Systemic Immune-Inflammatory Index as a Predictor of Lymph Node Metastasis in Endometrial Cancer

Prognostic value of pretreatment systemic immune-inflammation index in patients with endometrial cancer: a meta-analysis

Background: The present work focused on evaluating the systemic immune-inflammation index (SII) for its role in predicting endometrial cancer (EC) patient prognosis by meta-analysis. Methods: SII's role in predicting the prognosis of EC patients was analyzed by calculating combined hazard ratios (HRs) and 95% CIs. Results: As revealed by combined analysis, an increased SII predicted poor overall survival (HR = 2.01; 95% CI = 1.58-2.57; p < 0.001) as well as inferior progression-free survival (HR = 1.87; 95% CI = 1.36-2.58; p < 0.001) of EC. Conclusion: An increased SII score significantly predicted poor overall survival and progression-free survival in subjects with EC. The SII is suitable for predicting short- and long-term prognoses of patients with EC.

Preoperative evaluation and a nomogram prediction model for pelvic lymph node metastasis in endometrial cancer

OBJECTIVE

The primary objective of this study is to explore the preoperative risk factors of pelvic lymph node metastasis (PLNM) in endometrial cancer patients, and construct a nomogram prediction model.

MATERIALS AND METHODS

We retrospectively collected various preoperative clinical characteristics of patients and analyzed their relationship with PLNM. Logistic regression analysis was used to screen for independent risk factors for PLNM of endometrial cancer. A nomogram prediction model was constructed, the receiver operating characteristic (ROC), calibration curve and decision curve analysis (DCA) were constructed and used to assess discrimination, calibration, and net benefit.

RESULTS

Out of the 276 patients, 74 (26.81%) with postoperative pathological confirmation of PLNM. Multivariate logistic regressive analysis demonstrated that preoperative depth of myometrial invasion (DIM) ≥50% determined by Magnetic Resonance Imaging (MRI) (p = 0.003), carbohydrate antigen 125 (CA125) (p = 0.030), carbohydrate antigen 19-9 (CA 19-9) (p = 0.044), and platelet/lymphocyte ratio (PLR) (p = 0.025) could serve as independent risk factors for PLNM. A risk factors-based nomogram prediction model was constructed, which showed good discrimination (AUC = 0.841, p < 0.001) and good efficacy (C-index = 0.842) and good calibration (mean absolute error = 0.046). DCA showed that the model can provide clinical benefits.

CONCLUSIONS

Preoperative DIM ≥50% determined by MRI, serum CA 19-9, CA125 and PLR could be utilized to predict PLNM in endometrial cancer patients. This nomogram prediction model can provide preoperative help for evaluation and identification of patients with endometrial cancer, and provide a theoretical basis for clinical intervention.

The value of machine learning in preoperative identification of lymph node metastasis status in endometrial cancer: a systematic review and meta-analysis

Background

The early identification of lymph node metastasis status in endometrial cancer (EC) is a serious challenge in clinical practice. Some investigators have introduced machine learning into the early identification of lymph node metastasis in EC patients. However, the predictive value of machine learning is controversial due to the diversity of models and modeling variables. To this end, we carried out this systematic review and meta-analysis to systematically discuss the value of machine learning for the early identification of lymph node metastasis in EC patients.

Methods

A systematic search was conducted in Pubmed, Cochrane, Embase, and Web of Science until March 12, 2023. PROBAST was used to assess the risk of bias in the included studies. In the process of meta-analysis, subgroup analysis was performed according to modeling variables (clinical features, radiomic features, and radiomic features combined with clinical features) and different types of models in various variables.

Results

This systematic review included 50 primary studies with a total of 103,752 EC patients, 12,579 of whom had positive lymph node metastasis. Meta-analysis showed that among the machine learning models constructed by the three categories of modeling variables, the best model was constructed by combining radiomic features with clinical features, with a pooled c-index of 0.907 (95%CI: 0.886-0.928) in the training set and 0.823 (95%CI: 0.757-0.890) in the validation set, and good sensitivity and specificity. The c-index of the machine learning model constructed based on clinical features alone was not inferior to that based on radiomic features only. In addition, logistic regression was found to be the main modeling method and has ideal predictive performance with different categories of modeling variables.

Conclusion

Although the model based on radiomic features combined with clinical features has the best predictive efficiency, there is no recognized specification for the application of radiomics at present. In addition, the logistic regression constructed by clinical features shows good sensitivity and specificity. In this context, large-sample studies covering different races are warranted to develop predictive nomograms based on clinical features, which can be widely applied in clinical practice.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO, identifier CRD42023420774.

Preoperative Haematologic Markers for the Differentiation of Endometrial Cancer from Benign Endometrial Lesions in Postmenopausal Patients with Endometrial Masses

Purpose

The diagnostic value of preoperative hematological changes in endometrial cancer (EC) remains unclear. This study aimed to assess the role of preoperative hematologic parameters in differentiating EC from benign endometrial lesions in postmenopausal women with endometrial masses.

Methods

Preoperative laboratory variables were retrospectively reviewed in patients with malignant or benign endometrial lesions, and the significance of intergroup differences was assessed. Receiver operating characteristic curves were used to analyze the optimal cut-off values for each variable. Logistic regression analysis was used to identify the variables predicting the presence of endometrial malignancy.

Results

Preoperative laboratory variables of 176 patients (84 EC and 92 benign lesions) with endometrial masses were analyzed. Significant differences were observed between malignant and benign lesions in terms of WBC count, ANC, MCV, MPV, PDW, CA125, NLR, PMR, LMR, and SII (P < 0.05). Multivariate analyses showed that a high WBC count, high ANC, low MCV, low MPV, low PDW, high CA125, high NLR, high PMR, high LMR, and high SII independently predicted the presence of endometrial malignancy.

Conclusion

The combination markers, MPV+PDW+NLR, had good discriminatory power for the presence of malignancy (AUC 0.797). Our results suggest that hematologic markers could be useful for the differentiation of malignant and benign endometrial lesions.

The role of systemic immune inflammation index in predicting treatment success in tuboovarian abscesses

PURPOSE

The aim of this study was to determine the predictability of the systemic immune inflammation index (SII) on the response to medical treatment in tubo-ovarian abscess (TOA).

METHODS

296 patients with TOA in a tertiary center were enrolled in the study. Patients were divided into two groups: Group1 (n  =  165) included patients in whom medical treatment was successful, and Group2 (n  =  131) included patients in whom surgery was required. Demographic, sonographic and laboratory findings were compared between groups. SII was calculated using peripheral blood parameters [SII  =  (platelets ∗ neutrophils)/lymphocytes].

RESULTS

Age, BMI, gravida, parity, smoking and menopausal status, CRP levels of patients were similar in both groups (p  >  0.05). Mass size (4.398 ± 0.306 vs 7.683 ± 0.689, p  <  0.001), white blood cell (WBC) (8685.08 ± 3981.98 vs 9994.35 ± 4468.024, p = 0.008), Hb (12.18 ± 1.65 vs 11.68 ± 1.65, p = 0.010), platelet to lymphocyte ratio (PLR) (151.26 ± 74.83 vs 230.77 ± 140.25, p < 0.001), neutrophil to lymphocyte ratio (NLR) (4.21 ± 3.27 vs 6.07 ± 6.6, p  =  0.003), monocyte to lymphocyte ratio (MLR) (0.300 ± 0.177 vs 0.346 ± 0.203, p = 0.041) and SII (1014.18 ± 781.71 vs 2094.088 ± 2117.58, p < 0.001) were statistically higher in group 2. ROC Analysis was used to determine the predictability of the variables and PLR (AUC = 0.718, p  <  0.001), NLR (AUC = 0.593, p  =  0.593), MLR (AUC = 0.576, p  =  0.024), SII (AUC  =  0.723, p  <  0.001) and size of mass (AUC = 0.670, p  <  0.001) were found to be significant. The SII, size of mass and bilateral involvement of adnexa were found to be the strongest prognostic factors for surgical intervention (OR:1.004 (1.002-1.005), OR:1.018 (1.010-1.027), OR:3.397 (1.338-8.627); p  <  0.001, p  <  0.001, p  =  0.010 resspectively).

CONCLUSION

SII, size of mass and bilaterality can be used to predict medical treatment success in patients with TOA.

Correlation between burst suppression and postoperative delirium in elderly patients: a prospective study

OBJECTIVE

To explore the correlation between intraoperative burst suppression (BS) and postoperative delirium (POD) in elderly patients, and provide more ideas for reducing POD in clinical.

METHODS

Ninety patients, aged over 60 years, who underwent lumbar internal fixation surgery in our hospital were selected. General information of patients was obtained and informed consent was signed during preoperative visits. Patients were divided into burst suppression (BS) group and non-burst suppression (NBS) group by intraoperative electroencephalogram monitoring. Intraoperative systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), and heart rate (HR) were recorded, and the variation and minimum value were obtained by calculating. Hemoglobin (HGB), C-reactive protein (CRP), system immune inflammatory index (SII) at 24 and 72 h after surgery, the incidence of postoperative adverse reactions, postoperative hospital stay, and total cost were recorded after operation. POD assessment was performed using CAM within 7 days after surgery or until discharge. SPSS25.0 was used for statistical analysis.

RESULTS

Compared with the NBS group, the number of elderly patients with high frailty level in BS group was more (P = 0.048). There is correlation between BS and POD (OR: 4.954, 95%CI 1.034-23.736, P = 0.045), and most of the POD patients in BS group behave as hyperactive type.

CONCLUSION

The occurrence of intraoperative BS is associated with POD, and elderly patients with frailty are more likely to have intraoperative BS. BS can be used as a predictor of POD.

Utilizing the Systemic Immune-Inflammation Index and Blood-Based Biomarkers in Association with Treatment Responsiveness amongst Patients with Treatment-Resistant Bipolar Depression

(1) Background: Inflammation is associated with depressive illness and treatment resistance. This study assessed a novel inflammatory index, the Systemic Immune-Inflammation Index (SII), in patients diagnosed with treatment-resistant bipolar depression (TRBDD) before and after treatment with escitalopram (ESC) and celecoxib (CBX) add-on or ESC and placebo (PBO), and compared them to healthy control (HC) subjects. (2) Methods: This is a secondary biological analysis from a double-blind randomized placebo-controlled trial of CBX augmentation in TRBDD. Our subsample with available complete blood count (CBC) data included 52 TRBDD subjects, randomized into an ESC + CBX, (n = 29), an ESC + PBO arm (n = 23), and an HC group (n = 32). SII was calculated from the CBC with differential (SII = platelets x neutrophils/lymphocytes) at baseline and end of treatment (8 weeks). Blood inflammation biomarkers, growth factors, and kynurenine metabolites were determined at both timepoints. Depressive symptom severity was the primary outcome, using the HAMD-17 rating scale score to quantitate treatment response and remission rates. (3) Results: Baseline SII did not discriminate TRBDD from HC, nor was it associated with HAMD-17 score at any timepoint, although it was significantly associated with lower baseline VEGF (p = 0.011) and higher week 8 levels of IL1-β (p = 0.03) and CRP (p = 0.048). Post-treatment HAMD-17 was not independently predicted using baseline SII unless an interaction with age was present (p = 0.003 was included), even after relevant adjustments. A similar effect was seen with baseline neutrophils. (4) Conclusions: While SII was not an independent predictor of treatment outcome, elevated baseline SII was a predictor of poor treatment response amongst older patients with TRBDD.

Systemic immune-inflammation index is associated with coronary heart disease: a cross-sectional study of NHANES 2009-2018

Background

Inflammation has been linked to the development of coronary heart disease (CHD). The systemic immune inflammation index (SII) is a useful biomarker of systemic inflammation. Our study aimed to explore the correlation between SII and CHD.

Methods

We conducted a multivariate logistic regression analysis, smoothing curve fitting, and segmented model comparison on 15,905 participants with a CHD prevalence of 3.31% and a mean age of 46.97 years.

Results

Adjusting for gender, age, and race, we found a negative association between SII and CHD [odds ratio (OR) 0.66; 95% confidence interval (CI) 0.48, 0.90]. There was an inverse trend where increasing SII was associated with decreasing odds of CHD (p for trend = 0.0017). After further adjustment, the association was strengthened, with a similar trend (p for trend = 0.0639). Smoothing curve fitting demonstrated a gender-specific association between SII and CHD.

Conclusions

Our findings suggest that higher SII values may be associated with a higher incidence of CHD, which varies by gender. SII may be a cost-effective and convenient method to detect CHD. Further studies are needed to confirm the causality of these findings in a larger prospective cohort.

Metabolic syndrome score as an indicator in a predictive nomogram for lymph node metastasis in endometrial cancer

BACKGROUND

Lymph node metastasis (LNM) is an important factor affecting endometrial cancer (EC) prognosis. Current controversy exists as to how to accurately assess the risk of lymphatic metastasis. Metabolic syndrome has been considered a risk factor for endometrial cancer, yet its effect on LNM remains elusive. We developed a nomogram integrating metabolic syndrome indicators with other crucial variables to predict lymph node metastasis in endometrial cancer.

METHODS

This study is based on patients diagnosed with EC in Peking University People's Hospital between January 2004 and December 2020. A total of 1076 patients diagnosed with EC and who underwent staging surgery were divided into training and validation cohorts according to the ratio of 2:1. Univariate and multivariate logistic regression analyses were used to determine the significant predictive factors.

RESULTS

The prediction nomogram included MSR, positive peritoneal cytology, lymph vascular space invasion, endometrioid histological type, tumor size >  = 2 cm, myometrial invasion >  = 50%, cervical stromal invasion, and tumor grade. In the training group, the area under the curve (AUC) of the nomogram and Mayo criteria were 0.85 (95% CI: 0.81-0.90) and 0.77 (95% CI: 0.77-0.83), respectively (P < 0.01). In the validation group (N = 359), the AUC was 0.87 (95% CI: 0.82-0.93) and 0.80 (95% CI: 0.74-0.87) for the nomogram and the Mayo criteria, respectively (P = 0.01). Calibration plots revealed the satisfactory performance of the nomogram. Decision curve analysis showed a positive net benefit of this nomogram, which indicated clinical value.

CONCLUSION

This model may promote risk stratification and individualized treatment, thus improving the prognosis.

Association between systemic immune-inflammation index (SII) and survival outcome in patients with primary glioblastoma

The purpose was to evaluate the prognostic value of systemic immune-inflammation index (SII) in glioblastoma patients. A total of 100 patients were retrospectively analyzed. We performed Kaplan-Meier and Cox regression analyses to determine the prognostic significance of SII. A nomogram was constructed by incorporating independent prognostic variables. The predictive accuracies of nomograms were evaluated by Harrell concordance index (c-index) and receiver operating characteristic curve analysis; the clinical benefit was evaluated by decision curve analysis. A high SII (>510.8 × 109 cells/L) (hazard ratio = 1.672, P = .034) and neutrophil count (>3.9 × 109 cells/L) (hazard ratio = 1.923, P = .009) were independently related with poor outcome in glioblastoma patients based on Cox analysis. The nomogram incorporating SII showed a good predictive accuracy (c-index = 0.866). Preoperative SII and neutrophil count are potential prognostic biomarkers for overall survival in glioblastoma patients and the nomogram model that integrated the SII may be used to facilitate a comprehensive preoperative survival evaluation.

Systemic Inflammation Markers Associated with Bone Mineral Density in perimenopausal and Postmenopausal Women

Objective

The aim of this research was to determine whether systemic inflammatory indicators, including aggregate index of systemic inflammation (AISI), neutrophils lymphocyte to platelet ratio (NLPR), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI), are related to bone mineral density (BMD) in perimenopausal and postmenopausal women.

Methods

One hundred and eighty-one perimenopausal and 390 postmenopausal women were enrolled in this cross-sectional study. Continuous variables by analysis of variance and Kruskal Wallis test for comparing the clinical characteristics. Linear regression analysis was conducted to investigate the associations between inflammatory indicators with BMD. The comparison between the subgroups was performed using the nonparametric test and the T-test.

Results

AISI, NLPR, SII, and SIRI quartile values were inversely associated with BMD in menopausal women (P = 0.021; P = 0.047; P < 0.001; P < 0.001, respectively). After adjusting for confounding factors, four inflammatory indicators remained significantly associated with BMD (all P for trend <0.001). Analysis according to menopausal status demonstrated that AISI, SII, and SIRI were significantly correlated with mean femoral neck BMD in postmenopausal women (P for trend = 0.015, 0.004, and 0.001), but not significantly associated with BMD in perimenopausal women (P for trend = 0.248, 0.054, and 0.352) after adjustment for covariates.

Conclusion

The quartile values of AISI, SII, and SIRI were inversely associated with BMD in postmenopausal women, following adjustment for individual variables, hormone profiles and glucolipid metabolism profiles. AISI, SII, and SIRI have potential to be important tools for screening and prevention of bone loss in menopausal women in future clinical practice.

Platelet-to-Lymphocyte Ratio (PLR) as the Prognostic Factor for Recurrence/Residual Disease in HSIL Patients After LEEP

Purpose

The platelet-to-lymphocyte ratio (PLR) is considered correlated with cancer prognosis including cervical cancer, in addition to high-risk papillomavirus (HR-HPV) infection, of which the predictive value in prognosis of high-grade squamous intraepithelial lesions (HSILs) remains unknown. Here, the prognostic predictive value of PLR in HSIL after loop electrosurgical excision procedure (LEEP) was evaluated.

Patients and Methods

This study included 335 nonpregnant participants with histopathologically confirmed HSIL and 3- and 5-year follow-ups from the Fujian Cervical Lesions Screening Cohorts (FCLSCs) between September 2016 and September 2018. PLR and other variables were evaluated to identify the factors related to the recurrence/residual cervical intraepithelial neoplasia (CIN)-free survival (RFS), namely, the time from LEEP at baseline to first detection of recurrence/residual CIN or end of follow-up, by logistic and Cox regression.

Results

In the Kaplan‒Meier analysis, HR-HPV infection (p=0.049/0.012), higher PLR (p=0.031/0.038), and gland invasion (p=0.047) had a higher risk for recurrence/residual CIN at the 3-/5-year follow-up. The univariate logistic and Cox regression analyses showed significant differences and a higher cumulative risk in patients with HR-HPV infection (OR=3.917, p=0.026; HR=3.996, p=0.020) and higher PLR (OR=2.295, p=0.041; HR=2.161, p=0.030) at the 5-year follow-up. The findings by multivariate Cox regression analysis were similar, indicating a poor prognosis for patients with HR-HPV infection (HR=3.901, p=0.023) and higher PLR (HR=2.082, p=0.038) at the 5-year follow-up. The calibration plot showed a better model fit for RFS at the 3-year follow-up.

Conclusion

Preoperative PLR level and HR-HPV infection could be available markers for predicting recurrence/residual disease of HSIL after LEEP. Clinically, combining PLR with HR-HPV tests may provide novel evaluation method and reference for management in post-treatment patients with cervical precancerous lesions.

A Novel Marker for Predicting Fulminant Myocarditis: Systemic Immune-Inflammation Index

In myocarditis, the search for effective and appropriate prognostic biomarkers can help clinicians identify high-risk patients in a timely manner and make better medical decisions in clinical practice. The prognostic value of systemic immune-inflammatory index (SII), an innovate biomarker of inflammation, in fulminant myocarditis in children has not been assessed. This study aims to (1) determine the effect of SII and other inflammatory markers on the prognosis of patients with myocarditis, and (2) characterize other factors affecting adverse outcomes in myocarditis. All patients aged between 1 months and 18 years who admitted to Pediatric Emergency Department between January 1, 2015 and October 1, 2021 and were diagnosed with myocarditis were retrospectively analyzed. 106 Eligible subjects were enrolled (67% male, 12.5 years (IQR 6-16). Fulminant myocarditis developed in 16 (15%) of the patients. The median SII was 1927 (1147.75-3610.25) in the fulminant myocarditis group and 351 (251.75-531.25) in the non-fulminant group (p < 0.001). In estimation of fulminant myocarditis, AUC was 0.87 for WBC [95% confidence interval (CI) 0.72-1.00, p = 0.002], 0.94 for ANC (95% CI 0.85-1.00), p = 0.000), 0.92 for SII (95% CI 0.82-1.00, p = 0.000). Spearman's correlation analysis showed a significant negative correlation between SII and LVEF (r = 0.576, p < 0.001). The highest AUC values were associated with ANC, SII, and WBC levels to predict fulminant myocarditis. SII, a readily available biomarker from routine blood parameters, allows early recognition of negative outcomes and can independently predict the prognosis of myocarditis in children.

Development of Nomogram Models Based on Peripheral Blood Score and Clinicopathological Parameters to Predict Preoperative Advanced Stage and Prognosis for Epithelial Ovarian Cancer Patients

Purpose

Nutritional and inflammatory states are crucial in cancer development. The purpose of this study is to construct a scoring system grounded on peripheral blood parameters associated with nutrition and inflammation and explore its value in stage, overall survival (OS), and progression-free survival (PFS) prediction for epithelial ovarian cancer (EOC) patients.

Patients and Methods

Four hundred and fifty-three EOC patients were retrospectively identified and their clinical data and relevant peripheral blood parameters were collected. The ratio of neutrophil to lymphocyte, lymphocyte to monocyte, fibrinogen to lymphocyte, total cholesterol to lymphocyte and albumin level were calculated and dichotomized. A scoring system named peripheral blood score (PBS) was constructed. Univariate and multivariate Logistic or Cox regression analyses were used to select independent factors; these factors were then used to develop nomogram models of advanced stage and OS, PFS, respectively. The internal validation and DCA analysis were performed to evaluate models.

Results

Lower PBS indicated a better prognosis and higher PBS indicated inferior. High PBS is associated with advanced stage, high CA125, serous histological type, poor differentiation, and accompanied ascites. The logistic regression showed age, CA125, and PBS were independent factors for the FIGO III-IV stage. The nomogram models for advanced FIGO stage based on these factors showed good efficiency. FIGO stage, residual disease, and PBS were independent factors affecting OS and PFS, the nomogram models composed of these factors had good performance. DCA curves revealed the models augmented net benefits.

Conclusion

PBS can be a noninvasive biomarker for EOC patients' prognosis. The related nomogram models could be powerful, cost-effective tools to provide information of advanced stage, OS, and PFS for EOC patients.

Prognostic Value of Systemic Immune-Inflammation Index (SII) in Patients with Glioblastoma: A Comprehensive Study Based on Meta-Analysis and Retrospective Single-Center Analysis

Inflammation is related to cancer. The systemic immune-inflammation index (SII) has been linked to the prognosis of many types of cancer. The present study aimed to determine the prognostic value of the SII in glioblastoma (GBM) patients based on meta-analysis and single-center retrospective analysis. Relevant publications published before 1 October 2022 were identified by searching PubMed, EMBASE, Cochrane Library databases, and Web of Science. Moreover, 208 GBM patients from Zhongnan Hospital were incorporated. Kaplan−Meier and Cox regression analyses determined the prognostic significance of inflammatory markers. By combining these indicators, we developed scoring systems. Nomograms were also built by incorporating independent variables. The accuracies of nomograms were evaluated by Harrell’s concordance index (c-index) and the calibration curve. According to meta-analysis, an elevated SII predicted the worst overall survival (OS) (Hazard ratio [HR] = 1.87, p < 0.001). Furthermore, a higher SII (>510.8) (HR = 1.782, p = 0.007) also predicted a poorer outcome in a retrospective cohort. The scoring systems of SII-NLR (neutrophil-to-lymphocyte ratio) showed the best predictive power for OS. The nomogram without MGMT (c-index = 0.843) exhibited a similar accuracy to that with MGMT (c-index = 0.848). A pre-treatment SII is independently associated with OS in GBM. A nomogram integrating the SII-NLR score may facilitate a comprehensive survival evaluation independent of molecular tests in GBM.

Short- and Mid-Term Survival Prediction in Patients with Acute Type A Aortic Dissection Undergoing Surgical Repair: Based on the Systemic Immune-Inflammation Index

Purpose

The postoperative survival of patients with acute type A aortic dissection (aTAAD) remains unsatisfactory. The current study developed an easy-to-use survival prediction model and calculator.

Methods

A total of 496 patients with aTAAD undergoing surgical repair were included in this study. The systemic immune-inflammation index (SII) and other clinical features were collected and subjected to logistic and Cox regression analyses. The survival prediction model was based on Cox regression analyses and exhibited as a nomogram. For convenience of use, the nomogram was further developed into calculator software.

Results

We demonstrated that a higher preoperative SII was associated with in-hospital death (OR: 4.116, p < 0.001) and a higher postoperative overall survival rate (HR: 2.467, p < 0.001) in aTAAD patients undergoing surgical repair. A survival prediction model and calculator based on SII and four other clinical features were developed. The overall C-index of the model was 0.743. The areas under the curves (AUCs) of the 1- and 3-month and 1- and 3-year survival probabilities were 0.73, 0.71, 0.71 and 0.72, respectively. The model also showed good calibration and clinical utility.

Conclusion

Preoperative SII is significantly associated with postoperative survival. Based on SII and other clinical features, we created the first easy-to-use prediction model and calculator for predicting the postoperative survival rate in aTAAD patients, which showed good prediction performance.

Preoperative Prediction Value of Pelvic Lymph Node Metastasis of Endometrial Cancer: Combining of ADC Value and Radiomics Features of the Primary Lesion and Clinical Parameters

Objective

To investigate the value of apparent diffusion coefficient (ADC) value of endometrial cancer (EC) primary lesion and magnetic resonance imaging (MRI) three-dimensional (3D) radiomics features combined with clinical parameters for preoperative prediction of pelvic lymph node metastasis (PLNM).

Methods

A total of 136 patients with EC confirmed by postoperative pathology were retrospectively reviewed and analyzed. Patients were randomly divided into training set (n = 95) and test set (n = 41) at a ratio of 7 : 3. Radiomics features based on T₂WI, DWI, and contrast-enhanced T₁WI (CE-T₁WI) sequence were extracted and screened, and then radiomics score (Rads-score) was calculated. Clinical parameters and ADC value of EC primary lesion were measured and collected, and their correlation with PLNM was analyzed. Receiver operating characteristic (ROC) curve was plotted to assess the diagnostic efficacy of the model. A nomogram for PLNM was created based on the multivariate logistic regression model.

Results

The ADC value of the EC primary lesion showed inverse correlation with PLNM, while CA125 and Rads-score were positively associated with PLNM. A predictive model was proposed based on ADC value, Rads-score, CA125, and MR-reported pelvic lymph node status (PLNS) for PLNM in EC. The area under the curve (AUC) of the model is 0.940; the sensitivity and specificity (87.1% and 90.6%) of the model were significantly higher than that of the MRI morphological signs.

Conclusion

A combination of ADC value, MRI 3D radiomics features of the EC primary lesion, and clinical parameters generated a prediction model for PLNM in EC and had a good diagnostic performance; it was a useful supplement to MR-reported PLNS based on MRI morphological signs.