High preoperative CEA and systemic inflammation response index (C-SIRI) predict unfavorable survival of resectable colorectal cancer

Prognostic and clinicopathological value of systemic inflammation response index (SIRI) in patients with breast cancer: a meta-analysis

BACKGROUND

Many studies have explored the value of the systemic inflammation response index (SIRI) in predicting the prognosis of patients with breast cancer (BC); however, their findings remain controversial. Consequently, we performed the present meta-analysis to accurately identify the role of SIRI in predicting BC prognosis.

METHODS

PubMed, Embase, Cochrane Library, and Web of Science databases were comprehensively searched between their inception and February 10, 2024. The significance of SIRI in predicting overall survival (OS) and disease-free survival (DFS) in BC patients was analyzed by calculating pooled hazard ratios (HRs) and corresponding 95% confidence intervals (CIs).

RESULTS

Eight articles involving 2,997 patients with BC were enrolled in the present study. According to our combined analysis, a higher SIRI was markedly associated with dismal OS (HR = 2.43, 95%CI = 1.42-4.15, p < 0.001) but not poor DFS (HR = 2.59, 95%CI = 0.81-8.24, p = 0.107) in patients with BC. Moreover, based on the pooled results, a high SIRI was significantly related to T3-T4 stage (OR = 1.73, 95%CI = 1.40-2.14, p < 0.001), N1-N3 stage (OR = 1.61, 95%CI = 1.37-1.91, p < 0.001), TNM stage III (OR = 1.63, 95%CI = 1.34-1.98, p < 0.001), and poor differentiation (OR = 1.25, 95%CI = 1.02-1.52, p = 0.028).

CONCLUSION

According to our results, a high SIRI significantly predicted poor OS in patients with BC. Furthermore, elevated SIRI was also remarkably related to increased tumor size and later BC tumor stage. The SIRI can serve as a novel prognostic biomarker for patients with BC.

Relationship between systemic immune-inflammation index and osteoarthritis: a cross-sectional study from the NHANES 2005-2018

Objective

The study aimed to explore the relationship between systemic inflammatory response index (SIRI) levels and osteoarthritis (OA) using cross-sectional data from the National Health and Nutrition Examination Survey (NHANES) database from 2005 to 2018.

Methods

Using cross-sectional data from the NHANES database from 2005 to 2018, we included 11,381 study participants divided into OA (n = 1,437) and non-OA (n = 9,944) groups. Weighted multivariable regression models and subgroup analyses were employed to investigate the relationship between SIRI and OA. Additionally, restricted cubic spline models were used to explore nonlinear relationships.

Results

This study enrolled 11,381 participants aged ≥20 years, including 1,437 (14%) with OA. Weighted multivariable regression analysis in the fully adjusted Model 3 indicated a correlation between higher levels of SIRI (log2-transformed) and an increased OA risk (odds ratio: 1.150; 95% confidence interval: 1.000-1.323, p < 0.05). Interaction tests showed that the variables did not significantly affect this correlation (p for interaction all >0.05). Additionally, a restricted cubic spline model revealed a nonlinear relationship between log2(SIRI) and OA risk, with a threshold effect showing 4.757 as the critical value of SIRI. SIRI <4.757 showed almost unchanged OA risk, whereas SIRI >4.757 showed rapidly increasing OA risk.

Conclusion

The positive correlation between SIRI and OA risk, with a critical value of 4.757, holds clinical value in practical applications. Additionally, our study indicates that SIRI is a novel, clinically valuable, and convenient inflammatory biomarker that can be used to predict OA risk in adults.

Inflammation and Colorectal Cancer: A Meta-Analysis of the Prognostic Significance of the Systemic Immune-Inflammation Index (SII) and the Systemic Inflammation Response Index (SIRI)

The overall prognosis for colorectal cancer (CRC) remains challenging as the survival time varies widely, even in patients with the same stage of disease. Recent studies suggest prognostic relevance of the novel markers of systemic inflammation, the systemic immune-inflammation index (SII), and the systemic inflammation response index (SIRI). We conducted a comprehensive meta-analysis to assess the prognostic significance of the SII and the SIRI in CRC. We searched the relevant literature for observational studies, and random effects models were employed to conduct a statistical analysis using the metaanalysisonline.com platform. Pooled effect sizes were reported with hazard ratios (HRs) and corresponding 95% confidence intervals (CI). Data from 29 studies published between 2016 and 2024, comprising 10,091 participants, were included in our meta-analysis on SII. CRC patients with high SII levels had worse disease outcomes, which were associated with poor OS (HR: 1.75; 95% CI: 1.4-2.19) and poor PFS/DFS/RFS (HR: 1.25; 95% CI: 1.18-1.33). This increased risk of worse OS was present irrespective of the treatment strategy, sample size (<220 and ≥220), and cutoff used to define high and low SII (<550 and ≥550) groups. Based on data from five studies comprising 2362 participants, we found a strong association between the high SIRI and worse OS (HR: 2.65; 95% CI: 1.6-4.38) and DFS/RFS (HR: 2.04; 95% CI: 1.42-2.93). According to our results, both the SII and SIRI hold great promise as prognostic markers in CRC. Further validations are needed for their age- and stage-specific utility in the clinical routine.

Systemic Inflammatory Response Index (SIRI) is associated with all-cause mortality and cardiovascular mortality in population with chronic kidney disease: evidence from NHANES (2001-2018)

Objective

To examine the correlation between SIRI and the probability of cardiovascular mortality as well as all-cause mortality in individuals with chronic kidney disease.

Methods

A cohort of 3,262 participants from the US National Health and Nutrition Examination Survey (NHANES) database were included in the study. We categorized participants into five groups based on the stage of chronic kidney disease. A weighted Cox regression model was applied to assess the relationship between SIRI and mortality. Subgroup analyses, Kaplan-Meier survival curves, and ROC curves were conducted. Additionally, restricted cubic spline analysis was employed to elucidate the detailed association between SIRI and hazard ratio (HR).

Results

This study included a cohort of 3,262 individuals, of whom 1,535 were male (weighted proportion: 42%), and 2,216 were aged 60 or above (weighted proportion: 59%). Following adjustments for covariates like age, sex, race, and education, elevated SIRI remained a significant independent risk factor for cardiovascular mortality (HR=2.50, 95%CI: 1.62-3.84, p<0.001) and all-cause mortality (HR=3.02, 95%CI: 2.03-4.51, p<0.001) in CKD patients. The restricted cubic spline analysis indicated a nonlinear relationship between SIRI and cardiovascular mortality, with SIRI>1.2 identified as an independent risk factor for cardiovascular mortality in CKD patients.

Conclusion

Heightened SIRI independently poses a risk for both all-cause and cardiovascular mortality in chronic kidney disease patients, with potentially heightened significance in the early stages (Stage I to Stage III) of chronic kidney disease.

A prognostic nomogram integrating carcinoembryonic antigen levels for predicting overall survival in elderly patients with stage II-III colorectal cancer

Background

With the aging of the population, colorectal surgeons will have to face more elderly colorectal cancer (CRC) patients in the future. We aim to analyze independent risk factors affecting overall survival in elderly (age ≥65 years) patients with stage II-III CRC and construct a nomogram to predict patient survival.

Methods

A total of 3,016 elderly CRC patients with stage II-III were obtained from the SEER database. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) regression analyses were used to screen independent prognostic factors, and a survival prediction nomogram was constructed based on the results. The consistency index (C-index), decision curve analysis (DCA), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were used to compare the predictive ability between the nomogram and tumor-node-metastasis (TNM) stage system. All patients were classified into high-risk and low-risk groups based on risk scores calculated by nomogram. The Kaplan-Meier method was used to compare the survival differences between two groups.

Results

The 3- and 5-year area under the curve (AUC) values of the prediction nomogram model were 76.6% and 74.8%, respectively. The AIC, BIC, and C-index values of the nomogram model were 6,032.502, 15,728.72, and 0.707, respectively, which were better than the TNM staging system. Kaplan-Meier survival analysis showed a significant survival difference between high-risk and low-risk groups (P<0.0001).

Conclusions

We constructed a prediction nomogram for stage II-III elderly CRC patients by combining pre-treatment carcinoembryonic antigen (CEA) levels, which can accurately predict patient survival. This facilitates clinicians to accurately assess patient prognosis and identify high-risk patients to adopt more aggressive and effective treatment strategies.

Systemic Immune-Inflammation Index Predicts Tumor Recurrence after Radical Resection for Colorectal Cancer

The systemic inflammatory response is associated with tumor promotion and suppression. Accumulating evidence shows that peripheral blood markers of inflammatory response predict clinical outcomes in various human cancers. The aim of this study was to investigate the prognostic relevance of the inflammation-based biomarkers in colorectal cancer (CRC). We retrospectively analyzed 118 CRC patients who underwent curative resection between 2012 and 2017. The inflammation-based biomarkers were evaluated by using preoperative neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), prognostic nutritional index (PNI), and Glasgow prognostic score (GPS). Prognostic values were assessed by the Kaplan-Meier analysis for cancer-specific recurrence-free survival (RFS) and Cox proportional-hazards model. There were significant differences in the levels of NLR, PLR, SII, and SIRI between recurrence and non-recurrence group. The area under the curve (AUC) for SII was 0.710, which showed the highest value in the inflammation-based biomarkers. Multivariate analysis identified that SII (p = 0.0031) and lymph node metastasis (p = 0.0168) were independent prognostic factors for recurrence. High SII exhibited more dismal RFS than low SII in CRC patients with non-metastatic lymph node (p = 0.0002). Our study suggests that SII and lymph node metastasis could be useful indicators in predicting the recurrence of CRC patients. Additionally, SII could accurately stratify CRC patients with tumor recurrence by combining with lymph node metastasis. This result would be beneficial for determining the optimal therapeutic strategies after surgical resection for CRC.

Interpretable machine learning model based on the systemic inflammation response index and ultrasound features can predict central lymph node metastasis in cN0T1-T2 papillary thyroid carcinoma

Background

It is arguable whether individuals with T1-T2 papillary thyroid cancer (PTC) who have a clinically negative (cN0) diagnosis should undergo prophylactic central lymph node dissection (pCLND) on a routine basis. Many inflammatory indices, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and systemic immune-inflammatory index (SII), have been reported in PTC. However, the associations between the systemic inflammation response index (SIRI) and the risk of central lymph node metastasis (CLNM) remain unclear.

Methods

Retrospective research involving 1,394 individuals with cN0T1-T2 PTC was carried out, and the included patients were randomly allocated into training (70%) and testing (30%) subgroups. The preoperative inflammatory indices and ultrasound (US) features were used to train the models. To assess the forecasting factors as well as drawing nomograms, the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were utilized. Then eight interpretable models based on machine learning (ML) algorithms were constructed, including decision tree (DT), K-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost). The performance of the models was evaluated by incorporating the area under the precision-recall curve (auPR) and the area under the receiver operating characteristic curve (auROC), as well as other conventional metrics. The interpretability of the optimum model was illustrated via the shapley additive explanations (SHAP) approach.

Results

Younger age, larger tumor size, capsular invasion, location (lower and isthmus), unclear margin, microcalcifications, color Doppler flow imaging (CDFI) blood flow, and higher SIRI (≥0.77) were independent positive predictors of CLNM, whereas female sex and Hashimoto thyroiditis were independent negative predictors, and nomograms were subsequently constructed. Taking into account both the auROC and auPR, the RF algorithm showed the best performance, and superiority to XGBoost, CatBoost and ANN. In addition, the role of key variables was visualized in the SHAP plot.

Conclusions

An interpretable ML model based on the SIRI and US features can be used to predict CLNM in individuals with cN0T1-T2 PTC.

Prognostic value of the fibrinogen albumin ratio index (FARI) in nasopharyngeal carcinoma patients undergoing radiotherapy

There is mounting evidence that malnutrition and systemic inflammation status are involved in the prognosis of various cancers. In this study, we aimed to evaluate the prognostic value of the pretreatment fibrinogen-albumin ratio index (FARI) in nasopharyngeal carcinoma (NPC) patients receiving definite radiotherapy. NPC patients who received definite radiotherapy between January 2013 and December 2019 were included. A receiver operating characteristic (ROC) curve was used to determine the optimal cutoff value. The clinicopathological characteristics of the patients were compared via the Chi-square test. Survival curves were analyzed by the Kaplan‒Meier method. The prognostic factors were evaluated by univariate and multivariate analyses via Cox hazards regression analysis. A total of 225 patients were enrolled, and the median follow-up time was 48.5 months. High FARI was correlated with worse ECOG score (p = 0.003), higher EBV-DNA titer (p = 0.047), and more advanced clinical stage (p < 0.001). In the multivariable analysis, FARI independently predicted OS (HR 2.399, 95% CI 1.294-4.450, P < 0.001), PFS (HR 2.085, 95% CI 1.200-3.625, P = 0.009), and DMFS (HR 2.527, 95% CI 1.288-4.958, P < 0.001). The current findings suggest that a high pretreatment FARI is an independent predictor of OS, PFS and DMFS in NPC patients undergoing definite radiotherapy.