Identification of key pathways and genes in the progression of silicosis based on WGCNA.

Silicosis, induced by inhaling silica particles in workplaces, is one of the most common occupational diseases. The prognosis of silicosis and its consequent fibrosis is extremely poor due to limited treatment modalities and lack of understanding of the disease mechanisms. In this study, a Wistar rat model for silicosis fibrosis was established by intratracheal instillation of silica (0, 50, 100 and 200 mg/mL, 1 mL) with the evidence of Hematoxylin and Eosin (HE) and Masson staining and the expressions of inflammatory and fibrotic proteins of rats' lung tissues. RNA of lung tissues of rats exposed to 200 mg/mL silica particles and normal saline for 14 d and 28 d was extracted and sequenced to detect differentially expressed genes (DEGs) and to identify silicosis fibrosis-associated modules and hub genes by Weighted gene co-expression network analysis (WGCNA). Predictions of gene functions and signaling pathways were conducted using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. In this study, it has been demonstrated the promising role of the Hippo signaling pathway in silicosis fibrosis, which will be conducive to elucidating the specific mechanism of pulmonary fibrosis induced by silica and to determining molecular initiating event (MIE) and adverse outcome pathway (AOP) of silicosis fibrosis.

Breast Tumor Classification Based on a Computerized Breast Imaging Reporting and Data System Feature System.

OBJECTIVES: This work focused on extracting novel and validated digital high-throughput features to present a detailed and comprehensive description of the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) with the goal of improving the accuracy of ultrasound breast cancer diagnosis. METHODS: First, the phase congruency approach was used to segment the tumors automatically. Second, high-throughput features were designed and extracted on the basis of each BI-RADS category. Then features were selected based on the basis of a Student t test and genetic algorithm. Finally, the AdaBoost classifier was used to differentiate benign tumors from malignant ones. RESULTS: Experiments were conducted on a database of 138 pathologically proven breast tumors. The system was compared with 6 state-of-art BI-RADS feature extraction methods. By using leave-one-out cross-validation, our system achieved a highest overall accuracy of 93.48%, a sensitivity of 94.20%, a specificity of 92.75%, and an area under the receiver operating characteristic curve of 95.67%, respectively, which were superior to those of other methods. CONCLUSIONS: The experiments demonstrated that our computerized BI-RADS feature system was capable of helping radiologists detect breast cancers more accurately and provided more guidance for final decisions.

CHeart: A Conditional Spatio-Temporal Generative Model for Cardiac Anatomy.

Two key questions in cardiac image analysis are to assess the anatomy and motion of the heart from images; and to understand how they are associated with non-imaging clinical factors such as gender, age and diseases. While the first question can often be addressed by image segmentation and motion tracking algorithms, our capability to model and answer the second question is still limited. In this work, we propose a novel conditional generative model to describe the 4D spatio-temporal anatomy of the heart and its interaction with non-imaging clinical factors. The clinical factors are integrated as the conditions of the generative modelling, which allows us to investigate how these factors influence the cardiac anatomy. We evaluate the model performance in mainly two tasks, anatomical sequence completion and sequence generation. The model achieves high performance in anatomical sequence completion, comparable to or outperforming other state-of-the-art generative models. In terms of sequence generation, given clinical conditions, the model can generate realistic synthetic 4D sequential anatomies that share similar distributions with the real data. The code and the trained generative model are available at https://github.com/MengyunQ/CHeart.

Chronic Lead Exposure in Adult Mice: Associations with miR-671/CDR1as Regulation, NF-κB Signaling, and Alzheimer's Disease-like Pathology.

Long-term exposure to lead (Pb) can result in chronic damage to the body through accumulation in the central nervous system (CNS) leading to neurodegenerative diseases, such as Alzheimer's disease (AD). This study delves into the intricate role of miR-671/CDR1as regulation in the etiology of AD-like lesions triggered by chronic Pb exposure in adult mice. To emulate the chronic effects of Pb, we established a rodent model spanning 10 months of controlled Pb administration, dividing 52 C57BL/6J mice into groups receiving varying concentrations of Pb (1, 2, or 4 g/L) alongside an unexposed control. Blood Pb levels were monitored using serum samples to ensure accurate dosing and to correlate with observed toxicological outcomes. Utilizing the Morris water maze, a robust behavioral assay for assessing cognitive functions, we documented a dose-dependent decline in learning and memory capabilities among the Pb-exposed mice. Histopathological examination of the hippocampal tissue revealed tell-tale signs of AD-like neurodegeneration, characterized by the accumulation of amyloid plaques and neurofibrillary tangles. At the molecular level, a significant upregulation of AD-associated genes, namely amyloid precursor protein (APP), ß-secretase 1 (BACE1), and tau, was observed in the hippocampal tissue of Pb-exposed mice. This was accompanied by a corresponding surge in the protein levels of APP, BACE1, amyloid-ß (Aß), and phosphorylated tau (p-tau), further implicating Pb in the dysregulation of these key AD markers. The expression of CDR1as, a long non-coding RNA implicated in AD pathogenesis, was found to be suppressed in Pb-exposed mice. This observation suggests a potential mechanistic link between Pb-induced neurotoxicity and the dysregulation of the CDR1as/miR-671 axis, which warrants further investigation. Moreover, our study identified a dose-dependent alteration in the intracellular and extracellular levels of the transcription factor nuclear factor-kappa B (NF-κB). This finding implicates Pb in the modulation of NF-κB signaling, a pathway that plays a pivotal role in neuroinflammation and neurodegeneration. In conclusion, our findings underscored the deleterious effects of Pb exposure on the CNS, leading to the development of AD-like pathology. The observed modulation of NF-κB signaling and miR-671/CDR1as regulation provides a plausible mechanistic framework for understanding the neurotoxic effects of Pb and its potential contribution to AD pathogenesis.

GMRLNet: A Graph-Based Manifold Regularization Learning Framework for Placental Insufficiency Diagnosis on Incomplete Multimodal Ultrasound Data.

Multimodal analysis of placental ultrasound (US) and microflow imaging (MFI) could greatly aid in the early diagnosis and interventional treatment of placental insufficiency (PI), ensuring a normal pregnancy. Existing multimodal analysis methods have weaknesses in multimodal feature representation and modal knowledge definitions and fail on incomplete datasets with unpaired multimodal samples. To address these challenges and efficiently leverage the incomplete multimodal dataset for accurate PI diagnosis, we propose a novel graph-based manifold regularization learning (MRL) framework named GMRLNet. It takes US and MFI images as input and exploits their modality-shared and modality-specific information for optimal multimodal feature representation. Specifically, a graph convolutional-based shared and specific transfer network (GSSTN) is designed to explore intra-modal feature associations, thus decoupling each modal input into interpretable shared and specific spaces. For unimodal knowledge definitions, graph-based manifold knowledge is introduced to describe the sample-level feature representation, local inter-sample relations, and global data distribution of each modality. Then, an MRL paradigm is designed for inter-modal manifold knowledge transfer to obtain effective cross-modal feature representations. Furthermore, MRL transfers the knowledge between both paired and unpaired data for robust learning on incomplete datasets. Experiments were conducted on two clinical datasets to validate the PI classification performance and generalization of GMRLNet. State-of-the-art comparisons show the higher accuracy of GMRLNet on incomplete datasets. Our method achieves 0.913 AUC and 0.904 balanced accuracy (bACC) for paired US and MFI images, as well as 0.906 AUC and 0.888 bACC for unimodal US images, illustrating its application potential in PI CAD systems.

Breast Tumor Classification Based on MRI-US Images by Disentangling Modality Features.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and ultrasound (US), which are two common modalities for clinical breast tumor diagnosis besides Mammograms, can provide different and complementary information for the same tumor regions. Although many machine learning methods have been proposed for breast tumor classification based on either single modality, it remains unclear how to further boost the classification performance by utilizing paired multi-modality information with different dimensions. In this paper, we propose MRI-US multi-modality network (MUM-Net) to classify breast tumor into different subtypes based on 3D MR and 2D US images. The key insight of MUM-Net is that we explicitly distill modality-agnostic features for tumor classification. Specifically, we first adopt a discrimination-adaption module to decompose features into modality-agnostic and modality-specific ones with min-max training strategies. Then, we propose a feature fusion module to increase the compactness of the modality-agnostic features by utilizing an affinity matrix with nearest neighbour selection. We build a paired MRI-US breast tumor classification dataset containing 502 cases with three clinical indicators to validate the proposed method. In three tasks including lymph node metastasis, histological grade and Ki-67 level, MUM-Net achieves AUC scores of 0.8581, 0.8965 and 0.8577, outperforming other counterparts which are based on single task or single modality by a wide margin. In addition, we find that the extracted modality-agnostic features can help the network focus on the tumor regions in both modalities.

TN-USMA Net: Triple normalization-based gastrointestinal stromal tumors classification on multicenter EUS images with ultrasound-specific pretraining and meta attention.

PURPOSE: Accurate quantification of gastrointestinal stromal tumors' (GISTs) risk stratification on multicenter endoscopic ultrasound (EUS) images plays a pivotal role in aiding the surgical decision-making process. This study focuses on automatically classifying higher-risk and lower-risk GISTs in the presence of a multicenter setting and limited data. METHODS: In this study, we retrospectively enrolled 914 patients with GISTs (1824 EUS images in total) from 18 hospitals in China. We propose a triple normalization-based deep learning framework with ultrasound-specific pretraining and meta attention, namely, TN-USMA model. The triple normalization module consists of the intensity normalization, size normalization, and spatial resolution normalization. First, the image intensity is standardized and same-size regions of interest (ROIs) and same-resolution tumor masks are generated in parallel. Then, the transfer learning strategy is utilized to mitigate the data scarcity problem. The same-size ROIs are fed into a deep architecture with ultrasound-specific pretrained weights, which are obtained from self-supervised learning using a large volume of unlabeled ultrasound images. Meanwhile, tumors' size features are calculated from the same-resolution masks individually. Afterward, the size features together with two demographic features are integrated to the model before the final classification layer using a meta attention mechanism to further enhance feature representations. The diagnostic performance of the proposed method was compared with one radiomics-based method and two state-of-the-art deep learning methods. Four evaluation metrics, namely, the accuracy, the area under the receiver operator curve, the sensitivity, and the specificity were used to evaluate the model performance. RESULTS: The proposed TN-USMA model achieves an overall accuracy of 0.834 (95% confidence interval [CI]: 0.772, 0.885), an area under the receiver operator curve of 0.881 (95% CI: 0.825, 0.924), a sensitivity of 0.844 (95% CI: 0.672, 0.947), and a specificity of 0.832 (95% CI: 0.762, 0.888). The AUC significantly outperforms other two deep learning approaches (p < 0.05, DeLong et al). Moreover, the performance is stable under different variations of multicenter dataset partitions. CONCLUSIONS: The proposed TN-USMA model can successfully differentiate higher-risk GISTs from lower-risk ones. It is accurate, robust, generalizable, and efficient for potential clinical applications.

Three-dimensional breast tumor segmentation on DCE-MRI with a multilabel attention-guided joint-phase-learning network.

Accurate breast and tumor segmentations from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is vital in breast disease diagnosis. Here, we propose a novel attention-guided joint-phase-learning network for multilabel segmentation including the breast and tumors simultaneously and automatically. Instead of common multichannel inputs, our novel network consists of five separated streams designed for extracting comprehensive features for each DCE-MRI phase to fully use the dynamic intensity of enhanced images. A new time-signal intensity map was designed based on the DCE-MRI pixel-by-pixel values and added as an additional stream to reflect breast tumor dynamic variations. The multiple streams were fused in a fully connected layer to integrate the comprehensive tumor information. Weighted-loss was applied to the multilabel strategy to highlight breast tumor segmentation. In addition, the net applies the self-attention module with grid-based attention coefficients based on a global feature vector to emphasize breast regions and suppress irrelevant non-breast tissue features. We trained our method on 144 DCE-MRI datasets acquired from Philips and achieved mean Dice coefficients of 0.92 and 0.86 for breast and tumor segmentations that were superior to common networks with multichannel structures. The model was extended to an independent test set with 59 cases from two different MRI machines and achieved a Dice coefficient of 0.83 for breast tumor segmentation, which illustrates the robustness of our framework. The automatically generated masks can improve the accuracy and time of diagnosis of malignant and benign breast tumors. Qualitative comparisons illustrate that the proposed method has high precision and generalizability.

Temporally coherent cardiac motion tracking from cine MRI: Traditional registration method and modern CNN method.

PURPOSE: Cardiac motion tracking enables quantitative evaluation of myocardial strain, which is clinically interesting in cardiovascular disease research. However, motion tracking is difficult to perform manually. In this paper, we aim to develop and compare two fully automated motion tracking methods for the steady state free precession (SSFP) cine magnetic resonance imaging (MRI), and explore their use in real clinical scenario with different patient groups. METHODS: We proposed two automated cardiac motion tracking method: (a) a traditional registration-based method, named full cardiac cycle registration, which simultaneously tracks all cine frames within a full cardiac cycle by joint registration of all frames; and (b) a modern convolutional neural network (CNN)-based method, named Groupwise MotionNet, which enhances the temporal coherence by fusing motion along a continuous time scale. Both methods were evaluated on the healthy volunteer data from the MICCAI 2011 STACOM Challenge, as well as on patient data including hypertrophic cardiomyopathy (HCM) and myocardial infarction (MI). RESULTS: The full cardiac cycle registration method achieved an average end-point error (EPE) 2.89 ± 1.57 mm for cardiac motion tracking, with computation time of around 9 min per short-axis cine MRI (size 128 × 128, 30 cardiac phases). In comparison, the Groupwise MotionNet achieved an average EPE of 0.94 ± 1.59 mm, taking < 1 s for a full cardiac phases. Further experiments showed that registration method had stable performance, independent of patient cohort and MRI machine, while the CNN-based method relied on the training data to deliver consistently accurate results. CONCLUSION: Both registration-based and CNN-based method can track the cardiac motion from SSFP cine MRI in a fully automated manner, while taking temporal coherence into account. The registration method is generic, robust, but relatively slow; the CNN-based method trained with heterogeneous data was able to achieve high tracking accuracy with real-time performance.

Breast DCE-MRI radiomics: a robust computer-aided system based on reproducible BI-RADS features across the influence of datasets bias and segmentation methods.

PURPOSE: A highly accurate and robust computer-aided system based on quantitative high-throughput Breast Imaging Reporting and Data System (BI-RADS) features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can drive the success of radiomic applications in breast cancer diagnosis. We aim to build a stable system with highly reproducible radiomics features, which can make diagnostic performance independent of datasets bias and segmentation methods. METHOD: We applied a dataset of 267 patients including 136 malignant and 131 benign tumors from two MRI manufacturers, where 211 cases from a Philips system and 55 cases from a GE system. First, manual annotations, 3D-Unet and 2D-Unet were applied as different segmentation methods. Second, we designed and extracted 3172 features from six modalities of DCE-MRI based on BI-RADS. Third, the feature selection was conducted. Between-class distance was utilized to eliminate the effect of dataset bias caused by two machines. Concordance correlation coefficient, intraclass correlation coefficient and deviation were employed to evaluate the influence of three segmentation methods. We further eliminated features redundancy using genetic algorithm. Finally, three classifiers including support vector machine (SVM), the bagged trees and K-Nearest Neighbor were evaluated by their performance for diagnosing malignant and benign tumors. RESULTS: A total of 246 features were preserved to have high stability and reproducibility. The final feature set showed the robust performance under these factors and achieved the area under curve of 0.88, the accuracy of 0.824, the sensitivity of 0.844, the specificity of 0.807 in differentiating benign and malignant tumors with the SVM classifier using manually segmentation results. CONCLUSION: The final selected 246 features are reproducible and show little dependence on segmentation methods and data perturbation. The high stability and effectiveness of diagnosis across these factors illustrate that the preserved features can be used for prognostic analysis and help radiologists in the diagnosis of breast cancer.

Breast Calcifications Detection Based on Radiofrequency Signals by Quantitative Ultrasound Multi-parameter Fusion.

Breast calcifications indicate the high possibility of malignancy in the radiological assessment of breast lesions. However, it is difficult to detect them from traditional B-mode ultrasound images due to the resolution limit and speckle noise. In this paper, we proposed a novel automatic calcification detection method based on ultrasound radio frequency (RF) signals by quantitative multi-parameter fusion. The proposed method consists of four steps: selecting the region of interest (ROI), extracting multiple features on sliding windows that traverse the entire ROI, classifying the window with or without calcifications using the Adaptive Boosting classifier, and obtaining the detection result by a threshold filter. Experiments were conducted on a database of 130 experienced doctor-proven breast tumors with calcifications. Compared to manual annotation, the proposed method achieved an average accuracy of 88%. The experiments demonstrated that our computerized RF signals feature system was capable of helping radiologists detect tumor calcifications more accurately and provided more guidance for the final decision.

Radiomics Analysis on Ultrasound for Prediction of Biologic Behavior in Breast Invasive Ductal Carcinoma.

INTRODUCTION: In current clinical practice, invasive ductal carcinoma is always screened using medical imaging techniques and diagnosed using immunohistochemistry. Recent studies have illustrated that radiomics approaches provide a comprehensive characterization of entire tumors and can reveal predictive or prognostic associations between the images and medical outcomes. To better reveal the underlying biology, an improved understanding between objective image features and biologic characteristics is urgently required. PATIENTS AND METHODS: A total of 215 patients with definite histologic results were enrolled in our study. The tumors were automatically segmented using our phase-based active contour model. The high-throughput radiomics features were designed and extracted using a breast imaging reporting and data system and further selected using Student's t test, interfeature coefficients and a lasso regression model. The support vector machine classifier with threefold cross-validation was used to evaluate the relationship. RESULTS: The radiomics approach demonstrated a strong correlation between receptor status and subtypes (P < .05; area under the curve, 0.760). The appearance of hormone receptor-positive cancer and human epidermal growth factor receptor 2-negative cancer on ultrasound scans differs from that of triple-negative cancer. CONCLUSION: Our approach could assist clinicians with the accurate prediction of prognosis using ultrasound findings, allowing for early medical management and treatment.

Reproducibility of quantitative high-throughput BI-RADS features extracted from ultrasound images of breast cancer.

PURPOSE: Digital Breast Imaging Reporting and Data System (BI-RADS) features extracted from ultrasound images are essential in computer-aided diagnosis, prediction, and prognosis of breast cancer. This study focuses on the reproducibility of quantitative high-throughput BI-RADS features in the presence of variations due to different segmentation results, various ultrasound machine models, and multiple ultrasound machine settings. METHODS: Dataset 1 consists of 399 patients with invasive breast cancer and is used as the training set to measure the reproducibility of features, while dataset 2 consists of 138 other patients and is a validation set used to evaluate the diagnosis performances of the final reproducible features. Four hundred and sixty high-throughput BI-RADS features are designed and quantized according to BI-RADS lexicon. Concordance Correlation Coefficient (CCC) and Deviation (Dev) are used to assess the effect of the segmentation methods and Between-class Distance (BD) is used to study the influences of the machine models. In addition, the features jointly shared by two methodologies are further investigated on their effects with multiple machine settings. Subsequently, the absolute value of Pearson Correlation Coefficient (Rabs ) is applied for redundancy elimination. Finally, the features that are reproducible and not redundant are preserved as the stable feature set. A 10-fold Support Vector Machine (SVM) classifier is employed to verify the diagnostic ability. RESULTS: One hundred and fifty-three features were found to have high reproducibility (CCC > 0.9 & Dev < 0.1) within the manual and automatic segmentation. Three hundred and thirty-nine features were stable (BD < 0.2) at different machine models. Two feature sets shared the same 102 features, in which nine features were highly sensitive to the machine settings. Forty-six features were finally preserved after redundancy elimination. For the validation in dataset 2, the area under curve (AUC) of the 10-fold SVM classifier was 0.915. CONCLUSIONS: Three factors, segmentation results, machine models, and machine settings may affect the reproducibility of high-throughput BI-RADS features to various degrees. Our 46 reproducible features were robust to these factors and were capable of distinguishing benign and malignant breast tumors.