Discrimination between HER2-overexpressing, -low-expressing, and -zero-expressing statuses in breast cancer using multiparametric MRI-based radiomics.

OBJECTIVES: To explore the performance of multiparametric MRI-based radiomics in discriminating different human epidermal growth factor receptor 2 (HER2) expressing statuses (i.e., HER2-overexpressing, HER2-low-expressing, and HER2-zero-expressing) in breast cancer. METHODS: A total of 771 breast cancer patients from two institutions were retrospectively studied. Five-hundred-eighty-one patients from Institution I were divided into a training dataset (n1 = 407) and an independent validation dataset (n1 = 174); 190 patients from Institution II formed the external validation dataset. All patients were categorized into HER2-overexpressing, HER2-low-expressing, and HER2-zero-expressing groups based on pathologic examination. Multiparametric (including T2-weighted imaging with fat suppression [T2WI-FS], diffusion-weighted imaging [DWI], apparent diffusion coefficient [ADC], and dynamic contrast-enhanced [DCE]) MRI-based radiomics features were extracted and then selected from the training dataset using the least absolute shrinkage and selection operator (LASSO) regression. Three predictive models to discriminate HER2-overexpressing vs. others, HER2-low expressing vs. others, and HER2-zero-expressing vs. others were developed based on the selected features. The model performance was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS: Eleven radiomics features from DWI, ADC, and DCE; one radiomics feature from DWI; and 17 radiomics features from DWI, ADC, and DCE were selected to build three predictive models, respectively. In training, independent validation, and external validation datasets, radiomics models achieved AUCs of 0.809, 0.737, and 0.725 in differentiating HER2-overexpressing from others; 0.779, 0.778, and 0.782 in differentiating HER2-low-expressing from others; and 0.889, 0.867, and 0.813 in differentiating HER2-zero-expressing from others, respectively. CONCLUSIONS: Multiparametric MRI-based radiomics model may preoperatively predict HER2 statuses in breast cancer patients. CLINICAL RELEVANCE STATEMENT: The MRI-based radiomics models could be used to noninvasively identify the new three-classification of HER2 expressing status in breast cancer, which is helpful to the decision-making for HER2-target therapies. KEY POINTS: • Detecting HER2-overexpressing, HER2-low-expressing, and HER2-zero-expressing status in breast cancer patients is crucial for determining candidates for anti-HER2 therapy. • Radiomics features from multiparametric MRI significantly differed among HER2-overexpressing, HER2-low expressing, and HER2-zero-expressing breast cancers. • Multiparametric MRI-based radiomics could preoperatively evaluate three different HER2-expressing statuses and help to determine potential candidates for anti-HER2 therapy in breast cancer patients.

CT-Based Radiomics Models for Differentiation of Benign and Malignant Thyroid Nodules: A Multicenter Development and Validation Study.

BACKGROUND. CT is increasingly detecting thyroid nodules. Prior studies indicated a potential role of CT-based radiomics models in characterizing thyroid nodules, although these studies lacked external validation. OBJECTIVE. The purpose of this study was to develop and validate a CT-based radiomics model for the differentiation of benign and malignant thyroid nodules. METHODS. This retrospective study included 378 patients (mean age, 46.3 ± 13.9 [SD] years; 86 men, 292 women) with 408 resected thyroid nodules (145 benign, 263 malignant) from two centers (center 1: 293 nodules, January 2018 to December 2022; center 2: 115 nodules, January 2020 to December 2022) who underwent preoperative multiphase neck CT (noncontrast, arterial, and venous phases). Nodules from center 1 were divided into training (n = 206) and internal validation (n = 87) sets; all nodules from center 2 formed an external validation set. Radiologists assessed nodules for morphologic CT features. Nodules were manually segmented on all phases, and radiomic features were extracted. Conventional (clinical and morphologic CT), noncontrast CT radiomics, arterial phase CT radiomics, venous phase CT radiomics, multiphase CT radiomics, and combined (clinical, morphologic CT, and multiphase CT radiomics) models were established using feature selection methods and evaluated by ROC curve analysis, calibration-curve analysis, and decision-curve analysis. RESULTS. The combined model included patient age, three morphologic features (cystic change, "edge interruption" sign, abnormal cervical lymph nodes), and 28 radiomic features (from all three phases). In the external validation set, the combined model had an AUC of 0.923, and, at an optimal threshold derived in the training set, sensitivity of 84.0%, specificity of 94.1%, and accuracy of 87.0%. In the external validation set, the AUC was significantly higher for the combined model than for the conventional model (0.827), noncontrast CT radiomics model (0.847), arterial phase CT radiomics model (0.826), venous phase CT radiomics model (0.773), and multiphase CT radiomics model (0.824) (all p < .05). In the external validation set, the calibration curves indicated the lowest (i.e., best) Brier score for the combined model; in the decision-curve analysis, the combined model had the highest net benefit for most of the range of threshold probabilities. CONCLUSION. A combined model incorporating clinical, morphologic CT, and multiphase CT radiomics features exhibited robust performance in differentiating benign and malignant thyroid nodules. CLINICAL IMPACT. The combined radiomics model may help guide further management for thyroid nodules detected on CT.

The aging of glymphatic system in human brain and its correlation with brain charts and neuropsychological functioning.

This study aimed to investigate the aging of the glymphatic system in healthy adults, and to determine whether this change is correlated with the brain charts and neuropsychological functioning. Two independent brain 3.0 T MRI datasets were analyzed: a public dataset and our hospital-own dataset from two hospitals. The function of the glymphatic system was quantified by diffusion analysis along the perivascular space (ALPS) index via an automatic method. Brain charts were calculated online. Correlations of the ALPS index with the brain charts, age, gender, and neuropsychological functioning, as well as differences in ALPS index across age groups, were assessed. A total of 161 healthy volunteers ranging in age from 20 to 87 years were included. ALPS index was negatively correlated with the age in both independent datasets. Compared with that of the young group, the ALPS index was significantly lower in the elderly group. No significant difference was found in the ALPS index between different genders. In addition, the ALPS index was not significantly correlated with the brain charts and neuropsychological functioning. In conclusion, the aging of glymphatic system exists in healthy adults, which is not correlated with the changes of brain charts and neuropsychological functioning.

Gastric duplication cyst with ectopic pancreas in a teenager successfully resected by endoscopic submucosal dissection.

BACKGROUND: Gastric duplication cyst associated with ectopic pancreas is rare and we aimed to alert clinician to this congenital anomaly. CASE PRESENTATION: A 15-year-old girl presented with intermittent vomiting. Gastroscopy showed a submucosal tumor with an approximate diameter of 40 mm in the anterior wall of the gastric antrum. The lesion had a central umbilication and was diagnosed preliminarily as gastric ectopic pancreas with pseudocyst formation on the basis of its appearance. However, computed tomographic scan showed a thick-walled cystic lesion with an enhanced outline of the cystic wall in the antrum of stomach, suggestive of duplication cyst. Serum amylase was normal. Endoscopic ultrasonography revealed a solid-cystic lesion; the solid portion were inhomogeneously mixed with echoes, and had indistinct border to muscularis propria; the cystic portion had echogenic internal mucosal layer and distinct border to muscularis propria. Endoscopic submucosal dissection (ESD) was suggested for the patient to relieve symptoms and diagnose the lesion definitely. The operation procedure was uneventful and the solid-cystic lesion was resected completely. Histopathologic examination revealed that the solid portion was ectopic pancreas, and the cystic portion was gastric duplication cyst. After resection, the patient discharged successfully and neither symptoms nor tumors recurred during the 9 months follow-up period. CONCLUSIONS: This is the first case of a solid-cystic lesion with central umbilication in the stomach diagnosed as gastric duplication cyst associated with ectopic pancreas. ESD could be an optional treatment to provide a definitive diagnosis.

Atypical presentations of coronavirus disease 2019 (COVID-19) from onset to readmission.

BACKGROUND: To investigate the CT imaging and clinical features of three atypical presentations of coronavirus disease 2019 (COVID-19), namely (1) asymptomatic, (2) CT imaging-negative, and (3) re-detectable positive (RP), during all disease stages. METHODS: A consecutive cohort of 79 COVID-19 patients was retrospectively recruited from five independent institutions. For each presentation type, all patients were classified into atypical vs. typical groups (i.e., asymptomatic vs.symptomatic, CT imaging-negative vs. CT imaging-positive, and RP and non-RP,respectively). The chi-square test, Student's t test, and Kruskal-Wallis H test were performed to compare CT imaging and clinical features of atypical vs. typical patients for all three presentation categories. RESULTS: In our COVID-19 cohort, we found 12.7% asymptomatic patients, 13.9% CT imaging-negative patients, and 8.9% RP patients. The asymptomatic patients had fewer hospitalization days (P=0.043), lower total scores for bilateral lung involvement (P< 0.001), and fewer ground-glass opacities (GGOs) in the peripheral area (P< 0.001) than symptomatic patients. The CT imaging-negative patients were younger (P=0.002), had a higher lymphocyte count (P=0.038), had a higher lymphocyte rate (P=0.008), and had more asymptomatic infections (P=0.002) than the CT imaging-positive patients. The RP patients with moderate COVID-19 had lower total scores of for bilateral lung involvement (P=0.030) and a smaller portion of the left lung affected (P=0.024) than non-RP patients. Compared to their first hospitalization, RP patients had a shorter hospitalization period (P< 0.001) and fewer days from the onset of illness to last RNA negative conversion (P< 0.001) at readmission. CONCLUSIONS: Significant CT imaging and clinical feature differences were found between atypical and typical COVID-19 patients for all three atypical presentation categories investigated in this study, which may help provide complementary information for the effective management of COVID-19.

A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study.

OBJECTIVES: Rapid and accurate diagnosis of coronavirus disease 2019 (COVID-19) is critical during the epidemic. We aim to identify differences in CT imaging and clinical manifestations between pneumonia patients with and without COVID-19, and to develop and validate a diagnostic model for COVID-19 based on radiological semantic and clinical features alone. METHODS: A consecutive cohort of 70 COVID-19 and 66 non-COVID-19 pneumonia patients were retrospectively recruited from five institutions. Patients were divided into primary (n = 98) and validation (n = 38) cohorts. The chi-square test, Student's t test, and Kruskal-Wallis H test were performed, comparing 1745 lesions and 67 features in the two groups. Three models were constructed using radiological semantic and clinical features through multivariate logistic regression. Diagnostic efficacies of developed models were quantified by receiver operating characteristic curve. Clinical usage was evaluated by decision curve analysis and nomogram. RESULTS: Eighteen radiological semantic features and seventeen clinical features were identified to be significantly different. Besides ground-glass opacities (p = 0.032) and consolidation (p = 0.001) in the lung periphery, the lesion size (1-3 cm) is also significant for the diagnosis of COVID-19 (p = 0.027). Lung score presents no significant difference (p = 0.417). Three diagnostic models achieved an area under the curve value as high as 0.986 (95% CI 0.966~1.000). The clinical and radiological semantic models provided a better diagnostic performance and more considerable net benefits. CONCLUSIONS: Based on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished. A model composed of radiological semantic and clinical features has an excellent performance for the diagnosis of COVID-19. KEY POINTS: • Based on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished. • A diagnostic model for COVID-19 was developed and validated using radiological semantic and clinical features, which had an area under the curve value of 0.986 (95% CI 0.966~1.000) and 0.936 (95% CI 0.866~1.000) in the primary and validation cohorts, respectively.

Identification of Up- and Down-Regulated Proteins in Pemetrexed-Resistant Human Lung Adenocarcinoma: Flavin Reductase and Calreticulin Play Key Roles in the Development of Pemetrexed-Associated Resistance.

Drug resistance is one of the major causes of cancer chemotherapy failure. In the current study, we used a pair of lung adenocarcinoma cell lines, A549 and the pemetrexed-resistant A549/PEM cells, as a model to monitor resistance-dependent cellular responses and identify potential therapeutic targets. By means of 2D differential gel electrophoresis (2D-DIGE) and matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS), we investigated the global protein expression alterations induced by pemetrexed treatment and resistance. The proteomic result revealed that pemetrexed exposure obviously altered the expression of 81 proteins in the A549 cells, whereas no significant response was observed in the similarly treated A549/PEM cells, hence implying an association between these proteins and the drug-specific response. Moreover, 72 proteins including flavin reductase and calreticulin demonstrated differential expression between the A549 and A549/PEM cells, indicating baseline resistance. Additional tests employed siRNA silencing, protein overexpression, cell viability analysis, and analysis of apoptosis to examine and confirm the potency of flavin reductase and calreticulin proteins in the development of pemetrexed resistance. In summary, by using a proteomic approach, we identified numerous proteins, including flavin reductase and calreticulin, involved in pemetrexed drug resistance-developing mechanisms. Our results provide useful diagnostic markers and therapeutic candidates for pemetrexed-resistant lung cancer treatment.

Mitochondrial proteomics with siRNA knockdown to reveal ACAT1 and MDH2 in the development of doxorubicin-resistant uterine cancer.

Mitochondria are key organelles in mammary cells in responsible for a number of cellular functions including cell survival and energy metabolism. Moreover, mitochondria are one of the major targets under doxorubicin treatment. In this study, low-abundant mitochondrial proteins were enriched for proteomic analysis with the state-of-the-art two-dimensional differential gel electrophoresis (2D-DIGE) and matrix-assistant laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) strategy to compare and identify the mitochondrial protein profiling changes in response to the development of doxorubicin resistance in human uterine cancer cells. The mitochondrial proteomic results demonstrate more than fifteen hundred protein features were resolved from the equal amount pooled of three purified mitochondrial proteins and 101 differentially expressed spots were identified. In which, 39 out of these 101 identified proteins belong to mitochondrial proteins. Mitochondrial proteins such as acetyl-CoA acetyltransferase (ACAT1) and malate dehydrogenase (MDH2) have not been reported with the roles on the formation of doxorubicin resistance in our knowledge. Further studies have used RNA interference and cell viability analysis to evidence the essential roles of ACAT1 and MDH2 on their potency in the formation of doxorubicin resistance through increased cell viability and decreased cell apoptosis during doxorubicin treatment. To sum up, our current mitochondrial proteomic approaches allowed us to identify numerous proteins, including ACAT1 and MDH2, involved in various drug-resistance-forming mechanisms. Our results provide potential diagnostic markers and therapeutic candidates for the treatment of doxorubicin-resistant uterine cancer.

Identification of up- and down-regulated proteins in doxorubicin-resistant uterine cancer cells: reticulocalbin-1 plays a key role in the development of doxorubicin-associated resistance.

Drug resistance is a frequent cause of failure in cancer chemotherapy treatments. In this study, a pair of uterine sarcoma cancer lines, MES-SA, and doxorubicin-resistant partners, MES-SA/DxR-2µM cells and MES-SA/DxR-8µM cells, as a model system to investigate resistance-dependent proteome alterations and to identify potential therapeutic targets. We used two-dimensional differential gel electrophoresis (2D-DIGE) and matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) to perform this research and the results revealed that doxorubicin-resistance altered the expression of 208 proteins in which 129 identified proteins showed dose-dependent manners in response to the levels of resistance. Further studies have used RNA interference, H2A.X phosphorylation assay, cell viability analysis, and analysis of apoptosis against reticulocalbin-1 (RCN1) proteins, to prove its potency on the formation of doxorubicin resistance as well as the attenuation of doxorubicin-associated DNA double strand breakage. To sum up, our results provide useful diagnostic markers and therapeutic candidates such as RCN1 for the treatment of doxorubicin-resistant uterine cancer.

Increased glymphatic system activity in patients with mild traumatic brain injury.

Purpose: This study aims to investigate the glymphatic system activity changes in patients with mild traumatic brain injury (mTBI), particularly in MRI-negative patients, using analysis along the perivascular space (ALPS) technology. Methods: A total of 161 mTBI patients (age: 15-92 years old) and 28 healthy controls (age: 15-84 years old) were included in this retrospective study. The mTBI patients were divided into MRI-negative and MRI-positive groups. ALPS index was calculated automatically using whole-brain T1-MPRAGE imaging and diffusion tensor imaging. The Student's t and chi-squared tests were performed to compare the ALPS index, age, gender, course of disease, and Glasgow Coma Scale (GCS) score between groups. Correlations among ALPS index, age, course of disease and GCS score were computed using Spearman's correlation analysis. Results: Increased activity of the glymphatic system was suggested in mTBI patients based on ALPS index analysis, including the MRI-negative patients. There was a significant negative correlation between the ALPS index and age. In addition, a weak positive correlation between the ALPS index and course of disease was also observed. On the contrary, there was no significant correlation between the ALPS index and sex nor between the ALPS index and GCS score. Conclusion: Our study demonstrated that the activity level of the glymphatic system was enhanced in mTBI patients, even when their brain MRI scans were negative. These findings may provide novel insights for understanding the pathophysiology of mild TBI.

Prediction of histopathologic grades of myxofibrosarcoma with radiomics based on magnetic resonance imaging.

PURPOSE: To develop a radiomics-based model from preoperative magnetic resonance imaging (MRI) for predicting the histopathological grades of myxofibrosarcoma. METHODS: This retrospective study included 54 patients. The tumors were classified into high-grade and low-grade myxofibrosarcoma. The tumor size, signal intensity heterogeneity, margin, and surrounding tissue were evaluated on MRI. Using the least absolute shrinkage and selection operator (LASSO) algorithms, 1037 radiomics features were obtained from fat-suppressed T2-weighted images (T2WI), and a radiomics signature was established. Using multivariable logistic regression analysis, three models were built to predict the histopathologic grade of myxofibrosarcoma. A radiomics nomogram represents the integrative model. The three models' performance was evaluated using the receiver operating characteristics (ROC) and calibration curves. RESULTS: The high-grade myxofibrosarcoma had greater depth (P = 0.027), more frequent heterogeneous signal intensity at T2WI (P = 0.015), and tail sign (P = 0.014) than the low-grade tumor. The area under curve (AUC) of these conventional MRI features models was 0.648, 0.656, and 0.668, respectively. Seven radiomic features were selected by LASSO to construct the radiomics signature model, with an AUC of 0.791. The AUC of the integrative model based on radiomics signature and conventional MRI features was 0.875. The integrative model's calibration curve and insignificant Hosmer-Lemeshow test statistic (P = 0.606) revealed good calibration. CONCLUSION: An integrative model using radiomics signature and three conventional MRI features can preoperatively predict low- or high-grade myxofibrosarcoma.

The value of quantitative plaque analysis based on coronary computed tomography angiography in predicting the percutaneous coronary intervention outcome of chronic total occlusion lesions.

Background: Due to the uncertainty of the success of percutaneous coronary intervention (PCI) and the complexity of selecting suitable treatment cases, the interventional outcome of coronary chronic total occlusion (CTO) remains challenging. The purpose of this study was to evaluate the role of quantitative plaque analysis based on coronary computed tomography angiography (CCTA) in predicting the CTO-PCI outcome. Methods: We retrospectively included 78 patients with CTO (80 lesions) confirmed by invasive coronary angiography from July 2016 to December 2018. All patients underwent PCI treatment according to standard practice. A total of 47 lesions in 47 patients were successfully treated with PCI. PCI failed in the remaining 33 lesions in 31 patients. The following conventional CCTA morphologic parameters were evaluated and compared between the PCI-success and PCI-failure groups: stump morphology; occlusion length, tortuous course; CTO lesion calcium; bridging collateral vessel; retrograde collateral vessel; the appearance of the occluded distal segment; and quantitative CTO plaque characteristics, including total plaque volume, calcified plaque (CP) volume, noncalcified plaque (NCP) volume, low-density noncalcified plaque (LDNCP) volume, and plaque length. Univariate and multivariate logistic regression analyses were performed to determine independent parameters predictive of CTO-PCI outcomes. The predictive performances were assessed using receiver operating characteristic curve analysis. Results: The blunt stump was the only independent CCTA morphologic parameter to predict the outcome of CTO-PCI [odds ratio (OR): 10.807; P<0.001]. NCP volume (OR: 1.018; P<0.001), CP volume (OR: 1.026; P=0.049), and plaque length (OR: 1.058; P=0.037) were independent quantitative CTO plaque characteristics predictive of CTO-PCI outcomes. The plaque-based model combining NCP volume with CP volume and plaque length had a higher area under the curve (AUC =0.96) than did the morphology-based model that included blunt stump (AUC 0.68) in predicting the outcomes of CTO-PCI (P<0.001). Conclusions: The CCTA-based plaque characteristics, including NCP volume, CP volume, and plaque length, outperformed morphologic parameters in predicting the CTO-PCI outcomes.

Prescription system to calculate precise doses of Chinese herbal medicine to avoid toxic effects.

Background and objectives: Traditional Chinese Medicine (TCM) is a therapeutic system which has been practiced for thousands of years. Although for much of its history the decoction of medicinal herbs was the most common method of consuming the herbal treatments, TCM prescriptions are now primarily prepared using concentrated Chinese herbal extracts (CCHE) in powder or granular form. However, determining the precise dose of each single Chinese herbal constituent within a prescription creates a challenge in clinical practice due to the potential risk of toxicity. To alleviate this, we invented the Chinese Intelligence Prescription System (CIPS) to calculate the exact dose of each single herb within an individual prescription. Methods: In this study, we applied CIPS in a real-world setting to analyze clinical prescriptions collected and prepared at the TCM Pharmacy of China Medical University Hospital (CMUH). Results: Our investigation revealed that 3% of all prescriptions filled in a 1-month period contained inexact dosages, suggesting that more than 170,000 prescriptions filled in Taiwan in a given month may contain potentially toxic components. We further analyzed the data to determine the excess dosages and outline the possible associated side effects. Conclusions: In conclusion, CIPS offers TCM practitioners the ability to prepare exact Chinese herbal medicine (CHM) prescriptions in order to avoid toxic effects, thereby ensuring patient safety.

Histological Classification and Invasion Prediction of Thymoma by Machine Learning-Based Computed Tomography Imaging.

Purpose: The values of machine learning-based computed tomography (CT) imaging in histological classification and invasion prediction of thymoma were investigated. Methods: 181 patients diagnosed with thymoma by surgery or biopsy in Shantou Central Hospital between February 2017 and March 2022 were selected. According to the concept of simplified histological classification and the latest histological classification by the WHO, thymoma was divided into two groups, including low-risk (types A, AB, B1, and metaplastic type) and high-risk groups (types B2 and B3). CT images were reconstructed by filtering back projection (FBP) algorithm. CT image features were collected for statistical analysis. Results: The main symptoms of patients diagnosed with thymoma included respiratory tract infection, chest distress and shortness of breath, and chest pain. 35.91% of them suffered from complicated myasthenia gravis. Tumor size and position in low-risk and high-risk groups showed no statistical significance (P > 0.05). Tumor morphology and boundary between the two groups suggested statistical difference (P < 0.05). Whether tumor invaded adjacent tissues was apparently correlated with simplified histological classification (P < 0.01). The sensitivity and specificity of CT images for the invasion of mediastinal pleura or pericardium were around 90% and negative predictive values both reached above 95%. Those of the CT images for lung invasion were over 80%. The negative and positive predictive values were 93.54% and 63.82%, respectively. Those of the CT images for blood vessel invasion were 67.32% and 97.93%. The negative and positive predictive values were 98.21% and 83%, respectively. Conclusion: The machine learning-based CT image had significant values in the prediction of different histological classification and even invasion level.

Differential diagnosis of parotid gland tumours: Application of SWI combined with DWI and DCE-MRI.

BACKGROUND: Parotid tumours (PTs) have a variety of pathological types, and the surgical procedures differ depending on the tumour type. However, accurate diagnosis of PTs from the current preoperative examinations is unsatisfactory. METHODS: This retrospective study was approved by the Ethics Committee of our hospital, and the requirement for informed consent was waived. A total of 73 patients with PTs, including 55 benign and 18 malignant tumours confirmed by surgical pathology, were enrolled. All patients underwent diffusion-weighted imaging (DWI), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), susceptibility-weighted imaging (SWI), T2-weighted imaging (T2WI), and T1-weighted imaging (T1WI). The signal uniformity and capsule on T2WI, apparent diffusion coefficient (ADC) derived from DWI, semi-quantitative parameter time-intensity curve (TIC) pattern, and quantitative parameters including transfer constant (Ktrans), extravascular extracellular volume fraction (Ve), wash-out constant (Kep) calculated from DCE-MRI, and intratumoural susceptibility signal (ITSS) obtained from SWI were assessed and compared between benign and malignant PTs. Logistic regression analysis was used to select the predictive parameters for the classification of benign and malignant parotid gland tumours, and receiver operating characteristic (ROC) curve analysis was used to evaluate their diagnostic performance. RESULTS: Malignant PTs tended to exhibit a type C TIC pattern, whereas benign tumours tended to be type A and B (p < 0.001). Benign PTs had less ITSS than malignant tumours (p < 0.001). Multivariate analyses showed that ADC, Ve, and ITSS were predictors of tumour classification. ROC analysis showed that the area under the curve (AUC) of ADC, Ve, ITSS, and ADC combined with Ve were 0.623, 0.615, 0.826, and 0.782, respectively, in differentiating between malignant and benign PTs. When ITSS was added, the AUCs of ADC, Ve, and ADC combined with Ve increased to 0.882, 0.848, and 0.930, respectively. CONCLUSION: SWI offers incremental diagnostic value to DWI and DCE-MRI in the characterisation of parotid gland tumours.

Distinguishing COVID-19 From Influenza Pneumonia in the Early Stage Through CT Imaging and Clinical Features.

Background: Both coronavirus disease 2019 (COVID-19) and influenza pneumonia are highly contagious and present with similar symptoms. We aimed to identify differences in CT imaging and clinical features between COVID-19 and influenza pneumonia in the early stage and to identify the most valuable features in the differential diagnosis. Methods: Seventy-three patients with COVID-19 confirmed by real-time reverse transcription-polymerase chain reaction (RT-PCR) and 48 patients with influenza pneumonia confirmed by direct/indirect immunofluorescence antibody staining or RT-PCR were retrospectively reviewed. Clinical data including course of disease, age, sex, body temperature, clinical symptoms, total white blood cell (WBC) count, lymphocyte count, lymphocyte ratio, neutrophil count, neutrophil ratio, and C-reactive protein, as well as 22 qualitative and 25 numerical imaging features from non-contrast-enhanced chest CT images were obtained and compared between the COVID-19 and influenza pneumonia groups. Correlation tests between feature metrics and diagnosis outcomes were assessed. The diagnostic performance of each feature in differentiating COVID-19 from influenza pneumonia was also evaluated. Results: Seventy-three COVID-19 patients including 41 male and 32 female with mean age of 41.9 ± 14.1 and 48 influenza pneumonia patients including 30 male and 18 female with mean age of 40.4 ± 27.3 were reviewed. Temperature, WBC count, crazy paving pattern, pure GGO in peripheral area, pure GGO, lesion sizes (1-3 cm), emphysema, and pleural traction were significantly independent associated with COVID-19. The AUC of clinical-based model on the combination of temperature and WBC count is 0.880 (95% CI: 0.819-0.940). The AUC of radiological-based model on the combination of crazy paving pattern, pure GGO in peripheral area, pure GGO, lesion sizes (1-3 cm), emphysema, and pleural traction is 0.957 (95% CI: 0.924-0.989). The AUC of combined model based on the combination of clinical and radiological is 0.991 (95% CI: 0.980-0.999). Conclusion: COVID-19 can be distinguished from influenza pneumonia based on CT imaging and clinical features, with the highest AUC of 0.991, of which crazy-paving pattern and WBC count play most important role in the differential diagnosis.

Lung ultrasound correlates with radiographic severity and pattern in COVID-19 pneumonia: a preliminary study.

BACKGROUND: Coronavirus Disease 2019 (COVID-19) was outbreaking in late 2019 and a proportion of patients developed to pneumonia. Although chest CT is a pivotal diagnostic tool for COVID-19 pneumonia, CT is expensive and also radiological burden for patients. There is urgent to investigate the role of lung ultrasound (LUS) in diagnosing and monitoring COVID-19 pneumonia. METHODS: A total of 8 patients with confirmed cases of COVID-19 pneumonia in Shantou Central Hospital from January 2020 to February 2020 were retrospectively studied. All participants underwent chest HRCT and LUS examination; both were independently performed within 1 day of the other. The radiological patterns were reviewed by 2 radiologists who were blind to the clinical information. A senior ultrasound physician, blind to HRCT results and clinical data, performed bedside LUS in the isolation ward. The CT score was used (a semi-quantitative scoring system) to assess radiographic severity and extent. A B-lines score denoting the extent and severity of sonographic lesion was calculated by summing the number of B-lines on 18 scanning sites. RESULTS: B-lines (100%), pleural irregularities (25%), consolidation (25%), and pleural effusion (25%) were the main findings of LUS examination. Interstitial abnormalities, ground-glass opacities (GGO), consolidations and local or bilateral patchy shadowing were the main findings of HRCT examination. The findings of LUS and HRCT were compared point to point and high consistency was found between the 2 measurements. A significant correlation was also found between the B-lines score and CT score [r=0.96, 95% confidence interval (CI): 0.81 to 0.99, P=0.0001]. CONCLUSIONS: Both LUS patterns and B-lines score are significantly correlated with HRCT findings and score, respectively, supporting its role in assessing COVID-19 pneumonia severity, screening, and following up dynamic changes of pneumonia.

Electromagnetic navigation bronchoscopic localization versus percutaneous CT-guided localization for thoracoscopic resection of small pulmonary nodules.

BACKGROUND: Identification of small pulmonary nodules is challenging in a limited intrathoracic field during minimally invasive video-assisted thoracoscopic surgery (VATS), and preoperative localization is required. Various techniques have been reported with some failure and complications. Here, we compare the feasibility and safety between electromagnetic navigation bronchoscopic marking and computed tomography (CT)-guided percutaneous marking using indocyanine green (ICG) and iopamidol. METHODS: A total of 47 patients with small-sized pulmonary nodules, scheduled to undergo video-assisted thoracoscopic limited resection, were enrolled in this study. A mixture of diluted ICG and iopamidol was injected into the lung parenchyma as a marker, using CT-guided percutaneous or electromagnetic navigation bronchoscopic injection techniques and the results were examined and compared. RESULTS: A total of 35 and 12 patients underwent preoperative marking by percutaneous injection and electromagnetic navigation bronchoscopic injection, respectively, in which a marker was detected in 33/35 (94.3%) and 12/12 (100%) patients. No combination of these procedures was performed in any patient. All markers were successfully detected in three patients who underwent injection marking at two different lesion sites. Pneumothorax occurred in five patients (14%) in the percutaneous marking group, which was relieved in all patients without the necessity for chest tube drainage. No other complication was observed in this study. CONCLUSIONS: Electromagnetic navigation bronchoscopic injection techniques using indocyanine green fluorescence plus iopamidol are safe and effective, and comparable with CT-guided localization. Furthermore, a bronchoscopic approach enables marking of multiple lesion areas without increasing patient risk, especially for puncture-related pneumothorax. KEY POINTS: SIGNIFICANT FINDINGS OF THE STUDY: Either computed tomography (CT)-guided percutaneous or electromagnetic navigation bronchoscopic injection techniques can be used for preoperative marking of pulmonary nodules with indocyanine green (ICG) fluorescence. WHAT THIS STUDY ADDS: Indocyanine green (ICG) is a safe and easily detectable fluorescent marker for video-assisted thoracoscopic surgery (VATS). A bronchoscopic injection approach enables marking of multiple lesion areas without increasing the risk of pneumothorax.

Proteomic analysis of rhein-induced cyt: ER stress mediates cell death in breast cancer cells.

Rhein is a natural product purified from herbal plants such as Rheum palmatum, which has been shown to have anti-angiogenesis and anti-tumor metastasis properties. However, the biological effects of rhein on the behavior of breast cancers are not completely elucidated. To evaluate whether rhein might be useful in the treatment of breast cancer and its cytotoxic mechanism, we analyzed the impact of rhein treatment on differential protein expression as well as redox regulation in a non-invasive breast cancer cell line, MCF-7, and an invasive breast cancer cell line, MDA-MB-231, using lysine- and cysteine-labeling two-dimensional difference gel electrophoresis (2D-DIGE) combined with MALDI-TOF/TOF mass spectrometry. This proteomic study revealed that 73 proteins were significantly changed in protein expression; while 9 proteins were significantly altered in thiol reactivity in both MCF-7 and MDA-MB-231 cells. The results also demonstrated that rhein-induced cytotoxicity in breast cancer cells mostly involves dysregulation of cytoskeleton regulation, protein folding, the glycolysis pathway and transcription control. A further study also indicated that rhein promotes misfolding of cellular proteins as well as unbalancing of the cellular redox status leading to ER-stress. Our work shows that the current proteomic strategy offers a high-through-put platform to study the molecular mechanisms of rhein-induced cytotoxicity in breast cancer cells. The identified differentially expressed proteins might be further evaluated as potential targets in breast cancer therapy.