BMC Medical Imaging最新文献

Objective: This study aims to evaluate the efficacy of two free-breathing magnetic resonance imaging (MRI) sequences-spiral ultrashort echo time (spiral UTE) and radial volumetric interpolated breath-hold examination (radial VIBE).

Methods: Patients were prospectively enrolled between February 2021 and September 2022. All participants underwent both 3T MRI scanning, utilizing the radial VIBE sequence and spiral UTE sequence, as well as standard chest CT imaging. The CT and MRI examinations were conducted within a 7-day interval. Two radiologists assessed the image quality using a visual 5-point ordinal Likert scale, and pulmonary nodules identified on MRI were evaluated through comparison with CT as the reference standard.

Results: A total of 52 patients participated in this study, during which 82 pulmonary nodules were detected via CT imaging. The image quality scores for depicting pulmonary vasculature and airways using the spiral UTE sequence (4.61 ± 0.63; 4.76 ± 0.48) were significantly higher than those for the radial VIBE sequence (4.27 ± 0.87; 4.14 ± 0.82) (P < 0.05). However, for nodules smaller than 6 mm, the detection rate for the spiral UTE sequence (82.61%) was notably higher than that of the radial VIBE sequence (39.13%) (P < 0.05). Additionally, the detection rate for ground-glass nodules was higher with the spiral UTE sequence (75.00%) compared to the radial VIBE sequence (17.86%) (P < 0.05). The Pearson correlation coefficient (r) between radial VIBE and CT was 0.99 (P < 0.001), and the Pearson correlation coefficient (r) between spiral UTE and CT was also 0.99 (P < 0.001).

Conclusion: The spiral UTE sequence demonstrates superior capability in visualizing ground glass nodules, blood vessels, and airways. In cases where patients present with ground glass nodules, the spiral UTE sequence is the preferred choice. Conversely, when the nodules are solid or partially solid, it is advisable to opt for radial VIBE sequences that are time-efficient and exhibit fewer artifacts.

Purpose: We used knowledge discovery from radiomics of T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (T1C) for assessing relapse risk in patients with high-grade meningiomas (HGMs).

Methods: 279 features were extracted from each ROI including 9 histogram features, 220 Gy-level co-occurrence matrix features, 20 Gy-level run-length matrix features, 5 auto-regressive model features, 20 wavelets transform features and 5 absolute gradient statistics features. The datasets were randomly divided into two groups, the training set (~ 70%) and the test set (~ 30%). Combinations of data preprocessing methods, including normalization (Min-Max, Z-score, Mean), dimensionality reduction (Pearson Correlation Coefficients (PCC)), feature selector (max-Number, cluster) and ten-fold cross-validation were analyzed for their prediction performance. Kaplan-Meier curve, Cox proportional hazards regression model were used and concordance index (C-index), integrated Brier score (IBS) were selected. Model performance was assessed using the C-index.

Results: WHO grade, age, gender, histogram (Mean, Perc.90%, Perc.99%), Gray-level co-occurrence matrix (S(3, -3)DifVarnc, S(5, 5)Correlat, S(1, 0)SumEntrp, S(2, -2)InvDfMom), Teta1, WavEnLL_s-2 and GrVariance were identified as the significant recurrence factors. The pipeline using Mean_PCC_Cluster_10 of T1C yielded the highest efficiency with an IBS of 0.170, 0.188, 0.208 and C-index of 0.709, 0.705, 0.602 in the train, test and validation sets, respectively. The pipeline using MinMax_PCC_Cluster_19 of T2WI yielded the highest efficiency with an IBS of 0.189, 0.175, 0.185 and C-index of 0.783, 0.66, 0.649 in the train, test and validation sets. The pipeline using MinMax_PCC_Cluster_13 of T2WI + T1C yielded the highest efficiency with an IBS of 0.152, 0.164, 0.191 and C-index of 0.701, 0.656, 0.593 in the train, test and validation sets, respectively.

Conclusion: Knowledge discovery from MRI radiomic features can slightly help predict recurrence risk in HGMs. T2WI or T1C yielded better efficiency than T2WI + T1C. The parameters with the best power were Mean, Perc.99%, WavEnLL_s-2, Teta1 and GrVariance.

Problem: Breast cancer is a leading cause of death among women, and early detection is crucial for improving survival rates. The manual breast cancer diagnosis utilizes more time and is subjective. Also, the previous CAD models mostly depend on manmade visual details that are complex to generalize across ultrasound images utilizing distinct techniques. Distinct imaging tools have been utilized in previous works such as mammography and MRI. However, these imaging tools are costly and less portable than ultrasound imaging. Also, ultrasound imaging is a non-invasive method commonly used for breast cancer screening. Hence, the paper presents a novel deep learning model, BCDNet, for classifying breast tumors as benign or malignant using ultrasound images.

Aim: The primary aim of the study is to design an effective breast cancer diagnosis model that can accurately classify tumors in their early stages, thus reducing mortality rates. The model aims to optimize the weight and parameters using the RPAOSM-ESO algorithm to enhance accuracy and minimize false negative rates.

Methods: The BCDNet model utilizes transfer learning from a pre-trained VGG16 network for feature extraction and employs an AHDNAM classification approach, which includes ASPP, DTCN, 1DCNN, and an attention mechanism. The RPAOSM-ESO algorithm is used to fine-tune the weights and parameters.

Results: The RPAOSM-ESO-BCDNet-based breast cancer diagnosis model provided 94.5 accuracy rates. This value is relatively higher than the previous models such as DTCN (88.2), 1DCNN (89.6), MobileNet (91.3), and ASPP-DTC-1DCNN-AM (93.8). Hence, it is guaranteed that the designed RPAOSM-ESO-BCDNet produces relatively accurate solutions for the classification than the previous models.

Conclusion: The BCDNet model, with its sophisticated feature extraction and classification techniques optimized by the RPAOSM-ESO algorithm, shows promise in accurately classifying breast tumors using ultrasound images. The study suggests that the model could be a valuable tool in the early detection of breast cancer, potentially saving lives and reducing the burden on healthcare systems.

Background: Benign and malignant breast tumors differ in their microvasculature morphology and distribution. Histologic biomarkers of malignant breast tumors are also correlated with the microvasculature. There is a lack of imaging technology for evaluating the microvasculature. Ultrasound localization microscopy (ULM) can provide detailed microvascular architecture at super-resolution. The objective of this trial is to explore the role of ULM in distinguishing benign from malignant breast tumors and to explore the correlations between ULM qualitative and quantitative parameters and histologic biomarkers in malignant breast tumors.

Methods/design: This prospective and multicenter study will include 83 patients with breast tumors that will undergo ULM. 55 patients will be assigned to the malignant group, and 28 patients will be assigned to the benign group. The primary outcome is the differences in the qualitative parameters (microvasculature morphology, distribution, and flow direction) between benign and malignant breast tumors on ULM. Secondary outcomes include (1) differences in the quantitative parameters (microvasculature density, tortuosity, diameter, and flow velocity) between benign and malignant breast tumors based on ULM; (2) diagnostic performance of the qualitative parameters in distinguishing benign and malignant breast tumors; (3) diagnostic performance of the quantitative parameters in distinguishing benign and malignant breast tumors; (4) relationships between the qualitative parameters and histologic biomarkers in malignant breast tumors; (5) relationships between the quantitative parameters and histologic biomarkers in malignant breast tumors; and (6) the evaluation of inter-reader and intra-reader reproducibility.

Discussion: Detecting vascularity in breast tumors is of great significance to differentiate benign from malignant tumors and to predict histologic biomarkers. These histologic biomarkers, such as ER, PR, HER2 and Ki67, are closely related to prognosis evaluation. This trial will provide maximum information about the microvasculature of breast tumors and thereby will help with the formulation of subsequent differential diagnosis and the prediction of histologic biomarkers.

Trial registration number/date: Chinese Clinical Trial Registry ChiCTR2100048361/6th/July/2021. This study is a part of that clinical trial.

Background: US tools to quantify hepatic steatosis have recently been made clinically available by different manufacturers, but comparative data on their consistency are lacking.

Objective: US tools to quantify hepatic steatosis have recently been made clinically available by different manufacturers, but comparative data on their consistency are lacking. The aim of our study was to compare the diagnostic consistency for evaluating hepatic steatosis by two different US techniques, hepatorenal index by B-mode Ratio and attenuation coefficient by attenuation imaging (ATI).

Methods: Patients with suspicion or previously diagnosed of metabolic dysfunction-associated steatotic liver disease (MASLD) who attended fatty liver consulting room from June 2023 to September 2023 were prospectively recruited. Patients underwent two different US techniques of B-mode Ratio and ATI, and laboratory test were collected. According to previously proposed cut-off values, B-mode Ratio ≥ 1.22, 1.42, 1.54, and ATI ≥ 0.62, 0.70, and 0.78 dB/cm/MH were used for assessing of mild, moderate, and severe hepatic steatosis, respectively. Kappa consistency test was used to evaluate the consistency of hepatic steatosis.

Results: A total of 62 patients were enrolled, including 44 males (71.0%) with an age of (41 ± 13) years and a body mass index of (27.0 ± 3.5) kg/m². In the hyperlipidemia group, the B-mode Ratio and ATI were significantly higher than those in the non-hyperlipidemia group, with values of 1.68 ± 0.39 vs. 1.28 ± 0.35 (p = 0.001) and 0.74 ± 0.12 dB/cm/MH vs. 0.64 ± 0.11 dB/cm/MH (p = 0.005), respectively. The correlation coefficient between B-mode Ratio and ATI was 0.732 (p < 0.001). Using B-mode Ratio and ATI as diagnostic criteria for MASLD, the proportion of patients with MASLD was 79% and 82%, respectively. The Kappa coefficient for assessing MASLD was 0.90 (p < 0.001). Furthermore, these two different US techniques were used for grading hepatic steatosis, with no, mild, moderate, and severe steatosis accounting for 21%, 18%, 13%, and 48%, as well as 18%, 29%, 22%, and 31%, respectively. The linear weighted Kappa coefficient for staging hepatic steatosis was 0.78 (95% confidence interval: 0.68-0.87, p < 0.001).

Conclusion: The non-invasive methods of two different US techniques based on B-mode Ratio and ATI have good consistency for evaluating hepatic steatosis, and can be used for large-scale community screening.

Background: Monitoring fibrosis in patients with chronic liver disease (CLD) is an important management strategy. We have already reported a novel stacked microvascular imaging (SMVI) technique and an examiner scoring evaluation method to improve fibrosis assessment accuracy and demonstrate its high sensitivity. In the present study, we analyzed the effectiveness and objectivity of SMVI in diagnosing the liver fibrosis stage based on artificial intelligence (AI).

Methods: This single-center, cross-sectional study included 517 patients with CLD who underwent ultrasonography and liver stiffness testing between August 2019 and October 2022. A convolutional neural network model was constructed to evaluate the degree of liver fibrosis from stacked microvascular images generated by accumulating high-sensitivity Doppler (i.e., high-definition color) images from these patients. In contrast, as a method of judgment by the human eye, we focused on three hallmarks of intrahepatic microvessel morphological changes in the stacked microvascular images: narrowing, caliber irregularity, and tortuosity. The degree of liver fibrosis was classified into five stages according to etiology based on liver stiffness measurement: F0-1Low (< 5.0 kPa), F0-1High (≥ 5.0 kPa), F2, F3, and F4.

Results: The AI classification accuracy was 53.8% for a 5-class classification, 66.3% for a 3-class classification (F0-1Low vs. F0-1High vs. F2-4), and 83.8% for a 2-class classification (F0-1 vs. F2-4). The diagnostic accuracy for ≥ F2 was 81.6% in the examiner's score assessment, compared with 83.8% in AI assessment, indicating that AI achieved higher diagnostic accuracy. Similarly, AI demonstrated higher sensitivity and specificity of 84.2% and 83.5%, respectively. Comparing human judgement with AI judgement, the AI analysis was a superior model with a higher F1 score in the 2-class classification.

Conclusions: In detecting significant fibrosis (≥ F2) using the SMVI method, AI-based assessments are more accurate than human judgement; moreover, AI-based SMVI analysis eliminating human subjectivity bias and determining patients with objective fibrosis development is considered an important improvement.

Objective: This study was to develop a multi-parametric MRI radiomics model to predict preoperative Ki-67 status.

Materials and methods: A total of 120 patients with pathologically confirmed breast cancer were retrospectively enrolled and randomly divided into a training set (n = 84) and a validation set (n = 36). Radiomic features were derived from both the intratumoral and peritumoral regions, extending 5 mm from the tumor boundary, using magnetic resonance imaging (MRI). The MRI sequences employed included T2-weighted imaging (T2WI), dynamic contrast-enhanced (DCE) imaging, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps. The T-test and the Least Absolute Shrinkage and Selection Operator Cross-Validation (LASSO CV) were conducted for feature selection. Model_intra, model_peri, model_intra+peri were established by eleven supervised machine learning (ML) algorithms to predict the expression status of Ki-67 in breast cancer and were verified by the validation groups. The model's performance was evaluated by employing metrics such as the area under the curve (AUC), accuracy, sensitivity, and specificity.

Results: The features of intratumor, peritumor, intratumor + peritumor were extracted 851, 851 and 1702 samples respectively, 14, 23 and 35 features were selected by LASSO. ML algorithms based on model_intra and model_peri consistently yield AUCs that are below 80% in the validation set. Hower, Logistic regression (LR) and linear discriminant analysis (LDA) based on model_intra+peri demonstrated significant advantages over other algorithms, achieving AUCs of 0.92 and 0.98, accuracies of 0.94 and 0.97, sensitivities of 1 and 0.96, and specificities of 0.85 and 1 respectively in the validation set.

Conclusion: The integrated intra- and peritumoral radiomics model, developed using multiparametric MRI data and machine learning classifiers, exhibits significant predictive power for Ki-67 expression levels. This model could facilitate personalized clinical treatment strategies for individuals diagnosed with breast cancer (BC).

Clinical trial number: Not applicable.

Objective: The study aimed to evaluate the application value of computed tomography (CT) three-dimensional (3D) reconstruction technology in identifying benign and malignant lung nodules and characterizing the distribution of the nodules.

Methods: CT 3D reconstruction was performed for lung nodules. Pathological results were used as the gold standard to compare the detection rates of various lung nodule signs between conventional chest CT scanning and CT 3D reconstruction techniques. Additionally, the differences in mean diffusion coefficient values and partial anisotropy index values between male and female patients were analyzed.

Results: Pathologic confirmation identified 30 patients with benign lesions and 45 patients with malignant lesions. CT 3D reconstruction demonstrated higher diagnostic accuracy for lung nodule imaging signs compared to conventional CT scanning (P < 0.05). The mean diffusion coefficient values and partial anisotropy index values were lower in female patients compared to male patients in the lung nodule lesion area, lung perinodular edema area, and normal lung tissue (P < 0.05). Conventional CT scanning showed a benign accuracy rate of 63.33% and a malignant accuracy rate of 60.00%, whereas CT 3D imaging achieved a benign and malignant accuracy rate of 86.67% for both. The accuracy rates for CT 3D imaging were significantly higher than those for conventional CT scanning (P < 0.05).

Conclusion: CT 3D imaging technology demonstrates high diagnostic accuracy in differentiating benign from malignant lung nodules.

Invasive breast cancer diagnosis and treatment planning require an accurate assessment of human epidermal growth factor receptor 2 (HER2) expression levels. While immunohistochemical techniques (IHC) are the gold standard for HER2 evaluation, their implementation can be resource-intensive and costly. To reduce these obstacles and expedite the procedure, we present an efficient deep-learning model that generates high-quality IHC-stained images directly from Hematoxylin and Eosin (H&E) stained images. We propose a new IHC-GAN that enhances the Pix2PixHD model into a dual generator module, improving its performance and simplifying its structure. Furthermore, to strengthen feature extraction for HE-stained image classification, we integrate MobileNetV3 as the backbone network. The extracted features are then merged with those generated by the generator to improve overall performance. Moreover, the decoder's performance is enhanced by providing the related features from the classified labels by incorporating the adaptive instance normalization technique. The proposed IHC-GAN was trained and validated on a comprehensive dataset comprising 4,870 registered image pairs, encompassing a spectrum of HER2 expression levels. Our findings demonstrate promising results in translating H&E images to IHC-equivalent representations, offering a potential solution to reduce the costs associated with traditional HER2 assessment methods. We extensively validate our model and the current dataset. We compare it with state-of-the-art techniques, achieving high performance using different evaluation metrics, showing 0.0927 FID, 22.87 PSNR, and 0.3735 SSIM. The proposed approach exhibits significant enhancements over current GAN models, including an 88% reduction in Frechet Inception Distance (FID), a 4% enhancement in Learned Perceptual Image Patch Similarity (LPIPS), a 10% increase in Peak Signal-to-Noise Ratio (PSNR), and a 45% reduction in Mean Squared Error (MSE). This advancement holds significant potential for enhancing efficiency, reducing manpower requirements, and facilitating timely treatment decisions in breast cancer care.