Intravoxel incoherent motion assessment of liver fibrosis staging in MASLD.

PURPOSE: Partial correlation analysis was performed to account for the interference of steatosis changes and inflammatory factors, to determine the true correlation between fibrosis and IVIM parameters (Dfast, Dslow, and F), and to evaluate the diagnostic efficacy of IVIM for liver fibrosis. METHODS: A total of 106 patients with metabolic dysfunction-associated steatotic liver disease (MASLD) examined by IVIM from November 2016 to November 2023 at our hospital were retrospectively included. Preliminary analysis of each IVIM parameter and correlations with pathological findings were performed using Spearman correlation analysis, and partial correlation analysis was used to exclude the interference of other pathological factors, thus yielding the true correlations between IVIM parameters (Dfast, Dslow, and F) and pathology. The diagnostic efficacy of IVIM parameters for diagnosing MASLD was assessed via receiver operating characteristic (ROC) curve analysis. RESULTS: Spearman correlation analysis of all the IVIM parameters revealed correlations with steatosis, lobular inflammation, and ballooning. Partial correlation analysis indicated that Dfast was correlated with the pathological fibrosis stage (r = - 0.593, P < 0.001), Dslow was correlated with the pathological steatosis score (r = - 0.313, P < 0.05), and F was correlated with the pathological fibrosis stage and steatosis score (r = - 0.456 and 0.255, P < 0.001 and P < 0.05). In the diagnosis of hepatic fibrosis, significant hepatic fibrosis, advanced liver fibrosis and cirrhosis, Dfast achieved areas under the ROC curve of 0.763, 0.801, 0.853, and 0.897, respectively. The threshold values for diagnosing different fibrosis stages using Dfast (10-3 mm2/s) were 57.613, 54.587, 52.714, and 51.978, respectively. CONCLUSION: According to our partial correlation analysis, there was a moderate correlation between Dfast and F according to fibrosis stage, and Dfast was not influenced by inflammation or steatosis when diagnosing fibrosis in MASLD patients. A relatively close Dfast threshold is insufficient for accurately and noninvasively assessing various stages of MASLD fibrosis. In clinical practice, this approach can be considered an alternative method for the preliminary assessment of fibrosis in MASLD patients.

Rapid automated extracellular vesicle isolation and miRNA preparation on a cost-effective digital microfluidic platform.

As a prerequisite for extracellular vesicle (EV) -based studies and diagnosis, effective isolation, enrichment and retrieval of EV biomarkers are crucial to subsequent analyses, such as miRNA-based liquid biopsy for non-small-cell lung cancer (NSCLC). However, most conventional approaches for EV isolation suffer from lengthy procedure, high cost, and intense labor. Herein, we introduce the digital microfluidic (DMF) technology to EV pretreatment protocols and demonstrate a rapid and fully automated sample preparation platform for clinical tumor liquid biopsy. Combining a reusable DMF chip technique with a low-cost EV isolation and miRNA preparation protocol, the platform completes automated sample processing in 20-30 min, supporting immediate RT-qPCR analyses on EV-derived miRNAs (EV-miRNAs). The utility and reliability of the platform was validated via clinical sample processing for EV-miRNA detection. With 23 tumor and 20 non-tumor clinical plasma samples, we concluded that EV-miR-486-5p and miR-21-5p are effective biomarkers for NSCLC with a small sample volumn (20-40 µL). The result was consistent to that of a commercial exosome miRNA extraction kit. These results demonstrate the effectiveness of DMF in EV pretreatment for miRNA detection, providing a facile solution to EV isolation for liquid biopsy.

Assessment of Parafoveal Diabetic Macular Ischemia on Optical Coherence Tomography Angiography Images to Predict Diabetic Retinal Disease Progression and Visual Acuity Deterioration.

Importance: The presence of diabetic macular ischemia (DMI) on optical coherence tomography angiography (OCTA) images predicts diabetic retinal disease progression and visual acuity (VA) deterioration, suggesting an OCTA-based DMI evaluation can further enhance diabetic retinopathy (DR) management. Objective: To investigate whether an automated binary DMI algorithm using OCTA images provides prognostic value on DR progression, diabetic macular edema (DME) development, and VA deterioration in a cohort of patients with diabetes. Design, Setting, and Participants: In this cohort study, DMI assessment of superficial capillary plexus and deep capillary plexus OCTA images was performed by a previously developed deep learning algorithm. The presence of DMI was defined as images exhibiting disruption of fovea avascular zone with or without additional areas of capillary loss, while absence of DMI was defined as images presented with intact fovea avascular zone outline and normal distribution of vasculature. Patients with diabetes were recruited starting in July 2015 and were followed up for at least 4 years. Cox proportional hazards models were used to evaluate the association of the presence of DMI with DR progression, DME development, and VA deterioration. Analysis took place between June and December 2022. Main Outcomes and Measures: DR progression, DME development, and VA deterioration. Results: A total of 321 eyes from 178 patients were included for analysis (85 [47.75%] female; mean [SD] age, 63.39 [11.04] years). Over a median (IQR) follow-up of 50.41 (48.16-56.48) months, 105 eyes (32.71%) had DR progression, 33 eyes (10.28%) developed DME, and 68 eyes (21.18%) had VA deterioration. Presence of superficial capillary plexus-DMI (hazard ratio [HR], 2.69; 95% CI, 1.64-4.43; P < .001) and deep capillary plexus-DMI (HR, 3.21; 95% CI, 1.94-5.30; P < .001) at baseline were significantly associated with DR progression, whereas presence of deep capillary plexus-DMI was also associated with DME development (HR, 4.60; 95% CI, 1.15-8.20; P = .003) and VA deterioration (HR, 2.12; 95% CI, 1.01-5.22; P = .04) after adjusting for age, duration of diabetes, fasting glucose, glycated hemoglobin, mean arterial blood pressure, DR severity, ganglion cell-inner plexiform layer thickness, axial length, and smoking at baseline. Conclusions and Relevance: In this study, the presence of DMI on OCTA images demonstrates prognostic value for DR progression, DME development, and VA deterioration.

Burden, trends, and risk factors for breast cancer in China from 1990 to 2019 and its predictions until 2034: an up-to-date overview and comparison with those in Japan and South Korea.

BACKGROUND: The difference in epidemiological characteristics of breast cancer (BC) across countries is valuable for BC management and prevention. The study evaluated the up-to-date burden, trends, and risk factors of BC in China, Japan and South Korea during 1990-2019 and predicted the BC burden until 2034. METHODS: Data on incident cases, deaths, disability-adjusted life-years (DALYs) and age-standardized rate (ASR) of BC were extracted from the Global Burden of Disease Study 2019. Trend analysis and prediction until 2034 were conducted by estimated annual percentage change and a Bayesian age-period-cohort model, respectively. Besides, the attributable burden to BC risk factors was also estimated. RESULTS: In 2019, the number of BC incident cases, deaths and DALYs in China were 375,484, 96,306 and 2,957,453, respectively. The ASR of incidence increased, while that of death and DALYs decreased for Chinese females and Japanese and South Korean males during 1990-2019. High body-mass-index (BMI) was the largest contributor to Chinese female BC deaths and DALYs, while alcohol use was the greatest risk factor for Japanese and South Korean as well as Chinese males. The incident cases and deaths were expected to continue increase during 2020-2034 (except for Japanese female incident cases). CONCLUSIONS: China had the greatest burden of BC among the three countries. Incident cases and deaths of BC were projected to increase over the next 15 years in China, particularly among Chinese males. Effective prevention and management strategies are urgently necessary for BC control in China.

Noninvasive Assessment of APAP (N-acetyl-p-aminophenol)-Induced Hepatotoxicity Using Multiple MRI Parameters in an Experimental Rat Model.

BACKGROUND: Early detection and accurate assessment of N-acetyl-p-aminophenol (APAP)-induced hepatotoxicity can prevent further aggravation of liver injury and reduce the incidence of liver failure. PURPOSE: To evaluate the potential of multiple MRI parameters for assessing APAP-induced hepatotoxicity in an experimental rat model. STUDY TYPE: Prospective. ANIMAL MODEL: Twenty-one APAP-treated rats and 12 control rats. FIELD STRENGTH/SEQUENCE: A 3 T, T1 mapping, Gd-EOB-DTPA-enhanced MRI, and intravoxel incoherent motion (IVIM). ASSESSMENT: The severity of histological changes was assessed by a liver pathologist. Rat livers were pathologically classified into three groups: normal (n = 12), mild necrosis (n = 13), and moderate necrosis (n = 8). T1 relaxation time (T1) and diffusion parameters were measured. The reduction rate of T1 (ΔT1%) at different time points, the maximum value of ΔT1%, time period to the maximum value of ΔT1%, and time period from ΔT1max (%) to 2/3 value of ΔT1max (%) (ΔT1-T2/3) were calculated. Transporters activities like organic anion-transporting polypeptide 1 (oatp1) and multidrug resistance-associated protein 2 (mrp2) were compared among different necrotic groups. STATISTICAL TESTS: ANOVA/Kruskal-Wallis. Pearson/Spearman correlation. P < 0.05 was considered statistical significance. RESULTS: T1 Precontrast and ΔT1-T2/3 were strongly correlated with the severity of necrosis (r = 0.9094; r = 0.7978, respectively) and showed significant differences between the two groups. The apparent diffusion coefficient (ADC) and tissue diffusivity (D) values were significantly lower in the moderate necrosis group than in the normal and mild necrosis groups. The oatp1 activity of the necrosis groups was significantly reduced compared to that of the normal group, but the differences between normal and mild (P = 0.21), normal and moderate group (P = 0.56) were not significant. Meanwhile, enlargement of bile canaliculi and sparse microvilli was observed in the necrotic groups. CONCLUSION: MRI parameters such as precontrast T1 and ΔT1-T2/3 had promising potential in assessing the severity of early-stage hepatotoxicity in an APAP overdose rat model. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.

A MULTITASK DEEP-LEARNING SYSTEM FOR ASSESSMENT OF DIABETIC MACULAR ISCHEMIA ON OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY IMAGES.

PURPOSE: We aimed to develop and test a deep-learning system to perform image quality and diabetic macular ischemia (DMI) assessment on optical coherence tomography angiography (OCTA) images. METHODS: This study included 7,194 OCTA images with diabetes mellitus for training and primary validation and 960 images from three independent data sets for external testing. A trinary classification for image quality assessment and the presence or absence of DMI for DMI assessment were labeled on all OCTA images. Two DenseNet-161 models were built for both tasks for OCTA images of superficial and deep capillary plexuses, respectively. External testing was performed on three unseen data sets in which one data set using the same model of OCTA device as of the primary data set and two data sets using another brand of OCTA device. We assessed the performance by using the area under the receiver operating characteristic curves with sensitivities, specificities, and accuracies and the area under the precision-recall curves with precision. RESULTS: For the image quality assessment, analyses for gradability and measurability assessment were performed. Our deep-learning system achieved the area under the receiver operating characteristic curves >0.948 and area under the precision-recall curves >0.866 for the gradability assessment, area under the receiver operating characteristic curves >0.960 and area under the precision-recall curves >0.822 for the measurability assessment, and area under the receiver operating characteristic curves >0.939 and area under the precision-recall curves >0.899 for the DMI assessment across three external validation data sets. Grad-CAM demonstrated the capability of our deep-learning system paying attention to regions related to DMI identification. CONCLUSION: Our proposed multitask deep-learning system might facilitate the development of a simplified assessment of DMI on OCTA images among individuals with diabetes mellitus at high risk for visual loss.

Deep-Learning-Based Pre-Diagnosis Assessment Module for Retinal Photographs: A Multicenter Study.

Purpose: Artificial intelligence (AI) deep learning (DL) has been shown to have significant potential for eye disease detection and screening on retinal photographs in different clinical settings, particular in primary care. However, an automated pre-diagnosis image assessment is essential to streamline the application of the developed AI-DL algorithms. In this study, we developed and validated a DL-based pre-diagnosis assessment module for retinal photographs, targeting image quality (gradable vs. ungradable), field of view (macula-centered vs. optic-disc-centered), and laterality of the eye (right vs. left). Methods: A total of 21,348 retinal photographs from 1914 subjects from various clinical settings in Hong Kong, Singapore, and the United Kingdom were used for training, internal validation, and external testing for the DL module, developed by two DL-based algorithms (EfficientNet-B0 and MobileNet-V2). Results: For image-quality assessment, the pre-diagnosis module achieved area under the receiver operating characteristic curve (AUROC) values of 0.975, 0.999, and 0.987 in the internal validation dataset and the two external testing datasets, respectively. For field-of-view assessment, the module had an AUROC value of 1.000 in all of the datasets. For laterality-of-the-eye assessment, the module had AUROC values of 1.000, 0.999, and 0.985 in the internal validation dataset and the two external testing datasets, respectively. Conclusions: Our study showed that this three-in-one DL module for assessing image quality, field of view, and laterality of the eye of retinal photographs achieved excellent performance and generalizability across different centers and ethnicities. Translational Relevance: The proposed DL-based pre-diagnosis module realized accurate and automated assessments of image quality, field of view, and laterality of the eye of retinal photographs, which could be further integrated into AI-based models to improve operational flow for enhancing disease screening and diagnosis.

Assessment of liver fibrosis with liver and spleen magnetic resonance elastography, serum markers in chronic liver disease.

BACKGROUND: The accurate assessment of liver fibrosis is essential for patients with chronic liver disease. A liver biopsy is an invasive procedure that has many potential defects and complications. Therefore, noninvasive assessment techniques are of considerable value for clinical diagnosis. Liver and spleen magnetic resonance elastography (MRE) and serum markers have been proposed for quantitative and noninvasive assessment of liver fibrosis. This study aims to compare the diagnostic performance of liver and spleen stiffness measured by MRE, fibrosis index based on the 4 factors (FIB-4), aspartate aminotransferase-to-platelet ratio index (APRI), and their combined models for staging hepatic fibrosis. METHODS: One hundred and twenty patients with chronic liver disease underwent MRE scans. Liver and spleen stiffness were measured by the MRE stiffness maps. Serum markers were collected to calculate FIB-4 and APRI. Liver biopsies were used to identify pathologic grading. Spearman's rank correlation analysis evaluated the correlation between the parameters and fibrosis stages. Receiver operating characteristic (ROC) analysis evaluated the performance of the four individual parameters, a liver and spleen stiffness combined model, and an all-parameters combined model in assessing liver fibrosis. RESULTS: Liver stiffness, spleen stiffness, FIB-4, and APRI were all correlated with fibrosis stage (r=0.87, 0.64, 0.65, and 0.51, respectively, all P<0.001). Among the 4 individual diagnostic markers, liver stiffness showed the highest values in staging F1-4, F2-4, F3-4 and F4 (AUC =0.89, 0. 97, 0.95, and 0.95, all P<0.001). The AUCs of the liver and spleen stiffness combined model in the F1-4, F2-4, F3-4, and F4 staging groups were 0.89, 0.97, 0.95, and 0.96, respectively (all P<0.001). The corresponding AUCs of the all-parameters combined model were 0.90, 0.97, 0.95, and 0.96 (all P<0.001). The AUCs of the liver and spleen stiffness combined model were significantly higher than those of APRI, FIB-4 in the F2-4, F3-4, and F4 staging groups (all P<0.05). Both combined models were not significantly different from liver stiffness in staging liver fibrosis (all P>0.05). CONCLUSIONS: Liver stiffness measured with MRE had better diagnostic performance than spleen stiffness, APRI, and FIB-4 for fibrosis staging. The combined models did not significantly improve the diagnostic value compared with liver stiffness in staging fibrosis.