Explore the vast range of titles available.

Purpose It was found that 33.8–52.5% of patients experienced nausea and vomiting after Transcatheter Arterial Chemoembolization (TACE) for liver cancer, based on prior literature. But there are no models that predict this risk. In this study, we investigated the factors associated with nausea and vomiting after TACE and developed a predictive model to predict these adverse events. Method The study will include 401 patients who will be randomly assigned to the training group and validation group in a 7:3 ratio. An analysis of logistic regression was used to identify predictors and build a risk prediction model. Model performance was evaluated using the Area Under Curve (AUC), Calibration Curve, and Decision Curve Analysis (DCA). Results This study ultimately included 401 patients for TACE, of whom 132(32.92%) patients experienced nausea and vomiting. Multivariate analysis identified five independent risk predictors: history of vomiting, prophylactic use of antiemetics, postoperative pain, platinum, and anthracycline. The training group had an AUC of 0.839, and the validation group had an AUC of 0.742. They had calibration curves with P  = 0.208 and P  = 0.482, respectively. The DCA curves showed that the model had good clinical benefit at threshold probabilities greater than 20%. Conclusion A predictive model of nausea and vomiting after TACE has been developed, based on the individual risk factors, surgical factors and chemotherapy drug factors, with satisfactory predictive ability. This model can identify patients for TACE who are at high risk of nausea and vomiting. Our study provides an empirical basis for early detection, early diagnosis and early intervention of patients for TACE at high risk of nausea and vomiting.

Background The red blood cell distribution width-to-albumin ratio (RAR) is a novel biomarker that concurrently reflects nutritional status and inflammation. Unlike traditional cancer risk markers that focus on either inflammation or nutrition independently, RAR provides a more integrated assessment of these interrelated processes, making it a promising tool for cancer risk prediction. This study aims to investigate the relationship between RAR and the risk of digestive tract tumors (DTT), with particular emphasis on colorectal cancer (CC) and gastric cancer (GC). Methods This study explored the relationship between RAR and the risk of DTT using data from 32,953 participants in the 2005–2018 National Health and Nutrition Examination Survey (NHANES). Although weighted multivariate logistic regression models were used to adjust for potential confounders, residual confounding and selection bias may still affect the accuracy and generalizability of the findings, potentially influencing causal inferences. Additionally, subgroup analyses, interaction tests, and restricted cubic splines were performed to further examine potential associations. A two-sample Mendelian randomization analysis was also conducted to investigate the causal relationship between RAR and DTT. Results Among the participants, 234 were diagnosed with DTT, including 215 cases of CC and 19 cases of GC. Higher RAR levels were significantly associated with an increased risk of CC (OR = 1.48, 95% CI = 1.04–2.11, P  < 0.027), but not with GC (OR = 1.33, 95% CI = 0.45–3.94, P  = 0.60). A non-linear association between RAR and CC was also observed. Mendelian randomization analysis indicated that albumin was negatively associated with CC risk (OR = 0.84, 95% CI = 0.73–0.97), while erythrocyte distribution width (RDW) showed no significant association. Conclusion This study reveals a significant association between RAR and colorectal cancer (CC) risk, indicating that RAR may serve as a valuable biomarker for risk stratification. For individuals with abnormal RAR values, the integration of supplementary screening tools—such as fecal occult blood testing, colonoscopy, or additional biomarkers—could enhance early detection rates for CC. This strategy would allow healthcare providers to more effectively identify high-risk individuals and tailor personalized prevention strategies.

Background Colorectal cancer (CRC) incidence in Ghana has increased significantly in recent years. Despite medical advances, survival rates remain low, with challenges in CRC screening implementation contributing to late-stage diagnoses and poor outcomes. Methods This study updates CRC survival estimates and identifies key prognostic factors influencing outcomes in a Ghanaian cohort. We conducted a retrospective analysis of 281 CRC patients diagnosed between 2016 and 2022 at Komfo Anokye Teaching Hospital in Kumasi, Ghana. Data on patient demographics, tumour characteristics, and clinical variables were collected. Survival estimates were calculated using the Kaplan–Meier method, and the log-rank test was used to compare overall survival (OS). Multivariable Cox proportional hazards models identified independent prognostic factors. Results The median patient age was 54 years (95% CI: 53–58), with 27.8% under 45 years. Tumours were located in the colon (42.0%) and rectum (41.6%), with adenocarcinomas comprising 89.0% of cases. Stage III and IV diagnoses accounted for 39.1% and 29.9% of cases, respectively. There were 38.8% deaths, with a median OS of 45 months (95% CI: 32.5–57.5) and a 5-year OS rate of 39.3% (95% CI: 28.7–49.9). Poor OS was independently associated with stage II [aHR = 13.50, (95% CI: 1.74–104.75)], stage IV [aHR = 10.41, (95% CI: 1.16–93.11)], smoking [aHR = 1.72, (95% CI: 1.06–2.81)], multiple comorbidities [aHR = 2.72, (95% CI: 1.15–6.46)], and treatment with herbal medicine [aHR = 1.58, (95% CI: 1.04–2.38)]. Working in the informal sector was linked to improved OS [aHR = 0.57, (95% CI: 0.36–0.89)]. Conclusions CRC survival rates in this cohort were low, with a substantial proportion of patients experiencing mortality within five years following diagnosis. The findings highlight the critical need for early detection and intervention, particularly in younger populations to improve survival outcomes.

Background This study aims to establish a Bayesian network risk prediction model for gastric cancer using data mining methods. It explores both direct and indirect factors influencing the incidence of gastric cancer and reveals the interrelationships among these factors. Methods Data were collected from early cancer screenings conducted at the People’s Hospital of Lincang between 2022 and 2023. Initial variable selection was performed using Least Absolute Shrinkage and Selection Operator (Lasso) and Sliding Windows Sequential Forward Selection (SWSFS), and the screened variables and demographic characteristics features were used as variables for constructing the Bayesian network (BN) model. Subsequently, the performance of the models was evaluated, and the optimal model was selected for network mapping and Bayesian inference using the best model. Results The incidence rate of gastric cancer in this region’s high-risk population was determined to be 7.09%. The BN model constructed from the set of variables consisting of Lasso’s selection variables and demographic characteristics had better performance. A total of 12 variables were incorporated into the BN model to form a network structure consisting of 13 nodes and 18 edges. The model shows that age, gender, ethnicity, current address, upper gastrointestinal symptoms (nausea, acid reflux, vomiting), alcohol consumption, smoking, SGIM gastritis, and family history are important risk factors for gastric cancer development. Conclusion The Bayesian network model provides an intuitive framework for understanding the direct and indirect factors contributing to the early onset of gastric cancer, elucidating the interrelationships among these factors. Furthermore, the model demonstrates satisfactory predictive performance, which may facilitate the early detection of gastric cancer and enhance the levels of early diagnosis and treatment among high-risk populations.

Background Predicting lymph node metastasis (LNM) in colon cancer (CC) is crucial to treatment decision-making and prognosis. This study aimed to develop and validate a nomogram that estimates the risk of LNM in patients with CC using multiple clinical data from patients before surgery. Methods Clinicopathological data were collected from 412 CC patients who underwent Radical resection of CC. The training cohort consisted of 300 cases, and the external validation cohort consisted of 112 cases. The LASSO and multivariate logistic regression were used to select the predictors and construct the nomogram. The discrimination and calibration of the nomogram were evaluated by the ROC curve and calibration curve, respectively. The clinical application of the nomogram was assessed by decision curve analysis(DCA) and clinical impact curves(CIC). Results Eight independent factors associated with LNM were identified by multivariate logistic analysis: LN status on CT, tumor diameter on CT, differentiation, ulcer, intestinal obstruction, anemia, blood type, and neutrophil percentage. The online dynamic nomogram model constructed by independent factors has good discrimination and consistency. The AUC of 0.834(95% CI: 0.755–0.855) in the training cohort, 0.872(95%CI: 0.807–0.937) in the external validation cohort, and Internal validation showed that the corrected C statistic was 0.810. The calibration curve of both the training set and the external validation set indicated that the predicted outcome of the nomogram was highly consistent with the actual outcome. The DCA and CIC indicate that the model has clinical practical value. Conclusion Based on various simple parameters collected preoperatively, the online dynamic nomogram can accurately predict LNM risk in CC patients. The high discriminative ability and significant improvement of NRI and IDI indicate that the model has potential clinical application value.

Background Protein Kinase C iota (PKCι) has been implicated in cancer progression and chemoresistance, but its prognostic significance in solid tumors remains unclear. This study aimed to evaluate the expression of PKCι in tumor tissues and its association with clinicopathological features, survival outcomes, and chemotherapy response in patients with cancer. Methods A retrospective analysis was conducted on patients with confirmed cancer diagnoses. Immunohistochemistry (IHC) was used to assess PKCι expression levels in tumor tissues. Kaplan-Meier survival curves and univariate Cox analysis were applied to evaluate the impact of PKCι expression and other clinicopathological variables on overall survival (OS) and progression-free survival (PFS). The association between PKCι expression and chemotherapy resistance, particularly oxaliplatin resistance, was also investigated. Results PKCι overexpression was possibly correlated with advanced clinical stage, metastasis, organs involvement, vascular invasion, and perineural invasion. Patients with PKCι overexpression was possibly correlated with oxaliplatin resistance( P  = 0.014). Besides, Patients with PKCι overexpression exhibited significantly shorter PFS compared to those with low expression levels ( P  = 0.0006). Univariate Cox analysis confirmed that metastasis ( H.R. = 11.910, P  = 0.018) and clinical staging ( H.R. = 5.498, P  = 0.027) were independent prognostic factors for OS, whereas CEA levels ( H.R. = 3.497, P  = 0.005) was independent predictors of PFS. Conclusion PKCι overexpression is associated with tumor aggressiveness and chemotherapy resistance, indicating its potential as a prognostic biomarker and therapeutic target in cancer preliminarily.

Background This study examined the link between red cell distribution width – coefficient of variation (RDW-CV) levels, colon cancer, and all-cause mortality in the community population. Methods Five thousand one hundred twenty-four participants were included out of 131,030 from the NHANES 1999–2018 survey. Survival differences were analyzed with Kaplan-Meier curves, while multivariate Cox regression, restricted cubic spline models and threshold effect analysis assessed correlations. Subgroup analyses by sex and age were also performed. To ensure the robustness of our findings, sensitivity analyses and the E-value were performed. Results Over a median follow-up of 12.7 [10.3; 15.4] years, 124 deaths occurred. The KM survival curves showed that the greatest risk for colon cancer (Log-rank p  = 0.018) and all-cause mortality (Log-rank p  < 0.001) was at RDW-CV 13.4–13.9 (Q3), while the lowest risk for both was at RDW-CV 12.9–13.3 (Q2). Compared to Q1, there was an increased risk of colon cancer (adjusted HR = 2.43, 95% CI 1.32–4.48, p  = 0.005) and overall mortality (adjusted HR = 2.88, 95% CI 1.54–5.40, p  < 0.001). Adjusted models confirmed a positive association between RDW-CV and both outcomes. Threshold effects associated with RDW-CV and both outcomes were not found. A subgroup analysis revealed no significant interaction between RDW-CV and variables like age and sex for colon cancer and all-cause mortality (all p for interaction > 0.05). Sensitivity analysis indicates that the outcomes are stable. Conclusions RDW-CV is significantly associated with both colon cancer and all-cause mortality in community-dwelling individuals, suggesting a positive correlation. RDW-CV may serve as a simple and cost-effective indicator for assessing colon cancer risk and as a straightforward, economical marker for mortality prediction in the community.

Background Despite significant advances in prevention and early detection, colorectal cancer (CRC) is a leading cause of cancer mortality worldwide. Inadequate adherence and/or lack of knowledge of guidelines have shown to prevent adequate screening and surveillance recommendations and hinder effective screening programs. Objective Evaluate the implementation and real-world impact of a mobile app designed to optimize CRC screening and surveillance in accordance to recently updated guidelines. Methods A mobile app including ergonomic algorithms integrating all pertinent guideline information was created by a group of experts. Data were collected from Catalonia healthcare professionals using the app between February 2023 and May 2024. Users’ characteristics, consultation types, and patient data were analyzed to assess app’s implementation, usage patterns, and impact on CRC screening and surveillance outcomes. Results A total of 12,481 consultations were recorded; 3,054 (24.4%) screening and 9,427 (75.6%) post-polypectomy surveillance consultations. The app was increasingly and repeatedly used by professionals during the study period (72% retention rate). Among screening consultations, 2,082 (68.2%) patients were classified as average risk, suggesting the use of fecal occult blood test (FOBT) instead of colonoscopy. Among surveillance consultations, the app advised deferring follow-up colonoscopies and using FOBT instead in 4,748 (50%) patients based on negative index colonoscopy or the presence of low-risk polyps. Standard surveillance with colonoscopy at 3 years was recommended for 3,224 (34.1%) patients and intensive surveillance, requiring a colonoscopy at 1 year, was indicated for 749 (7.9%) patients. Conclusions A CRC screening and surveillance mobile app showed remarkable acceptance and uptake among healthcare professionals. Proper implementation of updated guidelines aided by the use of the app could significantly reduce the number of unnecessary screening and post-polypectomy surveillance colonoscopies, as well as help identifying high risk patients who require intensive surveillance. Clinical trial Not applicable.

Background/Aims This study aimed to investigate whether a real-time artificial intelligence (AI)-assisted polyp detection system can improve adenoma detection rates (ADRs) in real-world colonoscopy practice. Methods This single-center, retrospective, propensity score-matched study collected data from consecutive patients who underwent colonoscopy—either AI-assisted or standard colonoscopy— between March 2023 and February 2024. Propensity score matching was conducted to adjust for baseline characteristics across the groups. Results During the study period, 1,085 patients who underwent colonoscopy were eligible for inclusion. After propensity score matching, 474 patients who underwent AI-assisted colonoscopy and 474 who underwent standard colonoscopy were included in the primary analysis. The ADR was significantly higher in the AI-assisted colonoscopy group than in the standard colonoscopy group (35.9% vs. 26.4%; p  = 0.002). Additionally, the number of adenomas detected per colonoscopy was significantly higher in the AI-assisted group than in the standard group (0.69 ± 1.22 vs. 0.43 ± 0.91; p  < 0.001). However, the detection rates of advanced adenomas and sessile serrated lesions did not differ significantly between the two groups. Conclusion AI-assisted colonoscopy significantly improves ADRs in real-world colonoscopy practice.

Objectives Systematic evaluation of the efficacy and safety of reduction surgery in asymptomatic unresectable advanced gastric cancer. Materials and methods PubMed, EMBASE, Cochrane Library and Web of Science were searched from database inception to 12 July 2024. The Cochrane Risk of Bias Assessment Tool and Newcastle-Ottawa Scale were used to evaluate the quality and analyze the bias of the randomized controlled and non-randomized controlled studies included in this study, and RevMan (Version 5.4) was used to perform the meta-analysis. Results A total of 5 studies were finally included, including 1 randomized controlled study and 4 retrospective studies. The cumulative sample size was 1717 cases, including 701 cases in the reduced surgery group and 1016 cases in the non-surgical treatment group. The results of the Meta-analysis showed that the reduced surgery group did not offer a survival benefit compared with the non-surgical treatment group in terms of 1-year, 3-year, and 5-year survival rates. The reduced surgery group had a longer median survival time than the non-surgical group by 11.58 months. The incidence rate, morbidity rate, and mortality rate of the reduced surgery group were 5.5% and 6.5% higher than those of the non-surgical group, respectively. The incidence of perioperative complications and death rate in the reduced surgery group were 15% and 4%, respectively; about 3% of patients might have complications of the primary foci during non-surgical treatment and need palliative surgical resection. Conclusion Current evidence suggests that in asymptomatic patients with unresectable advanced gastric cancer, reduced surgery with resection of the primary site does not result in a long-term survival benefit. We look forward to more high-quality randomized controlled trials to provide more substantial evidence to support clinical practice.