The application of a novel platform of multiparametric magnetic resonance imaging in a bioenvironmental toxic carbon tetrachloride-induced mouse model of liver fibrosis.

The use of multiparametric magnetic resonance imaging (MRI) to distinguish complex histopathological changes in liver fibrosis has not yet been systematically established. The purpose of this study is to gauge the efficacy of a cutting-edge MRI platform for evaluating ecotoxicologically hazardous carbon tetrachloride (CCl4) induced liver fibrosis, while also scrutinizing the relationship between MRI and its histopathological features. Thirty-six mice were randomly divided into 6 groups, each with 6 mice. Control mice received an intraperitoneal injection of olive oil, while the experimental mice received different doses of intraperitoneal injection of CCl4. Both sets underwent this process twice per week over a duration of 5 weeks. MRI measurements encompassed T1WI, T2WI, T1 mapping, T2 mapping, T2* mapping. Liver fibrosis and inflammation were assessed and classified using Metavir and activity scoring systems. CCl4 successfully induced liver fibrosis in mice, showing an increasing extent of liver fibrosis and liver function damage with the increasing dosage of CCl4. Compared with the control group, T1, ΔT1, and T2 in the experimental group were considerably elevated (P < 0.05) than those in the control group. Spearman's correlation showed that the correlation of Native T1 and △T1 with fibrosis (r = 0.712, 0.678) was better than with inflammation (r = 0.688, 0.536). T2 correlation with inflammation (r = 0.803) was superior to fibrosis (r = 0.568). ROC analysis showed that the AUC of Native T1 was highest (0.906), followed by ΔT1 (0.852), while the AUC increased to 0.945 when all relevant MRI parameters were combined. T1 is the most potent MRI parameter for evaluating CCl4-induced liver fibrosis, followed by ΔT1. Meanwhile, T2 may not be suitable for evaluating liver fibrosis but is more suitable for evaluating liver inflammation.

Risk Prediction Model of 90-Day Mortality After Esophagectomy for Cancer.

Importance: Ninety-day mortality rates after esophagectomy are an indicator of the quality of surgical oncologic management. Accurate risk prediction based on large data sets may aid patients and surgeons in making informed decisions. Objective: To develop and validate a risk prediction model of death within 90 days after esophagectomy for cancer using the International Esodata Study Group (IESG) database, the largest existing prospective, multicenter cohort reporting standardized postoperative outcomes. Design, Setting, and Participants: In this diagnostic/prognostic study, we performed a retrospective analysis of patients from 39 institutions in 19 countries between January 1, 2015, and December 31, 2019. Patients with esophageal cancer were randomly assigned to development and validation cohorts. A scoring system that predicted death within 90 days based on logistic regression ß coefficients was conducted. A final prognostic score was determined and categorized into homogeneous risk groups that predicted death within 90 days. Calibration and discrimination tests were assessed between cohorts. Exposures: Esophageal resection for cancer of the esophagus and gastroesophageal junction. Main Outcomes and Measures: All-cause postoperative 90-day mortality. Results: A total of 8403 patients (mean [SD] age, 63.6 [9.0] years; 6641 [79.0%] male) were included. The 30-day mortality rate was 2.0% (n = 164), and the 90-day mortality rate was 4.2% (n = 353). Development (n = 4172) and validation (n = 4231) cohorts were randomly assigned. The multiple logistic regression model identified 10 weighted point variables factored into the prognostic score: age, sex, body mass index, performance status, myocardial infarction, connective tissue disease, peripheral vascular disease, liver disease, neoadjuvant treatment, and hospital volume. The prognostic scores were categorized into 5 risk groups: very low risk (score, ≥1; 90-day mortality, 1.8%), low risk (score, 0; 90-day mortality, 3.0%), medium risk (score, -1 to -2; 90-day mortality, 5.8%), high risk (score, -3 to -4: 90-day mortality, 8.9%), and very high risk (score, ≤-5; 90-day mortality, 18.2%). The model was supported by nonsignificance in the Hosmer-Lemeshow test. The discrimination (area under the receiver operating characteristic curve) was 0.68 (95% CI, 0.64-0.72) in the development cohort and 0.64 (95% CI, 0.60-0.69) in the validation cohort. Conclusions and Relevance: In this study, on the basis of preoperative variables, the IESG risk prediction model allowed stratification of an individual patient's risk of death within 90 days after esophagectomy. These data suggest that this model can help in the decision-making process when esophageal cancer surgery is being considered and in informed consent.