Prognostic value of D-dimer levels in patients with gastric cancer undergoing gastrectomy

Evaluating the predictive significance of D-dimer in conjunction with CA724 for the postoperative outcomes in gastric cancer: A retrospective cohort analysis

BACKGROUND

Gastric cancer represents a highly aggressive form of malignant tumor originating from the epithelial cells lining the gastric mucosa. Despite notable improvements in treatment approaches over the last few years, the associated mortality rate continues to be considerably high. Therefore, there is a pressing requirement for dependable biomarkers that can be utilized to predict and monitor prognosis, as well as to formulate targeted treatment strategies for patient groups at high risk.

METHODS

We conducted an analysis of data collected from patients who were diagnosed with gastric cancer and underwent radical gastrectomy at Shanxi Cancer Hospital from June 2017 to June 2018, with follow-up data gathered over a five-year duration until 2023. Patient follow-up information was sourced from the hospital's monitoring system. The analysis focused on the variances in effectiveness of D-dimer against different tumor markers through Cox stratification analysis. The tumor marker that exhibited the most pronounced impact was selected to formulate a novel combined indicator. Furthermore, we examined how this combined indicator influences five-year overall survival (OS) outcomes following gastric cancer surgery using Cox multivariate regression analysis.

RESULTS

The Cox multivariate regression analysis revealed that the effect value of the D_Dimer-CA724 Middle group on the overall survival rate post-surgery for gastric cancer was found to be 1.42 (1.13-1.78), p = 0.003 (<0.05), in comparison with the D_Dimer-CA724 Low group. For the D_Dimer-CA724 High group, the effect value on overall survival after gastric cancer surgery was 2.11 (1.65-2.68), p < 0.001. Additionally, the trend test results indicated a value of 1.46 (1.29-1.64) with p < 0.001, demonstrating statistical significance. When compared to the D_Dimer-CA724 Low group, both the D_Dimer-CA724 Middle and High groups showed markedly poorer prognoses, with increased risks of 42% and 111%, respectively, highlighting a highly significant finding in clinical practice.

CONCLUSION

The integrated measure of D-dimer and CA724, referred to as D-dimer_CA724, serves as an independent predictor for the postoperative outcomes of gastric cancer, demonstrating superior predictive capability compared to the individual markers. In clinical settings, patients with gastric cancer exhibiting elevated levels of D-dimer_CA724 tend to experience worse prognoses following surgery. This measure holds significant potential for widespread application and promotion within clinical practice.

The prognostic value of preoperative D-dimer to albumin ratio for overall survival and progression-free survival in colorectal cancer

Introduction: This study aimed to explore the predictive value of the D-dimer-to-albumin ratio (DAR) for progression-free survival (PFS) and overall survival (OS) in patients with colorectal cancer (CRC). Methods: The Kaplan-Meier method was used to plot survival curves for PFS and OS. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive efficacy of the DAR for PFS and OS in patients with CRC. Cox proportional hazards regression analysis was used to analyze prognostic factors influencing outcomes. A nomogram based on the DAR was constructed to predict 1-, 3-, and 5-year prognoses of patients with CRC; its predictive ability was evaluated using the concordance index (C-index) and calibration curves. Additionally, the clinical utility of the DAR-based nomogram was validated using an internal randomized validation cohort. Results: A total of 1,339 patients with CRC who underwent surgery were enrolled. The optimal cut-off value for DAR was determined to be 3.320, dividing patients into low (<3.320 [n = 470]) and high (≥3.320 [n = 869]) DAR groups. Compared with other composite immune inflammatory markers, DAR exhibited superior prognostic predictive efficacy. Patients with a high DAR had a significantly worse prognosis than those with a low DAR (PFS, 50.9% versus [vs.] 69.4%, p < 0.001; OS, 52.9% vs. 73.8%, p < 0.001). DAR also demonstrated significant prognostic stratification for most tumor subgroups, particularly in the stage III-IV subgroup and normal carcinoembryonic antigen subgroup. DAR has been identified as an independent predictive indicator of PFS/OS in patients with CRC. For every standard deviation increase in DAR, the risk for PFS/OS in patients with CRC was reduced by 9.5% (hazard ratio [HR] 1.095 [95% confidence interval (CI) 1.013-1.185]; p = 0.022) and 9.3% (HR 1.093 [95% CI 1.012-1.180]; p = 0.024), respectively. The DAR-based nomogram was confirmed to demonstrate good prognostic prediction accuracy and achieved high evaluation in the internal validation cohort. Conclusion: Preoperative DAR is a promising biomarker for predicting PFS and OS among patients with CRC. The DAR-based prognostic prediction nomogram may serve as an effective tool for the comprehensive assessment of prognosis in patients with CRC.

A novel nomogram based on GD for predicting prognosis in hepatocellular carcinoma

Purpose

The prognosis of liver cancer remains unfavorable nowadays, making the search for predictive biomarkers of liver cancer prognosis of paramount importance to guide clinical diagnosis and treatment. This study was conducted to explore more prognostic markers for most HCC.

Patients and methods

A total of 330 patients were enrolled in this study according to the inclusion and exclusion criteria. Follow-up data were collected for all patients until the cutoff date of the study, February 2023. In addition, patient outcomes were assessed with progression-free survival (PFS) and overall survival (OS). All statistical analysis was conducted using R 4.2.0 software.

Results

Univariate analysis illustrated that the GD [the product of gamma-glutamyl transpeptidase (GGT) concentration and D-dimer concentration, GD=GGT*D-dimer] levels were related to PFS (p<0.05) and OS (p<0.05). Kaplan-Meier survival curves and log-rank tests indicated a significant difference among different levels of GD (p<0.001). Multivariate analysis demonstrated GD as an independent prognostic factor for HCC. The C-indexes of nomogram were 0.77 and 0.76 in the training or validation cohort, respectively. Area Under the Curve (AUC) of 1-, 2-, 3-, and 4-year OS showed satisfactory accuracy, and the calibration curve illustrated brilliant consistence between the ideal and predicted values.

Conclusions

Herein, it was demonstrated that GD was an independent prognostic factor for HCC and revealed the potential to predict the PFS and OS in patients with HCC. Moreover, the nomogram based on GD illustrated a satisfactory prediction ability in comparison to other models without GD.

Prognostic prediction of idiopathic membranous nephropathy using interpretable machine learning

BACKGROUND

Established prognostic models of idiopathic membranous nephropathy (IMN) were limited to traditional modeling methods and did not comprehensively consider clinical and pathological patient data. Based on the electronic medical record (EMR) system, machine learning (ML) was used to construct a risk prediction model for the prognosis of IMN.

METHODS

Data from 418 patients with IMN were diagnosed by renal biopsy at the Fifth Clinical Medical College of Shanxi Medical University. Fifty-nine medical features of the patients could be obtained from EMR, and prediction models were established based on five ML algorithms. The area under the curve, recall rate, accuracy, and F1 were used to evaluate and compare the performances of the models. Shapley additive explanation (SHAP) was used to explain the results of the best-performing model.

RESULTS

One hundred and seventeen patients (28.0%) with IMN experienced adverse events, 28 of them had compound outcomes (ESRD or double serum creatinine (SCr)), and 89 had relapsed. The gradient boosting machine (LightGBM) model had the best performance, with the highest AUC (0.892 ± 0.052, 95% CI 0.840-0.945), accuracy (0.909 ± 0.016), recall (0.741 ± 0.092), precision (0.906 ± 0.027), and F1 (0.905 ± 0.020). Recursive feature elimination with random forest and SHAP plots based on LightGBM showed that anti-phospholipase A2 receptor (anti-PLA2R), immunohistochemical immunoglobulin G4 (IHC IgG4), D-dimer (D-DIMER), triglyceride (TG), serum albumin (ALB), aspartate transaminase (AST), β2-microglobulin (BMG), SCr, and fasting plasma glucose (FPG) were important risk factors for the prognosis of IMN. Increased risk of adverse events in IMN patients was correlated with high anti-PLA2R and low IHC IgG4.

CONCLUSIONS

This study established a risk prediction model for the prognosis of IMN using ML based on clinical and pathological patient data. The LightGBM model may become a tool for personalized management of IMN patients.

The prognostic and predictive value of plasma D-dimer in children with neuroblastoma: a 7-year retrospective analysis at a single institution

Purpose

Elevated plasma D-dimer level is a poor prognostic factor for many solid tumors. However, limited research has been conducted on D-dimer in children with neuroblastoma (NB), and its clinical significance remains unclear. The present study investigated the clinical and prognostic significance of D-dimer in pediatric NB patients.

Methods

A retrospective analysis of all newly admitted NB patients was conducted from January 2014 to December 2020. Baseline clinicopathological features, preoperative laboratory parameters, and follow-up information were collected. Univariate and multivariate analyses were performed to determine the relationship between D-dimer level, clinical features, and the prognostic value.

Results

Among 266 patients, the median value of D-dimer was 2.98 ng/mL, of which 132 patients showed elevated D-dimer levels before surgery (>2.98 ng/mL). Univariate analysis revealed that elevated D-dimer was significantly associated with age, hemoglobin, neutrophil-to-lymphocyte ratio, neuron-specific enolase, 24-hour vanillylmandelic acid, overall survival, and so on (P < 0.05). Patients with elevated D-dimer levels had shorter median overall survival time when compared with normal D-dimer levels (P = 0.01). The prognosis was better in patients with normal D-dimer levels when combined with lower age, ganglioneuroblastoma tumor type, lower stage on International Neuroblastoma Staging System, low-risk group, and without bone metastasis or bone marrow metastasis. The continuous increase of D-dimer level after treatment indicated tumor recurrence or progression.

Conclusion

A high D-dimer level is associated with low overall survival, and an elevated D-dimer level after treatment indicates tumor recurrence and progression. D-dimer can be used as one of the evaluation factors for NB treatment or prognosis.

Short-term prognostic role of peripheral lymphocyte subsets in patients with gastric cancer

BACKGROUND

Immune function is associated with clinical outcomes in patients with gastric cancer. This study aimed to explore the prognostic role of peripheral lymphocyte subsets in patients with gastric cancer after curative surgery.

METHODS

This retrospective study was conducted at a single tertiary referral hospital. We included patients diagnosed with gastric cancer who had undergone surgery and met the inclusion criteria. Clinicopathological characteristics and preoperative peripheral lymphocyte subset data were collected for the analysis. Recurrence-free survival (RFS) and overall survival were analyzed using the Kaplan-Meier curve and Cox hazard regression model. We used the Whitney test and Spearman test to analyze the correlation between lymphocyte subsets and clinicopathological characteristics.

RESULTS

This study included 171 patients with gastric cancer who underwent curative surgery. Multivariate analysis revealed that carcinoembryonic antigen (p < 0.01), carbohydrate antigen 19-9 (p < 0.001), lymph node metastases (p < 0.001), total T-cell count (p = 0.02), B-cell count (p < 0.01), and regulatory T-cell percentage (p < 0.01) were independent predictive factors associated with RFS.

CONCLUSIONS

Impaired immune function may lead to early recurrence following curative surgery. Our study showed that the characteristics of peripheral lymphocyte subsets (T, B, and Treg cells) were independent predictive factors for recurrence in patients with gastric cancer after surgery.

Prediction of Postoperative Pathologic Risk Factors in Cervical Cancer Patients Treated with Radical Hysterectomy by Machine Learning

Pretherapeutic serological parameters play a predictive role in pathologic risk factors (PRF), which correlate with treatment and prognosis in cervical cancer (CC). However, the method of pre-operative prediction to PRF is limited and the clinical availability of machine learning methods remains unknown in CC. Overall, 1260 early-stage CC patients treated with radical hysterectomy (RH) were randomly split into training and test cohorts. Six machine learning classifiers, including Gradient Boosting Machine, Support Vector Machine with Gaussian kernel, Random Forest, Conditional Random Forest, Naive Bayes, and Elastic Net, were used to derive diagnostic information from nine clinical factors and 75 parameters readily available from pretreatment peripheral blood tests. The best results were obtained by RF in deep stromal infiltration prediction with an accuracy of 70.8% and AUC of 0.767. The highest accuracy and AUC for predicting lymphatic metastasis with Cforest were 64.3% and 0.620, respectively. The highest accuracy of prediction for lymphavascular space invasion with EN was 59.7% and the AUC was 0.628. Blood markers, including D-dimer and uric acid, were associated with PRF. Machine learning methods can provide critical diagnostic prediction on PRF in CC before surgical intervention. The use of predictive algorithms may facilitate individualized treatment options through diagnostic stratification.

Hemostasis and cancer: Impact of haemostatic biomarkers for the prediction of clinical outcomes in patients with cancer

Patients with cancer are characterized by a dysregulation of the hemostatic system and systemic hypercoagulability. Different components of the hemostatic system are involved in tumor-promoting mechanisms including primary tumor growth, cancer cell invasion, immune evasion, angiogenesis, and the metastatic process. Therefore, different degrees of systemic hemostatic activation in patients with cancer can reflect distinct underlying biological phenotypes of cancer and seem to correlate with cancer aggressiveness. Peripheral blood levels of hemostatic biomarkers, indicating the activation status of different parts of the hemostatic system including the coagulation cascade, fibrinolytic activity, platelet activation, or endothelial activation, can be used to reflect cancer-associated systemic hypercoagulability. Thereby, hemostatic biomarkers represent promising candidates to investigate as surrogate markers for underlying cancer activity and progression dynamics and therefore as biomarkers for the prediction of clinical outcomes in cancer patients. In the present review, we provide an up-to-date summary of available data on hemostatic biomarkers for prognostication of overall survival and prediction of therapy response in patients with cancer, including specific oncologic treatment settings for potential clinical application. We provide a thorough discussion on potential clinical implementation and current limitations and highlight the most promising emerging biomarkers that might be used to contribute to risk-stratified, personalized oncologic decision making in the future.