A Semi-Supervised Learning Framework for Classifying Colorectal Neoplasia Based on the NICE Classification.

Labelling medical images is an arduous and costly task that necessitates clinical expertise and large numbers of qualified images. Insufficient samples can lead to underfitting during training and poor performance of supervised learning models. In this study, we aim to develop a SimCLR-based semi-supervised learning framework to classify colorectal neoplasia based on the NICE classification. First, the proposed framework was trained under self-supervised learning using a large unlabelled dataset; subsequently, it was fine-tuned on a limited labelled dataset based on the NICE classification. The model was evaluated on an independent dataset and compared with models based on supervised transfer learning and endoscopists using accuracy, Matthew's correlation coefficient (MCC), and Cohen's kappa. Finally, Grad-CAM and t-SNE were applied to visualize the models' interpretations. A ResNet-backboned SimCLR model (accuracy of 0.908, MCC of 0.862, and Cohen's kappa of 0.896) outperformed supervised transfer learning-based models (means: 0.803, 0.698, and 0.742) and junior endoscopists (0.816, 0.724, and 0.863), while performing only slightly worse than senior endoscopists (0.916, 0.875, and 0.944). Moreover, t-SNE showed a better clustering of ternary samples through self-supervised learning in SimCLR than through supervised transfer learning. Compared with traditional supervised learning, semi-supervised learning enables deep learning models to achieve improved performance with limited labelled endoscopic images.

The development and validation of automated machine learning models for predicting lymph node metastasis in Siewert type II T1 adenocarcinoma of the esophagogastric junction.

Background: Lymph node metastasis (LNM) is considered an essential prognosis factor for adenocarcinoma of the esophagogastric junction (AEG), which also affects the treatment strategies of AEG. We aimed to evaluate automated machine learning (AutoML) algorithms for predicting LNM in Siewert type II T1 AEG. Methods: A total of 878 patients with Siewert type II T1 AEG were selected from the Surveillance, Epidemiology, and End Results (SEER) database to develop the LNM predictive models. The patients from two hospitals in Suzhou were collected as the test set. We applied five machine learning algorithms to develop the LNM prediction models. The performance of predictive models was assessed using various metrics including accuracy, sensitivity, specificity, the area under the curve (AUC), and receiver operating characteristic (ROC) curve. Results: Patients with LNM exhibited a higher proportion of male individuals, a poor degree of differentiation, and submucosal infiltration, with statistical differences. The deep learning (DL) model demonstrated relatively good accuracy (0.713) and sensitivity (0.868) among the five models. Moreover, the DL model achieved the highest AUC (0.781) and sensitivity (1.000) in the test set. Conclusion: The DL model showed good predictive performance among five AutoML models, indicating the advantage of AutoML in modeling LNM prediction in patients with Siewert type II T1 AEG.

Association of the fat mass index with hepatic steatosis and fibrosis: evidence from NHANES 2017-2018.

Limited population-based studies discuss the association between fat mass index (FMI) and the risk of liver diseases. This investigation utilized data from the National Health and Nutrition Examination Survey (NHANES) to examine the linkage between the FMI and liver conditions, specifically steatosis and fibrosis. The study leveraged data from NHANES's 2017-2018 cross-sectional study, employing an oversampling technique to deal with sample imbalance. Hepatic steatosis and fibrosis were identified by vibration-controlled transient elastography. Receiver operating curve was used to assess the relationship of anthropometric indicators, e.g., the FMI, body mass index (BMI), weight-adjusted-waist index (WWI), percentage of body fat (BF%), waist-to-hip ratio (WHR), and appendicular skeletal muscle index (ASMI), with hepatic steatosis and fibrosis. In this study, which included 2260 participants, multivariate logistic regression models, stratified analyses, restricted cubic spline (RCS), and sharp regression discontinuity analyses were utilized. The results indicated that the WHR and the FMI achieved the highest area under the curve for identifying hepatic steatosis and fibrosis, respectively (0.720 and 0.726). Notably, the FMI presented the highest adjusted odds ratio for both hepatic steatosis (6.40 [4.91-8.38], p = 2.34e-42) and fibrosis (6.06 [5.00, 7.37], p = 5.88e-74). Additionally, potential interaction effects were observed between the FMI and variables such as the family income-to-poverty ratio, smoking status, and hypertension, all of which correlated with the presence of liver fibrosis (p for interaction < 0.05). The RCS models further confirmed a significant positive correlation of the FMI with the controlled attenuation parameter and liver stiffness measurements. Overall, the findings underscore the strong link between the FMI and liver conditions, proposing the FMI as a potential straightforward marker for identifying liver diseases.

Automated machine learning for early prediction of acute kidney injury in acute pancreatitis.

BACKGROUND: Acute kidney injury (AKI) represents a frequent and grave complication associated with acute pancreatitis (AP), substantially elevating both mortality rates and the financial burden of hospitalization. The aim of our study is to construct a predictive model utilizing automated machine learning (AutoML) algorithms for the early prediction of AKI in patients with AP. METHODS: We retrospectively analyzed patients who were diagnosed with AP in our hospital from January 2017 to December 2021. These patients were randomly allocated into a training set and a validation set at a ratio of 7:3. To develop predictive models for each set, we employed the least absolute shrinkage and selection operator (LASSO) algorithm along with AutoML. A nomogram was developed based on multivariate logistic regression analysis outcomes. The model's efficacy was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Additionally, the performance of the model constructed via AutoML was evaluated using decision curve analysis (DCA), feature importance, SHapley Additive exPlanations (SHAP) plots, and locally interpretable model-agnostic explanations (LIME). RESULTS: This study incorporated a total of 437 patients who met the inclusion criteria. Out of these, 313 were assigned to the training cohort and 124 to the validation cohort. In the training and validation cohorts, AKI occurred in 68 (21.7%) and 29(23.4%) patients, respectively. Comparative analysis revealed that the AutoML models exhibited enhanced performance over traditional logistic regression (LR). Furthermore, the deep learning (DL) model demonstrated superior predictive accuracy, evidenced by an area under the ROC curve of 0.963 in the training set and 0.830 in the validation set, surpassing other comparative models. The key variables identified as significant in the DL model within the training dataset included creatinine (Cr), urea (Urea), international normalized ratio (INR), etiology, smoking, alanine aminotransferase (ALT), hypertension, prothrombin time (PT), lactate dehydrogenase (LDH), and diabetes. CONCLUSION: The AutoML model, utilizing DL algorithm, offers considerable clinical significance in the early detection of AKI among patients with AP.

Deep learning to predict esophageal variceal bleeding based on endoscopic images.

OBJECTIVE: Esophageal varix (EV) bleeding is a particularly serious complications of cirrhosis. Prediction of EV bleeding requires extensive endoscopy experience; it remains unreliable and inefficient. This retrospective cohort study evaluated the feasibility of using deep learning (DL) to predict the 12-month risk of EV bleeding based on endoscopic images. METHODS: Six DL models were trained to perform binary classification of endoscopic images of EV bleeding. The models were subsequently validated using an external test dataset, then compared with classifications performed by two endoscopists. RESULTS: In the validation dataset, EfficientNet had the highest accuracy (0.910), followed by ConvMixer (0.898) and Xception (0.875). In the test dataset, EfficientNet maintained the highest accuracy (0.893), which was better than the endoscopists (0.800 and 0.763). Notably, one endoscopist displayed higher recall (0.905), compared with EfficientNet (0.870). When their predictions were assisted by artificial intelligence, the accuracies of the two endoscopists increased by 17.3% and 19.0%. Moreover, statistical agreement among the models was dependent on model architecture. CONCLUSIONS: This study demonstrated the feasibility of using DL to predict the 12-month risk of EV bleeding based on endoscopic images. The findings suggest that artificial intelligence-aided diagnosis will be a useful addition to cirrhosis management.

Public Imaging Datasets of Gastrointestinal Endoscopy for Artificial Intelligence: a Review.

With the advances in endoscopic technologies and artificial intelligence, a large number of endoscopic imaging datasets have been made public to researchers around the world. This study aims to review and introduce these datasets. An extensive literature search was conducted to identify appropriate datasets in PubMed, and other targeted searches were conducted in GitHub, Kaggle, and Simula to identify datasets directly. We provided a brief introduction to each dataset and evaluated the characteristics of the datasets included. Moreover, two national datasets in progress were discussed. A total of 40 datasets of endoscopic images were included, of which 34 were accessible for use. Basic and detailed information on each dataset was reported. Of all the datasets, 16 focus on polyps, and 6 focus on small bowel lesions. Most datasets (n = 16) were constructed by colonoscopy only, followed by normal gastrointestinal endoscopy and capsule endoscopy (n = 9). This review may facilitate the usage of public dataset resources in endoscopic research.

A nomogram based on platelet-to-lymphocyte ratio for predicting lymph node metastasis in patients with early gastric cancer.

Background: Preoperative assessment of the presence of lymph node metastasis (LNM) in patients with early gastric cancer (EGC) remains difficult. We aimed to develop a practical prediction model based on preoperative pathological data and inflammatory or nutrition-related indicators. Methods: This study retrospectively analyzed the clinicopathological characteristics of 1,061 patients with EGC who were randomly divided into the training set and validation set at a ratio of 7:3. In the training set, we introduced the least absolute selection and shrinkage operator (LASSO) algorithm and multivariate logistic regression to identify independent risk factors and construct the nomogram. Both internal validation and external validation were performed by the area under the receiver operating characteristic curve (AUC), C-index, calibration curve, and decision curve analysis (DCA). Results: LNM occurred in 162 of 1,061 patients, and the rate of LNM was 15.27%. In the training set, four variables proved to be independent risk factors (p < 0.05) and were incorporated into the final model, including depth of invasion, tumor size, degree of differentiation, and platelet-to-lymphocyte ratio (PLR). The AUC values were 0.775 and 0.792 for the training and validation groups, respectively. Both calibration curves showed great consistency in the predictive and actual values. The Hosmer-Lemeshow (H-L) test was carried out in two cohorts, showing excellent performance with p-value >0.05 (0.684422, 0.7403046). Decision curve analysis demonstrated a good clinical benefit in the respective set. Conclusion: We established a preoperative nomogram including depth of invasion, tumor size, degree of differentiation, and PLR to predict LNM in EGC patients and achieved a good performance.

Development of a scoring system for predicting primary resistance to venetoclax plus hypomethylating agents (HMAs) in acute myeloid leukemia patients.

In recent years, one of the most promising advances in the treatment of acute myeloid leukemia (AML) is the combination of a hypomethylating agent (HMA) with the BCL2 inhibitor venetoclax (VEN). To better understand the key factors associated with the response of VEN plus HMA, 212 consecutive AML patients were retrospectively recruited to establish and validate a scoring system for predicting the primary resistance to VEN-based induced therapy. All AML patients were divided randomly into a training set (n = 155) and a validation set (n = 57). Factors were selected using a multivariate logistic regression model, including FAB-M5, myelodysplastic syndrome-secondary acute myeloid leukemia (MDS-sAML), RUNX1-RUNX1T1 and FLT3-ITD mutation (FLT3-ITDm). A nomogram was then constructed including all these four predictors. The nomogram both presented a good performance of discrimination and calibration, with a C-index of 0.770 and 0.733 in the training and validation set. Decision curve analysis also indicated that the nomogram was feasible to make beneficial decisions. Eventually a total scoring system of 8 points was developed, which was divided into three risk groups: low-risk (score 0-2), medium-risk (score 3-4), and high-risk (score 5-8). There was a significant difference in the nonremission (NR) rate of these three risk groups (22.8% vs. 60.0% vs. 77.8%, p < 0.001). After adjustment of the other variables, patients in medium- or high-risk groups also presented a worse event-free survival (EFS) than that in the low-risk group (hazard ratio [HR] = 1.62, p = 0.03). In conclusion, we highlighted the response determinants of AML patients receiving a combination therapy of VEN plus HMAs. The scoring system can be used to predict the resistance of VEN, providing better guidance for clinical treatment.

Identification of gastric signet ring cell carcinoma based on endoscopic images using few-shot learning.

BACKGROUND: Deep learning (DL) models perform poorly when there are limited gastric signet ring cell carcinoma (SRCC) samples. Few-shot learning (FSL) can address the small sample problem. METHODS: EfficientNetV2-S was first pretrained on ImageNet and then pretrained on esophageal endoscopic images (i.e., base classes: normal vs. early cancer vs. advanced cancer) using transfer learning. Second, images of gastric benign ulcers, adenocarcinoma and SRCC, i.e., novel classes (n = 50 per class), were included. Image features were extracted as vectors using the dual pretrained EfficientNetV2-S. Finally, a k-nearest neighbor classifier was used to identify SRCC. The above proposed 3-way 3-shot FSL framework was conducted in three rounds. RESULTS: Dual pretrained FSL performed better than single pretrained FSL, endoscopists and traditional EfficientNetV2-S models. Dual pretrained FSL obtained the highest accuracy (79.4%), sensitivity (68.8%), recall (68.8%), precision (69.3%) and F1-score (0.691), and the senior endoscopist achieved the highest specificity of 93.6% when identifying SRCC. The macro-AUC and F1-score for dual pretraining (0.763 and 0.703, respectively) were higher than those for single pretraining (0.656 and 0.537, respectively), along with endoscopists and traditional EfficientNetV2-S models. The 2-way 3-shot FSL also performed better. CONCLUSION: The proposed FSL framework showed practical performance in the differentiation of SRCC on endoscopic images, suggesting the potential of FSL in the computer-aided diagnosis for rare diseases.

CT-based radiomics nomogram for overall survival prediction in patients with cervical cancer treated with concurrent chemoradiotherapy.

Background and purpose: To establish and validate a hybrid radiomics model to predict overall survival in cervical cancer patients receiving concurrent chemoradiotherapy (CCRT). Methods: We retrospectively collected 367 cervical cancer patients receiving chemoradiotherapy from the First Affiliated Hospital of Soochow University in China and divided them into a training set and a test set in a ratio of 7:3. Handcrafted and deep learning (DL)-based radiomics features were extracted from the contrast-enhanced computed tomography (CT), and the two types of radiomics signatures were calculated based on the features selected using the least absolute shrinkage and selection operator (LASSO) Cox regression. A hybrid radiomics nomogram was constructed by integrating independent clinical risk factors, handcrafted radiomics signature, and DL-based radiomics signature in the training set and was validated in the test set. Results: The hybrid radiomics nomogram exhibited favorable performance in predicting overall survival, with areas under the receiver operating characteristic curve (AUCs) for 1, 3, and 5 years in the training set of 0.833, 0.777, and 0.871, respectively, and in the test set of 0.811, 0.713, and 0.730, respectively. Furthermore, the hybrid radiomics nomogram outperformed the single clinical model, handcrafted radiomics signature, and DL-based radiomics signature in both the training (C-index: 0.793) and test sets (C-index: 0.721). The calibration curves and decision curve analysis (DCA) indicated that our hybrid nomogram had good calibration and clinical benefits. Finally, our hybrid nomogram demonstrated value in stratifying patients into high- and low-risk groups (cutoff value: 5.6). Conclusion: A high-performance hybrid radiomics model based on pre-radiotherapy CT was established, presenting strengths in risk stratification.

The prognostic role of fatty acid metabolism-related genes in patients with gastric cancer.

Background: With the deepening research on fatty acid metabolism, people have achieved a preliminary understanding of it in the development and prognosis of tumors. However, few studies are still on the expression pattern and prognostic value of fatty acid metabolism-related genes in gastric cancer (GC). Methods: We chose 93 genes relevant to fatty acid metabolism from the Gene Set Enrichment Analysis (GSEA) database. We analyzed differentially expressed genes (DEGs) in The Cancer Genome Atlas (TCGA) patients. Univariate Cox analysis and LASSO regression were used to select the genes most related to prognosis and therefore developed a prognosis model. In addition, a dataset of 76 samples from Gene Expression Omnibus (GEO) selected as a test set to aid in the development of a prognostic model. The prognostic relevance of this model was confirmed using Kaplan-Meier survival analysis, univariate/multivariate Cox analysis, and receiver operating characteristic (ROC) curve. Finally, enrichment analysis and protein-protein interaction (PPI) were used to analyze the functional differences of patients with different risk. Immune infiltration analysis based on CIBERSORT could check the infiltration degree and immune function changes of immune cell subtypes in patients with different risk groups. Results: Overexpression of ELOVL4, ADH4, CPT1C, and ADH1B was linked to poor overall survival (OS) in GC patients, according to our findings. Furthermore, according to prognostic factors, patients with lower risk score tend to have better prognosis than patients with higher risk score. In addition, we also found that the infiltration levels of B cells, dendritic cells, auxiliary T cells, mast cells, neutrophils and tumor-infiltrating lymphocytes in patients with high-risk group were significantly increased, and the type II IFN response of immune cells, CCR and MHC class I receptor functions were significantly enhanced, suggesting that the tumor microenvironment immune activity in patients with high-risk group was active. Conclusions: Four fatty acid metabolism-related genes were discovered to be closely connected to the prognosis of individuals with GC. Through analysis and verification, we believed that this prognostic model was reliable and instructive in the prediction of the prognosis of GC.

The Development of a Prediction Model Based on Random Survival Forest for the Postoperative Prognosis of Pancreatic Cancer: A SEER-Based Study.

Accurate prediction for the prognosis of patients with pancreatic cancer (PC) is a emerge task nowadays. We aimed to develop survival models for postoperative PC patients, based on a novel algorithm, random survival forest (RSF), traditional Cox regression and neural networks (Deepsurv), using the Surveillance, Epidemiology, and End Results Program (SEER) database. A total of 3988 patients were included in this study. Eight clinicopathological features were selected using least absolute shrinkage and selection operator (LASSO) regression analysis and were utilized to develop the RSF model. The model was evaluated based on three dimensions: discrimination, calibration, and clinical benefit. It found that the RSF model predicted the cancer-specific survival (CSS) of the postoperative PC patients with a c-index of 0.723, which was higher than the models built by Cox regression (0.670) and Deepsurv (0.700). The Brier scores at 1, 3, and 5 years (0.188, 0.177, and 0.131) of the RSF model demonstrated the model's favorable calibration and the decision curve analysis illustrated the model's value of clinical implement. Moreover, the roles of the key variables were visualized in the Shapley Additive Explanations plotting. Lastly, the prediction model demonstrates value in risk stratification and individual prognosis. In this study, a high-performance prediction model for PC postoperative prognosis was developed, based on RSF The model presented significant strengths in the risk stratification and individual prognosis prediction.

The relationship between Helicobacter pylori and pancreatic cancer: a meta-analysis.

Background: The relationship between Helicobacter pylori (H. pylori, HP) infection and pancreatic cancer would be investigated in this article. Methods: All cohort studies and case-control studies about H. pylori infection and pancreatic cancer up to October 2021 were searched in the databases of PubMed, Embase and Cochrane. The combined odds ratio (OR) and 95% confidence interval (CI) were calculated by R 4.1.0 software. Funnel plot and Egger test were used to evaluate publication bias. Results: A total of 17 studies which included 8 case-control studies, 5 nested case-control studies, and 4 cohort studies were included in this study, and the results of this article have confirmed that the H. pylori infection was significantly correlated with the occurrence of pancreatic cancer (OR =1.30, 95% CI: 1.02-1.64), especially in economically underdeveloped areas (OR =2.10, 95% CI: 1.44-3.05). However, negative results were obtained in the relationship between CagA + H. pylori and pancreatic cancer. Similarly, we also did not find an association between vacuolating cytotoxin gene A-positive strains (VacA-positive H. pylori) and pancreatic cancer. The heterogeneity of this study was significant. Through a sensitivity analysis by the leave-one-out method, we found the results remained unchanged on the whole but the correlation between H. pylori infection and the occurrence of pancreatic cancer in the Asian population was significant. The tests for funnel plot asymmetry indicated that there might be obvious publication bias in this study. After carrying out the Egger test, we proved the existence of the publication bias in this study, which could have a certain impact on the results. Discussion: Based on the currently available data, we confirm that H. pylori infection can increase the incidence of pancreatic cancer in general. CagA/VacA-positive H. pylori infection is not associated with the incidence of pancreatic cancer. H. pylori infection is significantly associated with the incidence of pancreatic cancer in economically underdeveloped areas, while the relationship between H. pylori infection and the incidence of pancreatic cancer in the Asian population is uncertain. In addition, more high-quality studies are needed to be included to confirm this conclusion.

The prognostic value and clinical significance of mitophagy-related genes in hepatocellular carcinoma.

Background: Mitophagy has been found to play a significant part in the cancer process in a growing number of studies in recent years. However, there is still a lack of study on mitophagy-related genes' (MRGs) prognostic potential and clinical significance in hepatocellular carcinoma (HCC). Methods: We employed bioinformatics and statistical knowledge to examine the transcriptome data of HCC patients in the TCGA and GEO databases, with the goal of constructing a multigene predictive model. Then, we separated the patients into high- and low-risk groups based on the score. The model's dependability was determined using principal components analysis (PCA), survival analysis, independent prognostic analysis, and receiver operating characteristic (ROC) analysis. Following that, we examined the clinical correlations, pharmacological treatment sensitivity, immune checkpoint expression, and immunological correlations between patients in high and low risk groups. Finally, we evaluated the variations in gene expression between high- and low-risk groups and further analyzed the network core genes using protein-protein interaction network analysis. Results: Prognostic models were built using eight genes (OPTN, ATG12, CSNK2A2, MFN1, PGAM5, SQSTM1, TOMM22, TOMM5). During validation, the prognostic model demonstrated high reliability, indicating that it could accurately predict the prognosis of HCC patients. Additionally, we discovered that typical HCC treatment medicines had varying impacts on patients classified as high or low risk, and that individuals classified as high risk are more likely to fail immunotherapy. Additionally, the high-risk group expressed more immunological checkpoints. The immunological status of patients in different risk categories varies as well, and patients with a high-risk score have a diminished ability to fight cancer. Finally, PPI analysis identified ten related genes with potential for research. Conclusion: Our prognostic model had good and reliable predictive ability, as well as clinical diagnosis and treatment guiding significance. Eight prognostic MRGs and ten network core genes merited further investigation.

Pancreatic cancer incidence and mortality patterns in 2006-2015 and prediction of the epidemiological trend to 2025 in China.

BACKGROUND: Due to dietary patterns, the aging population, and other high-risk factors, the occurrence of pancreatic cancer (PC) has been rapidly increasing in China. AIM: To present the epidemiological trends of PC in China over the past decade and the estimated trend in 2025 and to compare the international differences in PC morbidity and mortality. METHODS: This study used a series of nationally representative data from the National Central Cancer Registry of China (NCCR), the International Agency for Research on Cancer and the Institute for Health Metrics and Evaluation databases. Age-standardized data of the PC incidence and mortality from 2006 to 2015 in China were extracted from the NCCR database. Linear regression models were used to estimate the incidence and mortality rates of PC in 2025. RESULTS: The age-standardized rates of PC in China increased from 3.65 per 100000 in 2006 to 4.31 per 100000 in 2015 and were estimated to reach up to 5.52 per 100000 in 2025. The mortality went from 3.35 per 100000 in 2006 to 3.78 per 100000 in 2015, estimated to reach up to 4.6 per 100000 in 2025. The number of new cases and deaths was low before 45 years and the peak age of onset was 85-89 years. The incidence and mortality rates in men were higher than those in women regardless of the region in China. In addition, the incidence and mortality rates in China were higher than the average level around the world. Likewise, disability-adjusted life years attributed to PC in China were 197.22 years per 100000, above the average level around the world. CONCLUSION: This study presented an increasing trend of PC in China and differences in morbidity, mortality and disability-adjusted life years between Chinese and global populations. Efforts need to be made to decrease the PC incidence and improve patient outcomes.

Association of mucin family members with prognostic significance in pancreatic cancer patients: A meta-analysis.

OBJECTIVE: The role of biomarkers in the early diagnosis and prognosis prediction of tumors has been paid more and more attention by researchers. Mucins are markers that have been found to have an abnormal expression in many tumors in recent years, which have been proved to have a predictive effect on the prognosis of tumors such as cholangiocarcinoma and colon cancer. However, whether it can predict the prognosis of pancreatic cancer remains unknown. The purpose of our study is to investigate whether the mucins and their subtypes are related to the prognosis of patients with pancreatic cancer. METHODS: We systematically searched the Pubmed, Embase, and Cochrane Library for all eligible studies on the relationship between mucin and the prognosis of patients with pancreatic cancer up to November 2021. We used R 4.12 to calculate the combined risk ratio (HR) and 95% confidence interval (CI). For studies that did not provide HR values, we used scientific methods to calculate their values as accurately as possible. We used fixed effect model due to low heterogeneity. Subgroup analysis and sensitivity analysis were used to study heterogeneity. The funnel plot and Egger test were used to test whether the publication bias existed. The trim and filling method were used to evaluate the impact of publication bias on the results of the study. RESULTS: A total of 18 studies were included in this meta-analysis, including 4 subtypes of mucin family members and 1643 patients. There was a slight heterogeneity between studies (I2 = 24.4%, P = 0.14). Meta-analysis showed that MUC4 (HR = 2.04, 95%CI 1.21;3.45), MUC16 (HR = 2.10, 95%CI 1.31;3.37), and whole mucin (HR = 1.32, 95%CI 1.07;1.63). The expression level was negatively correlated with the prognosis of pancreatic cancer patients, MUC1 (HR = 1.09, 95%CI 0.77;1.54), MUC5 (HR = 1.03, 95%CI 0.47;2.25) The expression level was not related to the prognosis of pancreatic cancer patients. CONCLUSION: The meta-analysis demonstrated that the overall expression level of mucin and the expression levels of MUC4 and MUC16 were important prognostic predictors for pancreatic cancer patients. MUC1 and MUC5 had no predictive value for the prognosis of pancreatic cancer patients. Future studies should validate these and other promising biomarkers. TRIAL REGISTRATION: PROSPERO registration number is CRD42021291962. https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021291962.

Development of a Deep Learning Model for Malignant Small Bowel Tumors Survival: A SEER-Based Study.

Background This study aims to explore a deep learning (DL) algorithm for developing a prognostic model and perform survival analyses in SBT patients. Methods The demographic and clinical features of patients with SBTs were extracted from the Surveillance, Epidemiology and End Results (SEER) database. We randomly split the samples into the training set and the validation set at 7:3. Cox proportional hazards (Cox-PH) analysis and the DeepSurv algorithm were used to develop models. The performance of the Cox-PH and DeepSurv models was evaluated using receiver operating characteristic curves, calibration curves, C-statistics and decision-curve analysis (DCA). A Kaplan−Meier (K−M) survival analysis was performed for further explanation on prognostic effect of the Cox-PH model. Results The multivariate analysis demonstrated that seven variables were associated with cancer-specific survival (CSS) (all p < 0.05). The DeepSurv model showed better performance than the Cox-PH model (C-index: 0.871 vs. 0.866). The calibration curves and DCA revealed that the two models had good discrimination and calibration. Moreover, patients with ileac malignancy and N2 stage disease were not responding to surgery according to the K−M analysis. Conclusions This study reported a DeepSurv model that performed well in CSS in SBT patients. It might offer insights into future research to explore more DL algorithms in cohort studies.

Encapsulating peritoneal sclerosis in an AMA-M2 positive patient: A case report.

BACKGROUND: Encapsulating peritoneal sclerosis (EPS) is hard to diagnose because of nonspecific symptoms and signs. It is a general consensus that EPS is classified as primary and secondary. There have been several studies discovering some high-risk factors such as liver cirrhosis, of which AMA-M2 is a biomarker, and intra-abdominal surgery such as laparoscopic surgery. Imaging studies help to diagnose EPS and exploratory laparotomy might be an alternative if imaging fails. Nowadays, laparotomy plays a key role in treating EPS, especially when medical treatments do not work and medical therapy fails to ease patients' symptoms. CASE SUMMARY: A 58-year-old man complained of unexplained vomiting and abdominal distension 2 mo after laparoscopic cholecystectomy. Increased alkaline phosphatase and liver enzymes were discovered. An autoimmune liver disease test showed that AMA-M2 was positive. A gastroscopy revealed bile reflux gastritis. A magnetic resonance imaging scan showed a slight dilatation of the intrahepatic bile duct. A colonoscopy showed that there was a mucosal eminence lesion in the sigmoid colon (24 cm away from the anus), with a size of 3 cm × 3 cm and erosive surface. At last, the small intestine and the stomach were found to be encased in a cocoon-like membrane during the surgery. The membrane was dissected and adhesiolysis was done to release the trapped organs. The patient recovered and was discharged 44 d after the operation, and there was no recurrence during a follow-up period of 3 mo. CONCLUSION: AMA-M2 is a marker of primary biliary sclerosis and may help to make a preoperative diagnosis of EPS.

Relationships are between metformin use and survival in pancreatic cancer patients concurrent with diabetes: A systematic review and meta-analysis.

BACKGROUND: Increased risk and cancer-related mortality is observed in pancreatic cancer (PC) patients with diabetes mellitus (DM). Whether using metformin as glucose-lowering therapy can result in survival benefit in this group of patients is still unclear. METHODS: A meta-analysis of 21 studies that including 38,772 patients was performed to investigate the association between metformin and overall survival in patients with PC and concurrent DM. RESULTS: A significant survival benefit was observed in metformin treatment group compared with non-metformin group (hazard ratio [HR] = 0.83, 95% confidence interval [CI]: 0.74-0.91). These associations were observed in both subgroups of Asian countries (HR = 0.69, 95% CI: 0.60-0.79) and Western countries (HR = 0.86, 95% CI: 0.76-0.95), the former was more obvious. Survival benefit was gained for patients at early stage (HR = 0.75, 95% CI: 0.64-0.85) and mixed stage (HR = 0.81, 95% CI: 0.70-0.91), but not for patients at advanced stage (HR = 0.99, 95% CI: 0.74-1.24). Similarly, survival benefit was also observed in patients receiving surgery (HR = 0.82, 95% CI: 0.69-0.94) and comprehensive treatment (HR = 0.85, 95% CI: 0.77-0.93), but not in chemotherapy group (HR = 0.99, 95% CI: 0.67-1.30). No obvious benefit was suggested when pooled by time-varying COX model (HR = 0.94, 95% CI: 0.86-1.03). CONCLUSIONS: These results suggest that metformin is associated with survival benefit in patients with PC and concurrent DM. Further randomized controlled trials and prospective studies with larger sample sizes are required to confirm our findings.

Ultrasound-mediated destruction of paclitaxel and oxygen loaded lipid microbubbles for combination therapy in ovarian cancer xenografts.

We have synthesized multifunctional oxygen and paclitaxel loaded microbubbles (OPLMBs) for ultrasound mediated delivery of combination therapy in an ovarian cancer xenograft model. In comparison with other therapeutic options, intravenous injection of OPLMBs followed by ultrasound mediation yielded a superior therapeutic outcome. Immunohistochemical analyses of the dissected tumor tissue confirmed the increased tumor apoptosis and the reduced VEGF expression after treatment. Western Blot tests further confirmed the decreased expressions of HIF-1α and P-gp. Our experiment suggests that ultrasound mediation of OPLMBs may provide a promising drug delivery strategy for the combination treatment of ovarian cancer.