BMC Medical Imaging最新文献

Objective: This study explored the value of stomach ultrasound reporting and data system (Su-RADS) and two-dimensional shear wave elastography (2D-SWE) in the diagnosis of benign and malignant lesions of the gastric wall, evaluating the feasibility of combining the two methods for the diagnosis of gastric wall lesions.

Methods: 113 patients with gastric wall lesions were examined after oral gastric ultrasound contrast agent, and the grades of the gastric wall lesions were classified according to Su-RADS. Moreover, 2D-SWE was performed to measure the E value of the lesions. ROC curves were constructed to evaluate the diagnostic efficacy of Su-RADS, 2D-SWE and their combination for gastric wall lesions.

Results: The cutoff values for Emean and Emax were 8.01 kPa and 11.08 kPa, respectively. The sensitivity and specificity of 2D-SWE were 70.59%, 93.67% and 85.69%, 88.61%, respectively. The diagnostic sensitivity and specificity of Su-RADS were 91.18% and 82.28%, respectively. The AUC of combination of two methods was 0.951, which was greater than that of Su-RADS (0.940) or 2D-SWE alone (0.853, 0.903), and the sensitivity and specificity were 82.35% and 94.94%. The sensitivity and specificity of the combination of the two methods for the diagnosis of malignant gastric lesions were 82.35% and 94.94%, respectively. The AUC was 0.951, and the Youden index was 0.8064. The DeLong test was used to determine the AUC between the combination of two methods and 2D-SWE was P < 0.05.

Conclusion: Compared with Su-RADS or 2D-SWE alone, the combination of the two methods is more effective at diagnosing of gastric wall.And improved the specificity in the diagnosis of gastric wall lesions.

Background: The longitudinal vaginal septum and oblique vaginal septum are female müllerian duct anomalies that are relatively less diagnosed but severely fertility-threatening in clinical practice. Ultrasound imaging is commonly used to examine the two vaginal malformations, but in fact it's difficult to make an accurate differential diagnosis. This study is intended to assess the performance of multiple deep learning models based on ultrasonographic images for distinguishing longitudinal vaginal septum and oblique vaginal septum.

Methods: The cases and ultrasound images of longitudinal vaginal septum and oblique vaginal septum were collected. Two convolutional neural network (CNN)-based models (ResNet50 and ConvNeXt-B) and one base resolution variant of vision transformer (ViT)-based neural network (ViT-B/16) were selected to construct ultrasonographic classification models. The receiver operating curve analysis and four indicators including accuracy, sensitivity, specificity and area under the curve (AUC) were used to compare the diagnostic performance of deep learning models.

Results: A total of 70 cases with 426 ultrasound images were included for deep learning models construction using 5-fold cross-validation. Convolutional neural network-based models (ResNet50 and ConvNeXt-B) presented significantly better case-level discriminative efficacy with accuracy of 0.842 (variance, 0.004, 95%CI, [0.639-0.997]) and 0.897 (variance, 0.004, [95%CI, 0.734-1.000]), specificity of 0.709 (variance, 0.041, [95%CI, 0.505-0.905]) and 0.811 (variance, 0.017, [95%CI, 0.622-0.979]), and AUC of 0.842 (variance, 0.004, [95%CI, 0.639-0.997]) and 0.897 (variance, 0.004, [95%CI, 0.734-1.000]) than transformer-based model (ViT-B/16) with its accuracy of 0.668 (variance, 0.014, [95%CI, 0.407-0.920]), specificity of 0.572 (variance, 0.024, [95%CI, 0.304-0.831]) and AUC of 0.681 (variance, 0.030, [95%CI, 0.434-0.908]). There was no significance of AUC between ConvNeXt-B and ResNet50 (P = 0.841).

Conclusions: Convolutional neural network-based model (ConvNeXt-B) shows promising capability of discriminating longitudinal and oblique vaginal septa ultrasound images and is expected to be introduced to clinical ultrasonographic diagnostic system.

Aim: To assess the value of preoperatively contrast-enhanced MRI and clinical characteristics for identification of SMAD4-mutated pancreatic ductal adenocarcinoma (PDAC) patients.

Materials and methods: This retrospective study included patients with surgically confirmed PDAC from January 2016 to December 2022. Based on immunostaining results indicating the mutation of SMAD4, the enrolled participants were grouped into SMAD4-mutated PDAC and non-SMAD4-mutated PDAC. Contrast-enhanced MRI findings, clinical-pathological characteristics, and prognosis were recorded and reviewed. The pathological findings and clinical prognosis were compared between the two groups. Uni- and multivariable logistic regression analyses were further performed to determine the radiological and clinical predictive factors for the mutation of SMAD4.

Results: In total, 428 PDAC patients were enrolled and analyzed, who were grouped as SMAD4-mutated PDAC (n = 224) and non-SMAD4-mutated PDAC (n = 204). SMAD4-mutated PDAC demonstrated higher frequency of pathological fatty infiltration (83.4% vs. 74.2%, P = 0.016), peripheral nerve infiltration (84.4% vs. 76.5%, P = 0.039). and higher recurrence rates (43.6% vs. 58.9%, P = 0.045) than non-SMAD4-mutated PDAC. The 3-year recurrence-free survival rates were worse for SMAD4-mutated PDAC (28.7% vs. 39.1%). In multivariable logistic regression analyses, CA19-9 > 100 U/mL (odds ratio [OR] = 1.519, P = 0.041), CBD dilation (OR = 1.564, P = 0.036), and rim enhancement (OR = 1.631, P = 0.025) were independent predictive factors.

Conclusion: Rim enhancement, CBD dilation on contrast-enhanced MRI and higher CA19-9 level are promising radiological and clinical factors for identifying SMAD4-mutated PDAC.

Introduction: Gadolinium-based T1-weighted MRI sequence is the gold standard for the detection of active multiple sclerosis (MS) lesions. The performance of machine learning (ML) and deep learning (DL) models in the classification of active and non-active MS lesions from the T2-weighted MRI images has been investigated in this study.

Methods: 107 Features of 75 active and 100 non-active MS lesions were extracted by using SegmentEditor and Radiomics modules of 3D slicer software. Sixteen ML and one sequential DL models were created using the 5-fold cross-validation method and each model with its special optimized parameters trained using the training-validation datasets. Models' performances in test data set were evaluated by metric parameters of accuracy, precision, sensitivity, specificity, AUC, and F1 score.

Results: The sequential DL model achieved the highest AUC of 95.60% on the test dataset, demonstrating its superior ability to distinguish between active and non-active plaques. Among traditional ML models, the Hybrid Gradient Boosting Classifier (HGBC) demonstrated a commendable test AUC of 86.75%, while the Gradient Boosting Classifier (GBC) excelled in cross-validation with an AUC of 87.92%.

Conclusion: The performance of sixteen ML and one sequential DL models in the classification of active and non-active MS lesions was evaluated. The results of the study highlight the effectiveness of sequential DL approach and ensemble methods in achieving robust predictive performance, underscoring their potential applications in classifying MS plaques.

Background: Diffusion-weighted imaging (DWI) can be used for quantitative tumor assessment. DWI with different models may show different aspects of tissue characteristics.

Objective: To investigate the diagnostic performance of parameters derived from monoexponential, biexponential, stretched exponential magnetic resonance diffusion weighted imaging (DWI) and diffusion kurtosis imaging (DKI) in differentiating benign from malignant solitary pulmonary lesions (SPLs).

Method: Forty-four SPL subjects were selected according to the inclusion criteria. All patients underwent conventional and multi‑b DWI sequences. Monoexponential DWI and DKI model were fitted using least square method. Levenberg-Marquardt nonlinear fitting biexponential and stretched exponential DWI. Region of interests (ROIs) were described manually. Parameters between benign and malignant SPLs were compared using independent sample t test or the Mann-Whitney U test. Receiver operating characteristic (ROC) curves analysis was used to investigate the diagnostic performance of different DWI parameters. Correlation between all parameters were evaluated by using Spearman correlation.

Result: ADC, ADC_slow, α, DDC and D_app values were significantly lower in malignant SPL than in benign SPL (P < 0.001). K_app was significantly higher in malignant SPL than in benign SPL (P < 0.001). Among all subjects, ADC_slow was significantly lower than ADC (P < 0.05), while DDC and D_app were significantly higher than ADC (P < 0.05). When observing the ROC curves for distinguishing benign and malignant SPL, the AUC values of ADC, ADC_slow, DDC, D_app, and K_app were 0.904, 0.815, 0.942, 0.93, and 0.815, respectively. The DDC value has the highest area under ROC curve value. DeLong analysis showed no statistically significant difference in the area under ADC, DDC, and D_app curves. There were strong correlations among ADC, ADC_slow, ADC_fast, f, α, DDC, D_app, and K_app (P < 0.001).

Conclusion: Multi‑b DWI is a promising method for differentiating benign from malignant SPLs with high diagnostic accuracy. In addition, the DDC derived from stretched‑exponential model is the most promising DWI parameter for the differentiation of benign and malignant SPLs.

Trail registration: This study was a clinical trail study, with study protocol published at ClinicalTrails. Retrospectively registered number ChiCTR2300074258, date of registration 02/08/2023.

Objective: Identifying prognostic markers for clinical outcomes is crucial in selecting appropriate treatment options for patients with radioiodine-refractory (RAI-R) differentiated thyroid carcinoma (DTC). The aim of this study was to investigate the prognostic value of clinico-pathological features and semiquantitative [¹⁸F]FDG PET/CT metabolic parameters in predicting progression-free survival (PFS) in DTC patients with RAI-R.

Patients and methods: This prospective cohort study included 110 consecutive RAI-R DTC patients who were referred for [¹⁸F]FDG PET/CT imaging. The lesion standard uptake values (SUV)s, including SUVmax, SUVmean, SULpeak as well astotal metabolic tumor volume (tMTV)and total lesion glycolysis (tTLG) were measured. Disease progression was assessed using the Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 and/or Positron Emission Tomography Response Criteria in Solid Tumors (PERCIST) 1.0. PFS curves were plotted using Kaplan-Meier analysis. Univariate and multivariate Cox regression analyses were performed to identify the prognostic factors for PFS.

Results: [¹⁸F]FDG PET/CT metabolic parameters demonstrate predictive value for PFS in RAI-R DTC patients, with sensitivity ranging from 70.7% to 81% and specificity from 75% to 92.3% (p < 0.001). PFS was significantly worse in patients with SUVmax > 6.39 g/ml, SUVmean > 3.68 g/ml, SULpeak > 3.14 g/ml, tTLG > 4.23 g/ml × cm³, and tMTV > 1.24 cm³. Clinico-pathological factors including age > 55, aggressive variant and follicular histological subtype, extra-thyroidal extension of the primary tumor, stage III - IV disease at initial DTC diagnosis, distant metastases detected on [¹⁸F]FDG PET/CT, and metabolic parameters of [¹⁸F]FDG PET/CT associated with PFS in univariate analysis (p < 0.01). In multivariate analysis, extra-thyroidal extension (HR: 2.25; 95% CI: 1.22 - 4.16; p = 0.01), distant metastases on [¹⁸F]FDG PET/CT (HR: 2.98; 95%CI: 1.62 - 5.5; p < 0.001), and tMTV > 1.24 cm³ (HR: 4.17; 95% CI: 2.02 - 8.6; p < 0.001), were independent prognostic factors for PFS.

Conclusions: In addition to classic clinico-pathological factors, the semiquantitative [¹⁸F]FDG PET/CT metabolic parameters can be utilized for dynamic risk stratification for progression in RAI-R DTC patients. Furthermore, extra-thyroidal extension of the primary tumor, distant metastases, and tMTV > 1.24 cm³ are independent prognostic factors for PFS.

Purpose: Exploring the computed tomography (CT), magnetic resonance imaging (MRI), and fluorodeoxyglucose positron emission tomography (FDG-PET)/CT Multimodal Imaging Characteristics of Desmoplastic Small Round Cell Tumor (DSRCT) to enhance the diagnostic proficiency of this condition.

Methods: A retrospective analysis was performed on clinical data and multimodal imaging manifestations (CT, MRI, FDG-PET/CT) of eight cases of DSRCT. These findings were systematically compared with pathological results to succinctly summarize imaging features and elucidate their associations with both clinical and pathological characteristics.

Results: All eight cases within this cohort exhibited abdominal-pelvic masses, comprising six solitary masses and two instances of multiple nodules, except for one case located in the left kidney, the remaining cases lacked a clear organ source. On plain images, seven cases exhibited patchy areas of low density within the masses, four cases showed calcification within the masses. Post-contrast imaging displayed mild-to-moderate, uneven enhancement. Larger masses displayed patchy areas without significant enhancement at the center. In the four MRI examinations, T1-weighted images exhibited uneven, low signal intensity, while T2-weighted images demonstrated uneven high signal intensity. Imaging unveiled four cases of liver metastasis, four cases of ascites, seven cases of lymph node metastasis, three cases of diffuse peritoneal thickening, and one case involving left ureter invasion with obstruction. In the FDG-PET/CT examinations of seven cases, multiple abnormal FDG accumulations were observed in the abdominal cavity, retroperitoneum, pelvis, and liver. One postoperative case revealed a new metastatic focus near the colonic hepatic region. The range of maximum standardized uptake values (SUV_max) for all lesions are 6.62-11.15.

Conclusions: DSRCT is commonly seen in young men, and the imaging results are mostly multiple lesions with no clear organ source. Other common findings include intratumoral calcification, liver metastasis, ascites, peritoneal metastasis, and retroperitoneal lymph node enlargement. The combined use of CT, MRI and FDG-PET/CT can improve the diagnostic accuracy and treatment evaluation of DSRCT. However, it is imperative to underscore that the definitive diagnosis remains contingent upon pathological examination.

Purpose: The segmentation of target volume and organs at risk (OAR) was a significant part of radiotherapy. Specifically, determining the location and scale of the esophagus in simulated computed tomography images was difficult and time-consuming primarily due to its complex structure and low contrast with the surrounding tissues. In this study, an Enhanced Cross-stage-attention U-Net was proposed to solve the segmentation problem for the esophageal gross tumor volume (GTV) and clinical tumor volume (CTV) in CT images.

Methods: First, a module based on principal component analysis theory was constructed to pre-extract the features of the input image. Then, a cross-stage based feature fusion model was designed to replace the skip concatenation of original UNet, which was composed of Wide Range Attention unit, Small-kernel Local Attention unit, and Inverted Bottleneck unit. WRA was employed to capture global attention, whose large convolution kernel was further decomposed to simplify the calculation. SLA was used to complement the local attention to WRA. IBN was structed to fuse the extracted features, where a global frequency response layer was built to redistribute the frequency response of the fused feature maps.

Results: The proposed method was compared with relevant published esophageal segmentation methods. The prediction of the proposed network was MSD = 2.83(1.62, 4.76)mm, HD = 11.79 ± 6.02 mm, DC = 72.45 ± 19.18% in GTV; MSD = 5.26(2.18, 8.82)mm, HD = 16.22 ± 10.01 mm, DC = 71.06 ± 17.72% in CTV.

Conclusion: The reconstruction of the skip concatenation in UNet showed an improvement of performance for esophageal segmentation. The results showed the proposed network had better effect on esophageal GTV and CTV segmentation.

Background: Alzheimer's Disease is a neurodegenerative condition leading to irreversible and progressive brain damage, with possible features such as structural atrophy. Effective precision diagnosis is crucial for slowing disease progression and reducing the incidence rate and morbidity. Traditional computer-aided diagnostic methods using structural MRI data often focus on capturing such features but face challenges, like overfitting with 3D image analysis and insufficient feature capture with 2D slices, potentially missing multi-planar information, and the complementary nature of features across different orientations.

Methods: The study introduces MHAGuideNet, a classification method incorporating a guidance network utilizing multi-head attention. The model utilizes a pre-trained 3D convolutional neural network to direct the feature extraction of multi-planar 2D slices, specifically targeting the detection of features like structural atrophy. Additionally, a hybrid 2D slice-level network combining 2D CNN and 2D Swin Transformer is employed to capture the interrelations between the atrophy in different brain structures associated with Alzheimer's Disease.

Results: The proposed MHAGuideNet is tested using two datasets: the ADNI and OASIS datasets. The model achieves an accuracy of 97.58%, specificity of 99.89%, F1 score of 93.98%, and AUC of 99.31% on the ADNI test dataset, demonstrating superior performance in distinguishing between Alzheimer's Disease and cognitively normal subjects. Furthermore, testing on the independent OASIA test dataset yields an accuracy of 96.02%, demonstrating the model's robust performance across different datasets.

Conclusion: MHAGuideNet shows great promise as an effective tool for the computer-aided diagnosis of Alzheimer's Disease. Within the guidance of information from the 3D pre-trained CNN, the ability to leverage multi-planar information and capture subtle brain changes, including the interrelations between different structural atrophies, underscores its potential for clinical application.