An Externally Validated Nomogram for Predicting the Overall Survival of Patients With Diffuse Large B-Cell Lymphoma Based on Clinical Characteristics and Systemic Inflammatory Markers

Predictive value of prognostic nutritional index as prognostic biomarkers in patients with lymphoma: a systematic review and meta-analysis

BACKGROUND

Several research have indicated the significant potential of the Prognostic Nutritional Index (PNI) as a prognostic biomarker in lymphoma patients. However, there is some inconsistency in the findings of a few studies. Hence, to offer a thorough evaluation of the predictive significance of PNI in lymphoma patients, we performed a meta-analysis to examine the prognostic value of PNI for survival outcomes in lymphoma patients.

METHODS

We conducted a comprehensive search for pertinent works published up until December 2023 in databases such as PubMed, EMBASE, Cochrane Library, and Web of Science. We obtained hazard ratio (HR) data related to survival outcomes and computed aggregated HRs with their corresponding 95% confidence intervals (CIs) to evaluate the correlation between PNI and both overall survival (OS) and progression-free survival (PFS) in lymphoma patients.

RESULTS

By analyzing data from 1260 patients in 28 studies, we found that PNI levels were associated with prognosis in lymphoma patients. High PNI levels predicted that patients had longer OS (HR: 0.46, 95% CI 0.37-0.58, P < 0.05) and better PFS (HR: 0.56, 95% CI 0.45-0.70, P < 0.05). Subgroup analyses showed that the predictive ability of PNI for patient prognosis may differ depending on the type of lymphoma. In addition, we found that the critical PNI value had greater predictive potential at 40-45 and above 45.

CONCLUSION

Our study suggests a strong association between PNI and prognostic outcomes in lymphoma patients, indicating that PNI holds substantial prognostic value in this population.

Comparison of Cox regression and generalized Cox regression models to machine learning in predicting survival of children with diffuse large B-cell lymphoma

Background

The incidence of diffuse large B-cell lymphoma (DLBCL) in children is increasing globally. Due to the immature immune system in children, the prognosis of DLBCL is quite different from that of adults. We aim to use the multicenter large retrospective analysis for prognosis study of the disease.

Methods

For our retrospective analysis, we retrieved data from the Surveillance, Epidemiology and End Results (SEER) database that included 836 DLBCL patients under 18 years old who were treated at 22 central institutions between 2000 and 2019. The patients were randomly divided into a modeling group and a validation group based on the ratio of 7:3. Cox stepwise regression, generalized Cox regression and eXtreme Gradient Boosting (XGBoost) were used to screen all variables. The selected prognostic variables were used to construct a nomogram through Cox stepwise regression. The importance of variables was ranked using XGBoost. The predictive performance of the model was assessed by using C-index, area under the curve (AUC) of receiver operating characteristic (ROC) curve, sensitivity and specificity. The consistency of the model was evaluated by using a calibration curve. The clinical practicality of the model was verified through decision curve analysis (DCA).

Results

ROC curve demonstrated that all models except the non-proportional hazards and non-log linearity (NPHNLL) model, achieved AUC values above 0.7, indicating high accuracy. The calibration curve and DCA further confirmed strong predictive performance and clinical practicability.

Conclusions

In this study, we successfully constructed a machine learning model by combining XGBoost with Cox and generalized Cox regression models. This integrated approach accurately predicts the prognosis of children with DLBCL from multiple dimensions. These findings provide a scientific basis for accurate clinical prognosis prediction.

Construction and validation of a nomogram for predicting disease-free survival after radical resection of rectal cancer using perioperative inflammatory indicators

Background

Colorectal cancer is a common digestive tract malignancy that seriously affects patients' quality of life and survival time. Surgery is the main treatment modality, but postoperative prognosis varies greatly. This study sought to explore the impact of perioperative inflammatory indicators on disease-free survival (DFS) in patients after radical resection of rectal cancer and to construct a nomogram for clinical reference.

Methods

A retrospective analysis was performed on 304 primary rectal adenocarcinoma patients who underwent laparoscopic radical resection of rectal cancer at the Affiliated Hospital of Xuzhou Medical University from May 1, 2018 to September 30, 2020. The patients were divided into a training set (n=213) and a validation set (n=91) at a ratio of 7:3. The cut-off values of each inflammatory indicator based on the receiver operating characteristic (ROC) curve were determined and each indicator was divided into high and low groups. The least absolute shrinkage and selection operator (LASSO)-Cox regression model was used to analyze the independent risk factors affecting DFS, and a nomogram was established. The model was internally validated using the validation set, and the discrimination, calibration, and clinical application value of the nomogram were evaluated using ROC curve, calibration curve, and clinical decision curve analysis (DCA).

Results

Tumor-node-metastasis (TNM) stage III, neural invasion, preoperative neutrophil-to-lymphocyte ratio (NLR) ≥1.995, postoperative systemic immune-inflammation index (SII) ≥451.05, and Δpan-immune-inflammation value (ΔPIV) ≥144.36 (P<0.05) were independent factors for predicting the 3-year DFS of patients after rectal cancer surgery. The area under the ROC curve (AUC) of the nomogram was 0.811 [95% confidence interval (CI): 0.778-0.889] in the training set and 0.808 (95% CI: 0.785-0.942) in the validation set. The nomogram showed good calibration, indicating good consistency between predicted and actual risks. DCA demonstrated the clinical utility of the nomogram.

Conclusions

The nomogram constructed based on TNM stage III, neural invasion, preoperative NLR ≥1.995, postoperative SII ≥451.05, and ΔPIV ≥144.36 can predict the risk of 3-year DFS in patients undergoing curative surgery for rectal cancer, enabling strict postoperative follow-up and timely adjuvant treatment for high-risk patients.

Should we use nomograms for risk predictions in diffuse large B cell lymphoma patients? A systematic review

Models based on risk stratification are increasingly reported for Diffuse large B cell lymphoma (DLBCL). Due to a rising interest in nomograms for cancer patients, we aimed to review and critically appraise prognostic models based on nomograms in DLBCL patients. A literature search in PubMed/Embase identified 59 articles that proposed prognostic models for DLBCL by combining parameters of interest (e.g., clinical, laboratory, immunohistochemical, and genetic) between January 2000 and 2024. Of them, 40 studies proposed different gene expression signatures and incorporated them into nomogram-based prognostic models. Although most studies assessed discrimination and calibration when developing the model, many lacked external validation. Current nomogram-based models for DLBCL are mainly developed from publicly available databases, lack external validation, and have no applicability in clinical practice. However, they may be helpful in individual patient counseling, although careful considerations should be made regarding model development due to possible limitations when choosing nomograms for prognostication.

A Novel Inflammatory-Nutritional Prognostic Scoring System for Patients with Diffuse Large B Cell Lymphoma

Purpose

This study aimed to examine the predictive ability of inflammatory and nutritional markers and further establish a novel inflammatory nutritional prognostic scoring (INPS) system.

Patients and Methods

We collected clinicopathological and baseline laboratory data of 352 patients with DLBCL between April 2010 and January 2023 at the First affiliated hospital of Ningbo University. Eligible patients were randomly divided into training and validation cohorts (n = 281 and 71, respectively) in an 8:2 ratio. We used the least absolute shrinkage and selection operator (LASSO) Cox regression model to determine the most important factors among the eight inflammatory-nutritional variables. The impact of INPS on OS was evaluated using the Kaplan-Meier curve and the Log rank test. A prognostic nomogram was developed based on the multivariate Cox regression method. Then, we used the concordance index (C-index), calibration plot, and time-dependent receiver operating characteristic (ROC) analysis to evaluate the prognostic performance and predictive accuracy of the nomogram.

Results

Seven inflammatory-nutritional biomarkers, including neutrophil-lymphocyte ratio (NLR), prognostic nutritional index (PNI), body mass index (BMI), monocyte-lymphocyte ratio (MLR), prealbumin, C reactive protein, and D-dimer were selected using the LASSO Cox analysis to construct INPS, In the multivariate analysis, IPI-High-intermediate group, IPI-High group, high INPS were independently associated with OS, respectively. The prognostic nomogram for overall survival consisting of the above two indicators showed excellent discrimination. The C-index for the nomogram was 0.94 and 0.95 in the training and validation cohorts. The time-dependent ROC curves showed that the predictive accuracy of the nomogram for OS was better than that of the NCCN-IPI system.

Conclusion

The INPS based on seven inflammatory-nutritional indexes was a reliable and convenient predictor of outcomes in DLBCL patients.