Transfer learning with CNNs for efficient prostate cancer and BPH detection in transrectal ultrasound images.

Early detection of prostate cancer (PCa) and benign prostatic hyperplasia (BPH) is crucial for maintaining the health and well-being of aging male populations. This study aims to evaluate the performance of transfer learning with convolutional neural networks (CNNs) for efficient classification of PCa and BPH in transrectal ultrasound (TRUS) images. A retrospective experimental design was employed in this study, with 1380 TRUS images for PCa and 1530 for BPH. Seven state-of-the-art deep learning (DL) methods were employed as classifiers with transfer learning applied to popular CNN architectures. Performance indices, including sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), Kappa value, and Hindex (Youden's index), were used to assess the feasibility and efficacy of the CNN methods. The CNN methods with transfer learning demonstrated a high classification performance for TRUS images, with all accuracy, specificity, sensitivity, PPV, NPV, Kappa, and Hindex values surpassing 0.9400. The optimal accuracy, sensitivity, and specificity reached 0.9987, 0.9980, and 0.9980, respectively, as evaluated using twofold cross-validation. The investigated CNN methods with transfer learning showcased their efficiency and ability for the classification of PCa and BPH in TRUS images. Notably, the EfficientNetV2 with transfer learning displayed a high degree of effectiveness in distinguishing between PCa and BPH, making it a promising tool for future diagnostic applications.

Fusion extracted features from deep learning for identification of multiple positioning errors in dental panoramic imaging.

BACKGROUND: Dental panoramic imaging plays a pivotal role in dentistry for diagnosis and treatment planning. However, correctly positioning patients can be challenging for technicians due to the complexity of the imaging equipment and variations in patient anatomy, leading to positioning errors. These errors can compromise image quality and potentially result in misdiagnoses. OBJECTIVE: This research aims to develop and validate a deep learning model capable of accurately and efficiently identifying multiple positioning errors in dental panoramic imaging. METHODS AND MATERIALS: This retrospective study used 552 panoramic images selected from a hospital Picture Archiving and Communication System (PACS). We defined six types of errors (E1-E6) namely, (1) slumped position, (2) chin tipped low, (3) open lip, (4) head turned to one side, (5) head tilted to one side, and (6) tongue against the palate. First, six Convolutional Neural Network (CNN) models were employed to extract image features, which were then fused using transfer learning. Next, a Support Vector Machine (SVM) was applied to create a classifier for multiple positioning errors, using the fused image features. Finally, the classifier performance was evaluated using 3 indices of precision, recall rate, and accuracy. RESULTS: Experimental results show that the fusion of image features with six binary SVM classifiers yielded high accuracy, recall rates, and precision. Specifically, the classifier achieved an accuracy of 0.832 for identifying multiple positioning errors. CONCLUSIONS: This study demonstrates that six SVM classifiers effectively identify multiple positioning errors in dental panoramic imaging. The fusion of extracted image features and the employment of SVM classifiers improve diagnostic precision, suggesting potential enhancements in dental imaging efficiency and diagnostic accuracy. Future research should consider larger datasets and explore real-time clinical application.

Haploinsufficiency of A20 caused by a novel pathogenic missense variant of TNFAIP3 and successfully treated with anti-TNF and immunosuppressive therapies.

Loss-of-function of protein A20, encoded by TNFAIP3, leads to an early-onset haploinsufficiency of A20 (HA20). This study reports one Chinese child with HA20 and explores the genetic etiology of TNFAIP3 variant. The patient exhibited transient recurrent episodes of fever, intermittent signs of arthritis, gastrointestinal symptoms and multiple colonic ulcers. Laboratory tests revealed elevated inflammatory indicators and mild to moderate anemia. Genetic analysis identified a heterozygous de novo variant in his TNFAIP3 gene (c.740C＞T, p. P247L), which had never been reported before. The novel missense variation was validated to be pathogenic through causing insufficient expression of A20, over-activation of NF-κB signaling pathway and elevated levels of proinflammatory cytokines in response to stimulation by lipopolysaccharide. A combination of oral corticosteroids, TNF-α inhibitors and thalidomide freed him from symptoms and abnormal inflammatory indicators. Furthermore, continual improvement of the patient's condition was observed during a follow-up period of five months. We demonstrate a case with a de novo missense variant resulting in a loss-of-function of TNFAIP3, which expands the clinical spectrum of HA20. Cytokine antagonists and immunosuppressants may be effective drugs.

Classification of Color Fundus Photographs Using Fusion Extracted Features and Customized CNN Models.

This study focuses on overcoming challenges in classifying eye diseases using color fundus photographs by leveraging deep learning techniques, aiming to enhance early detection and diagnosis accuracy. We utilized a dataset of 6392 color fundus photographs across eight disease categories, which was later augmented to 17,766 images. Five well-known convolutional neural networks (CNNs)-efficientnetb0, mobilenetv2, shufflenet, resnet50, and resnet101-and a custom-built CNN were integrated and trained on this dataset. Image sizes were standardized, and model performance was evaluated via accuracy, Kappa coefficient, and precision metrics. Shufflenet and efficientnetb0demonstrated strong performances, while our custom 17-layer CNN outperformed all with an accuracy of 0.930 and a Kappa coefficient of 0.920. Furthermore, we found that the fusion of image features with classical machine learning classifiers increased the performance, with Logistic Regression showcasing the best results. Our study highlights the potential of AI and deep learning models in accurately classifying eye diseases and demonstrates the efficacy of custom-built models and the fusion of deep learning and classical methods. Future work should focus on validating these methods across larger datasets and assessing their real-world applicability.

Fusion-Extracted Features by Deep Networks for Improved COVID-19 Classification with Chest X-ray Radiography.

Convolutional neural networks (CNNs) have shown promise in accurately diagnosing coronavirus disease 2019 (COVID-19) and bacterial pneumonia using chest X-ray images. However, determining the optimal feature extraction approach is challenging. This study investigates the use of fusion-extracted features by deep networks to improve the accuracy of COVID-19 and bacterial pneumonia classification with chest X-ray radiography. A Fusion CNN method was developed using five different deep learning models after transferred learning to extract image features (Fusion CNN). The combined features were used to build a support vector machine (SVM) classifier with a RBF kernel. The performance of the model was evaluated using accuracy, Kappa values, recall rate, and precision scores. The Fusion CNN model achieved an accuracy and Kappa value of 0.994 and 0.991, with precision scores for normal, COVID-19, and bacterial groups of 0.991, 0.998, and 0.994, respectively. The results indicate that the Fusion CNN models with the SVM classifier provided reliable and accurate classification performance, with Kappa values no less than 0.990. Using a Fusion CNN approach could be a possible solution to enhance accuracy further. Therefore, the study demonstrates the potential of deep learning and fusion-extracted features for accurate COVID-19 and bacterial pneumonia classification with chest X-ray radiography.

Comprehensive analysis of the prognosis, tumor microenvironment, and immunotherapy response of SDHs in colon adenocarcinoma.

Background: Succinate dehydrogenase (SDH), one of the key enzymes in the tricarboxylic acid cycle, is mainly found in the mitochondria. SDH consists of four subunits encoding SDHA, SDHB, SDHC, and SDHD. The biological function of SDH is significantly related to cancer progression. Colorectal cancer (CRC) is one of the most common malignant tumors globally, whose most common histological subtype is colon adenocarcinoma (COAD). However, the correlation between SDH factors and COAD remains unclear. Methods: The data on pan-cancer was obtained from The Cancer Genome Atlas (TCGA) database. Kaplan-Meier survival analysis showed the prognostic ability of SDHs. The cBioPortal database reflected genetic variations of SDHs. The correlation analysis was conducted between SDHs and mitochondrial energy metabolism genes (MMGs) and the protein-protein interaction (PPI) network was built. Consequently, Univariate and Multivariate Cox Regression Analysis on SDHs and other clinical characteristics were conducted. A nomogram was established. The ssGSEA analysis visualized the association between SDHs and immune infiltration. Immunophenoscore (IPS) explored the correlation between SDHs and immunotherapy, and the correlation between SDHs and targeted therapy was investigated through Genomics of Drug Sensitivity in Cancer. Finally, qPCR and immunohistochemistry detected SDHs' expression. Results: After assessing SDHs differential expression in pan-cancer, we found that SDHB, SDHC, and SDHD benefit COAD patients. The cBioPortal database demonstrated that SDHA was the top gene in mutation frequency rank. Correlation analysis mirrored a strong link between SDHs and MMGs. We formulated a nomogram and found that SDHB, SDHC, SDHD, and clinical characteristics correlated with COAD patients' survival. For T helper cells, Th2 cells, and Tem, SDHA, SDHB, SDHC, and SDHD were significantly enriched in the high expression group. Moreover, COAD patients with high SDHA expression were more suitable for immunotherapy. And COAD patients with different SDHs' expression have different sensitivity to targeted drugs. Further verifying the gene and protein expression levels of SDHs, we found that the tissues were consistent with the bioinformatics analysis. Conclusions: Our study analyzed the expression and prognostic value of SDHs in COAD, explored the pathway mechanisms involved, and the immune cell correlations, indicating that SDHs might be biomarkers for COAD patients.

A Novel Pyroptosis-Related Gene Signature for Predicting the Prognosis and the Associated Immune Infiltration in Colon Adenocarcinoma.

Background: Pyroptosis has been demonstrated to be an inflammatory form of programmed cell death recently. However, the expression of pyroptosis-related genes (PRGs) in colon adenocarcinoma (COAD) and their correlations with prognosis remain unclear. Methods: Data of COAD patients were obtained from The Cancer Genome Atlas (TCGA) database to screen differentially expressed genes (DEGs). Univariate Cox regression analysis and the LASSO Cox regression analysis were applied to construct a gene signature. All COAD patients in TCGA cohort were separated into low-risk subgroup or high-risk subgroup via the risk score. Kaplan-Meier survival analysis and receiver operator characteristic (ROC) curves were adopted to assess its prognostic efficiency. COAD data from the GSE17537 datasets was used for validation. A prognostic nomogram was established to predict individual survival. The correlation between PRGs and immune cell infiltration in COAD was verified based on TIMER database. CIBERSORT analysis was utilized on risk subgroup as defined by model. The protein and mRNA expression level of PRGs were verified by HPA database and qPCR. Results: A total of 51 differentially expressed PRGs were identified in TCGA cohort. Through univariate Cox regression analysis and LASSO Cox regression analysis, a prognostic model containing 7 PRGs was constructed. Kaplan-Meier survival analysis indicated that patients in the low-risk subgroup exhibited better prognosis compared to those in the high-risk subgroup. Additionally, the area under the curve (AUC) of ROC is 0.60, 0.63, and 0.73 for 1-, 3-, and 5-year survival in TCGA cohort and 0.63, 0.65, and 0.64 in validation set. TIMER database showed a strong correlation between 7 PRGs and tumor microenvironment in COAD. Moreover, CIBERSORT showed significant differences in the infiltration of plasma cells, M0 macrophages, resting dendritic cells, and eosinophils between low-risk subgroup and high-risk subgroup. HPA database showed that protein expression level of SDHB, GZMA, BTK, EEF2K, and NR1H2 was higher in normal tissues. And the transcriptional level of CASP5, BTK, SDHB, GZMA, and RIPK3 was high in normal tissues. Conclusions: Our study identified a novel PRGs signature that could be used to predict the prognosis of COAD patients, which might provide a new therapeutic target for the treatment of COAD patients.

Segmentation of liver tumors with abdominal computed tomography using fully convolutional networks.

BACKGROUND: Dividing liver organs or lesions depicting on computed tomography (CT) images could be applied to help tumor staging and treatment. However, most existing image segmentation technologies use manual or semi-automatic analysis, making the analysis process costly and time-consuming. OBJECTIVE: This research aims to develop and apply a deep learning network architecture to segment liver tumors automatically after fine tuning parameters. METHODS AND MATERIALS: The medical imaging is obtained from the International Symposium on Biomedical Imaging (ISBI), which includes 3D abdominal CT scans of 131 patients diagnosed with liver tumors. From these CT scans, there are 7,190 2D CT images along with the labeled binary images. The labeled binary images are regarded as gold standard for evaluation of the segmented results by FCN (Fully Convolutional Network). The backbones of FCN are extracted from Xception, InceptionresNetv2, MobileNetv2, ResNet18, ResNet50 in this study. Meanwhile, the parameters including optimizers (SGDM and ADAM), size of epoch, and size of batch are investigated. CT images are randomly divided into training and testing sets using a ratio of 9:1. Several evaluation indices including Global Accuracy, Mean Accuracy, Mean IoU (Intersection over Union), Weighted IoU and Mean BF Score are applied to evaluate tumor segmentation results in the testing images. RESULTS: The Global Accuracy, Mean Accuracy, Mean IoU, Weighted IoU, and Mean BF Scores are 0.999, 0.969, 0.954, 0.998, 0.962 using ResNet50 in FCN with optimizer SGDM, batch size 12, and epoch 9. It is important to fine tuning the parameters in FCN model. Top 20 FNC models enable to achieve higher tumor segmentation accuracy with Mean IoU over 0.900. The occurred frequency of InceptionresNetv2, MobileNetv2, ResNet18, ResNet50, and Xception are 9, 6, 3, 5, and 2 times. Therefore, the InceptionresNetv2 has higher performance than others. CONCLUSIONS: This study develop and test an automated liver tumor segmentation model based on FCN. Study results demonstrate that many deep learning models including InceptionresNetv2, MobileNetv2, ResNet18, ResNet50, and Xception have high potential to segment liver tumors from CT images with accuracy exceeding 90%. However, it is still difficult to accurately segment tiny and small size tumors by FCN models.

Deep Transfer Learning for the Multilabel Classification of Chest X-ray Images.

Chest X-ray (CXR) is widely used to diagnose conditions affecting the chest, its contents, and its nearby structures. In this study, we used a private data set containing 1630 CXR images with disease labels; most of the images were disease-free, but the others contained multiple sites of abnormalities. Here, we used deep convolutional neural network (CNN) models to extract feature representations and to identify possible diseases in these images. We also used transfer learning combined with large open-source image data sets to resolve the problems of insufficient training data and optimize the classification model. The effects of different approaches of reusing pretrained weights (model finetuning and layer transfer), source data sets of different sizes and similarity levels to the target data (ImageNet, ChestX-ray, and CheXpert), methods integrating source data sets into transfer learning (initiating, concatenating, and co-training), and backbone CNN models (ResNet50 and DenseNet121) on transfer learning were also assessed. The results demonstrated that transfer learning applied with the model finetuning approach typically afforded better prediction models. When only one source data set was adopted, ChestX-ray performed better than CheXpert; however, after ImageNet initials were attached, CheXpert performed better. ResNet50 performed better in initiating transfer learning, whereas DenseNet121 performed better in concatenating and co-training transfer learning. Transfer learning with multiple source data sets was preferable to that with a source data set. Overall, transfer learning can further enhance prediction capabilities and reduce computing costs for CXR images.

Identifying the Posture of Young Adults in Walking Videos by Using a Fusion Artificial Intelligent Method.

Many neurological and musculoskeletal disorders are associated with problems related to postural movement. Noninvasive tracking devices are used to record, analyze, measure, and detect the postural control of the body, which may indicate health problems in real time. A total of 35 young adults without any health problems were recruited for this study to participate in a walking experiment. An iso-block postural identity method was used to quantitatively analyze posture control and walking behavior. The participants who exhibited straightforward walking and skewed walking were defined as the control and experimental groups, respectively. Fusion deep learning was applied to generate dynamic joint node plots by using OpenPose-based methods, and skewness was qualitatively analyzed using convolutional neural networks. The maximum specificity and sensitivity achieved using a combination of ResNet101 and the naïve Bayes classifier were 0.84 and 0.87, respectively. The proposed approach successfully combines cell phone camera recordings, cloud storage, and fusion deep learning for posture estimation and classification.

Recent advances in the aetiology of recurrent aphthous stomatitis (RAS).

Recurrent aphthous stomatitis (RAS) is the most common disease of oral mucosa, which almost attacks each individual once in their lifespan. Although plenty of factors have been suggested to play a role in the pathogenesis of RAS, the aetiology of RAS is still controversial, which might lead to limited clinical therapies in accordance with each RAS patient. This review mainly illustrates recent advances in potential causes associated with RAS in detail. Deeper comprehension of the aetiology of RAS will support doctors and researchers to make a better management of RAS patients and to discover new treatments. The aetiology of RAS is complicated, hence we should take a comprehensive view into its aetiology, with multiple potential factors being considered. Sample collection of RAS patients have greatly limited the progress in the aetiology of RAS. A research model of multiagency cooperation can help achieve perfect sample collection of year-round and multiposition.

Efficient Strike Artifact Reduction Based on 3D-Morphological Structure Operators from Filtered Back-Projection PET Images.

Positron emission tomography (PET) can provide functional images and identify abnormal metabolic regions of the whole-body to effectively detect tumor presence and distribution. The filtered back-projection (FBP) algorithm is one of the most common images reconstruction methods. However, it will generate strike artifacts on the reconstructed image and affect the clinical diagnosis of lesions. Past studies have shown reduction in strike artifacts and improvement in quality of images by two-dimensional morphological structure operators (2D-MSO). The morphological structure method merely processes the noise distribution of 2D space and never considers the noise distribution of 3D space. This study was designed to develop three-dimensional-morphological structure operators (3D MSO) for nuclear medicine imaging and effectively eliminating strike artifacts without reducing image quality. A parallel operation was also used to calculate the minimum background standard deviation of the images for three-dimensional morphological structure operators with the optimal response curve (3D-MSO/ORC). As a result of Jaszczak phantom and rat verification, 3D-MSO/ORC showed better denoising performance and image quality than the 2D-MSO method. Thus, 3D MSO/ORC with a 3 × 3 × 3 mask can reduce noise efficiently and provide stability in FBP images.

Integrating ECG Monitoring and Classification via IoT and Deep Neural Networks.

Anesthesia assessment is most important during surgery. Anesthesiologists use electrocardiogram (ECG) signals to assess the patient's condition and give appropriate medications. However, it is not easy to interpret the ECG signals. Even physicians with more than 10 years of clinical experience may still misjudge. Therefore, this study uses convolutional neural networks to classify ECG image types to assist in anesthesia assessment. The research uses Internet of Things (IoT) technology to develop ECG signal measurement prototypes. At the same time, it classifies signal types through deep neural networks, divided into QRS widening, sinus rhythm, ST depression, and ST elevation. Three models, ResNet, AlexNet, and SqueezeNet, are developed with 50% of the training set and test set. Finally, the accuracy and kappa statistics of ResNet, AlexNet, and SqueezeNet in ECG waveform classification were (0.97, 0.96), (0.96, 0.95), and (0.75, 0.67), respectively. This research shows that it is feasible to measure ECG in real time through IoT and then distinguish four types through deep neural network models. In the future, more types of ECG images will be added, which can improve the real-time classification practicality of the deep model.

Questionable necessity of nitroglycerin for diagnostic coronary artery examination using 320-row multi-detector computed tomography.

OBJECTIVE: This study aims to analyze and compare the diagnostic effectiveness of 320-row multi-detector computed tomography for coronary artery angiography (MDCTA) in subjects with and without sublingual vasodilator (nitroglycerin). MATERIALS AND METHODS: From September 2015 to September 2016, 70 individuals without history of major cardiovascular diseases who underwent MDCTA for health examination were retrospectively categorized into sublingual nitroglycerin (NTG) and non-NTG groups. Medical history, CT dose index (CTDI), and multi-slice CT images were compared between two groups. A diameter of coronary artery (DA, mm) was computed and analyzed. RESULTS: A total of 41 males and 29 females (mean age: 55.43±8.84 years, range: 34- 76) were reviewed. Normal and abnormal MDCTA findings were noted in 54 and 16 participants, respectively, with the detection rate of coronary artery disease being 23%. There was no significant difference in inter-observer variability of coronary CTA image quality and diagnosis between the NTG and non-NTG groups among three experienced radiologists. Although the percentage dilatation of left anterior descending branch (LAD), right coronary artery (RCA) and left circumflex branch (LCX) following in the NTG group were 12.4%, 12.8% and 25.3%, respectively (p < 0.01), there was no significant difference in image quality and diagnosis between the two groups. CONCLUSIONS: Despite the recommendation of routine nitroglycerin use for subjects undergoing computed tomography for coronary artery angiography, our results showed no significant advantage of its use in improving image quality and rate of diagnosis accuracy.

Combination of a Self-Regulation Module and Mobile Application to Enhance Treatment Outcome for Patients with Acne.

Background and Objectives: Acne, an inflammatory disorder of the pilosebaceous unit associated with both physiological and psychological morbidities, should be considered a chronic disease. The application of self-regulation theory and therapeutic patient education has been widely utilized in different health-related areas to help patient with a chronic disease to attain better behavioral modification. The present study aims at investigating the treatment efficacy of combining a self-regulation-based patient education module with mobile application in acne patients. Materials and Methods: This was one-grouped pretest-posttest design at a single tertiary referral center with the enrollment of 30 subjects diagnosed with acne vulgaris. Relevant information was collected before (week 0) and after (week 4) treatment in the present study, including the Acne Self-Regulation Inventory (ASRI), Cardiff Acne Disability Index (CADI), and Dermatology Life Quality Index (DLQI) that involved a questionnaire-based subjective evaluation of the patient's ability in self-regulation and quality of life as well as clinical Acne Grading Scores (AGS) that objectively assessed changes in disease severity. To reinforce availability and feasibility, an individualized platform was accessible through mobile devices for real-time problem solving between hospital visits. Results: Thirty subjects completed the designed experiment. An analysis of the differences between scores of pretest and posttest of ASRI demonstrated substantial elevations (p < 0.001). The questionnaire survey of CADI and DLQI dropped significantly after the application of a self-regulation-based patient education module with a mobile application, revealing substantial reductions in both parameters (p < 0.001). The sign test demonstrated a remarkably significant difference in AGS (Z = -7.38, p < 0.001), indicating notable improvement in the clinical severity of acne after treatment. Conclusions: After incorporating modern mobile application, a self-regulation-based therapeutic patient education module could significantly improve treatment outcomes among acne patients.

Classification for liver ultrasound tomography by posterior attenuation correction with a phantom study.

The B-mode ultrasound usually contains scattering speckle noise which reduces the detailed resolution of the target and is regarded as an intrinsic noise that interferes with diagnostic precision. The aim of this study was to classify hepatic steatosis through applying attenuation correction with a phantom to reduce speckle noise in liver ultrasound tomography in patients. This retrospective study applied three randomized groups signifying different liver statuses. A total of 114 patients' effective liver ultrasound images-30 normal, 44 fatty, and 40 cancerous-were included. The proposed depth attenuation correction method was first applied to images. Three regions of interest were manually drawn on the images. Next, five feature values for the regions of interest were calculated. Finally, the hybrid method of logistic regression and support vector machine was employed to classify the ultrasound images with 10-fold cross-validation. The accuracy, kappa statistic, and mean absolute error of the proposed hybrid method were 87.5%, 0.812, and 0.119, respectively, which were higher than those of the logistic regression method-75.0%, 0.548, and 0.280-or those of the support vector machine method-75.7%, 0.637, and 0.293-respectively. Therefore, the hybrid method has been proven to be more accurate and have better performance and less error than either single method. The hybrid method provided acceptable accuracy of classification in three liver ultrasound image groups after depth attenuation correction. In the future, the deep learning approaches may be considered for the application in classifying liver ultrasound images.

Opportunistic Breast Density Assessment in Women Receiving Low-dose Chest Computed Tomography Screening.

RATIONALE AND OBJECTIVES: Low-dose chest computed tomography (LDCT), increasingly being used for screening of lung cancer, may also be used to measure breast density, which is proven as a risk factor for breast cancer. In this study, we developed a segmentation method to measure quantitative breast density on CT images and correlated with magnetic resonance density. MATERIALS AND METHODS: Forty healthy women receiving both LDCT and breast magnetic resonance imaging (MRI) were studied. A semiautomatic method was applied to quantify the breast density on LDCT images. The intra- and interoperator reproducibility was evaluated. The volumetric density on MRI was obtained by using a well-established automatic template-based segmentation method. The breast volume (BV), fibroglandular tissue volume (FV), and percent breast density (PD) measured on LDCT and MRI were compared. RESULTS: The measurements of BV, FV, and PD on LDCT images yield highly consistent results, with the intraclass correlation coefficient of 0.999 for BV, 0.977 for FV, and 0.966 for PD for intraoperator reproducibility, and intraclass correlation coefficient of 0.953 for BV, 0.974 for FV, and 0.973 for PD for interoperator reproducibility. The BV, FV, and PD measured on LDCT and MRI were well correlated (all r ≥ 0.90). Bland-Altman plots showed that a larger BV and FV were measured on LDCT than on MRI. CONCLUSIONS: The preliminary results showed that quantitative breast density can be measured from LDCT, and that our segmentation method could yield a high reproducibility on the measured volume and PD. The results measured on LDCT and MRI were highly correlated. Our results showed that LDCT may provide valuable information about breast density for evaluating breast cancer risk.

Pneumoconiosis increases the risk of congestive heart failure: A nationwide population-based cohort study.

The purpose of the study was to determine the relationship between pneumoconiosis and congestive heart failure (CHF).We collected data from the National Health Insurance Research Database in Taiwan. The study sample comprised 8923 patients with pneumoconiosis and 35,692 nonpneumoconiosis controls enrolled from 2000 to 2011. Patients were followed up until the end of 2011 to evaluate the incidence of CHF. The risk of CHF was analyzed using Cox proportional hazard regression models, and the analysis accounted for factors such as sex, age, comorbidities, and air pollutants (µg/m).The overall incidence of CHF was higher in the pneumoconiosis cohort (15.7 per 1000 person-y) than in the nonpneumoconiosis cohort (11.2 per 1000 person-y), with a crude hazard ratio (HR) of 1.40 (P < 0.001). The HR for CHF was 1.38-fold greater in the pneumoconiosis cohort than in the nonpneumoconiosis cohort (P < 0.001) after the model was adjusted for age, sex, various comorbidities, and air pollutants (µg/m). The relative risk for CHF in the sex-specific pneumoconiosis cohort compared with the nonpneumoconiosis cohort was significant for men (adjusted HR = 1.40, 95% confidence interval = 1.21-1.62, P < 0.001). The incidence density rates of CHF increased with age; pneumoconiosis patients had a higher relative risk of CHF for all age group.Patients with pneumoconiosis were at higher risk for developing CHF than patients in the nonpneumoconiosis cohort, particularly in cases with coexisting coronary artery disease, hypertension, and chronic obstructive pulmonary disease.