BMC Medical Imaging最新文献

Objective: To assess whether diffusion and perfusion MRI derived parameters could non-invasively predict PD-L1 and Ki-67 status in primary central nervous system diffuse large B-cell lymphoma (PCNS-DLBCL).

Methods: We retrospectively analyzed DWI, DSC-PWI, and morphological MRI (mMRI) in 88 patients with PCNS-DLBCL. The mMRI features were compared using chi-square tests or Fisher exact test. Minimum ADC (ADC_min), mean ADC(ADC_mean), relative minimum ADC (rADC_min), relative mean ADC (rADC_mean), and relative maximum CBV (rCBV_max) values were compared in PCNS-DLBCL with different molecular status by using the Mann-Whitney U test. The diagnostic performances were evaluated by receiver operating characteristic curves.

Results: PCNS-DLBCL with high PD-L1 expression demonstrated a significantly higher ADC_min value than those with low PD-L1. The ADC_mean and rADC_mean values were significantly lower in PCNS-DLBCL with high Ki-67 status compared with those in low Ki-67 status. Other ADC, CBV parameters, and mMRI features did not show any association with these molecular statuses The diagnostic efficacy of ADC values in assessing PD-L1 and Ki-67 status was relatively low, with area under the curves (AUCs) values less than 0.7.

Conclusions: DWI-derived ADC values can provide some relevant information about PD-L1 and Ki-67 status in PCNS-DLBCL, but may not be sufficient to predict their expression due to the rather low diagnostic performance.

Background: Non-Alcoholic Steatohepatitis (NASH) is a crucial stage in the progression of Non-Alcoholic Fatty Liver Disease(NAFLD). The purpose of this study is to explore the clinical value of ultrasound features and radiological analysis in predicting the diagnosis of Non-Alcoholic Steatohepatitis.

Method: An SD rat model of hepatic steatosis was established through a high-fat diet and subcutaneous injection of CCl₄. Liver ultrasound images and elastography were acquired, along with serum data and histopathological results of rat livers.The Pyradiomics software was used to extract radiomic features from 2D ultrasound images of rat livers. The rats were then randomly divided into a training set and a validation set, and feature selection was performed through dimensionality reduction. Various machine learning (ML) algorithms were employed to build clinical diagnostic models, radiomic models, and combined diagnostic models. The efficiency of each diagnostic model for diagnosing NASH was evaluated using Receiver Operating Characteristic (ROC) curves, Clinical Decision Curve Analysis (DCA), and calibration curves.

Results: In the machine learning radiomic model for predicting the diagnosis of NASH, the Area Under the Curve (AUC) of ROC curve for the clinical radiomic model in the training set and validation set were 0.989 and 0.885, respectively. The Decision Curve Analysis revealed that the clinical radiomic model had the highest net benefit within the probability threshold range of > 65%. The calibration curve in the validation set demonstrated that the clinical combined radiomic model is the optimal method for diagnosing Non-Alcoholic Steatohepatitis.

Conclusion: The combined diagnostic model constructed using machine learning algorithms based on ultrasound image radiomics has a high clinical predictive performance in diagnosing Non-Alcoholic Steatohepatitis.

Uterine fibroids are common benign tumors originating from the uterus's smooth muscle layer, often leading to symptoms such as pelvic pain, and reproductive issues. Early detection is crucial to prevent complications such as infertility or the need for invasive treatments like hysterectomy. One of the main challenges in diagnosing uterine fibroids is the lack of specific symptoms, which can mimic other gynecological conditions. This often leads to under-diagnosis or misdiagnosis, delaying appropriate management. In this research, an attention based fine-tuned EfficientNetB0 model is proposed for the classification of uterine fibroids from ultrasound images. Attention mechanisms, permit the model to focus on particular parts of an image and move forward the model's execution by empowering it to specifically go to imperative highlights whereas overlooking irrelevant ones. The proposed approach has used a total of 1990 images divided into two classes: Non-uterine fibroid and uterine fibroid. The data augmentation methods have been connected to improve generalization and strength by exposing it to a wider range of varieties within the training data. The proposed model has obtained the value of accuracy as 0.99. Future research should focus on improving the accuracy and efficiency of diagnostic techniques, as well as evaluating their effectiveness in diverse populations with higher sensitivity and specificity for the detection of uterine fibroids, as well as biomarkers to aid in diagnosis.

Background: Flatfoot is a condition resulting from complex three-dimensional (3D) morphological changes. Most Previous studies have been constrained by using two-dimensional radiographs and non-weight-bearing conditions. The deformity in flatfoot is associated with the 3D morphology of the bone. These morphological changes affect the force line conduction of the hindfoot/midfoot/forefoot, leading to further morphological alterations. Given that a two-dimensional plane axis overlooks the 3D structural information, it is essential to measure the 3D model of the entire foot in conjunction with the definition under the standing position. This study aims to analyze the morphological changes in flatfoot using 3D measurements from weight-bearing CT (WBCT).

Method: In this retrospective comparative our CT database was searched between 4-2021 and 3-2022. Following inclusion criteria were used: Patients were required to exhibit clinical symptoms suggestive of flatfoot, including painful swelling of the medial plantar area or abnormal gait, corroborated by clinical examination and confirmatory radiological findings on CT or MRI. Healthy participants were required to be free of any foot diseases or conditions affecting lower limb movement. After applying the exclusion criteria (Flatfoot with other foot diseases), CT scans (mean age = 20.9375, SD = 16.1) confirmed eligible for further analysis. The distance, angle in sagittal/transverse/coronal planes, and volume of the two groups were compared on reconstructed 3D models using the t-test. Logistic regression was used to identify flatfoot risk factors, which were then analyzed using receiver operating characteristic curves and nomogram.

Result: The flatfoot group exhibited significantly lower values for calcaneofibular distance (p = 0.001), sagittal and transverse calcaneal inclination angle (p < 0.001), medial column height (p < 0.001), sagittal talonavicular coverage angle (p < 0.001), and sagittal (p < 0.001) and transverse (p = 0.015) Hibb angle. In contrast, the sagittal lateral talocalcaneal angle (p = 0.013), sagittal (p < 0.001) and transverse (p = 0.004) talocalcaneal angle, transverse talonavicular coverage angle (p < 0.001), coronal Hibb angle (p < 0.001), and sagittal (p < 0.001) and transverse (p = 0.001) Meary's angle were significantly higher in the flatfoot group. The sagittal Hibb angle (B =  - 0.379, OR = 0.684) and medial column height (B =  - 0.990, OR = 0.372) were identified as significant risk factors for acquiring a flatfoot.

Conclusion: The findings validate the 3D spatial position alterations in flatfoot. These include the abduction of the forefoot and prolapse of the first metatarsal proximal, the arch collapsed, subluxation of the talonavicular joint in the midfoot, adduction and valgus of the calcaneus, adduction and plantar ward movement of the talus in the hindfoot, along with the first metat

Background: Pneumoconiosis has a significant impact on the quality of patient survival. This study aims to evaluate the performance and application value of improved Unet network technology in the recognition and segmentation of lesion areas of lung CT images in patients with pneumoconiosis.

Methods: A total of 1212 lung CT images of patients with pneumoconiosis were retrospectively included. The improved Unet network was used to identify and segment the CT image regions of the patients' lungs, and the image data of the granular regions of the lungs were processed by the watershed and region growing algorithms. After random sorting, 848 data were selected into the training set and 364 data into the validation set. The experimental dataset underwent data augmentation and were used for model training and validation to evaluate segmentation performance. The segmentation results were compared with FCN-8s, Unet network (Base), Unet (Squeeze-and-Excitation, SE + Rectified Linear Unit, ReLU), and Unet + + networks.

Results: In the segmentation of lung CT granular region with the improved Unet network, the four evaluation indexes of Dice similarity coefficient, positive prediction value (PPV), sensitivity coefficient (SC) and mean intersection over union (MIoU) reached 0.848, 0.884, 0.895 and 0.885, respectively, increasing by 7.6%, 13.3%, 3.9% and 6.4%, respectively, compared with those of Unet network (Base), and increasing by 187.5%, 249.4%, 131.9% and 51.0%, respectively, compared with those of FCN-8s, and increasing by 14.0%, 31.2%, 4.7% and 9.7%, respectively, compared with those of Unet network (SE + ReLU), while the segmentation performance was also not inferior to that of the Unet + + network.

Conclusions: The improved Unet network proposed shows good performance in the recognition and segmentation of abnormal regions in lung CT images in patients with pneumoconiosis, showing potential application value for assisting clinical decision-making.

Background: Focal cortical dysplasia (FCD) is the most common epileptogenic developmental malformation. The diagnosis of FCD is challenging. We generated a radiomics nomogram based on multiparametric magnetic resonance imaging (MRI) to diagnose FCD and identify laterality early.

Methods: Forty-three patients treated between July 2017 and May 2022 with histopathologically confirmed FCD were retrospectively enrolled. The contralateral unaffected hemispheres were included as the control group. Therefore, 86 ROIs were finally included. Using January 2021 as the time cutoff, those admitted after January 2021 were included in the hold-out set (n = 20). The remaining patients were separated randomly (8:2 ratio) into training (n = 55) and validation (n = 11) sets. All preoperative and postoperative MR images, including T1-weighted (T1w), T2-weighted (T2w), fluid-attenuated inversion recovery (FLAIR), and combined (T1w + T2w + FLAIR) images, were included. The least absolute shrinkage and selection operator (LASSO) was used to select features. Multivariable logistic regression analysis was used to develop the diagnosis model. The performance of the radiomic nomogram was evaluated with an area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), calibration and clinical utility.

Results: The model-based radiomics features that were selected from combined sequences (T1w + T2w + FLAIR) had the highest performances in all models and showed better diagnostic performance than inexperienced radiologists in the training (AUCs: 0.847 VS. 0.664, p = 0.008), validation (AUC: 0.857 VS. 0.521, p = 0.155), and hold-out sets (AUCs: 0.828 VS. 0.571, p = 0.080). The positive values of NRI (0.402, 0.607, 0.424) and IDI (0.158, 0.264, 0.264) in the three sets indicated that the diagnostic performance of Model-Combined improved significantly. The radiomics nomogram fit well in calibration curves (p > 0.05), and decision curve analysis further confirmed the clinical usefulness of the nomogram. Additionally, the contrast (the radiomics feature) of the FCD lesions not only played a crucial role in the classifier but also had a significant correlation (r = -0.319, p < 0.05) with the duration of FCD.

Conclusion: The radiomics nomogram generated by logistic regression model-based multiparametric MRI represents an important advancement in FCD diagnosis and treatment.

Background: The ratio (E/Ea) of mitral Doppler inflow velocity to annular tissue Doppler wave velocity by transthoracic echocardiography and diaphragmatic excursion (DE) by diaphragm ultrasound have been confirmed to predict extubation outcomes. However, few studies focused on the predicting value of E/Ea and DE at different positions during a spontaneous breathing trial (SBT), as well as the effects of △E/Ea and △DE (changes in E/Ea and DE during a SBT).

Methods: This study was a reanalysis of the data of 60 difficult-to-wean patients in a previous study published in 2017. All eligible participants were organized into respiratory failure (RF) group and extubation success (ES) group within 48 h after extubation, or re-intubation (RI) group and non-intubation (NI) group within 1 week after extubation. The risk factors for respiratory failure and re-intubation including E/Ea and △E/Ea, DE and △DE at different positions were analyzed by multivariate logistic regression, respectively. The receiver operating characteristic (ROC) curves of E/Ea (septal, lateral, average) and DE (right, left, average) were compared with each other, respectively.

Results: Of the 60 patients, 29 cases developed respiratory failure within 48 h, and 14 of those cases required re-intubation within 1 week. Multivariate logistic regression showed that E/Ea were all associated with respiratory failure, while only DE (right) and DE (average) after SBT were related to re-intubation. There were no statistic differences among the ROC curves of E/Ea at different positions, nor between the ROC curves of DE. No statistical differences were shown in △E/Ea between RF and ES groups, while △DE (average) was remarkably higher in NI group than that in RI group. However, multivariate logistic regression analysis showed that △DE (average) was not associated with re-intubation.

Conclusions: E/Ea at different positions during a SBT could predict postextubation respiratory failure with no statistical differences among them. Likewise, only DE (right) and DE (average) after SBT might predict re-intubation with no statistical differences between each other.

Background: Due to the increasing incidence of ischaemic cerebrovascular diseases, the accurate assessment of internal carotid artery (ICA) stenosis is crucial for the development of treatment plans. This systematic review and meta-analysis aimed to evaluate the diagnostic value of computed tomography angiography (CTA) for severe ICAstenosis, thereby providing support for clinical decision-making and promoting diagnostic updates.

Methods: The PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang Database, VIP Database for Chinese Technical Periodicals (VIP), and Chinese Biomedical Literature (CBM) electronic databases were searched from inception to March 21, 2024, to identify publicly available research literature on the use of CTA to diagnose severe ICA stenosis. Literature screening, data extraction, and quality assessment were conducted based on the inclusion and exclusion criteria as well as the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) standards. Data analysis was performed using Stata 17.0 and Meta-Disc 1.4 software. The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of the included studies were calculated using Stata 17.0 software, and forest plots and summary receiver operating characteristic (SROC) curves were generated. The area under the curve (AUC) was calculated, and funnel plots were constructed to assess publication bias.

Results: A total of 16 studies with 2368 vascular segments were included. The meta-analysis revealed that the combined sensitivity and specificity of CTA for severe ICA stenosis were 0.93 (95% CI: 0.88 ~ 0.96) and 0.99 (95% CI: 0.96 ~ 1.00), respectively. The combined positive likelihood ratio and negative likelihood ratio were 92.0 (95% CI: 24.2 ~ 349.6) and 0.07 (95% CI: 0.04 ~ 0.13), respectively. The diagnostic odds ratio was 1302 (95% CI: 257 ~ 6606), and the AUC of the SROC curve was 0.98. The Deeks funnel plot suggested no publication bias among the included studies.

Conclusion: CTA demonstrated high sensitivity and specificity for diagnosing severe ICA stenosis. Therefore, this study provided important evidence for the accurate diagnosis and treatment of severe ICA stenosis. However, there was considerable heterogeneity among the included studies, thus indicating the need for additional high-quality prospective studies to confirm the clinical applicability of CTA.

Background: In Tanzania, triphasic abdominal Computed Tomography (CT) is a more accessible and non-invasive alternative for diagnosing esophageal varices, though its accuracy has not been thoroughly evaluated, therefore this study aimed to determine the diagnostic accuracy of triphasic abdominal CT in detecting esophageal varices using esophagogastroduodenoscopy (OGD) as the gold standard among patients with upper gastrointestinal bleeding at Muhimbili National Hospital (MNH).

Methods: This cross-sectional study was conducted at MNH from January 2021 to May 2023. We sampled upper gastrointestinal bleeding patients who underwent both OGD and triphasic abdominal CT using non-probability consecutive sampling. The sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and accuracy of triphasic abdominal CT were assessed against OGD findings.

Results: In a study of 200 participants, esophageal varices were detected in 54% by OGD and 53.5% by CT. We observed 105 true positives, 2 false positives, 90 true negatives, and 3 false negatives. Triphasic abdominal CT demonstrated a sensitivity of 97.2%, specificity of 97.8%, PPV of 98.1%, NPV of 96.8%, and an accuracy of 97.5%. Extraluminal findings included portal venous thrombosis in (22%), splenic collateral (51.5%), ascites (32%), hepatocellular carcinoma (13%), and periportal fibrosis (32%).

Conclusion: Triphasic abdominal Computed Tomography can be used as a reliable and non-invasive alternative modality for diagnosing and screening esophageal varices in resource-limited settings.

Purpose: This study investigated potential use of computed tomography (CT)-based parameters in the lumbar spine as a surrogate for magnetic resonance imaging (MRI)-based findings.

Methods: In this retrospective study, all individuals, who had a lumbar spine CT scan and MRI between 2006 and 2012 were reviewed (n = 198). Disc height (DH) and endplate degeneration (ED) were evaluated between Th12/L1-L5/S1. Statistics consisted of Spearman correlation and univariate/multivariable regression (adjusting for age and gender).

Results: The mean CT-DH increased kranio-caudally (8.04 millimeters (mm) at T12/L1, 9.17 mm at L1/2, 10.59 mm at L2/3, 11.34 mm at L3/4, 11.42 mm at L4/5 and 10.47 mm at L5/S1). MRI-ED was observed in 58 (29%) individuals. CT-DH and MRI-DH had strong to very strong correlations (rho 0.781-0.904, p < .001). MRI-DH showed higher absolute values than CT-DH (mean of 1.76 mm). There was a significant association between CT-DH and MRI-ED at L2/3 (p = .006), L3/4 (p = .002), L4/5 (p < .001) and L5/S1 (p < .001). A calculated cut-off point was set at 11 mm.

Conclusions: In the lumbar spine, there is a correlation between disc height on CT and MRI. This can be useful in trauma and emergency cases, where CT is readily available in the lack of an MRI. In addition, in the middle and lower part of the lumbar spine, loss of disc height on CT scans is associated with more pronounced endplate degeneration on MRIs. If the disc height on CT scans is lower than 11 mm, endplate degeneration on MRIs is likely more pronounced.

Level and design: Level III, a retrospective study.