation measured by fibroblast activation protein inhibitor (FAPI) imaging and myocardial deformation measured by Speckle‑tracking Echocardiography (STE) in patients with pulmonary hypertension (PH).

Methods: Clinical data of PH patients were collected [15 chronic thromboembolic pulmonary hypertension (CTEPH), 4 PAH, 1 PH with unclear and/or multifactorial mechanisms]. All of patients underwent FAPI imaging and echocardiography within one month. FAPI activity of right ventricle higher than that in the blood pool was defined as abnormal. The global and segmental maximum standardised uptake values (SUV_max) of the right ventricle were measured and further expressed as target-to-background ratio (TBR) with blood pool activity as background. right ventricular global longitudinal strain (RVGLS) and right ventricular free wall longitudinal strain (RVFWLS) including the basal-, mid-, and apical-segments were measured by STE.

Results: Eighteen patients with PH showed abnormal FAPI uptake in right ventricle. No significant differences were found between CTEPH and other types of PH. TBR of right ventricle had negative correlations with RVGLS (r = -0.597, P = 0.005) and RVFWLS (r = -0.586, P = 0.007) at global level. While, at regional level, significant correlation was only demonstrated between TBR of right ventricle free wall and RVFWLS in apical region (r = -0.530, P = 0.016) and middle region (r = -0.457, P = 0.043). Among the traditional Echocardiography parameters, TBR of right ventricle were positively associated with thickness of right ventricular anterior wall (RVAW) (r_s = 0.475, P = 0.034), and inversely with right ventricular systolic function [RVFAC (r = -0.586, P = 0.007) and TAPSE (r = -0.565, P = 0.009)].

Conclusion: FAPI imaging can partially reflect the right ventricular strain reduction in patients with PH.

Purposes: To develop a predictive model for recurrent intussusception based on abdominal ultrasound (US) images and abdominal radiographs.

Methods: A total of 3665 cases of intussusception were retrospectively collected from January 2017 to December 2022. The cohort was randomly assigned to training and validation sets at a 6:4 ratio. Two types of images were processed: abdominal grayscale US images and abdominal radiographs. These images served as inputs for the deep learning algorithm and were individually processed by five detection models for training, with each model predicting its respective categories and probabilities. The optimal models were selected individually for decision fusion to obtain the final predicted categories and their probabilities.

Results: With US, the VGG11 model showed the best performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.669 (95% CI: 0.635-0.702). In contrast, with radiographs, the ResNet18 model excelled with an AUC of 0.809 (95% CI: 0.776-0.841). We then employed two fusion methods. In the averaging fusion method, the two models were combined to reach a diagnostic decision. Specifically, a soft voting scheme was used to average the probabilities predicted by each model, resulting in an AUC of 0.877 (95% CI: 0.846-0.908). In the stacking fusion method, a meta-model was built based on the predictions of the two optimal models. This approach notably enhanced the overall predictive performance, with LightGBM emerging as the top performer, achieving an AUC of 0.897 (95% CI: 0.869-0.925). Both fusion methods demonstrated excellent performance.

Conclusions: Deep learning algorithms developed using multimodal medical imaging may help predict recurrent intussusception.

Clinical trial number: Not applicable.