Development and validation of a nomogram based on multiple preoperative immunoinflammatory indexes for survival prediction in patients with stage IA-IB endometrial cancer.

To evaluate the preoperative systemic immune-inflammation index (SII), advanced lung cancer inflammation index (ALI), neutrophil to lymphocyte ratio (NLR), and prognostic nutritional index (PNI) capacity to predict the prognosis of stage IA-IB endometrial carcinoma (EC) patients after operation, and establish a nomogram model to guide clinical practice. A total of 387 patients with EC (R0 resection, stage IA-IB) were assessed. Clinical information and the SII, NLR, ALI, and PNI values were obtained. The low and high ratio groups were separated using the receiver operating characteristic curve (ROC). Pearson's χ2-test or Fisher's exact test was used to determine their relationship with clinical variables. To determine the independent prognostic factors, Cox regression was utilized to do the univariate and multivariate survival analyses. The Kaplan-Meier method was used to draw the survival curve in our survival analysis. Depending upon the independent prognostic factors, the nomogram for Overall survival (OS) and Disease-free survival (DFS) nomogram was developed, and its discrimination ability was validated by the consistency index (C-index) and calibration curve. Cox regression analysis revealed that FIGO staging, Ki-67 expression level, PNI, and ALI are independent prognostic factors for both OS and DFS. Then a novel predictive nomogram was developed, and its C-index value for OS and DFS was 0.829 and 0.814, respectively. The calibration curves demonstrated consistency amid the predicted prognosis using the developed nomogram and the actual observed outcomes. The ALI and PNI could serve as readily available prognostic indicators for OS and DFS prediction in stage IA-IB EC patients. The nomogram developed owned superior power for OS and DFS prediction in stage IA-IB EC patients, and it would assist clinical oncologists in accurately predicting the individual's OS and DFS.

Zhang N ，Liu H ，Yang J ，Zhong F ... -  《American Journal of Translational Research》

Nomogram Based on Systemic Immune-Inflammation Index to Predict Survival of Tongue Cancer Patients Who Underwent Cervical Dissection.

Aim: The aim of this study was to evaluate the prognostic significance of the preoperative systemic immune-inflammation index (SII) and to establish a nomogram for prediction of survival of tongue squamous cell carcinoma (TSCC) patients who underwent primary surgery and cervical dissection. Methods: 120 patients diagnosed with TSCC who underwent primary tumor and neck dissection without preoperative treatment were included to develop the nomogram. This model was externally validated in an independent data cohort of 50 TSCC patients. X-tile software was used to identify the optimal cut-off value. Prognostic factors were identified by Univariate and multivariate analyses. A nomogram based on the multivariate analysis results was built to predict the survival rate and calibration curves and concordance index (C-index) were used to determine predictive and discriminatory capacity. Results: The optimal cut-off value was 569×109/L for SII. In the training cohort, a high preoperative SII (>569) was significantly related to tumor size, histological grade, depth of invasion, lymph node density (LND). A Kaplan-Meier survival analysis showed that patients with a lower SII had a significantly better 5-year overall survival (OS) and disease-free survival (DFS) than patients with high SII (80.8% vs. 43.5% and 72.7% vs. 36.2%, respectively, P<0.001). Univariate analyses of training cohort revealed that age, clinical stage, depth of invasion, LND, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and SII were significant prognostic factors for OS. Moreover, the receiver operating characteristics (ROC) curve showed that SII was superior to NLR and PLR for predicting clinical outcomes. However, multivariate analysis found that age, LND, and SII were independent risk factors for OS. The C-index of the nomograms based on independent prognostic factors was 0.716 for OS and 0.723 for DFS. The C-indexes for external validation of OS and DFS were 0.852 and 0.754, respectively. The calibration curves showed good agreement between predicted and actual observations of OS and DFS. Conclusion: SII can serve as a novel independent prognostic factor for OS and DFS of patients with TSCC. The prognostic nomogram based on SII is a reliable model for predicting survival of patients with TSCC after surgery.

Lu Z ，Yan W ，Liang J ，Yu M ，Liu J ，Hao J ，Wan Q ，Liu J ，Luo C ，Chen Y ... -  《Frontiers in Oncology》

[Relationship between preoperative inflammatory indexes and prognosis of patients with rectal cancer and establishment of prognostic nomogram prediction model].

Zhang L ，Shi FY ，Qin Q ，Liu GX ，Zhang HW ，Yan J ，Tan M ，Wang LZ ，Xue D ，Hu CH ，Zhang Z ，She JJ ... -  《-》

[Establishment and verification of a prognostic nomogram for survival of tongue squamous cell carcinoma patients who underwent cervical dissection].

Su J ，Wang X ，Sun Z 《-》

Preoperative Systemic Immune-Inflammation Index (SII) as a Superior Predictor of Long-Term Survival Outcome in Patients With Stage I-II Gastric Cancer After Radical Surgery.

Systemic immune-inflammation index (SII), calculated by immunoinflammatory cell counts of peripheral blood, is considered a predictor of survival outcome in several solid tumors, including gastric cancer (GC). However, there is no study focusing on the prognostic value of SII in the early stage of GC. This study aims to compare prognostic prediction capabilities of several inflammatory indices, nutritional indices, and tumor markers to further verify the superior prognostic value of SII in stage I-II GC patients after surgery. In this study, 548 patients (358 in the training group and 190 in the validation group) with stage I-II GC after radical surgery were retrospectively analyzed. The peripheral blood indices of interest were SII, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), advanced lung cancer inflammation index (ALI), systemic inflammation score (SIS), prognostic nutritional index (PNI), body mass index (BMI), albumin, carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), carbohydrate-associated antigen 19-9 (CA19-9), and alpha-fetoprotein (AFP). The time-dependent receiver operating characteristic (t-ROC) curves and the area under the curve (AUC) were used to determine the optimal cutoff value and prognostic ability of each parameter. Kaplan-Meier curves and multivariable Cox regression models were used to evaluate independent prognostic factors. The nomogram was constructed based on the result of bidirectional stepwise regression model. The optimal cutoff value of SII was 508.3. The 5-year overall survival rate of the low SII (SII-L) group was significantly higher than that of the high SII (SII-H) group (92% vs. 80%, P < 0.001), especially in the elderly and stage II patients (91% vs. 73%, P = 0.001; 86% vs. 67%, P = 0.003, respectively). The significant prognostic values of SII were consistent in most subgroups. In multivariate analysis, SII and CA19-9 were the only two independent prognostic hematology indices. The AUC value of SII (0.624) was greater than that of CA19-9 (0.528) and other prognostic parameters. Adding SII to the conventional model improved the predictive ability of 5-year overall survival as shown by the significantly increased net reclassification improvement (NRI) and integrated discrimination improvement (IDI) (P = 0.033, P = 0.053, respectively) and modestly improved consistency index (C-index) (increased by 1.6%). External validation of SII-based nomogram demonstrated favorable predictive performance and discrimination. In addition, interactive web dynamic nomogram was published to facilitate clinical use. SII is a simple but powerful index with a high predictive value to predict survival outcome in patients with stage I-II GC after radical operation. The SII-based nomogram can provide intuitive and accurate prognosis prediction of individual patients.

He K ，Si L ，Pan X ，Sun L ，Wang Y ，Lu J ，Wang X ... -  《Frontiers in Oncology》