Objective To assess the evolving disease burden of esophageal and gastric cancers in China from 1990 to 2021, with a focus on gender disparities, and construct a predictive model to forecast disease trends from 2022 to 2031, aiming to optimize targeted prevention strategies. MethodsEpidemiological data for esophageal and gastric cancers in China (1990-2021) were extracted from the Global Burden of Disease (GBD 2021) database. Temporal trends were analyzed using Joinpoint regression (version 4.9.1.0), and future trends were predicted via the GM (1,1) model under grey system theory. ResultsFrom 1990 to 2021, tobacco- and alcohol-attributable burdens of esophageal cancer increased, while tobacco- and diet-related burdens of gastric cancer declined. Deaths and disability-adjusted life years (DALY) for esophageal cancer rose by 26.38% and 17.89%, respectively; gastric cancer deaths increased by 18.95%, though DALY decreased by 1.22%. Both cancers exhibited significant declines in age-standardized mortality rates (−45.78% for esophageal cancer, −53.29% for gastric cancer) and age-standardized DALY rates (−51.36% for esophageal cancer, −57.58% for gastric cancer). Despite these reductions, China’s age-standardized mortality and DALY rates for both cancers remained consistently higher than global averages, with slower decline rates. Males exhibited disproportionately higher burdens than females. Predictive modeling projected continued but decelerating declines in disease burdens for both cancers by 2031. ConclusionOver three decades, China achieved measurable reductions in esophageal and gastric cancer burdens, though gastric cancer burdens remained higher than esophageal cancer. Persistent disparities relative to global levels, elevated male burdens, and aging demographics highlight the urgency for prioritized interventions targeting high-risk populations.
ObjectiveTo evaluate the predictive ability and clinical application value of artificial intelligence (AI) systems in the benign and malignant differentiation and pathological type of pulmonary nodules, and to summarize clinical application experience. MethodsA retrospective analysis was conducted on the clinical data of patients with pulmonary nodules admitted to the Department of Thoracic Surgery, Second Hospital of Lanzhou University, from February 2016 to February 2025. Firstly, pulmonary nodules were divided into benign and non-benign groups, and the discriminative abilities of AI systems and clinicians were compared. Subsequently, lung nodules reported as precursor glandular lesions (PGL), microinvasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) in postoperative pathological results were analyzed, comparing the efficacy of AI systems and clinicians in predicting the pathological type of pulmonary nodules. ResultsIn the analysis of benign/non-benign pulmonary nodules, clinical data from a total of 638 patients with pulmonary nodules were included, of which there were 257 males (10 patients and 1 patient of double and triple primary lesions, respectively) and 381 females (18 patients and 1 patient of double and triple primary lesions, respectively), with a median age of 55.0 (47.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis of the two groups of variables showed that, except for nodule location, the differences in the remaining variables were statistically significant (P<0.05). Multivariate logistic regression analysis showed that age, nodule type (subsolid pulmonary nodule), average density, burr sign, and vascular convergence sign were independent influencing factors for non-benign pulmonary nodules, among which age, nodule type (subsolid pulmonary nodule), burr sign, and vascular convergence sign were positively correlated with non-benign pulmonary nodules, while average density was negatively correlated with the occurrence of non-benign pulmonary nodules. The area under the receiver operating characteristic curve (AUC) of the malignancy risk value given by the AI system in predicting non-benign pulmonary nodules was 0.811, slightly lower than the 0.898 predicted by clinicians. In the PGL/MIA/IAC analysis, clinical data from a total of 411 patients with pulmonary nodules were included, of which there were 149 males (8 patients of double primary lesions) and 262 females (17 patients of double primary lesions), with a median age of 56.0 (50.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis results showed that, except for gender, nodule location, and vascular convergence sign, the differences in the remaining variables among the three groups of PGL, MIA, and IAC patients were statistically significant (P<0.05). Multinomial multivariate logistic regression analysis showed that the differences between the parameters in the PGL group and the MIA group were not statistically significant (P>0.05), and the maximum diameter and average density of the nodules were statistically different between the PGL and IAC groups (P<0.05), and were positively correlated with the occurrence of IAC as independent risk factors. The average AUC value, accuracy, recall rate, and F1 score of the AI system in predicting lung nodule pathological type were 0.807, 74.3%, 73.2%, and 68.5%, respectively, all better than the clinical physicians' prediction of lung nodule pathological type indicators (0.782, 70.9%, 66.2%, and 63.7% respectively). The AUC value of the AI system in predicting IAC was 0.853, and the sensitivity, specificity, and optimal cutoff value were 0.643, 0.943, and 50.0%, respectively. ConclusionThis AI system has demonstrated high clinical value in predicting the benign and malignant nature and pathological type of lung nodules, especially in predicting lung nodule pathological type, its ability has surpassed that of clinical physicians. With the optimization of algorithms and the adequate integration of multimodal data, it can better assist clinical physicians in formulating individualized diagnostic and treatment plans for patients with lung nodules.