Effects of continuous low-speed biogas agitation on anaerobic digestion of high-solids pig manure: Performance and microbial community.

This study aimed to investigate effects of continuous low-speed biogas agitation on the anaerobic digestion (AD) performance and microbial community of high-solids pig manure (total solids content of 10%). Our results reveal that at a biogas agitation intensity of 1.10 L/g feed VS/d, CH4 production increased by 16.67% compared to the non-agitated condition, the removal efficiency of H2S reached 63.18%, and the abundance of Methanosarcina was the highest. The presence of Hungateiclostridiaceae was associated with H2S concentrations. An increasing biogas agitation intensity led to an elevated pH and a decreased oxidation-reduction potential (ORP). Acetate concentrations, pH, and ORP values indicated changes in H2S concentrations. Sedimentibacter demonstrates the potential to indicate biogas agitation intensity and pH. We demonstrate that continuous low-speed biogas agitation effectively increases CH4 production and reduces H2S concentrations in AD of high-solids pig manure, offering a potential technical pathway for developing AD processes for high-solids pig manure, it also demonstrates that AD process can reduce the risk of pathogen and parasite transmission.

A New Method Used to Enhance the SPAIR Image of the Spine MRI.

BACKGROUND: Magnetic resonance imaging (MRI) is a handy diagnostic tool for orthopedic disorders, particularly spinal and joint diseases. METHODS: The lumbar intervertebral disc is visible in the T1 and T2 weight sequences of the spine MRI, which aids in diagnosing lumbar disc herniation, lumbar spine tuberculosis, lumbar spine tumors, and other conditions. The lumbar intervertebral disc cannot be seen accurately in the Spectral Attenuated Inversion Recovery (SPAIR) due to weaknesses in the fat and frequency offset parameters, which is not conducive to developing the intelligence diagnosis model of medical image. RESULTS: In order to solve this problem, we propose a composite framework, which is first to use the contrast limited adaptive histogram equalization (CLAHE) method to enhance the SPAIR image contrast of the spine MRI and then use the non-local means method to remove the noise of the image to ensure that the image contrast is uniform without losing details. We employ the Information Entropy (IE), Peak signal-to-noise ratio (PSNR), and feature similarity index measure (FSIM) to quantify image quality after enhancement by the composite framework. CONCLUSION: The outcomes of the experiments' output images and quantitative data indicate that our composite framework is better than others.

Rethinking Annotation Granularity for Overcoming Shortcuts in Deep Learning-based Radiograph Diagnosis: A Multicenter Study.

Purpose: To evaluate the ability of fine-grained annotations to overcome shortcut learning in deep learning (DL)-based diagnosis using chest radiographs. Materials and Methods: Two DL models were developed using radiograph-level annotations (disease present: yes or no) and fine-grained lesion-level annotations (lesion bounding boxes), respectively named CheXNet and CheXDet. A total of 34 501 chest radiographs obtained from January 2005 to September 2019 were retrospectively collected and annotated regarding cardiomegaly, pleural effusion, mass, nodule, pneumonia, pneumothorax, tuberculosis, fracture, and aortic calcification. The internal classification performance and lesion localization performance of the models were compared on a testing set (n = 2922); external classification performance was compared on National Institutes of Health (NIH) Google (n = 4376) and PadChest (n = 24 536) datasets; and external lesion localization performance was compared on the NIH ChestX-ray14 dataset (n = 880). The models were also compared with radiologist performance on a subset of the internal testing set (n = 496). Performance was evaluated using receiver operating characteristic (ROC) curve analysis. Results: Given sufficient training data, both models performed similarly to radiologists. CheXDet achieved significant improvement for external classification, such as classifying fracture on NIH Google (CheXDet area under the ROC curve [AUC], 0.67; CheXNet AUC, 0.51; P < .001) and PadChest (CheXDet AUC, 0.78; CheXNet AUC, 0.55; P < .001). CheXDet achieved higher lesion detection performance than CheXNet for most abnormalities on all datasets, such as detecting pneumothorax on the internal set (CheXDet jackknife alternative free-response ROC [JAFROC] figure of merit [FOM], 0.87; CheXNet JAFROC FOM, 0.13; P < .001) and NIH ChestX-ray14 (CheXDet JAFROC FOM, 0.55; CheXNet JAFROC FOM, 0.04; P < .001). Conclusion: Fine-grained annotations overcame shortcut learning and enabled DL models to identify correct lesion patterns, improving the generalizability of the models.Keywords: Computer-aided Diagnosis, Conventional Radiography, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms, Localization Supplemental material is available for this article © RSNA, 2022.

Efficacy Evaluation of Neoadjuvant Chemotherapy in Breast Cancer by MRI.

Breast cancer is a highly harmful malignancy, which often causes great distress to patients and seriously affects their physical and mental health. Breast cancer causes patients to experience decreased appetite, decreased eating, and indigestion, which in turn leads to malnutrition, body wasting, resistance, immune compromise, progressive anemia, cachexia, and, as a result, severe secondary infections. To investigate the efficacy evaluation of neoadjuvant chemotherapy in breast cancer by MRI, forty-eight subjects treated at the hospital from June 2014 to August 2019 were recruited. After the neoadjuvant chemotherapy, the patients were divided into two groups based on the results of histopathological examination, namely, the ineffective group (n = 14) and the effective group (n = 34). Changes in MRI indicators were compared between the two groups before and after the neoadjuvant chemotherapy. The maximum diameter of lesions decreased significantly after the neoadjuvant chemotherapy than before. The apparent diffusion coefficient (ADC) increased considerably, and the time-intensity curve (TIC) showed a transition from type III to type II/I and from type II to type I. MRI can indicate the maximum diameter of the breast cancer lesion, ADC, and TIC type. Therefore, it can be used to evaluate the efficacy of neoadjuvant chemotherapy for breast cancer and be widely applied in clinical practice.

Development and Evaluation of a Deep Learning Algorithm for Rib Segmentation and Fracture Detection from Multicenter Chest CT Images.

PURPOSE: To evaluate the performance of a deep learning-based algorithm for automatic detection and labeling of rib fractures from multicenter chest CT images. MATERIALS AND METHODS: This retrospective study included 10 943 patients (mean age, 55 years; 6418 men) from six hospitals (January 1, 2017 to December 30, 2019), which consisted of patients with and without rib fractures who underwent CT. The patients were separated into one training set (n = 2425), two lesion-level test sets (n = 362 and 105), and one examination-level test set (n = 8051). Free-response receiver operating characteristic (FROC) score (mean sensitivity of seven different false-positive rates), precision, sensitivity, and F1 score were used as metrics to assess rib fracture detection performance. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were employed to evaluate the classification accuracy. The mean Dice coefficient and accuracy were used to assess the performance of rib labeling. RESULTS: In the detection of rib fractures, the model showed an FROC score of 84.3% on test set 1. For test set 2, the algorithm achieved a detection performance (precision, 82.2%; sensitivity, 84.9%; F1 score, 83.3%) comparable to three radiologists (precision, 81.7%, 98.0%, 92.0%; sensitivity, 91.2%, 78.6%, 69.2%; F1 score, 86.1%, 87.2%, 78.9%). When the radiologists used the algorithm, the mean sensitivity of the three radiologists showed an improvement (from 79.7% to 89.2%), with precision achieving similar performance (from 90.6% to 88.4%). Furthermore, the model achieved an AUC of 0.93 (95% CI: 0.91, 0.94), sensitivity of 87.9% (95% CI: 83.7%, 91.4%), and specificity of 85.3% (95% CI: 74.6%, 89.8%) on test set 3. On a subset of test set 1, the model achieved a Dice score of 0.827 with an accuracy of 96.0% for rib segmentation. CONCLUSION: The developed deep learning algorithm was capable of detecting rib fractures, as well as corresponding anatomic locations on CT images.Keywords CT, Ribs© RSNA, 2021.

MommiNet-v2: Mammographic multi-view mass identification networks.

Many existing approaches for mammogram analysis are based on single view. Some recent DNN-based multi-view approaches can perform either bilateral or ipsilateral analysis, while in practice, radiologists use both to achieve the best clinical outcome. MommiNet is the first DNN-based tri-view mass identification approach, which can simultaneously perform bilateral and ipsilateral analysis of mammographic images, and in turn, can fully emulate the radiologists' reading practice. In this paper, we present MommiNet-v2, with improved network architecture and performance. Novel high-resolution network (HRNet)-based architectures are proposed to learn the symmetry and geometry constraints, to fully aggregate the information from all views for accurate mass detection. A multi-task learning scheme is adopted to incorporate both Breast Imaging-Reporting and Data System (BI-RADS) and biopsy information to train a mass malignancy classification network. Extensive experiments have been conducted on the public DDSM (Digital Database for Screening Mammography) dataset and our in-house dataset, and state-of-the-art results have been achieved in terms of mass detection accuracy. Satisfactory mass malignancy classification result has also been obtained on our in-house dataset.

Electrocardiograhic characteristics in patients with coronavirus infection: A single-center observational study.

BACKGROUND: A global outbreak of coronavirus disease (COVID-19), caused by severe acute respiratory coronavirus 2 (SARS-CoV-2), has emerged since December 2019, in Wuhan, China. However, electrocardiograhic (ECG) manifestations of patients with COVID-19 have not been fully described. We aim to investigate ECG characteristics in COVID-19 patients and risk factors of intensive care unit (ICU) admission. METHODS: This retrospective observational study included the patients with COVID-19 at the Wuhan Asia General hospital between February 10, and 26, 2020. Demographic, clinical, and ECG characteristics were collected, and comparisons were made between the ICU and non-ICU admission groups. Logistic regression was used to identify risk factors of ICU admission. RESULTS: Among 135 included patients (median age: 64 years [interquartile range: 48-72]), ST-T abnormalities (40%) were the most common ECG feature, followed by arrhythmias (38%). Cardiovascular disease (CVD) was presented in 48% of the patients. Six (4.4%) died during hospitalization, and 23 (17.0%) were admitted to the ICU. Compared with non-ICU group, the ICU group showed higher heart rate (p = .019) and P-wave duration (p = .039) and was more frequently associated with CVD (p < .001), ST-T abnormalities (p = .007), arrhythmias (p = .003), QTc interval prolongation (p = .003), and pathological Q waves (p < .001). Twenty-seven patients were re-examined ECG during admission, and 17 of them presented new findings compared with their initial ECG presentations. ST-T abnormalities (p = .040) and history of CVD (p = .0047) were associated with increased risk of ICU hospitalization. CONCLUSIONS: COVID-19 is frequently related to cardiovascular manifestations including ECG abnormalities and cardiovascular comorbidities. ST-T abnormalities and CVD at admission were associated with increased odds of ICU admission.

Serum IgM against SARS-CoV-2 correlates with in-hospital mortality in severe/critical patients with COVID-19 in Wuhan, China.

Severe/critical patients with coronavirus disease 2019 (COVID-19) have become the central issue in the current global pandemic due to their high mortality rate. However, the relationship between antibody response and clinical outcomes has not been well described in this group. We conducted a single-center, retrospective, cohort study to investigate the relationship between serum immunoglobulin G (IgG) and IgM and clinical outcomes in severe/critical patients with COVID-19. Seventy-nine severe/critical patients with COVID-19 admitted in Wuhan Asia General Hospital in Wuhan, China during January 22, 2020 to March 6, 2020 were included. Serum antibodies were measured at day 25 (SD, 7) post illness onset. The median IgG titer was 113 (IQR 81-167) AU/ml, and IgM titer was 50 (IQR, 23-105) AU/ml. Patients whose IgM titer ≥ 50 AU/ml had higher in-hospital mortality (p=0.026). IgM titer ≥ 50 AU/ml was also correlated with higher incidences of Acute Respiratory Distress Syndrome (ARDS) and sepsis shock. Antibody remeasurements were performed in 42 patients, where IgM titer declined significantly in survivors (p=0.031). Serum IgM titer changes according to the COVID-19 progression. The severe/critical patients with COVID-19 have a higher risk of clinical adverse events when IgM titer ≥ 50 AU/ml. Further decreasing of IgM could imply a better outcome in severe/critical cases.

CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma.

PURPOSE: Spread through air space (STAS) is a novel invasive pattern of lung adenocarcinoma and is also a risk factor for recurrence and worse prognosis of lung adenocarcinoma. The aims of this study are to develop and validate a computed tomography (CT)­based radiomics model for preoperative prediction of STAS in lung adenocarcinoma. METHODS AND MATERIALS: This retrospective study was approved by an institutional review board and included 462 (mean age, 58.06 years) patients with pathologically confirmed lung adenocarcinoma. STAS was identified in 90 patients (19.5%). Two experienced radiologists segmented and extracted radiomics features on preoperative thin-slice CT images with radiomics extension independently. Intraclass correlation coefficients (ICC) and Pearson's correlation were used to rule out those low reliable (ICC < 0.75) and redundant (r > 0.9) features. Univariate logistic regression was applied to select radiomics features which were associated with STAS. A radiomics-based machine learning predictive model using a random forest (RF) was developed and calibrated with fivefold cross-validation. The diagnostic performance of the model was measured by the area under the curve (AUC) of receiver operating characteristic (ROC). RESULTS: With univariate analysis, 12 radiomics features and age were found to be associated with STAS significantly. The RF model achieved an AUC of 0.754 (a sensitivity of 0.880 and a specificity of 0.588) for predicting STAS. CONCLUSION: CT-based radiomics model can preoperatively predict STAS in lung adenocarcinoma with good diagnosis performance. KEY POINTS: • CT-based radiomics and machine learning model can predict spread through air space (STAS) in lung adenocarcinoma with high accuracy. • The random forest (RF) model achieved an AUC of 0.754 (a sensitivity of 0.880 and a specificity of 0.588) for predicting STAS.

Risk of venous thromboembolism in Chinese pregnant women: Hong Kong venous thromboembolism study.

BACKGROUND: Previous Caucasian studies have described venous thromboembolism in pregnancy; however, little is known about its incidence during pregnancy and early postpartum period in the Chinese population. We investigated the risk of venous thromboembolism in a "real-world" cohort of pregnant Chinese women with no prior history of venous thromboembolism. METHODS: In this observational study, 15,325 pregnancies were identified in 14,162 Chinese women at Queen Mary Hospital, Hong Kong between January 2004 and September 2016. Demographic data, obstetric information, and laboratory and imaging data were retrieved and reviewed. RESULTS: The mean age at pregnancy was 32.4±5.3 years, and the median age was 33 years (interquartile range, 29-36 yr). Pre-existing or newly diagnosed diabetes mellitus was present in 627 women (4.1%); 359 (0.7%) women had pre-existing or newly detected hypertension. There was a small number of women with pre-existing heart disease and/or rheumatic conditions. Most deliveries (86.0%) were normal vaginal; the remaining were Cesarean section 2,146 (14.0%). The incidence of venous thromboembolism was 0.4 per 1,000 pregnancies, of which 83.3% were deep vein thrombosis and 16.7% were pulmonary embolism. In contrast to previous studies, 66.7% of venous thrombosis occurred in the first trimester. CONCLUSION: Chinese women had a substantially lower risk of venous thromboembolism during pregnancy and the postpartum period compared to that of Caucasians. The occurrence of pregnancy-related venous thromboembolism was largely confined to the early pregnancy period, probably related to the adoption of thromboprophylaxis, a lower rate of Cesarean section, and early mobilization.

Prediction of molecular subtypes of breast cancer using BI-RADS features based on a "white box" machine learning approach in a multi-modal imaging setting.

PURPOSE: To develop and validate an interpretable and repeatable machine learning model approach to predict molecular subtypes of breast cancer from clinical metainformation together with mammography and MRI images. METHODS: We retrospectively assessed 363 breast cancer cases (Luminal A 151, Luminal B 96, HER2 76, and BLBC 40). Eighty-two features defined in the BI-RADS lexicon were visually described. A decision tree model with the Chi-squared automatic interaction detector (CHAID) algorithm was applied for feature selection and classification. A 10-fold cross-validation was performed to investigate the performance (i.e., accuracy, positive predictive value, sensitivity, and F1-score) of the decision tree model. RESULTS: Seven of the 82 variables were derived from the decision tree-based feature selection and used as features for the classification of molecular subtypes including mass margin calcification on mammography, mass margin types of kinetic curves in the delayed phase, mass internal enhancement characteristics, non-mass enhancement distribution on MRI, and breastfeeding history. The decision tree model accuracy was 74.1%. For each molecular subtype group, Luminal A achieved a sensitivity, positive predictive value, and F1-score of 79.47%, 75.47%, and 77.42%, respectively; Luminal B showed a sensitivity, positive predictive value, and F1-score of 64.58%, 55.86%, and 59.90%, respectively; HER2 had a sensitivity, positive predictive value, and F1-scores of 81.58%, 95.38%, and 87.94%, respectively; BLBC showed sensitivity, positive predictive value, and F1-scores of 62.50%, 89.29%, and 73.53%, respectively. CONCLUSIONS: We applied a complete "white box" machine learning method to predict the molecular subtype of breast cancer based on the BI-RADS feature description in a multi-modal setting. By combining BI-RADS features in both mammography and MRI, the prediction accuracy is boosted and robust. The proposed method can be easily applied widely regardless of variability of imaging vendors and settings because of the applicability and acceptance of the BI-RADS.

Comparison of T1 Mapping and T1rho Values with Conventional Diffusion-weighted Imaging to Assess Fibrosis in a Rat Model of Unilateral Ureteral Obstruction.

RATIONALE AND OBJECTIVES: The aim of this study was to investigate the potential of magnetic resonance imaging (MRI) T1 mapping and T1 relaxation time in the rotating frame (T1rho) for assessment of renal fibrosis in a rat model of unilateral ureteral obstruction (UUO). MATERIALS AND METHODS: UUO was created in 36 rats. Six rats were scanned at each of the six time points (on days 0, 1, 3, 5, 10, and 15 after UUO). The contralateral kidneys were examined as controls. Hematoxylin-eosin, Masson's trichrome, and alpha-smooth muscle actin (α-SMA) antibody staining assays were performed. MRI data obtained with a 3.0T scanner were analyzed with α-SMA expression and Masson's staining. RESULTS: The T1 relaxation times and T1rho values increased, and the mean apparent diffusion coefficient (ADC) values decreased with time after UUO. Simple regression analysis indicated that the mean ADCs, T1 relaxation times, and T1rho values had strong correlations with the α-SMA expression levels (R2 = 0.34, R2 = 0.66, R2 = 0.71, respectively; P< .001) and positive Masson's staining (R2 = 0.38, R2 = 0.67, R2 = 0.65, respectively; P< .001). CONCLUSIONS: The T1 mapping and T1rho parameters had better correlations with α-SMA expression and Masson's staining than ADC values.

Role of Soluble ST2 Levels and Beta-Blockers Dosage on Cardiovascular Events of Patients with Unselected ST-Segment Elevation Myocardial Infarction.

BACKGROUND: Serum soluble ST2 (sST2) levels are elevated early after acute myocardial infarction and are related to adverse left ventricular (LV) remodeling and cardiovascular outcomes in ST-segment elevation myocardial infarction (STEMI). Beta-blockers (BB) have been shown to improve LV remodeling and survival. However, the relationship between sST2, final therapeutic BB dose, and cardiovascular outcomes in STEMI patients remains unknown. METHODS: A total of 186 STEMI patients were enrolled at the Wuhan Asia Heart Hospital between January 2015 and June 2015. All patients received standard treatment and were followed up for 1 year. Serum sST2 was measured at baseline. Patients were divided into four groups according to their baseline sST2 values (high >56 ng/ml vs. low ≤56 ng/ml) and final therapeutic BB dose (high ≥47.5 mg/d vs. low <47.5 mg/d). Cox regression analyses were performed to determine whether sST2 and BB were independent risk factors for cardiovascular events in STEMI. RESULTS: Baseline sST2 levels were positively correlated with heart rate (r = 0.327, P = 0.002), Killip class (r = 0.408, P = 0.000), lg N-terminal prohormone B-type natriuretic peptide (r = 0.467, P = 0.000), lg troponin I (r = 0.331, P = 0.000), and lg C-reactive protein (r = 0.307, P = 0.000) and negatively correlated to systolic blood pressure (r = -0.243, P = 0.009) and LV ejection fraction (r = -0.402, P = 0.000). Patients with higher baseline sST2 concentrations who were not titrated to high-dose BB therapy (P < 0.0001) had worse outcomes. Baseline high sST2 (hazard ratio [HR]: 2.653; 95% confidence interval [CI]: 1.201-8.929; P = 0.041) and final low BB dosage (HR: 1.904; 95% CI, 1.084-3.053; P = 0.035) were independent predictors of cardiovascular events in STEMI. CONCLUSIONS: High baseline sST2 levels and final low BB dosage predicted cardiovascular events in STEMI. Hence, sST2 may be a useful biomarker in cardiac pathophysiology.

Staging of rat liver fibrosis using monoexponential, stretched exponential and diffusion kurtosis models with diffusion weighted imaging- magnetic resonance.

Early diagnosis of liver fibrosis is important. The objective of this study was to explore the characteristics and to assess the accuracy of monoexponential, stretched exponential models (SEM), and diffusion kurtosis imaging (DKI) with diffusion-weighted imaging (DWI)-magnetic resonance imaging (MRI) in various stages of liver fibrosis in two standard rat models induced by carbon tetrachloride (CCl4) and biliary duct ligation (BDL). Parameters (ADC, Dapp, Kapp, DDC, α) were measured with a 3.0T MRI. Liver fibrosis stages (F0-F4) were defined by METAVIR scoring. Parameters (ADC, Dapp, DDC) were found to be negatively associated (r: -0.675~-0.789; P<0.05) with advancement of fibrosis stage. The analysis of receiver operating characteristic (ROC) curves illustrated that the areas under the curves (AUC) for ADC, Dapp, and DDC were 0.687~0.957, 0.805~0.938 and 0.876~1.000, respectively. The study showed that (ADC, Dapp, Kapp, DDC, α) from various diffusion models reflected pathological and physiological tissue changes. We conclude that SEM and DKI may provide more accurate information about diffusion, and non-Gaussian diffusion analysis may be a complementary tool for the assessment of liver fibrosis.

Prognostic Value of Plasma Long Noncoding RNA ANRIL for In-Stent Restenosis.

BACKGROUND In-stent restenosis (ISR) remains a major cause of failure of contemporary percutaneous revascularization therapies. Invasive biomarkers to improve the prognosis of ISR should be considered. This study aimed to investigate the association between plasma ANRIL expression and ISR. MATERIAL AND METHODS A total of 444 patients were included in this research. Serial coronary angiography was performed at baseline (before and after intervention) and within 36 months' follow-up. ISR was defined as >50% diameter stenosis at follow-up. ANRIL expression was quantified using reverse transcription-PCR. An area under the ROC curve (auROC) was generated to assess the diagnostic values of ANRIL. Logistic regression models were used to assess the independent risk factors for ISR. RESULTS Plasma ANRIL expression was significantly increased in patients with ISR, as compared with that in patients without ISR (1.6 [1.1-2.5] vs. 0.9 [0.6-1.3], P<0.001). The auROC (95% confidence interval [CI]) of plasma ANRIL in diagnosing ISR was 0.745 (0.687-0.811). Multiple logistic regression models indicated that drinking (odds ratio [OR]=2.09, 95% CI: 1.08-4.04, P=0.028), hypertension (OR=2.01, 95% CI: 1.14-3.57, P=0.017), diabetes (OR=3.15, 95% CI: 1.63-3.57, P<0.001), low-density lipoprotein (OR=3.14, 95% CI: 1.57-6.31, P=0.001), and ANRIL (OR=2.21, 95% CI: 1.68-2.92, P<0.001) were the independent risk factors for ISR. CONCLUSIONS We found that higher ANRIL expression is associated with ISR, indicating that ANRIL may be an optimal prognostic factor for ISR.

Association Between Imaging Characteristics and Different Molecular Subtypes of Breast Cancer.

RATIONALE AND OBJECTIVE: Breast cancer can be divided into four major molecular subtypes based on the expression of hormone receptor (estrogen receptor and progesterone receptor), human epidermal growth factor receptor 2, HER2 status, and molecular proliferation rate (Ki67). In this study, we sought to investigate the association between breast cancer subtype and radiological findings in the Chinese population. MATERIALS AND METHODS: Medical records of 300 consecutive invasive breast cancer patients were reviewed from the database: the Breast Imaging Reporting and Data System. The imaging characteristics of the lesions were evaluated. The molecular subtypes of breast cancer were classified into four types: luminal A, luminal B, HER2 overexpressed (HER2), and basal-like breast cancer (BLBC). Univariate and multivariate logistic regression analyses were performed to assess the association between the subtype (dependent variable) and mammography or 15 magnetic resonance imaging (MRI) indicators (independent variables). RESULTS: Luminal A and B subtypes were commonly associated with "clustered calcification distribution," "nipple invasion," or "skin invasion" (P <0.05). The BLBC subtype was more commonly associated with "rim enhancement" and persistent inflow type enhancement in delayed phase (P <0.05). HER2 overexpressed cancers showed association with persistent enhancement in the delayed phase on MRI and "clustered calcification distribution" on mammography (P <0.05). CONCLUSION: Certain radiological features are strongly associated with the molecular subtype and hormone receptor status of breast tumor, which are potentially useful tools in the diagnosis and subtyping of breast cancer.

Use of intra-aortic balloon pump support for oozing-type cardiac rupture after acute myocardial infarction.

Left ventricular free wall rupture usually leads to acute hemopericardium and sudden cardiac death resulting in cardiac tamponade. Rarely, only a few patients with subacute free wall rupture such as oozing-type ventricular rupture or left ventricular false aneurysm may permit time for pericardiocentesis and surgery. We report a 63-year-old man with ST-elevation myocardial infarction who underwent primary percutaneous coronary intervention about 12 hours from the onset, and cardiac tamponade occurred on the second day. An intra-aortic balloon pump (IABP) was immediately inserted for hemodynamic support. After 100 mL of pericardial fresh blood was drained from the percardial cavity, his hemodynamic collapse was promptly improved with IABP support. In the following 24 hours, about 600 mL of hemorrhagic pericardial fluid was drained. The most likely diagnosis was concerning for oozing-type ventricular rupture, and a conservative approach was decided. The patient survived to the acute phase under IABP support and was discharged with complete recovery.