Preoperative Differentiation of HER2-Zero and HER2-Low from HER2-Positive Invasive Ductal Breast Cancers Using BI-RADS MRI Features and Machine Learning Modeling.

BACKGROUND: Accurate determination of human epidermal growth factor receptor 2 (HER2) is important for choosing optimal HER2 targeting treatment strategies. HER2-low is currently considered HER2-negative, but patients may be eligible to receive new anti-HER2 drug conjugates. PURPOSE: To use breast MRI BI-RADS features for classifying three HER2 levels, first to distinguish HER2-zero from HER2-low/positive (Task-1), and then to distinguish HER2-low from HER2-positive (Task-2). STUDY TYPE: Retrospective. POPULATION: 621 invasive ductal cancer, 245 HER2-zero, 191 HER2-low, and 185 HER2-positive. For Task-1, 488 cases for training and 133 for testing. For Task-2, 294 cases for training and 82 for testing. FIELD STRENGTH/SEQUENCE: 3.0 T; 3D T1-weighted DCE, short time inversion recovery T2, and single-shot EPI DWI. ASSESSMENT: Pathological information and BI-RADS features were compared. Random Forest was used to select MRI features, and then four machine learning (ML) algorithms: decision tree (DT), support vector machine (SVM), k-nearest neighbors (k-NN), and artificial neural nets (ANN), were applied to build models. STATISTICAL TESTS: Chi-square test, one-way analysis of variance, and Kruskal-Wallis test were performed. The P values <0.05 were considered statistically significant. For ML models, the generated probability was used to construct the ROC curves. RESULTS: Peritumoral edema, the presence of multiple lesions and non-mass enhancement (NME) showed significant differences. For distinguishing HER2-zero from non-zero (low + positive), multiple lesions, edema, margin, and tumor size were selected, and the k-NN model achieved the highest AUC of 0.86 in the training set and 0.79 in the testing set. For differentiating HER2-low from HER2-positive, multiple lesions, edema, and margin were selected, and the DT model achieved the highest AUC of 0.79 in the training set and 0.69 in the testing set. DATA CONCLUSION: BI-RADS features read by radiologists from preoperative MRI can be analyzed using more sophisticated feature selection and ML algorithms to build models for the classification of HER2 status and identify HER2-low. TECHNICAL EFFICACY: Stage 2.

Acetylene Tunes Microbial Growth During Aerobic Cometabolism of Trichloroethene.

Microbial aerobic cometabolism is a possible treatment approach for large, dilute trichloroethene (TCE) plumes at groundwater contaminated sites. Rapid microbial growth and bioclogging pose a persistent problem in bioremediation schemes. Bioclogging reduces soil porosity and permeability, which negatively affects substrate distribution and contaminant treatment efficacy while also increasing the operation and maintenance costs of bioremediation. In this study, we evaluated the ability of acetylene, an oxygenase enzyme-specific inhibitor, to decrease biomass production while maintaining aerobic TCE cometabolism capacity upon removal of acetylene. We first exposed propane-metabolizing cultures (pure and mixed) to 5% acetylene (v v-1) for 1, 2, 4, and 8 d and we then verified TCE aerobic cometabolic activity. Exposure to acetylene overall decreased biomass production and TCE degradation rates while retaining the TCE degradation capacity. In the mixed culture, exposure to acetylene for 1-8 d showed minimal effects on the composition and relative abundance of TCE cometabolizing bacterial taxa. TCE aerobic cometabolism and incubation conditions exerted more notable effects on microbial ecology than did acetylene. Acetylene appears to be a viable approach to control biomass production that may lessen the likelihood of bioclogging during TCE cometabolism. The findings from this study may lead to advancements in aerobic cometabolism remediation technologies for dilute plumes.

Radiomics analysis for the prediction of locoregional recurrence of locally advanced oropharyngeal cancer and hypopharyngeal cancer.

PURPOSE: By radiomic analysis of the postcontrast CT images, this study aimed to predict locoregional recurrence (LR) of locally advanced oropharyngeal cancer (OPC) and hypopharyngeal cancer (HPC). METHODS: A total of 192 patients with stage III-IV OPC or HPC from two independent cohort were randomly split into a training cohort with 153 cases and a testing cohort with 39 cases. Only primary tumor mass was manually segmented. Radiomic features were extracted using PyRadiomics, and then the support vector machine was used to build the radiomic model with fivefold cross-validation process in the training data set. For each case, a radiomics score was generated to indicate the probability of LR. RESULTS: There were 94 patients with LR assigned in the progression group and 98 patients without LR assigned in the stable group. There was no significant difference of TNM staging, treatment strategies and common risk factors between these two groups. For the training data set, the radiomics model to predict LR showed 83.7% accuracy and 0.832 (95% CI 0.72, 0.87) area under the ROC curve (AUC). For the test data set, the accuracy and AUC slightly declined to 79.5% and 0.770 (95% CI 0.64, 0.80), respectively. The sensitivity/specificity of training and test data set for LR prediction were 77.6%/89.6%, and 66.7%/90.5%, respectively. CONCLUSIONS: The image-based radiomic approach could provide a reliable LR prediction model in locally advanced OPC and HPC. Early identification of those prone to post-treatment recurrence would be helpful for appropriate adjustments to treatment strategies and post-treatment surveillance.

Prediction of poor outcome in stroke patients using radiomics analysis of intraparenchymal and intraventricular hemorrhage and clinical factors.

PURPOSE: To build three prognostic models using radiomics analysis of the hemorrhagic lesions, clinical variables, and their combination, to predict the outcome of stroke patients with spontaneous intracerebral hemorrhage (sICH). MATERIALS AND METHODS: Eighty-three sICH patients were included. Among them, 40 patients (48.2%) had poor prognosis with modified Rankin scale (mRS) of 5 and 6 at discharge, and the prognostic model was built to differentiate mRS ≤ 4 vs. 5 + 6. The region of interest (ROI) of intraparenchymal hemorrhage (IPH) and intraventricular hemorrhage (IVH) were separately segmented. Features were extracted using PyRadiomics, and the support vector machine was applied to select features and build radiomics models based on IPH and IPH + IVH. The clinical models were built using multivariate logistic regression, and then the radiomics scores were combined with clinical variables to build the combined model. RESULTS: When using IPH, the AUC for radiomics, clinical, and combined model was 0.78, 0.82, and 0.87, respectively. When using IPH + IVH, the AUC was increased to 0.80, 0.84, and 0.90, respectively. The combined model had a significantly improved AUC compared to the radiomics by DeLong test. A clinical prognostic model based on the ICH score of 0-1 only achieved AUC of 0.71. CONCLUSIONS: The combined model using the radiomics score derived from IPH + IVH and the clinical factors could achieve a high accuracy in prediction of sICH patients with poor outcome, which may be used to assist in making the decision about the optimal care.

Assembling metal-organic cages as porous materials.

There is growing interest in metal-organic cages (MOCs) as porous materials owing to their processability in solution. The discrete molecular character and surface features of MOCs have a direct impact on the interactions between cages, enabling the final physical state of the materials to be tuned. In this tutorial review, we discuss how to use MOCs as core building units, highlighting the role played by surface functionalisation of MOCs in leading to porous materials in a range of states covering crystalline solids, soft matter, liquids and composites. We finish by providing an outlook on the opportunities for this work to serve as a foundation for the development of increasingly complex functional porous materials structured over various length scales.

Antinociceptive C19-diterpenoid alkaloids from the roots of Aconitum nagarum.

Three new C19-diterpenoid alkaloids, nagarumines A-C (1-3), together two known alkaloids, deoxyaconitine (4) and N-deethyldeoxyaconitine (5), were isolated from the roots of Aconitum nagarum. The structures of the new compounds were elucidated by spectral methods such as 1D and 2D (1H-1H COSY, HMQC, and HMBC) NMR spectroscopy, as well as high resolution mass spectrometry. The in vivo pharmacological studies revealed that nagarumine C (3) possessed comparable antinociceptive activity (ED50 = 76.0 µmol/kg) with the positive control drugs aspirin and acetaminophen.

Body Map of Droplet Distributions During Oropharyngeal Suction to Protect Health Care Workers From Airborne Diseases.

PURPOSE: Health care workers (HCWs), and in particular anesthesia providers, often must perform aerosol-generating medical procedures (AGMPs). However, no studies have analyzed droplet distributions on the bodies of HCWs during AGMPs. Therefore, the purpose of this study was to assess and analyze droplet distributions on the bodies of HCWs during suction of oral cavities with and without oral airways and during extubations. DESIGN: Using a quasi-experiemental design, we assumed the HCWs perform suction and extubation on intubated patients, and we prepared an intubated mannequin mimicking a patient. This study performed the oral suction and extubation on the intubated mannequin (with or without oral airways in place) and analyzed the droplet distributions. METHODS: We prepared a mannequin intubated with an 8.0 mm endotracheal tube, assuming the situation of general anesthesia. We designed the body mapping gown, and divided it into 10 areas including the head, neck, chest, abdomen, upper arms, forearms, and hands. We classified experiments into group O when suctions were performed on the mannequin with an oral airway, and into group X when the suctions were performed on the mannequin without an oral airway. An experienced board-certified anesthesiologist performed 10 oral suctions on each mannequin, and 10 extubations. We counted the droplets on the anesthesiologist's gown according to the divided areas after each procedure. FINDINGS: The mean droplet count after suction was 6.20 ± 2.201 in group O and 13.6 ± 4.300 in group X, with a significant difference between the two groups (P < .001). The right and left hands were the most contaminated areas in group O (2.8 ± 1.033 droplets and 2.0 ± 0.943 droplets, respectively). The abdomen, right hand, left forearm, and left hand showed many droplets in group X. (1.3 ± 1.337 droplets, 3.1 ± 1.792 droplets, 3.2 ± 3.910 droplets, and 4.3 ± 2.214 droplets, respectively). The chest, abdomen, and left hand presented significantly more droplets in group X than in group O. The trunk area (chest and abdomen) was exposed to more droplets during extubations than during suctions. CONCLUSIONS: During suctions, more droplets are splattered from mannequins without oral airways than from those with oral airways. The right and left hands were the most contaminated areas in group O. Moreover, the abdomen, right hand, left forearm, and left hand presented a lot of droplets in group X. In addition, extubations contaminate wider areas (the head, neck, chest and abdomen) of an HCW than suctions.

Assessment of breast lesions by the Kaiser score for differential diagnosis on MRI: the added value of ADC and machine learning modeling.

OBJECTIVES: To evaluate the diagnostic performance of Kaiser score (KS) adjusted with the apparent diffusion coefficient (ADC) (KS+) and machine learning (ML) modeling. METHODS: A dataset of 402 malignant and 257 benign lesions was identified. Two radiologists assigned the KS. If a lesion with KS > 4 had ADC > 1.4 × 10-3 mm2/s, the KS was reduced by 4 to become KS+. In order to consider the full spectrum of ADC as a continuous variable, the KS and ADC values were used to train diagnostic models using 5 ML algorithms. The performance was evaluated using the ROC analysis, compared by the DeLong test. The sensitivity, specificity, and accuracy achieved using the threshold of KS > 4, KS+ > 4, and ADC ≤ 1.4 × 10-3 mm2/s were obtained and compared by the McNemar test. RESULTS: The ROC curves of KS, KS+, and all ML models had comparable AUC in the range of 0.883-0.921, significantly higher than that of ADC (0.837, p < 0.0001). The KS had sensitivity = 97.3% and specificity = 59.1%; and the KS+ had sensitivity = 95.5% with significantly improved specificity to 68.5% (p < 0.0001). However, when setting at the same sensitivity of 97.3%, KS+ could not improve specificity. In ML analysis, the logistic regression model had the best performance. At sensitivity = 97.3% and specificity = 65.3%, i.e., compared to KS, 16 false-positives may be avoided without affecting true cancer diagnosis (p = 0.0015). CONCLUSION: Using dichotomized ADC to modify KS to KS+ can improve specificity, but at the price of lowered sensitivity. Machine learning algorithms may be applied to consider the ADC as a continuous variable to build more accurate diagnostic models. KEY POINTS: • When using ADC to modify the Kaiser score to KS+, the diagnostic specificity according to the results of two independent readers was improved by 9.4-9.7%, at the price of slightly degraded sensitivity by 1.5-1.8%, and overall had improved accuracy by 2.6-2.9%. • When the KS and the continuous ADC values were combined to train models by machine learning algorithms, the diagnostic specificity achieved by the logistic regression model could be significantly improved from 59.1 to 65.3% (p = 0.0015), while maintaining at the high sensitivity of KS = 97.3%, and thus, the results demonstrated the potential of ML modeling to further evaluate the contribution of ADC. • When setting the sensitivity at the same levels, the modified KS+ and the original KS have comparable specificity; therefore, KS+ with consideration of ADC may not offer much practical help, and the original KS without ADC remains as an excellent robust diagnostic method.

A deep learning-based method for the diagnosis of vertebral fractures on spine MRI: retrospective training and validation of ResNet.

PURPOSE: To improve the performance of less experienced clinicians in the diagnosis of benign and malignant spinal fracture on MRI, we applied the ResNet50 algorithm to develop a decision support system. METHODS: A total of 190 patients, 50 with malignant and 140 with benign fractures, were studied. The visual diagnosis was made by one senior MSK radiologist, one fourth-year resident, and one first-year resident. The MSK radiologist also gave the binary score for 15 qualitative imaging features. Deep learning was implemented using ResNet50, using one abnormal spinal segment selected from each patient as input. The T1W and T2W images of the lesion slice and its two neighboring slices were considered. The diagnostic performance was evaluated using tenfold cross-validation. RESULTS: The overall reading accuracy was 98, 96, and 66% for the senior MSK radiologist, fourth-year resident, and first-year resident, respectively. Of the 15 imaging features, 10 showed a significant difference between benign and malignant groups with p < = 0.001. The accuracy achieved by using the ResNet50 deep learning model for the identified abnormal vertebral segment was 92%. Compared to the first-year resident's reading, the model improved the sensitivity from 78 to 94% (p < 0.001) and the specificity from 61 to 91% (p < 0.001). CONCLUSION: Our deep learning-based model may provide information to assist less experienced clinicians in the diagnosis of spinal fractures on MRI. Other findings away from the vertebral body need to be considered to improve the model, and further investigation is required to generalize our findings to real-world settings.

A Comparison of the Clinical Outcomes of Thoracic Endovascular Repair for Acute Type B Aortic Dissection with Multichanneled and Double-Channeled Morphology.

In this study, we aim to investigate the clinical features and outcomes of multichanneled aortic dissection (MCAD) and double-channeled aortic dissection (DCAD) in acute type B aortic dissection (TBAD) patients who underwent thoracic endovascular aortic repair (TEVAR).In total, 479 consecutive acute TBAD patients treated with TEVAR from April 2002 to May 2020 were retrospectively enrolled in this study. The MCAD group was defined as those of multichanneled morphology by initial computed tomography angiography (CTA) (n = 61), whereas the DCAD group was defined as those with double-channeled morphology by initial CTA (n = 418). The clinical and morphological characteristics and short-term and long-term adverse events (30-day and > 30 days) were recorded and evaluated.No significant differences were noted between the 2 groups as regards demographics, comorbidity profiles, or initial feature of CTA. The incidence of true lumen compression was found to be significantly lower in the MCAD group compared with the DCAD group (8.2% versus 20.8%, P < 0.05). During the 65.37 ± 40.06 months of follow-up, there were no statistically significant differences in terms of 30-day mortality or the incidence of early adverse events between the 2 groups. The incidence rates of 5-year cumulative freedom from all-cause mortality and 5-year cumulative freedom from AD-related mortality were not significantly different between the MCAD and DCAD groups, whereas the 5-year cumulative freedom from adverse events were lower in the MCAD group compared to DCAD group (51.1% versus 72.5%, P < 0.05). In multivariate Cox regression models, only age > 60 years, pleural effusion, branch involvement, and length of the stent were independent predictors of mortality, whereas age > 60 years, pulse, pleural effusion, true lumen compression, widest diameter of the descending aorta, branch involvement, and length of stent were independent predictors of adverse aortic events.No significant difference was noted between the MCAD and DCAD groups in the 5-year mortality following, whereas patients with MCAD were found to have significantly lower AD-related events than patients with DCAD in long-term follow-up.

Differential diagnosis of benign and malignant vertebral fracture on CT using deep learning.

OBJECTIVES: To evaluate the performance of deep learning using ResNet50 in differentiation of benign and malignant vertebral fracture on CT. METHODS: A dataset of 433 patients confirmed with 296 malignant and 137 benign fractures was retrospectively selected from our spinal CT image database. A senior radiologist performed visual reading to evaluate six imaging features, and three junior radiologists gave diagnostic prediction. A ROI was placed on the most abnormal vertebrae, and the smallest square bounding box was generated. The input channel into ResNet50 network was 3, including the slice with its two neighboring slices. The diagnostic performance was evaluated using 10-fold cross-validation. After obtaining the malignancy probability from all slices in a patient, the highest probability was assigned to that patient to give the final diagnosis, using the threshold of 0.5. RESULTS: Visual features such as soft tissue mass and bone destruction were highly suggestive of malignancy; the presence of a transverse fracture line was highly suggestive of a benign fracture. The reading by three radiologists with 5, 3, and 1 year of experience achieved an accuracy of 99%, 95.2%, and 92.8%, respectively. In ResNet50 analysis, the per-slice diagnostic sensitivity, specificity, and accuracy were 0.90, 0.79, and 85%. When the slices were combined to ve per-patient diagnosis, the sensitivity, specificity, and accuracy were 0.95, 0.80, and 88%. CONCLUSION: Deep learning has become an important tool for the detection of fractures on CT. In this study, ResNet50 achieved good accuracy, which can be further improved with more cases and optimized methods for future clinical implementation. KEY POINTS: • Deep learning using ResNet50 can yield a high accuracy for differential diagnosis of benign and malignant vertebral fracture on CT. • The per-slice diagnostic sensitivity, specificity, and accuracy were 0.90, 0.79, and 85% in deep learning using ResNet50 analysis. • The slices combined with per-patient diagnostic sensitivity, specificity, and accuracy were 0.95, 0.80, and 88% in deep learning using ResNet50 analysis.

Prediction of breast cancer molecular subtypes on DCE-MRI using convolutional neural network with transfer learning between two centers.

OBJECTIVES: To apply deep learning algorithms using a conventional convolutional neural network (CNN) and a recurrent CNN to differentiate three breast cancer molecular subtypes on MRI. METHODS: A total of 244 patients were analyzed, 99 in training dataset scanned at 1.5 T and 83 in testing-1 and 62 in testing-2 scanned at 3 T. Patients were classified into 3 subtypes based on hormonal receptor (HR) and HER2 receptor: (HR+/HER2-), HER2+, and triple negative (TN). Only images acquired in the DCE sequence were used in the analysis. The smallest bounding box covering tumor ROI was used as the input for deep learning to develop the model in the training dataset, by using a conventional CNN and the convolutional long short-term memory (CLSTM). Then, transfer learning was applied to re-tune the model using testing-1(2) and evaluated in testing-2(1). RESULTS: In the training dataset, the mean accuracy evaluated using tenfold cross-validation was higher by using CLSTM (0.91) than by using CNN (0.79). When the developed model was applied to the independent testing datasets, the accuracy was 0.4-0.5. With transfer learning by re-tuning parameters in testing-1, the mean accuracy reached 0.91 by CNN and 0.83 by CLSTM, and improved accuracy in testing-2 from 0.47 to 0.78 by CNN and from 0.39 to 0.74 by CLSTM. Overall, transfer learning could improve the classification accuracy by greater than 30%. CONCLUSIONS: The recurrent network using CLSTM could track changes in signal intensity during DCE acquisition, and achieved a higher accuracy compared with conventional CNN during training. For datasets acquired using different settings, transfer learning can be applied to re-tune the model and improve accuracy. KEY POINTS: • Deep learning can be applied to differentiate breast cancer molecular subtypes. • The recurrent neural network using CLSTM could track the change of signal intensity in DCE images, and achieved a higher accuracy compared with conventional CNN during training. • For datasets acquired using different scanners with different imaging protocols, transfer learning provided an efficient method to re-tune the classification model and improve accuracy.

Visceral fat area to appendicular muscle mass ratio as a predictor for nonalcoholic fatty liver disease independent of obesity.

OBJECTIVE: Obesity and sarcopenia are known to be closely related to nonalcoholic fatty liver disease (NAFLD). We attempted to explore the combined influence of fat and muscle tissue on NAFLD by using visceral fat area to appendicular muscle mass ratio (VAR) as a novel parameter. MATERIAL AND METHODS: In this cross-sectional study, a total of 3255 adults (1399 men and 1856 women) coming for a health examination were enrolled. NAFLD was diagnosed using ultrasound and VAR was measured by bioelectrical impedance analyzer. RESULTS: The prevalence of NAFLD was 46.5% in men and 26.6% in women. VAR differed significantly between subjects with and without NAFLD (4.27 vs. 3.26 in men, 7.89 vs. 5.01 in women, respectively, p < .001). Logistic regression analysis determined VAR as a risk factor for NAFLD, and the multivariable-adjusted odds ratios in the highest VAR quartile was 9.57 (95%CI: 5.98-15.30) for men and 12.37 (95%CI: 6.37-24.05) for women. From the receiver operating characteristic analysis, the area under the curve was 0.767 and 0.834, with the suitable cut-off VAR value of 3.469 and 6.357 for men and women, respectively. To control the influence of obesity, all subjects were stratified according to their BMI. For each BMI group, individuals with VAR above the cut-off value had significant higher prevalence and risk of NAFLD, with odds ratios ranging from 1.76 to 4.75. CONCLUSIONS: Increased VAR is strongly associated with higher risk of NAFLD in both sexes independent of obesity and can serve as a screening reference for NAFLD.

Development of U-Net Breast Density Segmentation Method for Fat-Sat MR Images Using Transfer Learning Based on Non-Fat-Sat Model.

To develop a U-net deep learning method for breast tissue segmentation on fat-sat T1-weighted (T1W) MRI using transfer learning (TL) from a model developed for non-fat-sat images. The training dataset (N = 126) was imaged on a 1.5 T MR scanner, and the independent testing dataset (N = 40) was imaged on a 3 T scanner, both using fat-sat T1W pulse sequence. Pre-contrast images acquired in the dynamic-contrast-enhanced (DCE) MRI sequence were used for analysis. All patients had unilateral cancer, and the segmentation was performed using the contralateral normal breast. The ground truth of breast and fibroglandular tissue (FGT) segmentation was generated using a template-based segmentation method with a clustering algorithm. The deep learning segmentation was performed using U-net models trained with and without TL, by using initial values of trainable parameters taken from the previous model for non-fat-sat images. The ground truth of each case was used to evaluate the segmentation performance of the U-net models by calculating the dice similarity coefficient (DSC) and the overall accuracy based on all pixels. Pearson's correlation was used to evaluate the correlation of breast volume and FGT volume between the U-net prediction output and the ground truth. In the training dataset, the evaluation was performed using tenfold cross-validation, and the mean DSC with and without TL was 0.97 vs. 0.95 for breast and 0.86 vs. 0.80 for FGT. When the final model developed with and without TL from the training dataset was applied to the testing dataset, the mean DSC was 0.89 vs. 0.83 for breast and 0.81 vs. 0.81 for FGT, respectively. Application of TL not only improved the DSC, but also decreased the required training case number. Lastly, there was a high correlation (R2 > 0.90) for both the training and testing datasets between the U-net prediction output and ground truth for breast volume and FGT volume. U-net can be applied to perform breast tissue segmentation on fat-sat images, and TL is an efficient strategy to develop a specific model for each different dataset.

Mitochondria-targeted Re(I) complexes bearing guanidinium as ligands and their anticancer activity.

As the "powerhouse" of a cell, mitochondria maintain energy homeostasis, synthesize ATP via oxidative phosphorylation, generate ROS signaling molecules, and modulate cell apoptosis. Herein, three Re(I) complexes bearing guanidinium derivatives have been synthesized and characterized. All of these complexes exhibit moderate anticancer activity in HepG2, HeLa, MCF-7, and A549 cancer cells. Mechanism studies indicate that complex 3, [Re(CO)3(L)(Im)](PF6)2, can selectively localize in the mitochondria and induce cancer cell death through mitochondria-associated pathways. In addition, complex 3 can effectively depress the ability of cell migration, cell invasion, and colony formation.

Diagnosis of Benign and Malignant Breast Lesions on DCE-MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue.

BACKGROUND: Computer-aided methods have been widely applied to diagnose lesions detected on breast MRI, but fully-automatic diagnosis using deep learning is rarely reported. PURPOSE: To evaluate the diagnostic accuracy of mass lesions using region of interest (ROI)-based, radiomics and deep-learning methods, by taking peritumor tissues into consideration. STUDY TYPE: Retrospective. POPULATION: In all, 133 patients with histologically confirmed 91 malignant and 62 benign mass lesions for training (74 patients with 48 malignant and 26 benign lesions for testing). FIELD STRENGTH/SEQUENCE: 3T, using the volume imaging for breast assessment (VIBRANT) dynamic contrast-enhanced (DCE) sequence. ASSESSMENT: 3D tumor segmentation was done automatically by using fuzzy-C-means algorithm with connected-component labeling. A total of 99 texture and histogram parameters were calculated for each case, and 15 were selected using random forest to build a radiomics model. Deep learning was implemented using ResNet50, evaluated with 10-fold crossvalidation. The tumor alone, smallest bounding box, and 1.2, 1.5, 2.0 times enlarged boxes were used as inputs. STATISTICAL TESTS: The malignancy probability was calculated using each model, and the threshold of 0.5 was used to make a diagnosis. RESULTS: In the training dataset, the diagnostic accuracy was 76% using three ROI-based parameters, 84% using the radiomics model, and 86% using ROI + radiomics model. In deep learning using the per-slice basis, the area under the receiver operating characteristic (ROC) was comparable for tumor alone, smallest and 1.2 times box (AUC = 0.97-0.99), which were significantly higher than 1.5 and 2.0 times box (AUC = 0.86 and 0.71, respectively). For per-lesion diagnosis, the highest accuracy of 91% was achieved when using the smallest bounding box, and that decreased to 84% for tumor alone and 1.2 times box, and further to 73% for 1.5 times box and 69% for 2.0 times box. In the independent testing dataset, the per-lesion diagnostic accuracy was also the highest when using the smallest bounding box, 89%. DATA CONCLUSION: Deep learning using ResNet50 achieved a high diagnostic accuracy. Using the smallest bounding box containing proximal peritumor tissue as input had higher accuracy compared to using tumor alone or larger boxes. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2.

The chemokine CXCL1 and its receptor CXCR2 contribute to chronic stress-induced depression in mice.

Depression is increasingly recognized as an inflammatory disease, with inflammatory crosstalk in the brain contributing its pathogenesis. Life stresses may up-regulate inflammatory processes and promote depression. Although cytokines are central to stress-related immune responses, their contribution to stress-induced depression remains unclear. Here, we used unpredictable chronic mild stress (UCMS) to induce depression-like behaviors in mice, as assessed through a suite of behavioral tests. C-X-C motif chemokine ligand 1 (CXCL1)-related molecular networks responsible for depression-like behaviors were assessed through intrahippocampal microinjection of lenti-CXCL1, the antidepressant fluoxetine, the C-X-C motif chemokine receptor 2 (CXCR2) inhibitor SB265610, and the glycogen synthase kinase-3ß (GSK3ß) inhibitor AR-A014418. Modulation of apoptosis-related pathways and neuronal plasticity were assessed via quantification of cleaved caspase-3, B-cell lymphoma 2-associated X protein, cAMP response element-binding protein (CREB), and brain-derived neurotrophic factor (BDNF) protein expression. CXCL1/CXCL2 expression was correlated with depression-like behaviors in response to chronic stress or antidepressant treatment in the UCMS depression model. Intrahippocampal microinjection of lenti-CXCL1 increased depression-like behaviors, activated GSK3ß, increased apoptosis pathways, suppressed CREB activation, and decreased BDNF. Administration of the selective GSK3ß inhibitor AR-A014418 abolished the effects of lenti-CXCL1, and the CXCR2 inhibitor SB265610 prevented chronic stress-induced depression-like behaviors, inhibited GSK3ß activity, blocked apoptosis pathways, and restored BDNF expression. The CXCL1/CXCR2 axis appears to play a critical role in stress-induced depression, and CXCR2 is a potential novel therapeutic target for patients with depression.-Chai, H.-H., Fu, X.-C., Ma, L., Sun, H.-T., Chen, G.-Z., Song, M.-Y., Chen, W.-X., Chen, Y.-S., Tan, M.-X., Guo, Y.-W., Li, S.-P. The chemokine CXCL1 and its receptor CXCR2 contribute to chronic stress-induced depression in mice.

Need for Greater Attention to Joint Damage in Rhupus Patients: Results from an Ultrasound Study.

BACKGROUND The aim of this study was to evaluate the prevalence of inflammation and bone destruction of hand joints in rhupus patients through ultrasound examination. MATERIAL AND METHODS Ten rhupus patients and 33 systemic lupus erythematosus (SLE) patients with hand arthropathy were recruited in this single-center study, and the clinical features and ultrasound manifestations of these patients were analyzed. RESULTS We discovered that rhupus patients were older (47.31±4.35 years vs. 38.58±2.50 years, P=0.040), had longer duration of disease (median 72 months vs. median 12 months, P=0.040), had a higher positive rate (70% vs. 10.71%, P<0.001), and had higher titers of anti-CCP antibody (42.633±14.520 vs. 2.121±0.970, P<0.001) than SLE patients with arthropathy. More importantly, the prevalence rates of synovial hyperplasia (90% vs. 42.42%, P=0.008), synovitis (90% vs. 18.18%, P<0.001), synovial hyperplasia (70% vs. 10.71%, P<0.001), and bone destruction (70% vs. 6.06%, P<0.001) were higher in rhupus patients than in SLE patients with arthropathy. CONCLUSIONS Rhupus patients are more prone to develop synovitis, synovial hyperplasia, and bone destruction. Therefore, more attention should be paid to protection of the joints in rhupus patients.

Solitary vertebral metastatic glioblastoma in the absence of primary brain tumor relapse: a case report and literature review.

BACKGROUND: Metastatic glioblastoma presenting as a solitary osteolytic cervical vertebral mass without primary brain tumor relapse is extremely rare with only 1 reported case in the literature. Because of its rarity, it can be easily overlooked and misdiagnosed, posing a diagnostic dilemma. CASE PRESENTATION: A 51-year-old man with right temporal glioblastoma was initially treated by tumor resection, radiotherapy and chemotherapy. Eighteen months after surgery, he was readmitted with complaints of neck pain for 2 weeks. Follow-up magnetic resonance imaging (MRI) and fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) revealed a solitary FDG-avid osteolytic lesion in the 4th cervical vertebral body without other abnormal FDG-uptake in the body and in the absence of local recurrence at the resection cavity. Because of the sudden worsening situation and intractable neck pain, the patient underwent tumor resection. Postoperatively, the pain was obviously reduced and the situation was improved. Interestingly, the immunohistochemical findings of glial fibrillary acidic protein (GFAP) indicated the characteristic of metastatic glioblastoma, despite that the histopathological findings of Hematoxylin & Eosin (H&E) staining was suspicious of osteoclastoma. According to the clinical history, imaging findings, pathological and immunohistochemical results, a final diagnosis of solitary vertebral metastasis from glioblastoma without central nervous system (CNS) relapse was confirmed. Then, the patient received radiotherapy on spine and adjuvant chemotherapy with temozolomide. However, he died suddenly 2 months after the tumor resection, nearly 21 months after the initial diagnosis. CONCLUSION: We emphasize that metastatic glioblastoma should be considered in the differential diagnosis of a solitary FDG-avid osteolytic vertebral mass on PET/CT. And the diagnosis of extracranial metastasis (ECM) from glioblastoma can be achieved through clinical history, imaging findings, pathological examination, and immunohistochemical staining with GFAP.

Diagnosis of spinal lesions using perfusion parameters measured by DCE-MRI and metabolism parameters measured by PET/CT.

PURPOSE: To investigate the correlation of parameters measured by dynamic-contrast-enhanced MRI (DCE-MRI) and 18F-FDG PET/CT in spinal tumors, and their role in differential diagnosis. METHODS: A total of 49 patients with pathologically confirmed spinal tumors, including 38 malignant, six benign and five borderline tumors, were analyzed. The MRI and PET/CT were done within 3 days, before biopsy. On MRI, the ROI was manually placed on area showing the strongest enhancement to measure pharmacokinetic parameters Ktrans and kep. On PET, the maximum standardized uptake value SUVmax was measured. The parameters in different histological groups were compared. ROC was performed to differentiate between the two largest subtypes, metastases and plasmacytomas. Spearman rank correlation was performed to compare DCE-MRI and PET/CT parameters. RESULTS: The Ktrans, kep and SUVmax were not statistically different among malignant, benign and borderline groups (P = 0.95, 0.50, 0.11). There was no significant correlation between Ktrans and SUVmax (r = - 0.20, P = 0.18), or between kep and SUVmax (r = - 0.16, P = 0.28). The kep was significantly higher in plasmacytoma than in metastasis (0.78 ± 0.17 vs. 0.61 ± 0.18, P = 0.02); in contrast, the SUVmax was significantly lower in plasmacytoma than in metastasis (5.58 ± 2.16 vs. 9.37 ± 4.26, P = 0.03). In differential diagnosis, the AUC of kep and SUVmax was 0.79 and 0.78, respectively. CONCLUSIONS: The vascular parameters measured by DCE-MRI and glucose metabolism measured by PET/CT from the most aggressive tumor area did not show a significant correlation. The results suggest they provide complementary information reflecting different aspects of the tumor, which may aid in diagnosis of spinal lesions. These slides can be retrieved under Electronic Supplementary Material.