Visibility of esophageal squamous cell carcinoma under iodine staining on texture and color enhancement imaging.

Objective: Iodine staining on white light imaging (WLI) is the gold standard for detecting and demarcating esophageal squamous cell carcinoma (ESCC). We examined the effects of texture and color enhancement imaging (TXI) on improving the endoscopic visibility of ESCC under iodine staining. Methods: Twenty ESCC lesions that underwent endoscopic submucosal dissection were retrospectively included. The color difference between ESCC and the surrounding mucosa (ΔEe) on WLI, TXI, and narrow-band imaging was assessed, and ΔEe under 1% iodine staining on WLI and TXI. Furthermore, the visibility grade determined by endoscopists was evaluated on each imaging. Result: The median ΔEe was greater on TXI than on WLI (14.53 vs. 10.71, respectively; p < 0.005). Moreover, the median ΔEe on TXI under iodine staining was greater than the median ΔEe on TXI and narrow-band imaging (39.20 vs. 14.53 vs. 16.42, respectively; p < 0.005 for both). A positive correlation in ΔEe under iodine staining was found between TXI and WLI (correlation coefficient = 0.61, p < 0.01). Moreover, ΔEe under iodine staining on TXI in each lesion was greater than the corresponding ΔEe on WLI. The visibility grade assessed by endoscopists on TXI was also significantly greater than that on WLI under iodine staining (p < 0.01). Conclusions: The visibility of ESCC after iodine staining was greater on TXI than on WLI.

Comparison of Dental Caries Risk Assessment Using CaRisk- A Simple Mobile Based Application and WHO deft, DMFT Scores: A Cross Sectional Study.

Statement of the Problem: It is essential to address caries risk at an early stage for the prevention of dental caries. Mobile application CaRisk is designed in a particular way to self-assess the dental caries risk by the individual's themselves. Purpose: The current study aimed to assess the dental caries risk among age groups 5-6 and 35-44 using self-assessment caries risk mobile application CaRisk and compare it with the deft and DMFT values. Materials and Method: This cross-sectional study was conducted in Chennai, India; to evaluate the risk of dental caries in children aged 5 to 6 and adults aged 35 to 44. The scores of the mobile application CaRisk and the decayed- extracted- filled teeth (deft)/ decayed-missing-filled-teeth (DMFT) caries risk assessment were evaluated. Descriptive statistics were performed. The risk category was determined by frequency. Chi-square analysis was done to determine whether the DMFT scores and the CaRisk mobile app were associated. The correlation was performed between the CaRisk mobile application and DMFT scores. Results: Association was found between the caries risk assessment score of the mobile application CaRisk and the DMFT and deft scores of the adults and children for both the age groups 5-6 and 35-44 years respectively and it indicates that it was found to be statistically significant. Pearson's correlation was performed to assess the strength of association and R-values obtained for the age group 5-6 and 35-44 years respectively, which was statistically significant (0.892 and 0.840). Conclusion: This CaRisk mobile application scores correlate with the deft and DMFT scores and it is an effective self-diagnosis tool for assessing dental caries risk assessment. Further, it is suggested that the mobile application CaRisk should be tested among a huge population.

Improving skin cancer detection by Raman spectroscopy using convolutional neural networks and data augmentation.

Background: Our previous studies have demonstrated that Raman spectroscopy could be used for skin cancer detection with good sensitivity and specificity. The objective of this study is to determine if skin cancer detection can be further improved by combining deep neural networks and Raman spectroscopy. Patients and methods: Raman spectra of 731 skin lesions were included in this study, containing 340 cancerous and precancerous lesions (melanoma, basal cell carcinoma, squamous cell carcinoma and actinic keratosis) and 391 benign lesions (melanocytic nevus and seborrheic keratosis). One-dimensional convolutional neural networks (1D-CNN) were developed for Raman spectral classification. The stratified samples were divided randomly into training (70%), validation (10%) and test set (20%), and were repeated 56 times using parallel computing. Different data augmentation strategies were implemented for the training dataset, including added random noise, spectral shift, spectral combination and artificially synthesized Raman spectra using one-dimensional generative adversarial networks (1D-GAN). The area under the receiver operating characteristic curve (ROC AUC) was used as a measure of the diagnostic performance. Conventional machine learning approaches, including partial least squares for discriminant analysis (PLS-DA), principal component and linear discriminant analysis (PC-LDA), support vector machine (SVM), and logistic regression (LR) were evaluated for comparison with the same data splitting scheme as the 1D-CNN. Results: The ROC AUC of the test dataset based on the original training spectra were 0.886±0.022 (1D-CNN), 0.870±0.028 (PLS-DA), 0.875±0.033 (PC-LDA), 0.864±0.027 (SVM), and 0.525±0.045 (LR), which were improved to 0.909±0.021 (1D-CNN), 0.899±0.022 (PLS-DA), 0.895±0.022 (PC-LDA), 0.901±0.020 (SVM), and 0.897±0.021 (LR) respectively after augmentation of the training dataset (p<0.0001, Wilcoxon test). Paired analyses of 1D-CNN with conventional machine learning approaches showed that 1D-CNN had a 1-3% improvement (p<0.001, Wilcoxon test). Conclusions: Data augmentation not only improved the performance of both deep neural networks and conventional machine learning techniques by 2-4%, but also improved the performance of the models on spectra with higher noise or spectral shifting. Convolutional neural networks slightly outperformed conventional machine learning approaches for skin cancer detection by Raman spectroscopy.

Revolutionizing Healthcare: Qure.AI's Innovations in Medical Diagnosis and Treatment.

Qure.AI, a leading company in artificial intelligence (AI) applied to healthcare, has developed a suite of innovative solutions to revolutionize medical diagnosis and treatment. With a plethora of FDA-approved tools for clinical use, Qure.AI continually strives for innovation in integrating AI into healthcare systems. This article delves into the efficacy of Qure.AI's chest X-ray interpretation tool, "qXR," in medicine, drawing from a comprehensive review of clinical trials conducted by various institutions. Key applications of AI in healthcare include machine learning, deep learning, and natural language processing (NLP), all of which contribute to enhanced diagnostic accuracy, efficiency, and speed. Through the analysis of vast datasets, AI algorithms assist physicians in interpreting medical data and making informed decisions, thereby improving patient care outcomes. Illustrative examples highlight AI's impact on medical imaging, particularly in the diagnosis of conditions such as breast cancer, heart failure, and pulmonary nodules. AI can significantly reduce diagnostic errors and expedite the interpretation of medical images, leading to more timely interventions and treatments. Furthermore, AI-powered predictive analytics enable early detection of diseases and facilitate personalized treatment plans, thereby reducing healthcare costs and improving patient outcomes. The efficacy of AI in healthcare is underscored by its ability to complement traditional diagnostic methods, providing physicians with valuable insights and support in clinical decision-making. As AI continues to evolve, its role in patient care and medical research is poised to expand, promising further advancements in diagnostic accuracy and treatment efficacy.

Segmentation of retinal microaneurysms in fluorescein fundus angiography images by a novel three-step model.

Introduction: Microaneurysms serve as early signs of diabetic retinopathy, and their accurate detection is critical for effective treatment. Due to their low contrast and similarity to retinal vessels, distinguishing microaneurysms from background noise and retinal vessels in fluorescein fundus angiography (FFA) images poses a significant challenge. Methods: We present a model for automatic detection of microaneurysms. FFA images were pre-processed using Top-hat transformation, Gray-stretching, and Gaussian filter techniques to eliminate noise. The candidate microaneurysms were coarsely segmented using an improved matched filter algorithm. Real microaneurysms were segmented by a morphological strategy. To evaluate the segmentation performance, our proposed model was compared against other models, including Otsu's method, Region Growing, Global Threshold, Matched Filter, Fuzzy c-means, and K-means, using both self-constructed and publicly available datasets. Performance metrics such as accuracy, sensitivity, specificity, positive predictive value, and intersection-over-union were calculated. Results: The proposed model outperforms other models in terms of accuracy, sensitivity, specificity, positive predictive value, and intersection-over-union. The segmentation results obtained with our model closely align with benchmark standard. Our model demonstrates significant advantages for microaneurysm segmentation in FFA images and holds promise for clinical application in the diagnosis of diabetic retinopathy. Conclusion: The proposed model offers a robust and accurate approach to microaneurysm detection, outperforming existing methods and demonstrating potential for clinical application in the effective treatment of diabetic retinopathy.

The use of echocardiography compared to electrocardiogram when screening for left ventricular hypertrophy in hypertensive patients: A cross-sectional study.

Left ventricular hypertrophy (LVH) is often used as an indicator to assess hypertension-mediated organ damage (HMOD), alongside hypertensive retinopathy (HR) and nephropathy. Assessment of HMOD is crucial when making decisions about treatment optimization. Despite longstanding debate over its reliability to detect LVH, it is common practice to perform an electrocardiogram (ECG) instead of directly assessing left ventricular mass with echocardiography. In this study, the presence of LVH was evaluated using both ECG and echocardiography among consecutive patients suspected of therapy-resistant hypertension or secondary hypertension in the outpatient clinic of the Department of Internal Medicine at the Diakonessen Hospital, Utrecht, the Netherlands, between July 15, 2017, and July 31, 2020. The primary endpoints were the specificity and sensitivity of ECG as a diagnostic tool for LVH, with echocardiography serving as the reference method. Among the 329 participants, we identified 70 individuals (21.3%) with true LVH based on echocardiography. The ECG displayed a sensitivity of 47.9% and a specificity of 75.3%. Moreover, the area under the receiver operating characteristics curve was 0.604. In conclusion, ECG demonstrates limited value in identifying LVH. Considering the importance of accurately assessing HMOD for treatment optimization of hypertension, the role of ECG as a diagnostic tool for LVH is, therefore, questionable. Instead, we recommend employing standard echocardiography as a more reliable diagnostic.

Evaluation and management of knee osteoarthritis.

Knee osteoarthritis (KOA) significantly contributes to the global disability burden, with its incidence expected to escalate by 74.9% by 2050. The urgency to comprehend and tackle this condition is critical, necessitating an updated and thorough review of KOA. A systematic review up to February 26, 2024, has elucidated the principal aspects of KOA's pathogenesis, risk factors, clinical manifestations, and contemporary management paradigms. The origins of KOA are intricately linked to mechanical, inflammatory, and metabolic disturbances that impair joint function. Notable risk factors include age, obesity, and previous knee injuries. Diagnosis predominantly relies on clinical assessment, with radiographic evaluation reserved conditionally. The significance of rehabilitation assessments, informed by the International Classification of Functioning, Disability, and Health framework, is highlighted. Treatment strategies are diverse, prioritizing nonpharmacological measures such as patient education, exercise, and weight management, with pharmacological interventions considered adjuncts. Intra-articular injections and surgical options are contemplated for instances where conventional management is inadequate. KOA stands as a predominant disability cause globally, characterized by a complex etiology and profound effects on individuals' quality of life. Early, proactive management focusing on nonpharmacological interventions forms the cornerstone of treatment, aiming to alleviate symptoms and enhance joint function. This comprehensive review underscores the need for early diagnosis, individualized treatment plans, and the integration of rehabilitation assessments to optimize patient outcomes. Further research is needed to refine prevention strategies and improve management outcomes for KOA patients.

Gamified Crowdsourcing as a Novel Approach to Lung Ultrasound Data Set Labeling: Prospective Analysis.

BACKGROUND: Machine learning (ML) models can yield faster and more accurate medical diagnoses; however, developing ML models is limited by a lack of high-quality labeled training data. Crowdsourced labeling is a potential solution but can be constrained by concerns about label quality. OBJECTIVE: This study aims to examine whether a gamified crowdsourcing platform with continuous performance assessment, user feedback, and performance-based incentives could produce expert-quality labels on medical imaging data. METHODS: In this diagnostic comparison study, 2384 lung ultrasound clips were retrospectively collected from 203 emergency department patients. A total of 6 lung ultrasound experts classified 393 of these clips as having no B-lines, one or more discrete B-lines, or confluent B-lines to create 2 sets of reference standard data sets (195 training clips and 198 test clips). Sets were respectively used to (1) train users on a gamified crowdsourcing platform and (2) compare the concordance of the resulting crowd labels to the concordance of individual experts to reference standards. Crowd opinions were sourced from DiagnosUs (Centaur Labs) iOS app users over 8 days, filtered based on past performance, aggregated using majority rule, and analyzed for label concordance compared with a hold-out test set of expert-labeled clips. The primary outcome was comparing the labeling concordance of collated crowd opinions to trained experts in classifying B-lines on lung ultrasound clips. RESULTS: Our clinical data set included patients with a mean age of 60.0 (SD 19.0) years; 105 (51.7%) patients were female and 114 (56.1%) patients were White. Over the 195 training clips, the expert-consensus label distribution was 114 (58%) no B-lines, 56 (29%) discrete B-lines, and 25 (13%) confluent B-lines. Over the 198 test clips, expert-consensus label distribution was 138 (70%) no B-lines, 36 (18%) discrete B-lines, and 24 (12%) confluent B-lines. In total, 99,238 opinions were collected from 426 unique users. On a test set of 198 clips, the mean labeling concordance of individual experts relative to the reference standard was 85.0% (SE 2.0), compared with 87.9% crowdsourced label concordance (P=.15). When individual experts' opinions were compared with reference standard labels created by majority vote excluding their own opinion, crowd concordance was higher than the mean concordance of individual experts to reference standards (87.4% vs 80.8%, SE 1.6 for expert concordance; P<.001). Clips with discrete B-lines had the most disagreement from both the crowd consensus and individual experts with the expert consensus. Using randomly sampled subsets of crowd opinions, 7 quality-filtered opinions were sufficient to achieve near the maximum crowd concordance. CONCLUSIONS: Crowdsourced labels for B-line classification on lung ultrasound clips via a gamified approach achieved expert-level accuracy. This suggests a strategic role for gamified crowdsourcing in efficiently generating labeled image data sets for training ML systems.

Machine learning methods in automated detection of CT enterography findings in Crohn's disease: A feasibility study.

PURPOSE: Qualitative findings in Crohn's disease (CD) can be challenging to reliably report and quantify. We evaluated machine learning methodologies to both standardize the detection of common qualitative findings of ileal CD and determine finding spatial localization on CT enterography (CTE). MATERIALS AND METHODS: Subjects with ileal CD and a CTE from a single center retrospective study between 2016 and 2021 were included. 165 CTEs were reviewed by two fellowship-trained abdominal radiologists for the presence and spatial distribution of five qualitative CD findings: mural enhancement, mural stratification, stenosis, wall thickening, and mesenteric fat stranding. A Random Forest (RF) ensemble model using automatically extracted specialist-directed bowel features and an unbiased convolutional neural network (CNN) were developed to predict the presence of qualitative findings. Model performance was assessed using area under the curve (AUC), sensitivity, specificity, accuracy, and kappa agreement statistics. RESULTS: In 165 subjects with 29,895 individual qualitative finding assessments, agreement between radiologists for localization was good to very good (κ = 0.66 to 0.73), except for mesenteric fat stranding (κ = 0.47). RF prediction models had excellent performance, with an overall AUC, sensitivity, specificity of 0.91, 0.81 and 0.85, respectively. RF model and radiologist agreement for localization of CD findings approximated agreement between radiologists (κ = 0.67 to 0.76). Unbiased CNN models without benefit of disease knowledge had very similar performance to RF models which used specialist-defined imaging features. CONCLUSION: Machine learning techniques for CTE image analysis can identify the presence, location, and distribution of qualitative CD findings with similar performance to experienced radiologists.

Accurate classification of glomerular diseases by hyperspectral imaging and transformer.

BACKGROUND AND OBJECTIVE: In renal disease research, precise glomerular disease diagnosis is crucial for treatment and prognosis. Currently reliant on invasive biopsies, this method bears risks and pathologist-dependent variability, yielding inconsistent results. There is a pressing need for innovative diagnostic tools that enhance traditional methods, streamline processes, and ensure accurate and consistent disease detection. METHODS: In this study, we present an innovative Convolutional Neural Networks-Vision Transformer (CVT) model leveraging Transformer technology to refine glomerular disease diagnosis by fusing spectral and spatial data, surpassing traditional diagnostic limitations. Using interval sampling, preprocessing, and wavelength optimization, we also introduced the Gramian Angular Field (GAF) method for a unified representation of spectral and spatial characteristics. RESULTS: We captured hyperspectral images ranging from 385.18 nm to 1009.47 nm and employed various methods to extract sample features. Initial models based solely on spectral features achieved a accuracy of 85.24 %. However, the CVT model significantly outperformed these, achieving an average accuracy of 94 %. This demonstrates the model's superior capability in utilizing sample data and learning joint feature representations. CONCLUSIONS: The CVT model not only breaks through the limitations of existing diagnostic techniques but also showcases the vast potential of non-invasive, high-precision diagnostic technology in supporting the classification and prognosis of complex glomerular diseases. This innovative approach could significantly impact future diagnostic strategies in renal disease research. CONCISE ABSTRACT: This study introduces a transformative hyperspectral image classification model leveraging a Transformer to significantly improve glomerular disease diagnosis accuracy by synergizing spectral and spatial data, surpassing conventional methods. Through a rigorous comparative analysis, it was determined that while spectral features alone reached a peak accuracy of 85.24 %, the novel Convolutional Neural Network-Transformer (CVT) model's integration of spatial-spectral features via the Gramian Angular Field (GAF) method markedly enhanced diagnostic precision, achieving an average accuracy of 94 %. This methodological innovation not only overcomes traditional diagnostic limitations but also underscores the potential of non-invasive, high-precision technologies in advancing the classification and prognosis of complex renal diseases, setting a new benchmark in the field.

Machine Learning-based Cerebral Venous Thrombosis Diagnosis with Clinical Data.

OBJECTIVES: Cerebral Venous Thrombosis (CVT) poses diagnostic challenges due to the variability in disease course and symptoms. The prognosis of CVT relies on early diagnosis. Our study focuses on developing a machine learning-based screening algorithm using clinical data from a large neurology referral center in southern Iran. METHODS: The Iran Cerebral Venous Thrombosis Registry (ICVTR code: 9001013381) provided data on 382 CVT cases from Namazi Hospital. The control group comprised of adult headache patients without CVT as confirmed by neuroimaging and was retrospectively selected from those admitted to the same hospital. We collected 60 clinical and demographic features for model development and validation. Our modeling pipeline involved imputing missing values and evaluating four machine learning algorithms: generalized linear model, random forest, support vector machine, and extreme gradient boosting. RESULTS: A total of 314 CVT cases and 575 controls were included. The highest AUROC was reached when imputation was used to estimate missing values for all the variables, combined with the support vector machine model (AUROC=0.910, Recall=0.73, Precision=0.88). The best recall was achieved also by the support vector machine model when only variables with less than 50% missing rate were included (AUROC=0.887, Recall=0.77, Precision=0.86). The random forest model yielded the best precision by using variables with less than 50% missing rate (AUROC=0.882, Recall=0.61, Precision=0.94). CONCLUSION: The application of machine learning techniques using clinical data showed promising results in accurately diagnosing CVT within our study population. This approach offers a valuable complementary assistive tool or an alternative to resource-intensive imaging methods.

[Interpretation of the 2024 American Diabetes Association guidelines for the comprehensive management of non-alcoholic fatty liver disease combined with diabetes mellitus].

Non-alcoholic fatty liver disease (NAFLD) is a common concomitant disease in adults with type 2 diabetes mellitus (T2DM) and prediabetes. Therefore, T2DM/NAFLD patient populations are at high risk for cardiovascular disease. The occurrence and progression of non-alcoholic fatty liver disease-related liver fibrosis and cardiovascular disease have a severe impact on the patient's prognosis and mortality rate. The American Diabetes Association's 2024 "Guidelines for the Standardized Management of Diabetes" put forward recommendations relevant to the screening, evaluation, treatment, and management of NAFLD in T2DM and prediabetic populations, as well as liver fibrosis. The important measures for decelerating liver inflammation and fibrosis progression and the risk of cardiovascular disease are based on improvements in lifestyle methods, weight loss, and blood sugar control.

[Pay attention to the diagnosis and treatment of "easily neglected" complications in liver cirrhosis].

Managing cirrhosis complications is an important measure for improving patients' clinical outcomes. Therefore, in order to provide a complete disease assessment and comprehensive treatment, improve quality of life, and improve the prognosis for patients with cirrhosis, it is necessary to pay attention to complications such as thrombocytopenia and portal vein thrombosis in addition to common or severe complications such as ascites, esophagogastric variceal bleeding, hepatic encephalopathy, and hepatorenal syndrome. The relevant concept that an effective albumin concentration is more helpful in predicting the cirrhosis outcome is gradually being accepted; however, the detection method still needs further standardization and commercialization.

[Characteristics of liver volume and pathological changes with different stages of liver fibrosis in chronic liver disease].

Objective: To measure the overall and lobulated volume of the liver with different degrees of liver fibrosis and to further observe pathological changes such as liver microvasculature, hepatocyte apoptosis, and regeneration in order to understand the macroscopic volume changes of the liver during liver fibrosis and its relationship with liver tissue microscopic pathology in patients with chronic liver disease. Methods: 53 patients with chronic hepatitis B, alcoholic fatty liver disease, autoimmune liver disease, nonalcoholic fatty liver disease, and drug-induced chronic liver disease who underwent both liver biopsy tissue and abdominal magnetic resonance imaging were collected. Patients were divided into early (F1-2), middle (F3-4), and late (F5-6) in accordance with the Ishak fibrosis stage and Masson stain. The liver and spleen volumes were measured using ITK-SNAP software. CD31 immunohistochemical staining was used to reflect intrahepatic angiogenesis. Ki67 and HNF-4α multiplex immunohistochemical staining were used to reflect hepatocyte regeneration. GS staining was used to determine parenchymal extinction lesions. TUNEL staining was used to observe hepatocyte apoptosis. Spearman correlation analysis was used to analyze the relationship between liver volume changes and liver histopathological changes. Results: As liver fibrosis progressed, the total liver volume and right lobe liver volume gradually decreased (P<0.05), while the spleen volume gradually increased (P<0.05). The expression of CD31 and GS gradually increased (P<0.05), and the expression of Ki67 first increased and then decreased (P<0.05). The positivity rate of CD31 was negatively correlated with the right lobe liver volume (r=-0.609, P<0.001) and the total liver volume (r=-0.363, P=0.017). The positivity rate of Ki67 was positively correlated with the right lobe liver volume (r=0.423, P=0.018), while the positivity rate of apoptotic cells was significantly negatively correlated with the total liver volume (r=-0.860, P<0.001). The positivity rate of GS was negatively correlated with the right lobe liver volume (r=-0.440, P=0.002), and the number of PELs was negatively correlated with RV (r=-0.476, P=0.013). The CD31 positive staining area was negatively correlated with the Ki67 positive staining area(r=-0.511, P=0.009). Conclusion: As liver fibrosis progresses, patients with chronic liver disease have a depletion in total liver volume and right lobe liver volume, and this is mainly in correlation with fewer liver cells and liver tissue microvasculature disorders.

[Analysis of clinical and genetic characteristics of the severe liver disease phenotype in patients with hepatolenticular degeneration].

Objective: To investigate the clinical and genetic characteristics and predictive role of the severe liver disease phenotype in patients with hepatolenticular degeneration (HLD). Methods: Inpatients with HLD confirmed at Xinhua Hospital affiliated with Shanghai Jiao Tong University School of Medicine from January 1989 to December 2022 were selected as the research subjects. Clinical classification was performed according to the affected organs. Patients with liver disease phenotypes were classified into the liver disease group and further divided into the severe liver disease group and the ordinary liver disease group. The clinical characteristics and genetic variations were compared in each group of patients. The predictive indicators of patients with severe liver disease were analyzed by multiple regression. Statistical analysis was performed using the t-test, Mann-Whitney U test, or χ(2) test according to different data. Results: Of the 159 HLD cases, 142 were in the liver disease group (34 in the severe liver disease group and 108 in the ordinary liver disease group), and 17 were in the encephalopathy group. The median age of onset was statistically significantly different between the liver disease group and the encephalopathy group [12.6 (7.0, 13.3) years versus 16.9 (11.0, 21.5) years, P<0.01]. 156 ATP7B gene mutation sites were found in 83 cases with genetic testing results, of which 54 cases carried the p.Arg778Leu gene mutation (allele frequency 46.2%). Compared with patients with other types of gene mutations (n=65), patients with homozygous p.Arg778Leu mutations (n=18) had lower blood ceruloplasmin and albumin levels, a higher prognostic index, Child-Pugh score, an international normalized ratio, and prothrombin time (P<0.05). Hemolytic anemia, corneal K-F ring, homozygous p.Arg778Leu mutation, and multiple laboratory indexes in the severe liver disease group were statistically significantly different from those in the ordinary liver disease group (P<0.05). Multivariate logistic regression analysis showed that the predictive factors for severe liver disease were homozygous p.Arg778Leu mutation, total bilirubin, and bile acids (ORs=16.512, 1.022, 1.021, 95% CI: 1.204-226.425, 1.005-1.039, and 1.006-1.037, respectively, P<0.05). The drawn ROC curve demonstrated a cutoff value of 0.215 3, an AUC of 0.953 2, and sensitivity and specificity of 90.91% and 92.42%, respectively. Conclusion: Liver disease phenotypes are common in HLD patients and have an early onset. Total bilirubin, bile acids, and the homozygous p.Arg778Leu mutation of ATP7B is related to the severity of liver disease in HLD patients, which aids in predicting the occurrence and risk of severe liver disease.

[MRI manifestations of 40 cases with the hepatic epithelioid hemangioendothelioma classification based on the morphology and size].

Objective: To explore the MRI characteristics of the hepatic epithelioid hemangioendothelioma (HEHE) classification according to morphology and size. Methods: The clinical, pathological, and MRI imaging data of 40 cases with HEHE confirmed pathologically from December 2009 to September 2021 were retrospectively analyzed. A paired sample t-test was used for comparison between the two groups. Results: There were 40 cases (5 solitary, 24 multifocal, 9 local fusion, and 2 diffuse fusion) and 214 lesions (163 nodules, 31 masses, and 20 fusion foci). The most common features of lesions were subcapsular growth and capsular depression. The signal intensity of lesions ≤1cm was usually uniform with whole or ring enhancement. Nodules and mass-like lesions ≥1cm on a T1-weighted image had slightly reduced signal intensity or manifested as a halo sign. Target signs on a T2-weighted image were characterized by: target or centripetal enhancement; fusion-type lesions; irregular growth and hepatic capsular retraction, with ring or target-like enhancement in the early stage of fusion and patchy irregular enhancement in the late stage; blood vessels traversing or accompanied by malformed blood vessels; focal bleeding; an increasing proportion of extrahepatic metastases and abnormal liver function with the type of classified manifestation; primarily portal vein branches traversing; and reduced overall intralesional bleeding rate (17%). Lollipop signs were presented in 19 cases, with a high expression rate in mass-type lesions (42%). The fusion lesions were expressed, but the morphological manifestation was atypical. The diffusion-weighted imaging mostly showed high signal or target-like high signal. An average apparent diffusion coefficient of lesions was (1.56±0.36) ×10(-3)mm(2)/s, which was statistically significantly different compared with that of adjacent normal liver parenchyma (t=8.28, P<0.001). Conclusion: The MRI manifestations for the HEHE classification are closely related to the morphology and size of the lesions and have certain differences and characteristics that are helpful for the diagnosis of the disease when combined with clinical and laboratory examinations.

[Chinese guideline for diagnosis and treatment of adult acute lymphoblastic leukemia (2024)].

Adult acute lymphoblastic leukemia (ALL) is one of the most common acute leukemia in adults. There are relatively unified diagnostic criteria and systematic treatment regimens reported by different research groups in the world. Since 2013, three versions of expert consensus/guidelines on the diagnosis and treatment of adult ALL in China have been published, which are of great significance for improving the level of diagnosis and treatment of this disease. In 2022, the classification of ALL (precursor lymphocyte neoplasms-lymphoblastic leukemias/lymphomas) had been updated in the WHO classification of haematolymphoid tumors, and some new concepts had been proposed. In recent years, the rapid development of immunotherapy has improved the efficacy of adult ALL, and commercial antibodies and CAR-T cell products have been available in China, the clinical practice is increasing. In order to promote the standardization of clinical diagnosis and treatment of adult ALL, by referring to the latest guidelines and literatures all over the world, this guideline may contribute to the better understanding of diagnosis, treatment and efficacy monitoring for adult ALL.

[Chinese expert consensus on diagnosis and treatment of high risk multiple myeloma (2024)].

High-risk multiple myeloma (HRMM) refers to patients with multiple myeloma whose overall survival time is less than 2-3 years under current standardized diagnosis and treatment. By combining various static and dynamic prognostic factors, risk stratification is performed to identify HRMM patients early and treat patients with personalized strategies, with the aim of significantly improving adverse survival outcomes in HRMM patients. Although the clinical value of HRMM has reached a consensus domestically in recent years, there still exist confusions and ambiguities in the definition, high-risk factors, risk stratification, and treatment of HRMM, necessitating standardization. In order to enhance the diagnostic and treatment capabilities of Chinese physicians in HRMM, the Professional Committee of Hematologic Malignancies of the Chinese Anti-Cancer Association (CACA) and the Multiple Myeloma Expert Committee of the Chinese Society of Clinical Oncology (CSCO) have organized relevant experts to develop this consensus. This consensus aims to clarify the definition of HRMM, high-risk factors, and risk stratification system, and provide treatment recommendations for HRMM, thereby improving the quality of life and prognosis of Chinese HRMM patients.

[Clinical characteristics and prognostic analysis of 34 patients with primary thyroid lymphoma].

Objective: To investigate the clinical characteristics, diagnosis, treatment, and prognosis of primary thyroid lymphoma (PTL) . Methods: A retrospective analysis was conducted on the clinical and pathological data of 34 newly diagnosed PTL patients admitted to Beijing Tongren Hospital from September 2010 to February 2023. The Kaplan-Meier survival curve and Log-rank test were used for survival analysis, and the Cox regression model was applied for univariate analysis of prognostic factors. Results: All 34 PTL patients presented with cervical mass as the initial clinical manifestation. There were 9 males and 25 females. The pathological diagnosis was diffuse large B-cell lymphoma (DLBCL) in 29 patients and mucosa-associated lymphoid tissue (MALT) lymphoma in 5 patients. Among the DLBCL patients, 6 had B symptoms, 17 had an Eastern Cooperative Oncology Group (ECOG) score of ≥2, the Ann Arbor staging was stage Ⅰ-Ⅱ in 21 cases and stage Ⅲ-Ⅳ in 8 cases, the tumor diameter was ≥10 cm in 4 cases, and 14 had concurrent Hashimoto thyroiditis; 27 cases received chemotherapy, with 21 cases achieving complete remission (CR), 2 cases partial remission (PR), and 6 cases of disease progression; the 5-year progression-free survival and overall survival rates were 78.9% and 77.4%, respectively; univariate survival analysis showed that B symptoms, tumor diameter ≥10 cm, and Ann Arbor stage Ⅲ-Ⅳ were significant factors affecting patient prognosis (P<0.05). MALT lymphoma patients were all in stages Ⅰ-Ⅱ, had an ECOG score of 0-1, and were without B symptoms. All patients underwent surgical resection, with 4 cases achieving CR and 1 case PR. Conclusion: PTL is more common in females with concurrent Hashimoto thyroiditis, with the majority of pathological types being B-cell lymphoma. The main treatment is chemotherapy, supplemented by radiotherapy and surgery, and the prognosis is relatively favorable.

Patient-reported observations on medical procedure timeliness (PROMPT) in breast cancer: a qualitative study.

PURPOSE: Timeliness of care is an important healthcare outcome measure. The objective of this study was to explore patient perspectives on the timeliness of breast cancer diagnosis and treatment at accredited breast cancer centers. METHODS: In this qualitative study, 1 hour virtual interviews were conducted with participants 18-75 years old who were diagnosed and treated for stage 0-III breast cancer at a National Accreditation Program for Breast Centers facility from 2018 to 2022. Thematic analysis was used to identify key themes of participant experiences. RESULTS: Twenty-eight participants were interviewed. Two thematic domains were identified: etiologies of expedited or delayed care and the impact of delayed or expedited care on patients. Within these domains, multiple themes emerged. For etiologies of expedited or delayed care, participants discussed (1) the effect of scheduling appointments, (2) the COVID-19 pandemic, (3) dissatisfaction with the timeline for various parts of the diagnostic workup, and (4) delays related to patient factors, including socioeconomic status. For the impact of expedited or delayed care, patients discussed (1) the emotional and mental impact of waiting, (2) the importance of communication and clear expectations, and (3) the impact of electronic health portals. Patients desired each care interval (e.g., the time from mammogram to breast biopsy) to be approximately 7 days, with longer intervals sometimes preferred prior to surgery. CONCLUSION: These patient interviews identify areas of delay and provide patient-centered, actionable items to improve the timeliness of breast cancer care.