A clinical model predicting the risk of esophageal high-grade lesions in opportunistic screening: a multicenter real-world study in China.

BACKGROUND AND AIMS: Prediction models for esophageal squamous cell carcinoma are not common, and no model targeting a clinical population has previously been developed and validated. We aimed to develop a prediction model for estimating the risk of high-grade esophageal lesions for application in clinical settings and to validate the performance of this model in an external population. METHODS: The model was developed based on the results of endoscopic evaluation of 5624 outpatients in one hospital in a high-risk region in northern China and was validated using 5765 outpatients who had undergone endoscopy in another hospital in a non-high-risk region in southern China. Predictors were selected with unconditional logistic regression analysis. The Akaike information criterion was used to determine the final structure of the model. Discrimination was estimated using the area under the receiver operating characteristic curve (AUC). Calibration was assessed using a calibration plot with an intercept and slope. RESULTS: The final prediction model contained 5 variables, including age, smoking, body mass index, dysphagia, and retrosternal pain. This model generated an AUC of 0.871 (95% confidence interval, 0.842-0.946) in the development set, with an AUC of 0.862 after bootstrapping. The 5-variable model was superior to a single age model. In the validation population, the AUC was 0.843 (95% confidence interval, 0.793-0.894). This model successfully stratified the clinical population into 3 risk groups and showed high ability for identifying concentrated groups of cases. CONCLUSIONS: Our model for esophageal high-grade lesions has a high predictive value. It has the potential for application in clinical opportunistic screening to aid decision making for both health care professionals and individuals.

Advances in combined neuroendocrine carcinoma of lung cancer.

Lung cancer incidence and mortality rates are increasing worldwide, posing a significant public health challenge and an immense burden to affected families. Lung cancer encompasses distinct subtypes, namely, non-small-cell lung cancer (NSCLC) and small-cell lung cancer (SCLC). In clinical investigations, researchers have observed that neuroendocrine tumors can be classified into four types: typical carcinoid, atypical carcinoid, small-cell carcinoma, and large-cell neuroendocrine carcinoma based on their unique features. However, there exist combined forms of neuroendocrine cancer. This study focuses specifically on combined pulmonary carcinomas with a neuroendocrine component. In this comprehensive review article, the authors provide an overview of combined lung cancers and present two pathological images to visually depict these distinctive subtypes.

Negative causal exploration of systemic sclerosis: a Mendelian randomization analysis.

Systemic sclerosis (SSc), also known as scleroderma, is an autoimmune-related connective tissue disease with a complex and unknown pathophysiological mechanism with genes association. Several articles have reported a high prevalence of thyroid disease in SSc patients, while one study suggested a potential contribution of appendicitis to the development of SSc. To investigate this causal association, we conducted Mendelian randomization (MR) analysis using instrumental variables (IVs) to assess exposure and outcome. In the MR study involving two cohorts, all analyses were conducted using the TwoSampleMR package in R (version 4.3.0). Single nucleotide polymorphisms (SNPs) meeting a statistically significant threshold of 5E-08 were included in the analysis. Multiple complementary approaches including MR-IVW, MR-Egger, weighted median, simple mode, and weighted mode were employed to estimated the relationship between the exposure and outcome. Leave-one-out analysis and scatter plots were utilized for further investigation. Based on the locus-wide significance level, all of the MR analysis consequences manifested no causal association between the risk of appendicitis with SSc (IVW OR 0.319, 95% CI 0.063-14.055, P = 0.966). Negative causal effects of autoimmune thyroiditis (AT) on SSc (IVW OR 0.131, 95% CI 0.816-1.362, P = 0.686), Graves' disease (GD) on SSc (IVW OR 0.097, 95% CI 0.837-1.222, P = 0.908), and hypothyroidism on SSc (IVW OR 1.136, 95% CI 0.977-1.321, P = 0.096) were derived. The reverse MR revealed no significant causal effect of SSc on thyroid disease. According to the sensitivity analysis, horizontal pleiotropy was unlikely to distort the causal estimates. The consequences indicated no significant association between AT, GD, and hypothyroidism with SSc. Similarly, there was no observed relationship with appendicitis.

Development and external validation of a quantitative diagnostic model for malignant gastric lesions in clinical opportunistic screening: A multicenter real-world study.

BACKGROUND: Clinical opportunistic screening is a cost-effective cancer screening modality. This study aimed to establish an easy-to-use diagnostic model serving as a risk stratification tool for identification of individuals with malignant gastric lesions for opportunistic screening. METHODS: We developed a questionnaire-based diagnostic model using a joint dataset including two clinical cohorts from northern and southern China. The cohorts consisted of 17,360 outpatients who had undergone upper gastrointestinal endoscopic examination in endoscopic clinics. The final model was derived based on unconditional logistic regression, and predictors were selected according to the Akaike information criterion. External validation was carried out with 32,614 participants from a community-based randomized controlled trial. RESULTS: This questionnaire-based diagnostic model for malignant gastric lesions had eight predictors, including advanced age, male gender, family history of gastric cancer, low body mass index, unexplained weight loss, consumption of leftover food, consumption of preserved food, and epigastric pain. This model showed high discriminative power in the development set with an area under the receiver operating characteristic curve (AUC) of 0.791 (95% confidence interval [CI]: 0.750-0.831). External validation of the model in the general population generated an AUC of 0.696 (95% CI: 0.570-0.822). This model showed an ideal ability for enriching prevalent malignant gastric lesions when applied to various scenarios. CONCLUSION: This easy-to-use questionnaire-based model for diagnosis of prevalent malignant gastric lesions may serve as an effective prescreening tool in clinical opportunistic screening for gastric cancer.