Preoperative peripheral blood inflammatory markers especially the fibrinogen-to-lymphocyte ratio and novel FLR-N score predict the prognosis of patients with early-stage resectable extrahepatic cholangiocarcinoma

The prognostic impact of preoperative CA19-9 on resectable cholangiocarcinoma: a comprehensive systematic review and meta-analysis

BACKGROUND

This study aimed to determine the prognostic survival impact of preoperative carbohydrate CA19-9 levels in resectable cholangiocarcinoma.

METHODS

PubMed, Web of Science, Cochrane, and Embase databases were searched for articles published through April 28, 2023. The relationship between preoperative serum carbohydrate antigen 19-9 and the prognosis of patients with resectable cholangiocarcinoma was analyzed. Heterogeneity between studies was assessed by means of the I2 statistic. We also performed subgroup analyses based on anatomical site of the tumor, geographic region, time of occurrence, and different levels of CA19-9. A random effects model was performed to express effect sizes as Hazard Ratio (HR) with 95% confidence intervals (CIs).

RESULTS

A total of 60 original studies were eligible for inclusion, with a total of 15,031 patients with all sites, including 9014 males. The overall Hazard Ratio for all studies was 1.90 (95% CI 1.74-2.07, p < 0.001, Z = 14.59). Publication bias was suggested by the Begg's test (p = 0.014 < 0.05), and the overall HR was 1.66 (95% CI 1.53-1.80, p < 0.001, Z = 12.027) after the trim-and-filling method. Subgroup analyses showed that intrahepatic cholangiocarcinoma (HR = 2.00, 95% CI 1.79-2.23), extrahepatic cholangiocarcinoma (HR = 1.65, 95% CI 1.49-1.82), hilar cholangiocarcinoma (HR = 1. 82, 95% CI 1.60-2.07), and distal cholangiocarcinoma (HR = 1.66, 95% CI 1.27-2.15) were predicted to be linked with prognosis. Elevated CA19-9 levels were associated with an increased risk of death.

CONCLUSIONS

This meta-analysis showed that elevated CA19-9 levels were correlated with a poor prognosis in cholangiocarcinoma. In future, more distal and hilar cholangiocarcinoma should be included in statistical studies to improve the accuracy of our conclusions.

Clinical study on changes of peripheral blood immune function indicators in adults with newly diagnosed glioblastoma during the peri-radiotherapy period

To analyze the changes of immune function-related indicators with newly diagnosed glioblastoma before and after radiotherapy and their clinical significance. Clinical data of 104 patients were analyzed. The independent samples t-test or chi-square test was used to compare changes in immune function indicators and to ascertain the differences between groups with different doses or volumes. The grading of the lowest lymphocyte count during radiotherapy was compared. The log-rank (Mantel - Cox) test of the Kaplan - Meier method was used to compare the survival rate, and the relationship of radiotherapy-related parameters, with the survival rate was evaluated by using the Spearman correlation coefficient. A Cox regression model was used to determine the relationship between various immune function indicators and prognosis. The percentages of total T lymphocytes and CD4+ T cells, the CD4-to-CD8 subset ratio, and the percentages of B cells and NKT cells showed an overall decreasing trend, whereas the percentages of CD8+ T cells and NK cells displayed an overall increasing trend. The lower CD4+ T cell percentage and CD4/CD8 ratio after radiotherapy were independent risk factors for OS. Short OS was observed in patients with grade 3 or 4 lymphopenia or with low levels of hemoglobin and serum albumin before radiotherapy. The percentage of CD4+ T cells and the CD4/CD8 ratio were higher in patients with the low tumor-irradiated volume and irradiated volume and dose of the OAR, than in patients from the corresponding high indicator group. Different irradiation dose or volume can differentially alter various immune function indicators.

Machine learning algorithms able to predict the prognosis of gastric cancer patients treated with immune checkpoint inhibitors

BACKGROUND

Although immune checkpoint inhibitors (ICIs) have demonstrated significant survival benefits in some patients diagnosed with gastric cancer (GC), existing prognostic markers are not universally applicable to all patients with advanced GC.

AIM

To investigate biomarkers that predict prognosis in GC patients treated with ICIs and develop accurate predictive models.

METHODS

Data from 273 patients diagnosed with GC and distant metastasis, who un-derwent ≥ 1 cycle(s) of ICIs therapy were included in this study. Patients were randomly divided into training and test sets at a ratio of 7:3. Training set data were used to develop the machine learning models, and the test set was used to validate their predictive ability. Shapley additive explanations were used to provide insights into the best model.

RESULTS

Among the 273 patients with GC treated with ICIs in this study, 112 died within 1 year, and 129 progressed within the same timeframe. Five features related to overall survival and 4 related to progression-free survival were identified and used to construct eXtreme Gradient Boosting (XGBoost), logistic regression, and decision tree. After comprehensive evaluation, XGBoost demonstrated good accuracy in predicting overall survival and progression-free survival.

CONCLUSION

The XGBoost model aided in identifying patients with GC who were more likely to benefit from ICIs therapy. Patient nutritional status may, to some extent, reflect prognosis.

Prognostic Assessment of Colorectal Cancer Patients after Laparoscopic Surgery: A Comprehensive Evaluation of the Glasgow Prognostic Score and Fibrinogen-to-Prealbumin Ratio

BACKGROUND Previous studies have shown that systemic inflammation and suboptimal nutritional status are associated with poor cancer prognosis. This study aims to investigate the prognostic value of preoperative Glasgow prognostic score (GPS) and fibrinogen-to-prealbumin ratio (FPR) in patients with CRC (colorectal cancer) after laparoscopic surgery. MATERIAL AND METHODS In this study, the clinical data of 112 patients with CRC who underwent laparoscopic surgery were retrospectively analyzed, and the 3-year and 5-year survival rates of these patients were evaluated. In addition, the prognostic role of preoperative FPR and GPS in CRC patients was assessed using X-tile software, Kaplan-Meier analysis, and Cox regression analysis. Receiver operating characteristic (ROC) curves were generated to assess the predictive value of FPR, GPS, and FPR-GPS for the survival of these patients. RESULTS The results revealed a significant negative correlation between high FPR, elevated GPS, and overall survival (OS) in patients with CRC. Univariate and multivariate Cox regression analyses identified GPS (HR=3.207, 95% CI: 1.746~6.126), FPR (HR=2.669, 95% CI: 1.052~6.772), and lymph node metastasis (HR=2.222, 95% CI: 1.199~4.115) as independent prognostic indicators for overall survival. The ROC analysis demonstrated that the prediction based on FPR and GPS outperformed a single indicator in accurately predicting the prognosis of CRC patients. CONCLUSIONS Combining the preoperative FPR with the GPS contributes to accurate prognosis assessment for CRC patients after laparoscopic surgery. Patients exhibiting high FPR and GPS values are associated with a worse prognosis.

Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal cholangiocarcinoma

BACKGROUND

Distal cholangiocarcinoma (dCCA), originating from the common bile duct, is greatly associated with a dismal prognosis. A series of different studies based on cancer classification have been developed, aimed to optimize therapy and predict and improve prognosis. In this study, we explored and compared several novel machine learning models that might lead to an improvement in prediction accuracy and treatment options for patients with dCCA.

METHODS

In this study, 169 patients with dCCA were recruited and randomly divided into the training cohort (n = 118) and the validation cohort (n = 51), and their medical records were reviewed, including survival outcomes, laboratory values, treatment strategies, pathological results, and demographic information. Variables identified as independently associated with the primary outcome by least absolute shrinkage and selection operator (LASSO) regression, the random survival forest (RSF) algorithm, and univariate and multivariate Cox regression analyses were introduced to establish the following different machine learning models and canonical regression model: support vector machine (SVM), SurvivalTree, Coxboost, RSF, DeepSurv, and Cox proportional hazards (CoxPH). We measured and compared the performance of models using the receiver operating characteristic (ROC) curve, integrated Brier score (IBS), and concordance index (C-index) following cross-validation. The machine learning model with the best performance was screened out and compared with the TNM Classification using ROC, IBS, and C-index. Finally, patients were stratified based on the model with the best performance to assess whether they benefited from postoperative chemotherapy through the log-rank test.

RESULTS

Among medical features, five variables, including tumor differentiation, T-stage, lymph node metastasis (LNM), albumin-to-fibrinogen ratio (AFR), and carbohydrate antigen 19-9 (CA19-9), were used to develop machine learning models. In the training cohort and the validation cohort, C-index achieved 0.763 vs. 0.686 (SVM), 0.749 vs. 0.692 (SurvivalTree), 0.747 vs. 0.690 (Coxboost), 0.745 vs. 0.690 (RSF), 0.746 vs. 0.711 (DeepSurv), and 0.724 vs. 0.701 (CoxPH), respectively. The DeepSurv model (0.823 vs. 0.754) had the highest mean area under the ROC curve (AUC) than other models, including SVM (0.819 vs. 0.736), SurvivalTree (0.814 vs. 0.737), Coxboost (0.816 vs. 0.734), RSF (0.813 vs. 0.730), and CoxPH (0.788 vs. 0.753). The IBS of the DeepSurv model (0.132 vs. 0.147) was lower than that of SurvivalTree (0.135 vs. 0.236), Coxboost (0.141 vs. 0.207), RSF (0.140 vs. 0.225), and CoxPH (0.145 vs. 0.196). Results of the calibration chart and decision curve analysis (DCA) also demonstrated that DeepSurv had a satisfactory predictive performance. In addition, the performance of the DeepSurv model was better than that of the TNM Classification in C-index, mean AUC, and IBS (0.746 vs. 0.598, 0.823 vs. 0.613, and 0.132 vs. 0.186, respectively) in the training cohort. Patients were stratified and divided into high- and low-risk groups based on the DeepSurv model. In the training cohort, patients in the high-risk group would not benefit from postoperative chemotherapy (p = 0.519). In the low-risk group, patients receiving postoperative chemotherapy might have a better prognosis (p = 0.035).

CONCLUSIONS

In this study, the DeepSurv model was good at predicting prognosis and risk stratification to guide treatment options. AFR level might be a potential prognostic factor for dCCA. For the low-risk group in the DeepSurv model, patients might benefit from postoperative chemotherapy.

An overview of extrahepatic cholangiocarcinoma: from here to where?

Extrahepatic cholangiocarcinoma (eCCA) contains perihilar cholangiocarcinoma and distal cholangiocarcinoma both of which can arise at any point of the biliary tree and originate from disparate anatomical sites. Generally, the incidence of eCCA is increasing globally. Though surgical resection is the principal treatment of choice for the early stages of eCCA, optimal survival remains restricted by the high risk of recurrence when most patients are present with unresectable disease or distant metastasis. Furthermore, both intra- and intertumoral heterogeneity make it laborious to determine molecularly targeted therapies. In this review, we mainly focused on current findings in the field of eCCA, mostly including epidemiology, genomic abnormalities, molecular pathogenesis, tumor microenvironment, and other details while a summary of the biological mechanisms driving eCCA may shed light on intricate tumorigenesis and feasible treatment strategies.