Time-dependent Receiver Operating Characteristic Curve Research Articles

Identification and validation of 5-methylcytosine-associated genes in diffuse large B-cell lymphoma

5-methylcytosine modifications play a significant role in carcinogenesis; however, studies exploring 5-methylcytosine-related genes in diffuse large B-cell lymphoma patients are lacking. In this study, we aimed to understand the potential role and clinical prognostic impact of 5-methylcytosine regulators in diffuse large B-cell lymphoma and identify a prognostic biomarker based on 5-methylcytosine-associated genes. Gene expression profiles and corresponding clinical information of diffuse large B-cell lymphoma patients and normal controls were obtained from The Cancer Genome Atlas, Gene Expression Omnibus, and Genotype-Tissue Expression databases. Diffuse large B-cell lymphoma was divided into three clusters according to the 5-methylcytosine regulators, and differentially expressed genes were screened among the three clusters. Univariate Cox and Lasso-Cox regression analyses were used to screen prognostic genes and construct a prognostic risk model. Kaplan-Meier curve analysis, univariate and multivariate Cox regression analyses, and time-dependent receiver operator characteristic curve analysis were used to evaluate prognostic factors. GSVA was used to enrich potential pathways associated with 5-methylcytosine modification patterns. SsGSEA and CIBERSORT were used to assess immune cell infiltration. Six 5-methylcytosine-related genes (TUBB4A, CD3E, ZNF681, HAP1, IL22RA2, and POSTN) were used to construct a prognostic risk model, which was proved to have a good predictive effect. In addition, univariate and multivariate Cox regression risk scores were independent prognostic factors for diffuse large B-cell lymphoma. Further analysis showed that the 5-methylcytosine risk score was significantly correlated with immune cell infiltration and immune checkpoint of diffuse large B-cell lymphoma. Our study reveals for the first time a potential role for 5-methylcytosine modifications in diffuse large B-cell lymphoma, provides novel insights for future studies on diffuse large B-cell lymphoma, and offers potential prognostic biomarkers and therapeutic targets for patients with diffuse large B-cell lymphoma.

Association between high sensitivity cardiac troponin and mortality risk in the non-diabetic population: findings from the National Health and Nutrition Examination Survey

ObjectiveWe investigated the association of high-sensitivity cardiac troponin (Hs-cTn) with all-cause and cardiovascular mortality in non-diabetic individuals.MethodsThis study included 10,393 participants without known diabetes and cardiovascular disease from the US National Health and Nutrition Examination Survey (NHANES). Serum Hs-cTnI and Hs-cTnT concentrations were measured. Prediabetes was defined as fasting blood glucose between 100 and 125 mg/dL or HbA1c between 5.7 and 6.4%. Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for mortality risk. Time-dependent receiver operating characteristics (tROC) curves were utilized to measure the predictive performance of the biomarkers. Net Reclassification Improvement (NRI) were calculated to estimate the improvement in risk classification for adding Hs-cTnT or Hs-cTnI to the standard models based on Framingham risk factors.ResultsThe mean age of the participants was 48.1 ± 19.1 years, with 53.3% being female and 25.8% being prediabetic. After multivariable adjustment, compared to those with Hs-cTnI concentration less than the limit of detection, the HRs (95% CIs) of the participants with Hs-cTnI concentration higher than the 99th upper reference limit were 1.74 (1.35, 2.24) for all-cause mortality and 2.10 (1.36, 3.24) for cardiovascular mortality. The corresponding HRs (95% CIs) for Hs-cTnT were 2.07 (1.53, 2.81) and 2.92 (1.47, 5.80) for all-cause and cardiovascular mortality. There was a significant interaction between prediabetes and Hs-cTnI on the mortality risk; a positive relationship was only observed in prediabetic individuals. No interaction was observed between prediabetes and Hs-cTnT on mortality risk. The Areas Under tROC indicated both Hs-cTnT and Hs-cTnI show better predictive performance in cardiovascular mortality than in all-cause mortality. NRI (95% CI) for adding Hs-cTnT to the standard model were 0.25 (0.21, 0.27) and 0.33 (0.26, 0.39) for all-cause and cardiovascular mortality. The corresponding NRI (95% CI) for Hs-cTnI were 0.04 (0, 0.06) and 0.07 (0.01, 0.13).ConclusionsElevated blood levels of Hs-cTnI and Hs-cTnT are associated with increased mortality. Measurement of Hs-cTnT in non-diabetic subjects, particularly those with prediabetes, may help identify individuals at an increased risk of cardiovascular disease and provide early and more intensive risk factor modification.

Screening of potential biomarkers for polycystic ovary syndrome and identification of expression and immune characteristics.

Polycystic ovary syndrome (PCOS) seriously affects the fertility and health of women of childbearing age. We look forward to finding potential biomarkers for PCOS that can aid clinical diagnosis. We acquired PCOS and normal granulosa cell (GC) expression profiles from the Gene Expression Omnibus (GEO) database. After data preprocessing, differentially expressed genes (DEGs) were screened by limma package, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and Gene Set Enrichment Analysis (GSEA) were performed. Recursive feature elimination (RFE) algorithm and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis were used to acquire feature genes as potential biomarkers. Time-dependent receiver operator characteristic curve (ROC curve) and Confusion matrix were used to verify the classification performance of biomarkers. Then, the expression characteristics of biomarkers in PCOS and normal cells were analyzed, and the insulin resistance (IR) score of samples was computed by ssGSEA. Immune characterization of biomarkers was evaluated using MCP counter and single sample gene set enrichment analysis (ssGSEA). Finally, the correlation between biomarkers and the scores of each pathway was assessed. We acquired 93 DEGs, and the enrichment results indicated that most of DEGs in PCOS group were significantly enriched in immune-related biological pathways. Further screening results indicated that JDP2 and HMOX1 were potential biomarkers. The area under ROC curve (AUC) value and Confusion matrix of the two biomarkers were ideal when separated and combined. In the combination, the training set AUC = 0.929 and the test set AUC = 0.917 indicated good diagnostic performance of the two biomarkers. Both biomarkers were highly expressed in the PCOS group, and both biomarkers, which should be suppressed in the preovulation phase, were elevated in PCOS tissues. The IR score of PCOS group was higher, and the expression of JDP2 and HMOX1 showed a significant positive correlation with IR score. Most immune cell scores and immune infiltration results were significantly higher in PCOS. Comprehensive analysis indicated that the two biomarkers had strong correlation with immune-related pathways. We acquired two potential biomarkers, JDP2 and HMOX1. We found that they were highly expressed in the PCOS and had a strong positive correlation with immune-related pathways.

The Nomogram predicting the overall survival of patients with pancreatic cancer treated with radiotherapy: a study based on the SEER database and a Chinese cohort.

Patients with pancreatic cancer (PC) have a poor prognosis. Radiotherapy (RT) is a standard palliative treatment in clinical practice, and there is no effective clinical prediction model to predict the prognosis of PC patients receiving radiotherapy. This study aimed to analyze PC's clinical characteristics, find the factors affecting PC patients' prognosis, and construct a visual Nomogram to predict overall survival (OS). SEER*Stat software was used to collect clinical data from the Surveillance, Epidemiology, and End Results (SEER) database of 3570 patients treated with RT. At the same time, the relevant clinical data of 115 patients were collected from the Affiliated Cancer Hospital of Zhengzhou University. The SEER database data were randomly divided into the training and internal validation cohorts in a 7:3 ratio, with all patients at The Affiliated Cancer Hospital of Zhengzhou University as the external validation cohort. The lasso regression was used to screen the relevant variables. All non-zero variables were included in the multivariate analysis. Multivariate Cox proportional risk regression analysis was used to determine the independent prognostic factors. The Kaplan-Meier(K-M) method was used to plot the survival curves for different treatments (surgery, RT, chemotherapy, and combination therapy) and calculate the median OS. The Nomogram was constructed to predict the survival rates at 1, 3, and 5 years, and the time-dependent receiver operating characteristic curves (ROC) were plotted with the calculated curves. Calculate the area under the curve (AUC), the Bootstrap method was used to plot the calibration curve, and the clinical efficacy of the prediction model was evaluated using decision curve analysis (DCA). The median OS was 25.0, 18.0, 11.0, and 4.0 months in the surgery combined with chemoradiotherapy (SCRT), surgery combined with radiotherapy, chemoradiotherapy (CRT), and RT alone cohorts, respectively. Multivariate Cox regression analysis showed that age, N stage, M stage, chemotherapy, surgery, lymph node surgery, and Grade were independent prognostic factors for patients. Nomogram models were constructed to predict patients' OS. 1-, 3-, and 5-year Time-dependent ROC curves were plotted, and AUC values were calculated. The results suggested that the AUCs were 0.77, 0.79, and 0.79 for the training cohort, 0.79, 0.82, and 0.81 for the internal validation cohort, and 0.73, 0.93, and 0.88 for the external validation cohort. The calibration curves Show that the model prediction probability is in high agreement with the actual observation probability, and the DCA curve shows a high net return. SCRT significantly improves the OS of PC patients. We developed and validated a Nomogram to predict the OS of PC patients receiving RT.

Construction and validation of a nomogram for predicting the prognosis of patients with lymph node-positive invasive micropapillary carcinoma of the breast: based on SEER database and external validation cohort

BackgroundInvasive micropapillary carcinoma (IMPC) of the breast is a rare subtype of breast cancer with high incidence of aggressive clinical behavior, lymph node metastasis (LNM) and poor prognosis. In the present study, using the Surveillance, Epidemiology, and End Results (SEER) database, we analyzed the clinicopathological characteristics and prognostic factors of IMPC with LNM, and constructed a prognostic nomogram.MethodsWe retrospectively analyzed data for 487 breast IMPC patients with LNM in the SEER database from January 2010 to December 2015, and randomly divided these patients into a training cohort (70%) and an internal validation cohort (30%) for the construction and internal validation of the nomogram, respectively. In addition, 248 patients diagnosed with IMPC and LNM at the Fourth Hospital of Hebei Medical University from January 2010 to December 2019 were collected as an external validation cohort. Lasso regression, along with Cox regression, was used to screen risk factors. Further more, the discrimination, calibration, and clinical utility of the nomogram were assessed based on the consistency index (C-index), time-dependent receiver operating characteristic (ROC), calibration curve, and decision curve analysis (DCA).ResultsIn summary, we identified six variables including molecular subtype of breast cancer, first malignant primary indicator, tumor grade, AJCC stage, radiotherapy and chemotherapy were independent prognostic factors in predicting the prognosis of IMPC patients with LNM (P < 0.05). Based on these factors, a nomogram was constructed for predicting 3- and 5-year overall survival (OS) of patients. The nomogram achieved a C-index of 0.789 (95%CI: 0.759-0.819) in the training cohort, 0.775 (95%CI: 0.731-0.819) in the internal validation cohort, and 0.788 (95%CI: 0.756-0.820) in the external validation cohort. According to the calculated patient risk score, the patients were divided into a high-risk group and a low-risk group, which showed a significant difference in the survival prognosis of the two groups (P<0.0001). The time-dependent ROC curves, calibration curves and DCA curves proved the superiority of the nomogram.ConclusionsWe have successfully constructed a nomogram that could predict 3- and 5-year OS of IMPC patients with LNM and may assist clinicians in decision-making and personalized treatment planning.

MRI-based radiomic signatures for pretreatment prognostication in cervical cancer.

Accurate pretherapeutic prognostication is important for tailoring treatment in cervical cancer (CC). To investigate whether pretreatment MRI-based radiomic signatures predict disease-specific survival (DSS) in CC. Retrospective. CC patients (n = 133) allocated into training(T) (nT = 89)/validation(V) (nV = 44) cohorts. T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) at 1.5T or 3.0T. Radiomic features from segmented tumors were extracted from T2WI and DWI (high b-value DWI and apparent diffusion coefficient (ADC) maps). Radiomic signatures for prediction of DSS from T2WI (T2rad ) and T2WI with DWI (T2 + DWIrad ) were constructed by least absolute shrinkage and selection operator (LASSO) Cox regression. Area under time-dependent receiver operating characteristics curves (AUC) were used to evaluate and compare the prognostic performance of the radiomic signatures, MRI-derived maximum tumor size ≤/> 4 cm (MAXsize ), and 2018 International Federation of Gynecology and Obstetrics (FIGO) stage (I-II/III-IV). Survival was analyzed using Cox model estimating hazard ratios (HR) and Kaplan-Meier method with log-rank tests. The radiomic signatures T2rad and T2 + DWIrad yielded AUCT /AUCV of 0.80/0.62 and 0.81/0.75, respectively, for predicting 5-year DSS. Both signatures yielded better or equal prognostic performance to that of MAXsize (AUCT /AUCV : 0.69/0.65) and FIGO (AUCT /AUCV : 0.77/0.64) and were significant predictors of DSS after adjusting for FIGO (HRT /HRV for T2rad : 4.0/2.5 and T2 + DWIrad : 4.8/2.1). Adding T2rad and T2 + DWIrad to FIGO significantly improved DSS prediction compared to FIGO alone in cohort(T) (AUCT 0.86 and 0.88 vs. 0.77), and FIGO with T2 + DWIrad tended to the same in cohort(V) (AUCV 0.75 vs. 0.64, p = 0.07). High radiomic score for T2 + DWIrad was significantly associated with reduced DSS in both cohorts. Radiomic signatures from T2WI and T2WI with DWI may provide added value for pretreatment risk assessment and for guiding tailored treatment strategies in CC.

The neutrophil–lymphocyte ratio as a risk factor for all-cause and cardiovascular mortality among individuals with diabetes: evidence from the NHANES 2003–2016

BackgroundEvidence regarding the neutrophil–lymphocyte ratio (NLR) and mortality risk in diabetes patients is scarce. This study investigated the relationship of the NLR with all-cause and cardiovascular mortality risk in diabetes patients.MethodsDiabetes patients (n = 3251) from seven National Health and Nutrition Examination Survey (NHANES) cycles (2003–2016) were included in this study. The cause of death and mortality status of the participants were obtained from National Death Index records. Restricted cubic spline (RCS) was used to visualize the association of the NLR with mortality risk. The maximally selected rank statistics method (MSRSM) was used to determine the optimal NLR cutoff value corresponding to the most significant association with survival outcomes. Weighted multivariable Cox regression models and subgroup analyses were adopted to assess the association of the NLR with all-cause and cardiovascular mortality. Time-dependent receiver operating characteristic curve (ROC) analysis was conducted to evaluate the accuracy of the NLR in predicting survival outcomes.ResultsDuring a median follow-up of 91 months (interquartile range, 55–131 months), 896 (27.5%) of the 3251 diabetes patients died, including 261 (8.0%) with cardiovascular deaths and 635 (19.5%) with noncardiovascular deaths. The RCS regression analysis showed a positive linear association between the NLR and all-cause and cardiovascular mortality (both p > 0.05 for nonlinearity) in diabetes patients. Participants were divided into higher (> 3.48) and lower (≤ 3.48) NLR groups according to the MSRSM. In the multivariable-adjusted model, compared with participants with a lower NLR, those with a higher NLR had a significantly higher risk of both all-cause (HR 2.03, 95% confidence interval (CI) 1.64–2.51, p < 0.0001) and cardiovascular mortality (HR 2.76, 95% CI 1.84–4.14, p < 0.0001). The association was consistent in subgroup analyses based on age, sex, smoking status, drinking status, and hypertension, with no significant interaction between the aforementioned characteristics and the NLR (p interaction > 0.05). The time-dependent ROC curve showed that the areas under the curve of the 1-, 3-, 5-, and 10-year survival rates were 0.72, 0.66, 0.64, and 0.64 for all-cause mortality and 0.69, 0.71, 0.69 and 0.65, respectively, for cardiovascular mortality.ConclusionAn elevated NLR is independently associated with increased all-cause and cardiovascular mortality in diabetes patients.

Dynamic Risk Prediction of Treatment Discontinuation Using Patient-Reported Outcomes Data in the Phase III NSABP B-35 Trial.

The dynamic prediction provided by partly conditional models offers valuable insights into the treatment discontinuation risks using PRO data collected over time from clinical trial participants. This tool may benefit healthcare professionals in identifying patients at high risk of premature treatment discontinuation and support interventions to prevent potential discontinuation.

The value of the preoperative Naples prognostic score in predicting prognosis in gallbladder cancer surgery patients

PurposeThe Naples prognostic score (NPS) is a comprehensive prognostic model that includes inflammatory and nutrition-related indicators and is increasingly used as a prognostic score for various malignant tumors. Given its predictive effect on prognosis in patients with gallbladder cancer, it is currently unclear. This study aimed to investigate the role of preoperative NPS in predicting prognosis in gallbladder cancer surgery patients.Patients and methodsA retrospective analysis was performed for 135 patients who underwent radical surgery for gallbladder cancer without preoperative treatment between March 2011 and January 2020. NPS was calculated by measuring the preoperative total cholesterol value, serum albumin value, neutrophil–lymphocyte ratio (NLR), and lymphocyte-monocyte ratio (LMR). They were then divided into 3 groups (groups 0, 1, and 2) based on NPS scores. Survival analysis was performed using the Kaplan–Meier method and log-rank test. Univariate and multivariate Cox proportional hazards models were used to identify independent prognostic factors. Plot time-dependent receiver operating characteristic (ROC) curves to compare the prognostic value of scoring systems. Finally, a nomogram model was developed with independent prognostic factors.ResultsMultivariate analysis showed that NPS was an independent risk factor affecting OS (HR = 3.417, p < 0.05). The time-dependent ROC curve results showed that NPS had a better predictive value on survival prognosis than other indicators. The nomogram constructed according to independent factors such as NPS has a good predictive ability for OS.ConclusionAs a simple and reliable tool, the NPS has important predictive value in the survival prognosis of gallbladder cancer patients. The nomogram model constructed by NPS will help determine prognosis and make individualized treatment decisions.

Utility of the revised FIGO2023 staging with molecular classification in endometrial cancer

ObjectivesMolecular classification was introduced in endometrial cancer staging following the transition of the International Federation of Gynecology and Obstetrics (FIGO) 2008 to FIGO2023. In the early stages, p53 abnormal endometrial carcinoma with myometrial involvement was upstaged to stage IICm, in addition to the downstaging of POLE mutation endometrial cancer to stage IAm. This study compared the goodness of fit and discriminatory ability of FIGO2008, FIGO2023 without molecular classification (FIGO2023), and FIGO2023 with molecular classification (FIGO2023m); no study has been externally validated to date. MethodsThe study included 265 patients who underwent initial surgery at the National Cancer Center Hospital between 1997 and 2019 and were pathologically diagnosed with endometrial cancer. The three classification systems were compared using Harrell's concordance index (C-index), Akaike information criterion (AIC), and time-dependent receiver operating characteristic (ROC) curves. A higher C-index score and a lower AIC value indicated a more accurate model. ResultsAmong the three classification systems, FIGO2023m had the lowest AIC value (FIGO2023m: 455.925; FIGO2023: 459.162; FIGO2008: 457.901), highest C-index (FIGO2023m: 0.768; FIGO2023: 0.743; FIGO2008: 0.740), and superior time-dependent ROC curves within 1 year after surgical resection. In the stage IIIC, patients with p53 abnormalities had considerably lower 5-year overall survival than those with a p53 wild-type pattern (24.3% vs. 83.7%, p = 0.0005). ConclusionsFIGO2023m had the best discriminatory ability compared with FIGO2008 and FIGO2023. Even in advanced stages, p53 status was a poor prognostic factor. When feasible, molecular subtypes can be added to the staging criteria to allow better prognostic prediction in all stages.

Tumor-associated endothelial cell prognostic risk model and tumor immune environment modulation in liver cancer based on single-cell and bulk RNA sequencing: Experimental verification

BackgroundTo build a prognostic and immunotherapeutic response prediction model for liver cancer based on marker genes of tumor-associated endothelial cell (TEC). MethodSingle cell sequencing data from Gene Expression Omnibus (GEO) liver cancer patients were utilized to identify TEC subpopulations. Models were built from transcriptomic and clinical data of TCGA liver cancer patients. The GSE76427 and ICGC databases were used as independent validation sets. Time-dependent receiver operating characteristic (ROC) curves and Kaplan-Meier curves were used to verify the ability of the model to predict survival. XCELL, TIMER, QUANTISEQ, CIBERSORT, CIBERSORT-ABS, and ssGSEA were applied to evaluate tumor immune cell infiltration. The TIDE score was used to predict the effect of immunotherapy. Immune blockade checkpoint gene, tumor mutational load and GSVA enrichment analyses were further explored. The expression levels of candidate genes were measured and validated by real-time PCR between liver cancer tissues and adjacent nontumor liver tissues. ResultsEighty-seven genes were identified as marker genes for TECs. IGFBP3, RHOC, S100A16, FSCN1, and CLEC3B were included in the constructed prognostic model. Time-dependent ROC curve values were higher than 0.700 in both the model and validation groups. The low risk group exhibited high immune cell infiltration and function than the higher risk group. The TIDE score indicated that the low-risk group benefited more from immunotherapy than the high-risk group. The risk score and multiple immune blockade checkpoint genes and immune-related pathways were strongly correlated. ConclusionNovel signatures of TEC marker genes showed a powerful ability to predict prognosis and immunotherapy response in patients with liver cancer.

Comparison of the effectiveness of chemotherapy combined with immunotherapy and chemotherapy alone in advanced biliary tract cancer and construction of the nomogram for survival prediction based on the inflammatory index and controlling nutritional status score.

To analyze the effectiveness of combining immune checkpoint inhibitors (ICIs) with first-line therapy in patients with advanced biliary tract cancer (BTC) and explore the biomarkers affecting the prognosis of immunotherapy, to construct a nomogram for the prediction of survival. A retrospective study was conducted to include a total of 209 patients with advanced BTC treated in the first line from 2018 to 2022, divided into a combination therapy group (n = 129) and a chemotherapy-only group (n = 80) according to whether ICIs were applied in combination. Univariate and multifactorial COX regression analyses were performed on variables that may affect prognosis to identify independent influences on patient prognosis, and this was used to create nomograms, which were then prospectively validated and calibrated. The median progression-free survival (mPFS) and median overall survival (mOS) of patients in the combination therapy group were higher than those in the chemotherapy alone group [hazard ratio (HR) = 1.152, 95% confidence interval (CI): 0.7848-1.692, p = 0.0004, and HR = 1.067, 95% CI: 0.7474-1.524, p = 0.0016]. The objective response rate (ORR) of patients in the combination therapy and chemotherapy alone groups was 39.5% (51/129) vs. 27.5% (22/80), and the disease control rate (DCR) between the two groups was 89.9% (116/129) vs. 83.8% (67/80). Univariate analysis revealed the gender, presence of long-term tobacco and alcohol, degree of histological differentiation, serum albumin level, presence of liver metastases, presence of multi-visceral metastases, response, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), glycoprotein antigen 19-9 (CA19-9), systemic inflammatory index (SII), and controlling nutritional status (CONUT) scores were statistically significant with patient prognosis (all P values < 0.05). Multi-factor COX regression analysis was continued for the above variables, and the results showed that NLR, MLR, PLR, SII, and CONUT scores were independent influences on patients' OS (all p values < 0.05). A nomogram (C-index 0.77, 95% CI: 0.71-0.84) was created based on these independent influences and later validated using a validation cohort (C-index 0.75, 95% CI: 0.68-0.81). The time-dependent receiver operator characteristic curve (ROC) showed that the area under curve (AUC) of the training cohort patients at 12, 18, and 24months was 0.72 (95% CI: 0.63-0.81), 0.75 (95% CI: 0.67-0.85), and 0.77 (95% CI: 0.66-0.87) and the AUC of the validation cohort was 0.69 (95% CI: 0.58-0.79), 0.74 (95% CI: 0.65-0.87), and 0.71 (95% CI: 0.64-0.89), respectively. Finally, calibration was performed using calibration curves, and the results showed that nomograms based on inflammatory metrics and CONUT scores could be used to assess survival (12, 18, and 24months) in patients with advanced BTC treated with ICIs in the first line. Patients with advanced BTC benefit more from first-line treatment with standard chemotherapy in combination with ICIs than with chemotherapy alone. In addition, nomograms based on inflammatory metrics and CONUT scores can be used to predict survival at 12, 18, and 24months in patients with advanced BTC treated with ICIs.

Naples Prognostic Score is an Independent Prognostic Factor in Patients Undergoing Hepatectomy for Hepatocellular Carcinoma.

Nutritional and inflammatory status has been reported to be associated with the prognosis of hepatocellular carcinoma (HCC), but many studies did not include all biomarkers simultaneously. The present study aimed to determine the impact of Naples prognostic score (NPS) on the long-term survival in patients undergoing hepatectomy for HCC. Patients with HCC after curative resection were eligible. Then, all patients were stratified into three groups according to the NPS. Clinical features and survival outcomes were compared among the three groups. Independent prognostic factors were determined by COX analysis. The time dependent receiver operating characteristic (ROC) curves were used to compare prognostic performance with other immunonutrition scoring systems. A total of 476 patients were enrolled eventually. Baseline characteristics showed that patients with higher NPS had a higher proportion of poor liver function and advanced tumor features. Accordingly, Kaplan-Meier survival curves showed that patients with higher NPS had a lower rate of overall survival (OS) and recurrence-free survival (RFS). Multivariable COX analysis demonstrated that NPS was an independent risk factor of OS (NPS group 2 vs 1: HR=1.958, 95% CI: 1.038-3.369, p = 0.038; NPS group 3 vs 1: HR=2.608, 95% CI: 1.358-5.008, p=0.004, respectively) and RFS (NPS group 2 vs 1: HR=2.014, 95% CI: 1.299-2-3.124, p=0.002; NPS group 3 vs 1: HR=2.002, 95% CI: 1.262-3.175, p=0.003, respectively). The time-dependent ROC curve showed that NPS was superior to other models in prognostic performance and discriminatory power for long-term survival (median AUC 0.675, 95% CI: 0.586-0.712, P < 0.05). The NPS is a simple tool strongly associated with long-term survival in patients undergoing curative hepatectomy for HCC.

Prognostic Factors and a Predictive Nomogram of Cancer-Specific Survival of Epithelial Ovarian Cancer Patients with Pelvic Exenteration Treatment.

The aim of this study was to explore prognostic factors, develop and internally validate a prognostic nomogram model, and predict the cancer-specific survival (CCS) of epithelial ovarian cancer (EOC) patients with pelvic exenteration (PE) treatment. A total of 454 EOC patients from the Surveillance, Epidemiology, and End Results (SEER) database were collected according to the inclusion criteria and randomly divided into the training (n = 317) and validation (n = 137) cohorts. Prognostic factors of EOC patients with PE treatment were explored by univariate and multivariate stepwise Cox regression analyses. A predictive nomogram was constructed based on selected risk factors. The predictive power of the constructed nomogram was assessed by the time-dependent receiver operating characteristic (ROC) curve. Kaplan-Meier (KM) curve stratified by patients' nomoscore was also plotted to assess the risk stratification of the established nomogram. In internal validation, the C index, calibration curve, and decision curve analysis (DCA) were employed to assess the discrimination, calibration, and clinical utility of the models, respectively. In the training cohort, age, histological type, Federation of Gynecology and Obstetrics (FIGO) stage, number of examined lymph nodes, and number of positive lymph nodes were found to be independent prognostic factors of postoperative CSS. A practical nomogram model of EOC patients with PE treatment was constructed based on these selected risk factors. Time-dependent ROC curves and KM curves showed the superior predictive capability and excellent clinical stratification of the nomogram in both training and validation cohorts. In the internal validation, the C index, calibration plots, and DCA in the training and validation cohorts confirmed that the nomogram presents a high level of prediction accuracy and clinical applicability. Our nomogram exhibited satisfactory survival prediction and prognostic discrimination. It is a user-friendly tool with high clinical pragmatism for estimating prognosis and guiding the long-term management of EOC patients with PE treatment.

Combination of the Ratio Between Negative and Harvested Lymph Nodes and Metastatic Lymph Node Count as a Prognostic Indicator in Stage III Colon Cancer: A Retrospective Cohort Study.

This study aimed to investigate the relationship between the ratio of negative lymph nodes (NLN) number to the number of metastatic lymph nodes (MLN) and the harvested lymph nodes (HLN) number ratio survival rate and compare its prognostic value. This retrospective cohort study included 207 stage III colon cancer patients between 2010 and 2018 at a single center. NLN/MLN and NLN/HLN cut-off values were determined with the receiver operating characteristic (ROC) curve according to 5-year survival. The patients were divided into high-risk and low-risk groups according to the cut-off value. These 2 groups were evaluated according to the clinicopathological data of the patients. The time-dependent ROC curve showed the optimal cut-off values of NLN as 3.86 and .79, respectively. These values show 83 patients in the high-risk group and 124 in the low-risk group. There was no difference between the groups in tumor localization and T stage. According to Kaplan-Meier survival analysis, mean survival was 35.88 months in the high-risk group and 50.18 months in the low-risk group. The risk of death in the high-risk group was 305% compared to the low-risk group (Hazard Ratio: 3.05, 95% 1.91 - 4.88) (P < .001). NLNs are among the critical prognostic factors in colon cancer. Although NLNs have a positive correlation with the survival rate of the patients, there is no statistical difference in tumor T stage and localization.

Development and validation of a prognostic model for esophageal cancer patients with liver metastasis: a cohort study based on surveillance, epidemiology, and end results database.

Our objective is to examine the independent prognostic risk factors for patients with Esophageal Cancer with Liver Metastasis (ECLM) and to develop a predictive model. In this study, clinical data were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Cox regression analysis was employed to identify independent prognostic factors and construct nomograms based on the results of multivariate regression. The predictive performance of the nomograms was assessed using several methods, including the consistency index (C-index), calibration curve, time-dependent receiver-operating characteristic curve (ROC), and decision curve analysis (DCA). Additionally, Kaplan-Meier survival curves were generated to demonstrate the variation in overall survival between groups. A total of 1163 ECLM patients were included in the study. Multivariate Cox analysis revealed that age, tumor differentiation grade, bone metastasis, therapy, and income were independently associated with overall survival (OS) in the training set. Subsequently, a prognostic nomogram was constructed based on these independent predictors. The C-index values were 0.739 and 0.715 in the training and validation sets, respectively. The area under the curve (AUC) values at 0.5, 1, and 2years were all higher than 0.700. Calibration curves indicated that the nomogram accurately predicted OS. Decision curve analysis (DCA) showed moderately positive net benefits. Kaplan-Meier survival curves demonstrated significant differences in survival between high- and low-risk groups, which were divided based on the nomogram risk score. The nomogram we developed for ECLM patients has demonstrated good predictive capability, allowing clinicians to accurately evaluate patient prognosis and identify those at high risk, thereby facilitating the development of personalized treatment plans.

Predictive and prognostic markers from endoscopic ultrasound with biopsies during definitive chemoradiation therapy in esophageal squamous cell carcinoma

IntroductionEndoscopic ultrasound (EUS) may play a role in evaluating treatment response after definitive chemoradiation therapy (dCRT) for esophageal squamous cell carcinoma (ESCC). This study explored the prognostic markers of EUS with biopsies and developed two nomograms for survival prediction.MethodsA total of 821 patients newly diagnosed with ESCC between January 2015 and December 2019 were reviewed. We investigated the prognostic value of the changes in tumor imaging characteristics and histopathological markers by an interim response evaluation, including presence of stenosis, ulceration, tumor length, tumor thickness, lumen involvement, and tumor remission. Independent prognostic factors of progression-free survival (PFS) and overall survival (OS) were determined using Cox regression analysis and further selected to build two nomogram models for survival prediction. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to respectively assess its discriminatory capacity, predictive accuracy, and clinical usefulness.ResultsA total of 155 patients were enrolled in this study and divided into the training (109 cases) and testing (46 cases) cohorts. Tumor length, residual tumor thickness, reduction in tumor thickness, lumen involvement, and excellent remission (ER) of spatial luminal involvement in ESCC (ER/SLI) differed significantly between responders and non-responders. For patients undergoing dCRT, tumor stage (P = 0.001, 0.002), tumor length (P = 0.013, 0.008), > 0.36 reduction in tumor thickness (P = 0.004, 0.004) and ER/SLI (P = 0.041, 0.031) were independent prognostic markers for both PFS and OS. Time-dependent ROC curves, calibration curves, and DCA indicated that the predicted survival rates of our two established nomogram models were highly accurate.ConclusionOur nomogram showed high accuracy in predicting PFS and OS for ESCC after dCRT. External validation and complementation of other biomarkers are needed in further studies.

The novel dynamic nomogram and risk classification system constructed for predicting post-surgical overall survival and mortality risk in primary chondrosarcoma: a population study based on SEER database.

Surgery is the predominant method to improve the prognosis of primary chondrosarcoma patients. We aimed to construct the first reliable nomogram to predict the post-surgical overall survival (OS) of primary chondrosarcoma patients. We downloaded all primary chondrosarcoma patients treated with surgery between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database, and randomized them into training set (60%) and validation set (40%). Cox proportional regression analysis was applied to the training set to identify independent prognostic variables, and then constructed a nomogram for predicting 3-, 5-, and 8-year OS. The Harrell's concordance index (C-index), receiver operating characteristic curve (ROC), the area under curve (AUC), calibration curve and decision curve analysis (DCA) was used to assess the predictive efficacy and clinical applicability of the nomogram. The nomogram was also compared with The American Joint Committee on Cancer (AJCC) staging system. A total of 1005 post-surgical primary chondrosarcoma patients were included in this study. We finally identified five independent prognostic variables to construct the nomogram, being age, grade, tumor size, disease stage and histological type. The C-index results showed that the prediction performance of the nomogram was significantly better than the AJCC staging system. In the training set, (C-index: 0.805, 95% CI 0.879-0.730 vs 0.686, 95% CI 0.606-0.766); in the validation set, (C-index: 0.811, 95% CI 0.895-0.727 vs 0.697, 95% CI 0.647-0.799). Additionally, the AUC values generated by the ROC were all greater than 0.8, which also indicated the excellent predictive performance of the nomogram. The calibration curves showed that the predicted survival rate was highly similar to the actual. Time-dependent ROC and DCA showed that the nomogram has better predictive performance and net clinical benefits than the AJCC staging system. Finally, a risk stratification system based on nomogram was constructed. We successfully constructed and validated the first nomogram that could reliably predict 3-, 5-, and 8-year post-surgical OS in primary chondrosarcoma patients. Furthermore, the web-based dynamic nomogram could be more conveniently applied to clinic, providing assistance to surgeons and patients.

Inference for covariate-adjusted time-dependent prognosticaccuracy measures.

Evaluating the prognostic performance of candidate markers for future disease onset or progression is one of the major goals in medical research. A marker's prognostic performance refers to how well it separates patients at the high or low risk of a future disease state. Often the discriminative performance of a marker is affected by the patient characteristics (covariates). Time-dependent receiver operating characteristic (ROC) curves that ignore the informativeness of the covariates will lead to biased estimates of the accuracy parameters. We propose a time-dependent ROC curve that accounts for the informativeness of the covariates in the case of censored data. We propose inverse probability weighted (IPW) estimators for estimating the proposed accuracy parameters. We investigate the performance of the IPW estimators through simulation studies and real-life data analysis.

Web-based nomogram and risk stratification system constructed for predicting the overall survival of older adults with primary kidney cancer after surgical resection.

Kidney cancer (KC) is one of the most common malignant tumors in adults which particularly affects the survival of elderly patients. We aimed to construct a nomogram to predict overall survival (OS) in elderly KC patients after surgery. Information on all primary KC patients aged more than 65years and treated with surgery between 2010 and 2015 was downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression analysis was used to identify the independent prognostic factors. Consistency index (C-index), receiver operating characteristic curve (ROC), the area under curve (AUC), and calibration curve were used to assess the accuracy and validity of the nomogram. Comparison of the clinical benefits of nomogram and the TNM staging system is done by decision curve analysis (DCA) and time-dependent ROC. A total of 15,989 elderly KC patients undergoing surgery were included. All patients were randomly divided into training set (N = 11,193, 70%) and validation set (N = 4796, 30%). The nomogram produced C-indexes of 0.771 (95% CI 0.751-0.791) and 0.792 (95% CI 0.763-0.821) in the training and validation sets, respectively, indicating that the nomogram has excellent predictive accuracy. The ROC, AUC, and calibration curves also showed the same excellent results. In addition, DCA and time-dependent ROC showed that the nomogram outperformed the TNM staging system with better net clinical benefits and predictive efficacy. Independent influencing factors for postoperative OS in elderly KC patients were sex, age, histological type, tumor size, grade, surgery, marriage, radiotherapy, and T-, N-, and M-stage. The web-based nomogram and risk stratification system could assist surgeons and patients in clinical decision-making.