Objective:This study aimed to analyze the prognostic risk factors for hepatoid adenocarcinoma of the stomach (HAS) and construct two nomogram-based clinical prediction models to predict overall survival (OS) and recurrence-free survival (RFS) in patients with HAS.Methods:Data were retrospectively collected from 82 patients (64 males, 18 females; mean age 60.3 ± 9.4 years) who underwent radical gastrectomy and were pathologically diagnosed with gastric hepatoid adenocarcinoma at the First Medical Center of the PLA General Hospital between February 2006 and September 2023. Statistical analyses were conducted using SPSS 25.0 and R 4.3.2. Survival analyses were performed using the Kaplan-Meier method, and univariate analyses were used to identify clinical and pathological factors associated with prognosis. Variables with P<0.05 in the univariate analysis were included in multivariate Cox regression models to identify independent risk factors for OS and RFS. These factors were incorporated into the prediction models to construct nomograms. The discriminatory power of the models was assessed using the area under the curve (AUC) of receiver operating characteristic (ROC) analyses, while calibration curves, decision curve analysis (DCA), and comparisons with the 8th edition of the TNM staging system of the American Joint Committee on Cancer (AJCC) were employed to evaluate model performance. Results:Among the 82 patients, 36 (43.9%) exhibited vascular infiltration, 61 (74.4%) had nerve infiltration, and lymph node metastasis was observed in 60 cases (73.2%). Pathological stages I, II, III, and IV were distributed as 11 (13.4%), 26 (31.7%), 44 (53.7%), and 1 (1.2%) cases, respectively. Inflammatory markers included neutrophil-to-lymphocyte ratio (NLR) ≥ 4.33 in 22 cases (26.8%), platelet-to-lymphocyte ratio (PLR) ≥ 142.2 in 50 cases (61.0%), monocyte-to-lymphocyte ratio (MLR) ≥ 0.411 in 22 cases (26.8%), α-fetoprotein (AFP) ≥ 2.48 μg/L in 64 cases (78.0%), and C-reactive protein (CRP) ≥ 7.506 mg/L in 12 cases (14.6%). Among the 82 patients, 3 cases (3.6%) were lost to follow-up. The median follow-up time was 52 (range: 8–147) months, with a median OS of 61(2–147) months. The 1-year and 3-year OS rates were 78.5% and 58.5%, respectively, while the 1-year and 3-year RFS rates were 77.3% and 60.3%, respectively. Multivariate analysis identified several independent risk factors influencing OS in patients with HAS: advanced pathological stage, MLR ≥ 0.411, AFP ≥ 2.545 μg/L, and CRP ≥ 7.51 mg/L. The hazard ratios (HRs) and 95% confidence intervals (CIs) were as follows: 5.218 (1.230–22.143), 2.610 (1.287–5.294), 2.950 (1.013–8.589), and 2.594 (1.145–5.877), respectively (all P < 0.05). For RFS, advanced pathological stage, PLR ≥ 152.0, and MLR ≥ 0.411 were independent risk factors, with HRs (95% CIs) of 4.735 (1.080–20.760), 3.759 (1.259–11.226), and 2.714 (1.218–6.048), respectively (all P < 0.05). The AUC values for OS prediction at 1 year, 3 years, and 5 years were 0.7765, 0.7525, and 0.7702, respectively. For RFS, the AUC values were 0.7304, 0.8137, and 0.8307 at 1 year, 3 years, and 5 years, respectively. The calibration curves demonstrated strong agreement between nomogram- predicted outcomes and observed survival data. DCA indicated that both TNM staging and the nomogram-based clinical prediction models provided a net positive benefit in predicting OS and RFS in HAS patients, with the nomogram model demonstrating superior performance. Conclusion:The nomogram-based clinical prediction models developed in this study demonstrated robust performance in predicting long-term OS and RFS in patients with HAS.

Objective To assess changes in composition of oral microbiota in oral squamous cell carcinoma(OSCC)by systematic review and Meta-analysis.Methods Computer searches were conducted in databases such as PubMed,Embase,Cochrane Library,Web of Science,Wanfang Data and CNKI to gather research on the oral microbiome of OSCC.The search covered the period from the establishment of databases to March 25 2025.Literature was screened and data extracted according to literature inclusion and exclusion criteria,and Meta-analysis of changes in the abundance of bacterial genera in included studies was performed using Stata 17.0.Results A total of 23 studies involving 1 718 participants were included.These studies were divided into two categories:(1)case-control studies(n=14)and(2)cancer tissue versus paired paracancerous tissue(n=9).At the genus level,Fusobacterium was increased in abundance in OSCC patients(SMD=0.52,95%CI:0.39-0.65,P＜0.001)and cancer tissue(SMD=0.89,95%CI:0.55-1.24,P＜0.001).Porphyromonas was increased abundance in OSCC patients(SMD=0.17,95%CI:0.02-0.33,P=0.030)and cancer tissue(SMD=0.31,95%CI:0.10-0.53,P=0.005).Streptococcus was decreased in OSCC(SMD=-0.43,95%CI:-0.85--0.01,P=0.044)and cancer tissue(SMD=-0.66,95%CI:-0.96--0.37,P＜0.001).Conclusion Fusobacterium and Porphyromonas increase and Streptococcus decrease in OSCC patients and cancer tissue,suggesting that oral flora dysbiosis is associated with the development of OSCC.

Objective:This study aimed to analyze the prognostic risk factors for hepatoid adenocarcinoma of the stomach (HAS) and construct two nomogram-based clinical prediction models to predict overall survival (OS) and recurrence-free survival (RFS) in patients with HAS.Methods:Data were retrospectively collected from 82 patients (64 males, 18 females; mean age 60.3 ± 9.4 years) who underwent radical gastrectomy and were pathologically diagnosed with gastric hepatoid adenocarcinoma at the First Medical Center of the PLA General Hospital between February 2006 and September 2023. Statistical analyses were conducted using SPSS 25.0 and R 4.3.2. Survival analyses were performed using the Kaplan-Meier method, and univariate analyses were used to identify clinical and pathological factors associated with prognosis. Variables with P<0.05 in the univariate analysis were included in multivariate Cox regression models to identify independent risk factors for OS and RFS. These factors were incorporated into the prediction models to construct nomograms. The discriminatory power of the models was assessed using the area under the curve (AUC) of receiver operating characteristic (ROC) analyses, while calibration curves, decision curve analysis (DCA), and comparisons with the 8th edition of the TNM staging system of the American Joint Committee on Cancer (AJCC) were employed to evaluate model performance. Results:Among the 82 patients, 36 (43.9%) exhibited vascular infiltration, 61 (74.4%) had nerve infiltration, and lymph node metastasis was observed in 60 cases (73.2%). Pathological stages I, II, III, and IV were distributed as 11 (13.4%), 26 (31.7%), 44 (53.7%), and 1 (1.2%) cases, respectively. Inflammatory markers included neutrophil-to-lymphocyte ratio (NLR) ≥ 4.33 in 22 cases (26.8%), platelet-to-lymphocyte ratio (PLR) ≥ 142.2 in 50 cases (61.0%), monocyte-to-lymphocyte ratio (MLR) ≥ 0.411 in 22 cases (26.8%), α-fetoprotein (AFP) ≥ 2.48 μg/L in 64 cases (78.0%), and C-reactive protein (CRP) ≥ 7.506 mg/L in 12 cases (14.6%). Among the 82 patients, 3 cases (3.6%) were lost to follow-up. The median follow-up time was 52 (range: 8–147) months, with a median OS of 61(2–147) months. The 1-year and 3-year OS rates were 78.5% and 58.5%, respectively, while the 1-year and 3-year RFS rates were 77.3% and 60.3%, respectively. Multivariate analysis identified several independent risk factors influencing OS in patients with HAS: advanced pathological stage, MLR ≥ 0.411, AFP ≥ 2.545 μg/L, and CRP ≥ 7.51 mg/L. The hazard ratios (HRs) and 95% confidence intervals (CIs) were as follows: 5.218 (1.230–22.143), 2.610 (1.287–5.294), 2.950 (1.013–8.589), and 2.594 (1.145–5.877), respectively (all P < 0.05). For RFS, advanced pathological stage, PLR ≥ 152.0, and MLR ≥ 0.411 were independent risk factors, with HRs (95% CIs) of 4.735 (1.080–20.760), 3.759 (1.259–11.226), and 2.714 (1.218–6.048), respectively (all P < 0.05). The AUC values for OS prediction at 1 year, 3 years, and 5 years were 0.7765, 0.7525, and 0.7702, respectively. For RFS, the AUC values were 0.7304, 0.8137, and 0.8307 at 1 year, 3 years, and 5 years, respectively. The calibration curves demonstrated strong agreement between nomogram- predicted outcomes and observed survival data. DCA indicated that both TNM staging and the nomogram-based clinical prediction models provided a net positive benefit in predicting OS and RFS in HAS patients, with the nomogram model demonstrating superior performance. Conclusion:The nomogram-based clinical prediction models developed in this study demonstrated robust performance in predicting long-term OS and RFS in patients with HAS.

Objective To assess changes in composition of oral microbiota in oral squamous cell carcinoma(OSCC)by systematic review and Meta-analysis.Methods Computer searches were conducted in databases such as PubMed,Embase,Cochrane Library,Web of Science,Wanfang Data and CNKI to gather research on the oral microbiome of OSCC.The search covered the period from the establishment of databases to March 25 2025.Literature was screened and data extracted according to literature inclusion and exclusion criteria,and Meta-analysis of changes in the abundance of bacterial genera in included studies was performed using Stata 17.0.Results A total of 23 studies involving 1 718 participants were included.These studies were divided into two categories:(1)case-control studies(n=14)and(2)cancer tissue versus paired paracancerous tissue(n=9).At the genus level,Fusobacterium was increased in abundance in OSCC patients(SMD=0.52,95%CI:0.39-0.65,P＜0.001)and cancer tissue(SMD=0.89,95%CI:0.55-1.24,P＜0.001).Porphyromonas was increased abundance in OSCC patients(SMD=0.17,95%CI:0.02-0.33,P=0.030)and cancer tissue(SMD=0.31,95%CI:0.10-0.53,P=0.005).Streptococcus was decreased in OSCC(SMD=-0.43,95%CI:-0.85--0.01,P=0.044)and cancer tissue(SMD=-0.66,95%CI:-0.96--0.37,P＜0.001).Conclusion Fusobacterium and Porphyromonas increase and Streptococcus decrease in OSCC patients and cancer tissue,suggesting that oral flora dysbiosis is associated with the development of OSCC.

Medical image is a powerful tool to assist doctors in the diagnosis and treatment planning.Nowadays,the segmentation of medical images is no longer limited to manual segmentation methods.Traditional methods and deep learning methods have been used to achieve more accurate results in medical image segmentation.Herein some innovative medical image segmentation methods in recent years are reviewed.By elaborating on the innovations of deep learning methods(SAM,SegNet,Mask R-CNN,and U-NET)and traditional methods(active contour model and threshold segmentation model),the differences and similarities between them are compared.The summary of medical image segmentation methods and the prospect is expected to help researchers better grasp and familiarize themselves with research status and development trend.