Pretreatment serum albumin-to-alkaline phosphatase ratio is an independent prognosticator of survival in patients with metastatic gastric cancer

Efficacy and safety of tranexamic acid in the treatment of gastric cancer complicated with upper gastrointestinal bleeding

OBJECTIVE

To investigate the efficacy and safety of tranexamic acid (TXA) in preventing upper gastrointestinal (GI) bleeding in patients with gastric cancer.

METHODS

The clinical data of patients with gastric cancer complicated with acute non-operative GI bleeding treated in the Fourth Hospital of Hebei Medical University from 2020 to 2022 were collected and retrospectively analyzed. The survival status of the patients was followed up by telephone. The dataset of 168 patients was divided into a control group (n=85) and a TXA group (n=83), at a 1:1 ratio. The patients in the control group were treated with esomeprazole, and the patients in the TXA group received additional TXA. The hemostatic effect, rebleeding rate, and mortality of patients were compared between the two groups. The Cox proportional hazard model was used to evaluate the overall survival of patients as well as the related risk factors.

RESULTS

The success rate of hemostasis and the normal blood coagulation rate in the TXA group were significantly higher than those in the control group (P=0.003 and P=0.016). The secondary bleeding rate, thrombus formation rate and digestive tract perforation rate in the TXA group were significantly lower than those in the control group (P=0.002, P=0.003 and P=0.035). The improvement of all indicators in the TXA group was better than that in the control group (all P<0.05). For patients with gastric cancer complicated with acute GI bleeding treated with TXA, the Cox proportional hazard model identified III~IV stage, time of TXA treatment, surgical treatment after hemorrhage, and an increase of D-dimer as independent risk factors for upper GI bleeding (all P<0.05).

CONCLUSION

TXA can be an effective treatment for patients with gastric cancer complicated by GI bleeding.

Prediction model of ocular metastasis from primary liver cancer: Machine learning-based development and interpretation study

BACKGROUND

Ocular metastasis (OM) is a rare metastatic site of primary liver cancer (PLC). The purpose of this study was to establish a clinical predictive model of OM in PLC patients based on machine learning (ML).

METHODS

We retrospectively collected the clinical data of 1540 PLC patients and divided it into a training set and an internal test set in a 7:3 proportion. PLC patients were divided into OM and non-ocular metastasis (NOM) groups, and univariate logistic regression analysis was performed between the two groups. The variables with univariate logistic analysis p < 0.05 were selected for the ML model. We constructed six ML models, which were internally verified by 10-fold cross-validation. The prediction performance of each ML model was evaluated by receiver operating characteristic curves (ROCs). We also constructed a web calculator based on the optimal performance ML model to personalize the risk probability for OM.

RESULTS

Six variables were selected for the ML model. The extreme gradient boost (XGB) ML model achieved the optimal differential diagnosis ability, with an area under the curve (AUC) = 0.993, accuracy = 0.992, sensitivity = 0.998, and specificity = 0.984. Based on these results, an online web calculator was constructed by using the XGB ML model to help clinicians diagnose and treat the risk probability of OM in PLC patients. Finally, the Shapley additive explanations (SHAP) library was used to obtain the six most important risk factors for OM in PLC patients: CA125, ALP, AFP, TG, CA199, and CEA.

CONCLUSION

We used the XGB model to establish a risk prediction model of OM in PLC patients. The predictive model can help identify PLC patients with a high risk of OM, provide early and personalized diagnosis and treatment, reduce the poor prognosis of OM patients, and improve the quality of life of PLC patients.

Development and validation of a nomogram prediction model based on albumin-to-alkaline phosphatase ratio for predicting the prognosis of gallbladder carcinoma

Gallbladder carcinoma (GBC) is a rare biliary tract cancer with a high recurrence rate and a poor prognosis. Albumin-alkaline phosphatase ratio (AAPR) has been demonstrated to be a prognostic predictor for several cancers, but its predictive value for GBC patients remains unknown. The aim of this study was to investigate the predictive role of AAPR in GBC patients and to develop a novel nomogram prediction model for GBC patients. We retrospectively collected data from 80 patients who underwent surgery at the Hospital of 81st Group Army PLA as a training cohort. Data were collected from 70 patients with the same diagnosis who underwent surgery at the First Affiliated Hospital of Hebei North University as an external verification cohort. The optimal cut-off value of AAPR was determined using X-tile software. A nomogram for the overall survival (OS) based on multivariate Cox regression analysis was developed and validated using calibration curves, Harrell's concordance index, the receiver operating characteristic curves, and decisive curve analyses. The optimal cut-off value of AAPR was .20. Univariate and multivariate Cox regression analyses demonstrated that BMI (p = .043), R0 resection (p = .001), TNM stage (p = .005), and AAPR (p = .017) were independent risk factors for GBC patients. In terms of consistency, discrimination, and net benefit, the nomogram incorporating these four independent risk factors performed admirably. AAPR is an independent predictor of GBC patients undergoing surgery, and a novel nomogram prediction model based on AAPR showed superior predictive ability.