Multiparametric MRI-based radiomics analysis for prediction of lymph node metastasis and survival outcome in gastric cancer: A dual-center study.

RATIONALE AND OBJECTIVES: Gastric cancer (GC) is highly heterogeneous, and accurate preoperative assessment of lymph node status remains challenging. We aimed to develop a multiparametric MRI-based model for predicting lymph node metastasis (LNM) in GC and to explore its prognostic implications. MATERIALS AND METHODS: In this dual-center retrospective study, 479 GC patients undergoing preoperative multiparametric MRI before radical gastrectomy were enrolled. 1595 imaging features were extracted from T2-weighted imaging, apparent diffusion coefficient maps, and contrast-enhanced T1-weighted imaging (cT1WI), respectively. Feature selection steps, including the Boruta and Simulated Annealing algorithms, were conducted to identify key features. Different radiomics models (RMs) based on the single- and multiple-sequence were constructed. The performance of various RMs in predicting LNM was assessed in terms of discrimination, calibration, and clinical usefulness. Additionally, Kaplan-Meier survival curves were employed to estimate differences in disease-free survival (DFS) and overall survival (OS). RESULTS: The multi-sequence radiomics model (MRM) achieved area under the curves (AUCs) of 0.774 [95 % confidence interval (CI), 0.703-0.845], 0.721 (95 % CI, 0.593-0.850), and 0.720 (95 % CI, 0.639-0.801) in the training and two validation cohorts, respectively, outperforming the single-sequence RMs. Notably, the RM derived from cT1WI demonstrated superior performance compared to the other two single-sequence models. Furthermore, the proposed MRM exhibited a significant association with DFS and OS in GC patients (both P < 0.05). CONCLUSION: The multiparametric MRI-based radiomics model, derived from primary lesions, demonstrated moderate performance in predicting LNM and survival outcomes in patients with GC, which could provide valuable insights for personalized treatment strategies.

A combined model using pre-treatment CT radiomics and clinicopathological features of non-small cell lung cancer to predict major pathological responses after neoadjuvant chemoimmunotherapy.

OBJECTIVE: To investigate the relationship between clinical pathological characteristics, pretreatment CT radiomics, and major pathologic response (MPR) of non-small cell lung cancer (NSCLC) after neoadjuvant chemoimmunotherapy, and to establish a combined model to predict the major pathologic response of neoadjuvant chemoimmunotherapy. METHODS: A retrospective study of 211 patients with NSCLC who underwent neoadjuvant chemoimmunotherapy and surgical treatment from January 2019 to April 2021 was conducted. The patients were divided into two groups: the MPR group and the non-MPR group. Pre-treatment CT images were segmented using ITK SNAP software to extract radiomics features using Python software. Then a radiomics model, a clinical model, and a combined model were constructed and validated using a receiver operating characteristic (ROC) curve. Finally, Delong's test was used to compare the three models. RESULTS: The radiomics model achieved an AUC of 0.70 (95 % CI: 0.62-0.78) in the training group and 0.60 (95 % CI: 0.45-0.76) in the validation group. RECIST assessment results were screened from all clinical characteristics as independent factors for MPR with multivariate logistic regression analysis. The AUC of the clinical model for predicting MPR was 0.66 (95 % CI: 0.59-0.73) in the training group and 0.77 (95 % CI: 0.66-0.87) in the validation group. The combined model with combined radiomics and clinicopathological characteristics achieved an AUC was 0.76 (95 % CI: 0.68-0.84) in the training group, and 0.80 (95 % CI: 0.67-0.92) in the validation group. Delong's test showed that the AUC of the combined model was significantly higher than that of the radiomics model alone in both the training group (P = 0.0067) and the validation group (P = 0.0009).The calibration curve showed good agreement between predicted and actual MPR. Clinical decision curve analysis showed that the combined model was superior to radiomics alone. CONCLUSIONS: Radiomics model can predict MPR in NSCLC after neoadjuvant chemoimmunotherapy with similar accuracy to RECIST assessment criteria. The combined model based on pretreatment CT radiomics and clinicopathological features showed better predictive power than independent radiomics model or independent clinicopathological features, suggesting that it may be more useful for guiding personalized neoadjuvant chemoimmunotherapy treatment strategies.

Prediction of HER2 Expression in Gastric Adenocarcinoma Based On Preoperative Noninvasive Multimodal 18F-FDG PET/CT Imaging.

RATIONALE AND OBJECTIVES: This study aims to investigate the role of a flourine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) multimodal radiomics model in predicting the status of human epidermal growth factor receptor 2 (HER2) expression preoperatively in cases of gastric adenocarcinoma. MATERIALS AND METHODS: This retrospective study included 133 patients with gastric adenocarcinoma who were classified into training (n = 93) and validation (n = 40) cohorts in a ratio of 7:3. Features were selected using Least Absolute Shrinkage and Selection Operator and Extreme Gradient Boosting (XGBoost) methods; further, prediction models were constructed using logistic regression and XGBoost. These models were evaluated and validated using area under the curve (AUC), decision curves, and calibration curves to select the best-performing model. RESULTS: Six different models were established to predict HER2 expression. Among these, the comprehensive model, which integrates seven clinical features, one CT feature, and five PET features, demonstrated AUC values of 0.95 (95% confidence interval [CI]: 0.89-1.00) and 0.76 (95% CI: 0.52-1.00) in the training and validation cohorts, respectively. Compared with other models, this model exhibited a superior net benefit on the decision curve and demonstrated good alignment agreement with the observed values on the calibration curve. Based on these findings, we constructed a nomogram for visualizing the model, providing a noninvasive preoperative method for predicting HER2 expression. CONCLUSION: The preoperative 18F-FDG PET/CT multimodal radiomics model can effectively predict HER2 expression in patients with gastric adenocarcinoma, thereby guiding clinical decision-making and advancing the field of precision medicine.

Deep learning radio-clinical signatures for predicting neoadjuvant chemotherapy response and prognosis from pretreatment CT images of locally advanced gastric cancer patients.

BACKGROUND: Early noninvasive screening of patients who would benefit from neoadjuvant chemotherapy (NCT) is essential for personalized treatment of locally advanced gastric cancer (LAGC). The aim of this study was to identify radio-clinical signatures from pretreatment oversampled computed tomography (CT) images to predict the response to NCT and prognosis of LAGC patients. METHODS: LAGC patients were retrospectively recruited from six hospitals from January 2008 to December 2021. An SE-ResNet50-based chemotherapy response prediction system was developed from pretreatment CT images preprocessed with an imaging oversampling method (i.e. DeepSMOTE). Then, the deep learning (DL) signature and clinic-based features were fed into the deep learning radio-clinical signature (DLCS). The predictive performance of the model was evaluated based on discrimination, calibration, and clinical usefulness. An additional model was built to predict overall survival (OS) and explore the survival benefit of the proposed DL signature and clinicopathological characteristics. RESULTS: A total of 1060 LAGC patients were recruited from six hospitals; the training cohort (TC) and internal validation cohort (IVC) patients were randomly selected from center I. An external validation cohort (EVC) of 265 patients from five other centers was also included. The DLCS exhibited excellent performance in predicting the response to NCT in the IVC [area under the curve (AUC), 0.86] and EVC (AUC, 0.82), with good calibration in all cohorts ( P >0.05). Moreover, the DLCS model outperformed the clinical model ( P <0.05). Additionally, we found that the DL signature could serve as an independent factor for prognosis [hazard ratio (HR), 0.828, P =0.004]. The concordance index (C-index), integrated area under the time-dependent ROC curve (iAUC), and integrated Brier score (IBS) for the OS model were 0.64, 1.24, and 0.71 in the test set. CONCLUSION: The authors proposed a DLCS model that combined imaging features with clinical risk factors to accurately predict tumor response and identify the risk of OS in LAGC patients prior to NCT, which can then be used to guide personalized treatment plans with the help of computerized tumor-level characterization.

Risk Stratification and Overall Survival Prediction in Advanced Gastric Cancer Patients Based on Whole-Volume MRI Radiomics.

BACKGROUND: The prognosis of advanced gastric cancer (AGC) patients has attracted much attention, but there is a lack of evaluation method. MRI-based radiomics has the potential to evaluate AGC patients' prognosis. PURPOSE: To identify and validate the risk stratification and overall survival (OS) in AGC patients using MRI-based radiomics. STUDY TYPE: Retrospective. SUBJECTS: A total of 233 patients (168 males, 63.6 ± 11.1 years; 65 females, 59.7 ± 11.8 years) confirmed AGC were collected. The data were randomly divided into a training (164) and validation set (69). SEQUENCE: A 3.0 T, axial T2-weighted, diffusion-weighted imaging, and contrast-enhanced T1-weighted (CE-T1WI). ASSESSMENT: Radiologist 1 segmented 233 patients and radiologist 2 segmented randomly 50 patients on CE-T1WI. The risk score (RS) was summed by each sample based on the radiomics features and correlation coefficients. Patients were followed up for 7-67 months (median 41; 138 dead and 95 alive). STATISTICAL TESTS: The intraclass correlation coefficient (ICC) and Kappa value were calculated. Differences in survival analysis were assessed by Kaplan-Meier curves and log-rank test. Cox-regression analysis was performed to identify the radiomics features and clinical indicators associated with OS. The calibration curves were built to assess the model. A two-tailed P value < 0.05 was considered statistically significant. RESULTS: Integrated with age, lymphovascular invasion (LVI) and RS, a survival combined model was built. The area under the curve (AUC) for predicting 3-year and 5-year OS was 0.765 and 0.788 in the training set, 0.757 and 0.729 in the validation set. There was no significant difference between the radiomics model and survival combined model for 3-year (0.690 vs. 0.757, P = 0.425) and 5-year OS (0.687 vs. 729, P = 0.412) in the validation set. The calibration curves showed a high degree of fit for the survival combined model. DATA CONCLUSION: This study established a survival combined model that might help AGC patients in future clinical decision-making. EVIDENCE LEVEL: 33 TECHNICAL EFFICACY: Stage 5.

A combined predictive model based on radiomics features and clinical factors for disease progression in early-stage non-small cell lung cancer treated with stereotactic ablative radiotherapy.

Purpose: To accurately assess disease progression after Stereotactic Ablative Radiotherapy (SABR) of early-stage Non-Small Cell Lung Cancer (NSCLC), a combined predictive model based on pre-treatment CT radiomics features and clinical factors was established. Methods: This study retrospectively analyzed the data of 96 patients with early-stage NSCLC treated with SABR. Clinical factors included general information (e.g. gender, age, KPS, Charlson score, lung function, smoking status), pre-treatment lesion status (e.g. diameter, location, pathological type, T stage), radiation parameters (biological effective dose, BED), the type of peritumoral radiation-induced lung injury (RILI). Independent risk factors were screened by logistic regression analysis. Radiomics features were extracted from pre-treatment CT. The minimum Redundancy Maximum Relevance (mRMR) and the Least Absolute Shrinkage and Selection Operator (LASSO) were adopted for the dimensionality reduction and feature selection. According to the weight coefficient of the features, the Radscore was calculated, and the radiomics model was constructed. Multiple logistic regression analysis was applied to establish the combined model based on radiomics features and clinical factors. Receiver Operating Characteristic (ROC) curve, DeLong test, Hosmer-Lemeshow test, and Decision Curve Analysis (DCA) were used to evaluate the model's diagnostic efficiency and clinical practicability. Results: With the median follow-up of 59.1 months, 29 patients developed progression and 67 remained good controlled within two years. Among the clinical factors, the type of peritumoral RILI was the only independent risk factor for progression (P< 0.05). Eleven features were selected from 1781 features to construct a radiomics model. For predicting disease progression after SABR, the Area Under the Curve (AUC) of training and validation cohorts in the radiomics model was 0.88 (95%CI 0.80-0.96) and 0.80 (95%CI 0.62-0.98), and AUC of training and validation cohorts in the combined model were 0.88 (95%CI 0.81-0.96) and 0.81 (95%CI 0.62-0.99). Both the radiomics and the combined models have good prediction efficiency in the training and validation cohorts. Still, DeLong test shows that there is no difference between them. Conclusions: Compared with the clinical model, the radiomics model and the combined model can better predict the disease progression of early-stage NSCLC after SABR, which might contribute to individualized follow-up plans and treatment strategies.

Radiomics based on pretreatment MRI for predicting distant metastasis of nasopharyngeal carcinoma: A preliminary study.

Objective: To explore the feasibility of predicting distant metastasis (DM) of nasopharyngeal carcinoma (NPC) patients based on MRI radiomics model. Methods: A total of 146 patients with NPC pathologically confirmed, who did not exhibit DM before treatment, were retrospectively reviewed and followed up for at least one year to analyze the DM risk of the disease. The MRI images of these patients including T2WI and CE-T1WI sequences were extracted. The cases were randomly divided into training group (n=116) and validation group (n=30). The images were filtered before radiomics feature extraction. The least absolute shrinkage and selection operator (LASSO) regression was used to develop the dimension of texture parameters and the logistic regression was used to construct the prediction model. The ROC curve and calibration curve were used to evaluate the predictive performance of the model, and the area under curve (AUC), accuracy, sensitivity, and specificity were calculated. Results: 72 patients had DM and 74 patients had no DM. The AUC, accuracy, sensitivity and specificity of the model were 0. 80 (95% CI: 0.72~0. 88), 75.0%, 76.8%, 73.3%. and0.70 (95% CI: 0.51~0.90), 66.7%, 72.7%, 63.2% in training group and validation group, respectively. Conclusion: The radiomics model based on logistic regression algorithm has application potential for evaluating the DM risk of patients with NPC.

Prognostic significance of serum tumor marker normalization in the perioperative period for patients with advanced gastric cancer.

Background: Neoadjuvant chemotherapy (NCT) was developed to improve the prognosis of patients with advanced gastric cancer (AGC). Some studies have confirmed the diagnostic and prognostic value of various serum tumor markers in gastric cancer. However, most of these studies were focused on the value of preoperative and postoperative tumor markers in patients undergoing surgery with or without adjuvant therapy, and only a few studies focused on AGC patients undergoing NCT. Methods: We retrospectively analyzed the data of consecutive patients with histologically confirmed AGC who received NCT prior to surgical resection at Zhejiang Cancer Hospital from January 2010 to September 2018. The prognostic impact of tumor markers before and after NCT, including Carcinoembryonic antigen (CEA), Carbohydrate antigen199 (CA199), Carbohydrate antigen125 (CA125), Alpha-FetoProtein (AFP), Carbohydrate antigen242 (CA242), and Carbohydrate antigen724 (CA724), were evaluated using Kaplan-Meier log-rank survival analysis. The association between tumor marker normalization during preoperative chemotherapy and clinicopathological characteristics was also investigated. Results: Four hundred and seventy-two patients were included in the study. The levels of CEA, CA199, CA125, CA242, and CA724 before NCT could predict prognosis, and the levels of CA199, CA125, CA242, and CA724 after NCT were correlated with prognosis. The overall survival (OS) rate decreased with an increasing number of positive tumor markers before and after preoperative chemotherapy. Tumor marker abnormalization after NCT was not related to chemotherapy, whereas patients with tumor marker normalization after NCT obtained survival benefits. Conclusions: Tumor markers before and after NCT, such as CA199, CA125, CA242, and CA724, have a discriminatory ability for patients with GC. The normalization of tumor markers after NCT was associated with better survival.

Successful colonoscopic removal of a foreign body that caused sigmoid colon perforation: a case report.

Large bowel perforation is an acute abdominal emergency requiring rapid diagnosis for proper treatment. The high mortality rate associated with large bowel perforation underlines the importance of an accurate and timely diagnosis. Computed tomography is useful for diagnosis of ingested foreign bodies, and endoscopic repair using clips can be an effective treatment of colon perforations. We herein describe a 78-year-old man with sigmoid colon perforation caused by accidental swallowing of a jujube pit. The jujube pit had become stuck in the wall of the sigmoid colon and was successfully removed by colonoscopy, avoiding an aggressive surgery. As a result of developments in endoscopic techniques, endoscopic closure has become a feasible option for the management of intestinal perforation.

Novel typing of iliac vein compression in asymptomatic individuals evaluated by contrast enhanced CT.

PURPOSE: Compression of the iliac vein between the iliac artery and lumbosacral vertebra can cause iliac vein compression syndrome (IVCS). The purpose of this study is to assess compression characteristics and establish a new sub-typing in asymptomatic IVCS individuals using contrast-enhanced CT. METHODS: A retrospective analysis of abdomen contrast-enhanced CT images from 195 asymptomatic subjects with iliac vein compressed was investigated. Patients had no history of venous pathology, and images were collected from June 2018 to January 2019. Qualitative and quantitative characteristics of compression were examined including the location, pattern, minor diameter, area, and the percentage compression on an orthogonal section by the post-processing of multiple planar reconstruction and volume rendering. RESULTS: There were 107 females and 88 males with age range 18-92 years. The most common site of iliac vein compression was localized to the left common iliac vein (LCIV) (178/195, 91.3%). Notably, four compression types (type I-IV) were established according to the compression location, with type II being the most common. The four compression types had differences in the upper limit and fluctuation range of compression. It was found that the average level of iliac vein compression was below 25%. The compression degree of the left common iliac vein in type II was relatively concentrated, and the upper limit of compression was close to 70%. CONCLUSION: Asymptomatic iliac vein compression was categorized according to compression location. The proposal of four types might help clinicians to predict which IVCS patients would benefit from interventional therapy.

Pancreatic Serous Cystic Neoplasms and Mucinous Cystic Neoplasms: Differential Diagnosis by Combining Imaging Features and Enhanced CT Texture Analysis.

OBJECTIVE: To establish a diagnostic model by combining imaging features with enhanced CT texture analysis to differentiate pancreatic serous cystadenomas (SCNs) from pancreatic mucinous cystadenomas (MCNs). MATERIALS AND METHODS: Fifty-seven and 43 patients with pathology-confirmed SCNs and MCNs, respectively, from one center were analyzed and divided into a training cohort (n = 72) and an internal validation cohort (n = 28). An external validation cohort (n = 28) from another center was allocated. Demographic and radiological information were collected. The least absolute shrinkage and selection operator (LASSO) and recursive feature elimination linear support vector machine (RFE_LinearSVC) were implemented to select significant features. Multivariable logistic regression algorithms were conducted for model construction. Receiver operating characteristic (ROC) curves for the models were evaluated, and their prediction efficiency was quantified by the area under the curve (AUC), 95% confidence interval (95% CI), sensitivity and specificity. RESULTS: Following multivariable logistic regression analysis, the AUC was 0.932 and 0.887, the sensitivity was 87.5% and 90%, and the specificity was 82.4% and 84.6% with the training and validation cohorts, respectively, for the model combining radiological features and CT texture features. For the model based on radiological features alone, the AUC was 0.84 and 0.91, the sensitivity was 75% and 66.7%, and the specificity was 82.4% and 77% with the training and validation cohorts, respectively. CONCLUSION: This study showed that a logistic model combining radiological features and CT texture features is more effective in distinguishing SCNs from MCNs of the pancreas than a model based on radiological features alone.

Gastroduodenitis associated with ulcerative colitis: A case report.

BACKGROUND: Ulcerative colitis (UC) is defined as a chronic inflammatory bowel disease that can occur in any part of the large bowel. In addition, UC affects only the large bowel except for backwash ileitis and pouchitis, whereas Crohn's disease (CD) affects the entire digestive tract. Inflammatory bowel disease (IBD) patients tend to be diagnosed with CD or indeterminate colitis when combined with gastric lesion. However, in recent years, some UC patients are reported to have various degrees of lesions in gastroduodenum. Here, we report a case of gastroduodenitis associated with UC (GDUC). CASE SUMMARY: A 25-year-old man with a history of Klippel-Trenaunay syndrome presented to the hospital with mucopurulent bloody stool and epigastric persistent colic pain for 2 wk. Continuous superficial ulcers and spontaneous bleeding were observed under colonoscopy. Subsequent gastroscopy revealed mucosa with diffuse edema, ulcers, errhysis, and granular and friable changes in the stomach and duodenal bulb, which were similar to the appearance of the rectum. After ruling out other possibilities according to a series of examinations, a diagnosis of GDUC was considered. The patient hesitated about intravenous corticosteroids, so he received a standardized treatment with pentasa of 3.2 g/d. After 0.5 mo of treatment, the patient's symptoms achieved complete remission. Follow-up endoscopy and imaging findings showed no evidence of recurrence for 26 mo. CONCLUSION: The occurrence of gastrointestinal involvement in UC is rare, which may open a new window for studying the etiology and pathogenesis of UC. Physicians should consider broad differential diagnosis by endoscopic biopsy and laboratory examinations.

Molecular Magnetic Resonance Imaging with Contrast Agents for Assessment of Inflammatory Bowel Disease: A Systematic Review.

Background and Aims: Magnetic resonance imaging (MRI) has taken an important role in the diagnosis of inflammatory bowel diseases (IBD). In the wake of current advances in nanotechnology, the drug delivery industry has seen a surge of nanoparticles advertising high specificity in target imaging. Given the rapid development of the field, this review has assembled related articles to explore whether molecular contrast agents can improve the diagnostic capability on gastrointestinal imaging, especially for IBD. Methods: Relevant articles published between 1998 and 2018 from a literature search of PubMed and EMBASE were reviewed. Data extraction was performed on the studies' characteristics, experimental animals, modelling methods, nanoparticles type, magnetic resonance methods, and means of quantitative analysis. Results: A total of 8 studies were identified wherein the subjects were animals, and all studies employed MR equipment. One group utilized a perfluorocarbon solution and the other 7 groups used either magnetic nanoparticles or gadolinium- (Gd-) related nanoparticles for molecular contrast. With ultrasmall superparamagnetic iron oxide (USPIO) particles and Gd-related nanoparticles, signal enhancements were found in the mucosa or with focal lesion of IBD-related model in T1-weighted images (T1WI), whereas superparamagnetic iron oxide (SPIO) particles showed a signal decrease in the intestinal wall of the model in T1WI or T2-weighted images. The signal-to-noise ratio (SNR) was employed to analyze bowel intensity in 3 studies. And the percentage of normalized enhancement was used in 1 study for assessing the severity of inflammation. Conclusion: Molecular MRI with contrast agents can improve the early diagnosis of IBD and quantitate the severity of inflammation in experimental studies.

Evaluation of Lymph Node Metastasis in Advanced Gastric Cancer Using Magnetic Resonance Imaging-Based Radiomics.

Objective: To develop and evaluate a diffusion-weighted imaging (DWI)-based radiomic nomogram for lymph node metastasis (LNM) prediction in advanced gastric cancer (AGC) patients. Overall Study: This retrospective study was conducted with 146 consecutively included pathologically confirmed AGC patients from two centers. All patients underwent preoperative 3.0 T magnetic resonance imaging (MRI) examination. The dataset was allocated to a training cohort (n = 71) and an internal validation cohort (n = 47) from one center along with an external validation cohort (n = 28) from another. A summary of 1,305 radiomic features were extracted per patient. The least absolute shrinkage and selection operator (LASSO) logistic regression and learning vector quantization (LVQ) methods with cross-validations were adopted to select significant features in a radiomic signature. Combining the radiomic signature and independent clinical factors, a radiomic nomogram was established. The MRI-reported N staging and the MRI-derived model were built for comparison. Model performance was evaluated considering receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA). Results: A two-feature radiomic signature was found significantly associated with LNM (p < 0.01, training and internal validation cohorts). A radiomic nomogram was established by incorporating the clinical minimum apparent diffusion coefficient (ADC) and MRI-reported N staging. The radiomic nomogram showed a favorable classification ability with an area under ROC curve of 0.850 [95% confidence interval (CI), 0.758-0.942] in the training cohort, which was then confirmed with an AUC of 0.857 (95% CI, 0.714-1.000) in internal validation cohort and 0.878 (95% CI, 0.696-1.000) in external validation cohort. Meanwhile, the specificity, sensitivity, and accuracy were 0.846, 0.853, and 0.851 in internal validation cohort, and 0.714, 0.952, and 0.893 in external validation cohort, compensating for the MRI-reported N staging and MRI-derived model. DCA demonstrated good clinical use of radiomic nomogram. Conclusions: This study put forward a DWI-based radiomic nomogram incorporating the radiomic signature, minimum ADC, and MRI-reported N staging for individualized preoperative detection of LNM in patients with AGC.

Correlation of fibrinogen-like protein 2 with progression of acute pancreatitis in rats.

AIM: To examine fibrinogen-like protein 2 (fgl2) expression during taurocholate-induced acute pancreatitis progression in rats and its correlation with pancreatic injury severity. METHODS: Forty-eight male Sprague-Dawley rats were randomly divided into the severe acute pancreatitis (SAP) group (n = 24) and the sham operation (SO) group (n = 24). Sodium taurocholate (4% at doses of 1 mL/kg body weight) was retrogradely injected into the biliopancreatic ducts of the rats to induce SAP. Pancreatic tissues were prepared immediately after sacrifice. At the time of sacrifice, blood was obtained for determination of serum amylase activity and isolation of peripheral blood mononuclear cells (PBMCs). Pancreatic tissue specimens were obtained for routine light microscopy including hematoxylin and eosin staining, and the severity of pancreatic injury was evaluated 1, 4 and 8 h after induction. Expression of fgl2 mRNA was measured in the pancreas and PBMCs using reverse transcription polymerase chain reaction. Expression of fgl2 protein was evaluated in pancreatic tissues using Western blotting and immunohistochemical staining. Masson staining was also performed to observe microthrombosis. RESULTS: At each time point, levels of fgl2 mRNAs in pancreatic tissues and PBMCs were higher (P < 0.05) in the SAP group than in the SO group. For pancreatic tissue in SAP vs SO, the levels were: after 1 h, 3.911 ± 1.277 vs 1.000 ± 0.673; after 4 h, 9.850 ± 3.095 vs 1.136 ± 0.609; and after 8 h, 12.870 ± 3.046 vs 1.177 ± 0.458. For PBMCs in SAP vs SO, the levels were: after 1 h, 2.678 ± 1.509 vs 1.000 ± 0.965; after 4 h, 6.922 ± 1.984 vs 1.051 ± 0.781; and after 8 h, 13.533 ± 6.575 vs 1.306 ± 1.179. Levels of fgl2 protein expression as determined by Western blotting and immunohistochemical staining were markedly up-regulated (P < 0.001) in the SAP group compared with those in the SO group. For Western blotting in SAP vs SO, the results were: after 1 h, 2.183 ± 0.115 vs 1.110 ± 0.158; after 4 h, 2.697 ± 0.090 vs 0.947 ± 0.361; and after 8 h, 3.258 ± 0.094 vs 1.208 ± 0.082. For immunohistochemical staining in SAP vs SO, the results were: after 1 h, 1.793 ± 0.463 vs 0.808 ± 0.252; after 4 h, 4.535 ± 0.550 vs 0.871 ± 0.318; and after 8 h, 6.071 ± 0.941 vs 1.020 ± 0.406. Moreover, we observed a positive correlation in the pancreas (r = 0.852, P < 0.001) and PBMCs (r = 0.735, P < 0.001) between fgl2 expression and the severity of pancreatic injury. Masson staining showed that microthrombosis (%) in rats with SAP was increased (P < 0.001) compared with that in the SO group and it was closely correlated with fgl2 expression in the pancreas (r = 0.842, P < 0.001). For Masson staining in SAP vs SO, the results were: after 1 h, 26.880 ± 9.031 vs 8.630 ± 3.739; after 4 h, 53.750 ± 19.039 vs 8.500 ± 4.472; and after 8 h, 80.250 ± 12.915 vs 10.630 ± 7.003. CONCLUSION: Microthrombosis due to fgl2 overexpression contributes to pancreatic impairment in rats with SAP, and fgl2 level may serve as a biomarker during early stages of disease.