Diagnostic performance on multiple parameters of real-time ultrasound shear wave elastography for evaluating nonalcoholic fatty liver disease: A rabbit model.

OBJECTIVE: To study the diagnostic value of real-time ultrasound shear wave elastography (US-SWE) in evaluating the histological stages of nonalcoholic fatty liver disease (NAFLD) in a rabbit model. MATERIALS AND METHODS: Twenty-one 8-week-old rabbits were fed a high-fat, high-cholesterol diet (experimental groups), and seven rabbits were fed a standard diet (control group). All rabbits underwent real-time US-SWE at various time points to document the histological stages of NAFLD. We categorized the histological stages as normal, NAFL, borderline nonalcoholic steatohepatitis (NASH), and NASH. We measured the elastic modulus of the liver parenchyma and analyzed the diagnostic efficacy of real-time US-SWE using the area under receiver operating characteristic curve (AUC) for the four histological stages. RESULTS: The mean, minimum, and maximum elastic modulus increase for NAFL, borderline NASH, and NASH. For the mean, minimum, and maximum elastic modulus, AUCs are 0.891 (95% confidence interval [CI]: 0.716-0.977), 0.867 (95% CI: 0.686-0.965), and 0.789 (95% CI:0.594-0.919) for differentiating normal liver from liver with NAFLD, respectively; AUCs are 0.846 (95% CI: 0.660-0.954), 0.818 (95% CI: 0.627-0.937), and 0.797 (95% CI:0.627-0.913) for differentiating normal liver or liver with NAFL from liver with borderline NASH or NASH, respectively; AUCs are 0.889 (95% CI: 0.713-0.976), 0.787 (95% CI: 0.591-0.918), and 0.895 (95% CI:0.720-0.978) for differentiating liver with NASH from liver with lower severity NAFLD or normal liver, respectively. CONCLUSIONS: Real-time US-SWE is an accurate, noninvasive technique for evaluating the histological stages of NAFLD by measuring liver stiffness. We recommend using the mean elastic modulus to differentiate the histological stages, with the minimum and maximum elastic modulus as valuable complements.

NAFLD evaluation: Which is more appropriate, multislice computed tomography or ultrasound real-time shear wave elastography?

OBJECTIVE: This study aims to assess the value of ultrasound real-time shear wave elastography (US-SWE) for evaluation of nonalcoholic fatty liver disease (NAFLD) in a rabbit model compared with multislice computed tomography (MSCT). MATERIAL AND METHODS: Twenty-six rabbits were fed with high-fat, high-cholesterol diet and six rabbits were fed with a standard diet. All rabbits were performed with MSCT and US-SWE at various time points to measure changes in liver parenchyma. The diagnostic efficiency of US-SWE was analyzed using receiver operating characteristics (ROC) curves compared with MSCT based on the liver pathology. RESULTS: The statistically significant differences in the areas under the ROC curves between using MSCT and US-SWE modalities were detected to discriminate between normal vs. NAFLD or higher severity pathology. Similarly, for normal or NAFLD vs. borderline or NASH livers, statistically significant differences between using US-SWE and MSCT modalities were also detected for nonalcoholic steatohepatitis (NASH) vs. lower severity pathology. CONCLUSIONS: MSCT, but not US-SWE, had a better ability to differentiate normal or NAFLD livers from higher severity NAFLD livers. However, the diagnostic efficiency of US-SWE was superior to that of MSCT for differentiating NASH from normal or lower severity NAFLD.

Quantification of hepatic steatosis in histologic images by deep learning method.

OBJECTIVE: To develop and test a novel method for automatic quantification of hepatic steatosis in histologic images based on the deep learning scheme designed to predict the fat ratio directly, which aims to improve accuracy in diagnosis of non-alcoholic fatty liver disease (NAFLD) with objective assessment of the severity of hepatic steatosis instead of subjective visual estimation. MATERIALS AND METHODS: Thirty-six 8-week old New Zealand white rabbits of both sexes were fed with high-cholesterol, high-fat diet and sacrificed under deep anesthesia at various time points to obtain the pathological specimen. All rabbits were performed by multislice computed tomography for surveillance to measure density changes of liver parenchyma. A deep learning scheme using a convolutional neural network was developed to directly predict the liver fat ratio based on the pathological images. The average error value, standard deviation, and accuracy (error <5%) were evaluated and compared between the deep learning scheme and manual segmentation results. The Pearson's correlation coefficient was also calculated in this study. RESULTS: The deep learning scheme performs successfully on rabbit liver histologic data, showing a high degree of accuracy and stability. The average error value, standard deviation, and accuracy (error <5%) were 3.21%, 4.02%, and 79.10% for the cropped images, 2.22%, 1.92%, and 88.34% for the original images, respectively. The strong positive correlation was also observed for cropped images (R = 0.9227) and original images (R = 0.9255) in comparison to labeled fat ratio. CONCLUSIONS: This new deep learning scheme may aid in the quantification of steatosis in the liver and facilitate its treatment by providing an earlier clinical diagnosis.

Application of Dynamic Enhanced Magnetic Resonance Imaging Texture Analysis Combined with ADCs in Predicting Pelvic Lymph Metastasis of Prostate Cancer.

OBJECTIVE: The application value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) texture analysis combined with apparent diffusion coefficient (ADC) in predicting pelvic lymph node metastasis of prostate cancer was explored. METHODS: The clinical and imaging data of 151 patients with prostate cancer admitted to The Affiliated Tumor Hospital of Guizhou Medical University from November 2019 to November 2021 were retrospectively analysed. According to the final pathological diagnosis results, they were divided into two groups: Metastasis group (n = 63, pelvic lymph node metastasis) and non-metastasis group (n = 88, no pelvic lymph node metastasis). The DCE-MRI texture parameters and ADCs of the two groups were compared using Omni-Kinetics software and MADC software packages. The receiver operating characteristic (ROC) curve was used in evaluating the predictive value of each method and their combination, and Spearman rank correlation analysis was used in evaluating their correlation. RESULTS: The volume transfer (Ktrans) and interstitium-to-plasmarate rate constant (Kep) in the metastatic group were significantly higher than those in the non-metastatic group (p < 0.001). However, no significant difference in extravascular extracellular space volume fraction (Ve) was found between the groups (p > 0.05). The ADC of the metastatic group was lower (p < 0.001). The Ktrans and Kep values were positively correlated with pelvic lymph node metastasis of prostate cancer (r = 0.580, 0.684; p < 0.001), and the ADC was negatively correlated with pelvic lymph node metastasis of prostate cancer (r = -0.478; p < 0.001). The ROC curve showed that the area under the curve (AUC) of DCE-MRI texture analysis parameters Ktrans and Kep combined with ADC was large, and the prediction efficiency increased. The AUC, sensitivity and specificity were 0.974, 95.20% and 93.20% (p < 0.001), respectively. CONCLUSIONS: DCE-MRI texture analysis combined with ADC value can accurately predict pelvic lymphatic metastasis of prostate cancer, which is helpful for the selection and formulation of clinical treatment plans and has certain guiding value for the implementation of pelvic lymph node clearing in patients.

Diagnostic efficiency on ultrasound shear wave elastography in evaluation of steatosis severity for non-alcoholic fatty liver disease: a rat model.

BACKGROUND: The pathological feature of steatosis affects the elasticity values measured by shear wave elastography (SWE) is still controversial in non-alcoholic fatty liver disease (NAFLD). The aim of this study is to demonstrate the influence of steatosis on liver stiffness measured by SWE on a rat model with NAFLD and analyze feasibility of SWE for grading steatosis in absence of fibrosis. METHODS: Sixty-six rats were fed with methionine choline deficient diet or standard diet to produce various stages of steatosis; 48 rats were available for final analysis. Rats underwent abdominal ultrasound SWE examination and pathological assessment. Liver histopathology was analyzed to assess the degree of steatosis, inflammation, ballooning, and fibrosis according to the non-alcoholic fatty liver disease activity score. The diagnostic performance of SWE for differentiating steatosis stages was estimated according to the receiver operating characteristic (ROC) curve. Decision curve analysis (DCA) was conducted to determine clinical usefulness and the areas under DCA (AUDCAs) calculated. RESULTS: In multivariate analysis, steatosis was an independent factor affecting the mean elastic modules (B = 1.558, P < 0.001), but not inflammation (B = - 0.031, P = 0.920) and ballooning (B = 0.216, P = 0.458). After adjusting for inflammation and ballooning, the AUROC of the mean elasticity for identifying S ≥ S1 was 0.956 (95%CI: 0.872-0.998) and the AUDCA, 0.621. The AUROC for distinguishing S ≥ S2 and S = S3 was 0.987 (95%CI: 0.951-1.000) and 0.920 (95%CI: 0.816-0.986) and the AUDCA was 0.506 and 0.256, respectively. CONCLUSIONS: Steatosis is associated with liver stiffness and SWE may have the feasibility to be introduced as an assistive technology in grading steatosis for patients with NAFLD in absence of fibrosis.

Full of twists and turns: Collecting duct carcinoma presenting as persistent cough.

We report a case of collecting duct carcinoma (CDC) in a 60-year-old man who presented with persistent cough, low back pain, and weight loss. Contrast-enhanced CT of chest and abdomen revealed a mass in the medulla of the middle and upper parts of the right kidney, with spread into perirenal tissue, vascular invasion, and distant metastasis. First renal biopsy only showed inflammation. Repeat biopsy and histopathological examination and immunohistochemistry confirmed CDC. The patient died 2 months after diagnosis despite interventional therapy, chemotherapy, and targeted therapy. This case is being reported because of its rarity and unusual presentation.

Treatment Response Prediction Using Ultrasound-Based Pre-, Post-Early, and Delta Radiomics in Neoadjuvant Chemotherapy in Breast Cancer.

OBJECTIVE: To develop and validate a radiomics nomogram based on pre-treatment, early treatment ultrasound (US) radiomics features combined with clinical characteristics for early prediction of response to neoadjuvant chemotherapy (NAC) in breast cancer. METHOD: A total of 217 patients with histological results of breast cancer receiving four to eight cycles of NAC before surgery from January 2018 to December 2020 were enrolled. Patients from the study population were randomly separated into a training set (n = 152) and a validation set (n = 65) at a ratio of 7:3. A total of 788 radiomics features were extracted from each region of interest in the US image at pre-treatment baseline (radiomic signature, RS1), early treatment (after completion of two cycles of NAC, RS2) and delta radiomics (calculated between the pre-treatment and post-treatment features, Delta RS). The Max-Relevance and Min-Redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) regression were applied for feature selection. The predictive nomogram was built based on the radiomics signature combined with clinicopathological risk factors. Discrimination, calibration, and prediction performance were further evaluated in the validation set. RESULTS: Of the 217 breast masses, 127 (58.5%) were responsive to NAC and 90 (41.5%) were non-responsive. Following feature selection, nine features in RS1, 11 features in RS2, and eight features in Delta RS remained. With multivariate analysis, the RS1, RS2, Delta RS, and Ki-67 expression were independently associated with breast NAC response. However, the performance of the Delta RS (AUC Delta RS = 0.743) was not higher than RS1 (AUC RS1 = 0.722, PDelta vs RS1 = 0.086) and RS2 (AUC RS2 = 0.811, PDelta vs RS2 = 0.173) with the Delong test. The nomogram incorporating RS1, RS2, and Ki-67 expression showed better predictive ability for NAC response with an area under the curve (AUC) of 0.866 in validation cohorts than either the single RS1 (AUC 0.725) or RS2 (AUC 0.793) or Ki-67 (AUC 0.643). CONCLUSION: The nomogram incorporating pre-treatment and early-treatment US radiomics features and Ki-67 expression showed good performance in terms of NAC response in breast cancer, thereby providing valuable information for individual treatment and timely adjustment of chemotherapy regimens.

Clinical features and magnetic resonance image analysis of 15 cases of demyelinating leukoencephalopathy induced by levamisole.

The aim of this study was to explore the diagnostic value of magnetic resonance imaging (MRI) for levamisole-induced demyelinating leukoencephalopathy. The clinical features and MRI findings of 15 patients with levamisole-induced demyelinating leukoencephalopathy were retrospectively analyzed. The abnormality rate of the patients was demonstrated to be 100% by MRI, and scattered multiple cerebral foci were observed in all of the patients. The majority of the foci were located at the centrum ovale, peri-lateral cerebral ventricles and basal ganglia, while the remainder were located in the brain stem and cerebellum, as well as in the white matter regions of the temporal, frontal, apical and occipital lobes. In addition, mottling and ring-shaped enhancements were observed. The study demonstrated that MRI effectively displays demyelinating leukoencephalopathy, and that the combination of MRI with the medical history of the patient is of significance for the early diagnosis, differentiation and treatment of demyelinating leukoencephalopathy.