Development and Validation of a Practical Prognostic Coagulation Index for Patients with Esophageal Squamous Cell Cancer

Clinical value of measuring plasma D-dimer levels in patients with esophageal cancer

BACKGROUND

Esophageal cancer represents a significant public health concern; however, reliable diagnostic and prognostic markers have not been established. This study aimed to investigate the clinical value of plasma D-dimer levels in patients with esophageal cancer.

METHODS

Overall, 120 patients with esophageal cancer who underwent radical surgical resection at our department between January 2019 and 2020 were included (esophageal cancer group). Plasma D-dimer levels were measured preoperatively and on postoperative days 1 and 14. Additionally, 60 healthy participants (control group) with measured plasma D-dimer levels were included. The preoperative D-dimer levels and positive D-dimer test rates were compared between the groups. The 3-year survival rate in patients with esophageal cancer was calculated using the Kaplan-Meier method.

RESULTS

Preoperative D-dimer concentration in the esophageal cancer group was (0.65 ± 0.859 µg/mL) significantly higher than that in the control group (0.32 ± 0.369 µg/mL). The positivity rate in the esophageal cancer group (35.0%, 42/120) was significantly higher than that in the control group (15%, 9/60). D-dimer concentrations were significantly higher 1 day postoperatively than preoperatively. Conversely, D-dimer concentrations were significantly lower 14 days postoperatively than preoperatively. Patients in the esophageal cancer group with plasma D-dimer concentrations ≤ 0.5 µg/mL had significantly higher 3-year survival rates than those with higher concentrations. In the logistic multivariate analysis, tumor pathological stage and preoperative plasma D-dimer levels were independent prognostic factors of 3-year survival rates in patients with esophageal cancer.

CONCLUSION

Plasma D-dimer concentrations are clinically valuable in esophageal cancer diagnosis, postoperative recurrence monitoring, and prognosis prediction.

Laboratory blood parameters and machine learning for the prognosis of esophageal squamous cell carcinoma

Background

In contemporary study, the death of esophageal squamous cell carcinoma (ESCC) patients need precise and expedient prognostic methodologies.

Objective

To develop and validate a prognostic model tailored to ESCC patients, leveraging the power of machine learning (ML) techniques and drawing insights from comprehensive datasets of laboratory-derived blood parameters.

Methods

Three ML approaches, including Gradient Boosting Machine (GBM), Random Survival Forest (RSF), and the classical Cox method, were employed to develop models on a dataset of 2521 ESCC patients with 27 features. The models were evaluated by concordance index (C-index) and time receiver operating characteristics (Time ROC) curves. We used the optimal model to evaluate the correlation between features and prognosis and divide patients into low- and high-risk groups by risk stratification. Its performance was analyzed by Kaplan-Meier curve and the comparison with AJCC8 stage. We further evaluate the comprehensive effectiveness of the model in ESCC subgroup by risk score and KDE (kernel density estimation) plotting.

Results

RSF's C-index (0.746) and AUC (three-year AUC 0.761, five-year AUC 0.771) had slight advantage over GBM and the classical Cox method. Subsequently, 14 features such as N stage, T stage, surgical margin, tumor length, age, Dissected LN number, MCH, Na, FIB, DBIL, CL, treatment, vascular invasion, and tumor grade were selected to build the model. Based on these, we found significant difference for survival rate between low-(3-year OS 81.8%, 5-year OS 69.8%) and high-risk (3-year OS 25.1%, 5-year OS 11.5%) patients in training set, which was also verified in test set (all P < 0.0001). Compared with the AJCC8th stage system, it showed a greater discriminative ability which is also in good agreement with its staging ability.

Conclusion

We developed an ESCC prognostic model with good performance by clinical features and laboratory blood parameters.

A nomogram prognostic model for early hepatocellular carcinoma with diabetes mellitus after primary liver resection based on the admission characteristics

Background: Patients diagnosed with early-stage hepatocellular carcinoma (HCC) and diabetes mellitus (DM) are at a higher risk of experiencing complications and facing increased mortality rates. Hence, it is crucial to develop personalized clinical strategies for this particular subgroup upon their admission. The objective of this study is to determine the key prognostic factors in early HCC patients who received liver resection combined with DM and develop a practical personalized model for precise prediction of overall survival in these individuals. Method: A total of 1496 patients diagnosed hepatitis B virus (HBV) - related liver cancer from Beijing You'an Hospital were retrospectively enrolled, spanning from 1 January 2014, to 31 December 2019, and ultimately, 622 eligible patients of hepatocellular carcinoma (HCC) patients with diabetes were included in this present investigation. A multivariate COX regression analysis was conducted to identify prognostic factors that are independent of each other and develop a nomogram. The performance of the nomogram was evaluated using various statistical measures such as the C-index, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) in both the training and validation groups. Survival rates were estimated using the Kaplan-Meier method. Results: The study included a total of 622 early HCC patients who underwent liver resection combined with DM. Random Forrest model and Multivariate Cox regression analysis revealed that drinking, tumor number, monocyte-to-lymphocyte ratio, white blood cell count and international normalized ratio at admission were identified as independent prognostic factors for early HCC patients who underwent liver resection combined with DM. The nomogram demonstrated good predictive performance in the training and validation cohorts based on the C-index values of 0 .756 and 0 .739 respectively, as well as the area under the curve values for 3-, 5-, and 8-year overall survival (0.797, 0.807, 0.840, and 0.725, 0.791, 0.855). Calibration curves and decision curve analysis indicated high accuracy and net clinical benefit rates. Furthermore, the nomogram successfully stratified enrolled patients into low-risk and high-risk groups based on their risk of overall survival. The difference in overall survival between these two groups was statistically significant in both the training and validation cohorts (p < 0.0001 and p = 0.0064). Conclusion: Our results indicate that the admission characteristics demonstrate a highly effective ability to predict the overall survival of early HCC patients who have undergone liver resection in combination with DM. The developed model has the potential to support healthcare professionals in making more informed initial clinical judgments for this particular subgroup of patients.

Coagulation parameters for the differential diagnosis of pancreatic cancer in the early stage: a retrospective study

BACKGROUND

In recent years, conventional coagulation (CC) and thromboelastography (TEG) parameters have been reported to be closely related to the progression of pancreatic cancer (PC). However, the potential utility of these parameters in differentiating benign and malignant pancreatic diseases is still unclear.

OBJECTIVES

A retrospective study was conducted to evaluate the efficacy of coagulation parameters in differentiating pancreatic cancer/early stage pancreatic cancer (EPC, TNM stages I and II) from benign control conditions, and to further explore whether coagulation parameters could improve the differential value of CA199.

METHODS

Receiver operating characteristic (ROC) curves and logistic regression analysis were used to identify the diagnostic value of each coagulation parameter or combination of parameters.

RESULTS

Compared with benign pancreatic disease (BPD), patients with pancreatic malignant tumors had significant coagulation disorders, specifically manifested as abnormal increases or decreases in several CC and TEG parameters (such as activated partial thromboplastin time (APTT), fibrinogen (FIB), D-dimer (DD2), K time, R time, Angle, maximum amplitude (MA), coagulation index (CI), and Ly30). In the training group, ROC curve showed that FIB, DD2, Angle, MA, and CI had favorable efficacy at differentiating PC or EPC from BPD (for PC, AUC = 0.737, 0.654, 0.627, 0.602, 0.648; for EPC, AUC = 0.723, 0.635, 0.630, 0.614, 0.648). However, several combined diagnostic indicators based on FIB, DD2 and CI failed to outperform the individual coagulation indexes in diagnostic efficiency. Combinations of certain coagulation indexes with CA199 outperformed CA199 alone at identifying PC or EPC, especially FIB + CA199 (for PC, AUC = 0.904; for EPC, AUC = 0.905), FIB + DD2 + CA199 (for PC, AUC = 0.902; for EPC, AUC = 0.900), FIB + CI + CA199 (for PC, AUC = 0.906; for EPC, AUC = 0.906), and FIB + DD2 + CI + CA199 (for PC, AUC = 0.905; for EPC, AUC = 0.900). The results from a validation set also confirmed that these combinations have advantageous diagnostic value for PC and EPC.

CONCLUSIONS

A significant hypercoagulable state was common in PC. Some CC and TEG parameters are valuable in the differential diagnosis of benign and malignant pancreatic diseases. In addition, coagulation indexes combined with CA199 can further enhance the differential diagnosis efficacy of CA199 in PC and EPC.

A prognostic nomogram that includes MPV in esophageal squamous cell carcinoma

BACKGROUND

Mean platelet volume (MPV), as a marker of platelet activity, has been shown to be an efficient prognostic biomarker in several types of cancer. Using MPV, this study aimed to create and validate a prognostic nomogram to the overall survival in esophageal squamous cell carcinoma (ESCC) patients.

METHODS

The nomogram was constructed and tested using data from a retrospective study of 1893 patients who were randomly assigned to the training and testing cohorts with a 7:3 randomization. In order to screen out the optimal predictors for overall survival (OS), we conducted the LASSO-cox regression, univariate, and multivariate cox regression analyses. Subsequently, the predictive accuracy of the nomogram was validated in both the training and the testing cohorts. Finally, decision curve analysis (DCA) was used to confirm clinical validity.

RESULTS

Age, MPV, nerve invasion, T stage, and N stage were found as independent prognostic variables for OS and were further developed into a nomogram. The nomogram's prediction accuracy for 1-, 3-, and 5-year OS was 0.736, 0.749, 0.774, and 0.724, 0.719, 0.704 in the training and testing cohorts, respectively. Furthermore, DCA results indicated that nomograms outperformed the AJCC 8th and conventional T, N staging systems in both the training and testing cohorts.

CONCLUSIONS

The nomogram, in conjunction with MPV and standard clinicopathological markers, could improve the accuracy of prediction of OS in ESCC patients.

The prognostic role of coagulation markers in the progression and metastasis of laryngeal squamous cell carcinoma

BACKGROUND

The application of coagulation-related markers in laryngeal squamous cell carcinoma(LSCC) remains unclear. This study explored the prognostic role of coagulation markers in the progression and metastasis of LSCC.

METHODS

Coagulation markers of patients with LSCC receiving surgery in the Second Affiliated Hospital of Fujian Medical University in China, from January 2013 to May 2022 were retrospectively analyzed and compared with those of contemporary patients with benign laryngeal diseases. The relationship between clinicopathological features of LSCC and coagulation markers was analyzed with the chi-square and rank sum tests. The ROC curve analysis was utilized to evaluate the diagnostic efficacy of seven coagulation markers for LSCC and its different clinicopathological features, and to find the optimal cutoff value of each coagulation marker.

RESULTS

303 patients with LSCC and 533 patients with benign laryngeal diseases were included in the present analysis. Compared to the control group, prothrombin time (PT) (p < 0.001), activated partial thromboplastin time (APTT) (p = 0.001), and Fib (p < 0.001) in patients with LSCC were significantly higher, while mean platelet volume (MPV) (p < 0.001) was significantly shorter. Significant increases were detected in PT (Z = 14.342, p = 0.002), Fib (Z = 25.985, p < 0.001), platelet count (PC) (Z = 12.768, p = 0.005), PCT (Z = 9.178, p = 0.027), MPV (F = 2.948, p = 0.033) in T4 stage. Fib had the highest prognostic value among the seven coagulation markers in different T stages (AUC = 0.676, p < 0.001), N stages (AUC = 0.717, p < 0.001), tumor stage (AUC = 0.665, p < 0.001), differentiation degree (AUC = 0.579, p = 0.022), and neurovascular invasion (AUC = 0.651, p = 0.007). Fib (Z = 25.832, p < 0.001), PC (Z = 23.842, p < 0.001), and PCT (Z = 20.15, p < 0.001) in N1 and N3 stages were significantly higher than in N0 stage. PT (Z = 12.174, p = 0.007), Fib (Z = 23.873, p < 0.001), PC (Z = 17.785, p < 0.001), and PCT (Z = 14.693, p = 0.002) were significantly higher in stage IV than in stage I and II. APTT (Z=-1.983, p = 0.047), Fib (Z=-2.68, p = 0.007), PC (Z=-2.723, p = 0.006), and PCT (Z=-2.592, p = 0.01) increased significantly when the tumor invaded neurovascular tissue.

CONCLUSIONS

Coagulation markers have the potential to act as biomarkers for predicting pathological features of LSCC. The high level of Fib was helpful for the diagnosis of LSCC and the detection of advanced LSCC.

TRIAL REGISTRATION

Not applicable.

Machine learning‑based prediction of survival prognosis in esophageal squamous cell carcinoma

The current prognostic tools for esophageal squamous cell carcinoma (ESCC) lack the necessary accuracy to facilitate individualized patient management strategies. To address this issue, this study was conducted to develop a machine learning (ML) prediction model for ESCC patients' survival management. Six ML approaches, including Rpart, Elastic Net, GBM, Random Forest, GLMboost, and the machine learning-extended CoxPH method, were employed to develop risk prediction models. The model was trained on a dataset of 1954 ESCC patients with 27 clinical features and validated on a dataset of 487 ESCC patients. The discriminative performance of the models was assessed using the concordance index (C-index). The best performing model was used for risk stratification and clinical evaluation. The study found that N stage, T stage, surgical margin, tumor grade, tumor length, sex, MPV, AST, FIB, and Mg are the important feature for ESCC patients' survival. The machine learning-extended CoxPH model, Elastic Net, and Random Forest had similar performance in predicting the mortality risk of ESCC patients, and outperformed GBM, GLMboost, and Rpart. The risk scores derived from the CoxPH model effectively stratified ESCC patients into low-, intermediate-, and high-risk groups with distinctly different 3-year overall survival (OS) probabilities of 80.8%, 58.2%, and 29.5%, respectively. This risk stratification was also observed in the validation cohort. Furthermore, the risk model demonstrated greater discriminative ability and net benefit than the AJCC8th stage, suggesting its potential as a prognostic tool for predicting survival events and guiding clinical decision-making. The classical algorithm of the CoxPH method was also found to be sufficiently good for interpretive studies.

The potential of high-order features of routine blood test in predicting the prognosis of non-small cell lung cancer

BACKGROUND

Numerous studies have demonstrated that the high-order features (HOFs) of blood test data can be used to predict the prognosis of patients with different types of cancer. Although the majority of blood HOFs can be divided into inflammatory or nutritional markers, there are still numerous that have not been classified correctly, with the same feature being named differently. It is an urgent need to reclassify the blood HOFs and comprehensively assess their potential for cancer prognosis.

METHODS

Initially, a review of existing literature was conducted to identify the high-order features (HOFs) and classify them based on their calculation method. Subsequently, a cohort of patients diagnosed with non-small cell lung cancer (NSCLC) was established, and their clinical information prior to treatment was collected, including low-order features (LOFs) obtained from routine blood tests. The HOFs were then computed and their associations with clinical features were examined. Using the LOF and HOF data sets, a deep learning algorithm called DeepSurv was utilized to predict the prognostic risk values. The effectiveness of each data set's prediction was evaluated using the decision curve analysis (DCA). Finally, a prognostic model in the form of a nomogram was developed, and its accuracy was assessed using the calibration curve.

RESULTS

From 1210 documents, over 160 blood HOFs were obtained, arranged into 110, and divided into three distinct categories: 76 proportional features, 6 composition features, and 28 scoring features. Correlation analysis did not reveal a strong association between blood features and clinical features; however, the risk value predicted by the DeepSurv LOF- and HOF-models is significantly linked to the stage. Results from DCA showed that the HOF model was superior to the LOF model in terms of prediction, and that the risk value predicted by the blood data model could be employed as a complementary factor to enhance the prognosis of patients. A nomograph was created with a C-index value of 0.74, which is capable of providing a reasonably accurate prediction of 1-year and 3-year overall survival for patients.

CONCLUSIONS

This research initially explored the categorization and nomenclature of blood HOF, and proved its potential in lung cancer prognosis.