Prognostic Nomogram Research Articles

Developing hypoxia and lactate metabolism-related molecular subtypes and prognostic signature for clear cell renal cell carcinoma through integrating machine learning.

The microenvironment of clear cell renal cell carcinoma (ccRCC) is characterized by hypoxia and increased lactate production. However, the impact of hypoxia and lactate metabolism on ccRCC remains incompletely understood. In this study, a new molecular subtype is developed based on hypoxia-related genes (HRGs) and lactate metabolism-related genes (LMRGs), aiming to create a tool that can predict the survival rate, immune microenvironment status, and responsiveness to treatment of ccRCC patients. We obtained RNA-seq data and clinical information of patients with ccRCC from TCGA and GEO. HRGs and LMRGs are sourced from the Molecular Signatures Database. Integrating 10 machine learning algorithms and 101 frameworks, we constructed a prognostic model related to hypoxia and lactate metabolism. Its accuracy and reliability are evaluated through constructing prognostic nomograms, drawing ROC curves, and validating with clinical datasets. Additionally, risk subgroups are evaluated based on functional enrichment, tumor mutational burden (TMB), immune cell infiltration degree, and immune checkpoint expression level. Finally, we evaluate the responsiveness of risk subgroups to immunotherapy and determine personalized drugs for specific risk subgroups. 85 valuable prognostic genes were screened out. Functional enrichment analysis shows that the group with high-risk hypoxia and lactate metabolism-related genes scores (HLMRGS) is mainly involved in the activation of immune-related activities, while the low risk HLMRGS group is more active in metabolic and tumor-related pathways. At the same time, differences in the cellular functional states in the tumor microenvironment between the high risk HLMRGS group and the low risk HLMRGS group were observed. Finally, potential drugs for specific risk subgroups were determined. We have developed a novel prognostic signature that integrates hypoxia and lactate metabolism. It is expected to become an effective tool for prognosis prediction, immunotherapy and personalized medicine of ccRCC.

A prognostic nomogram for patients with III-IV nasopharyngeal carcinoma based on dynamic changes in the inflammatory and nutrition index.

The purpose of the study was to explore the value of dynamic changes in inflammatory and nutritional index after comprehensive treatment in patients with stage III-IVA nasopharyngeal carcinoma (NPC). A prognostic model was also established and validated for progression-free survival (PFS) of patients. We retrospectively selected 279 NPC patients with stage III-IVA. Their general clinical data and hematological index were collected and then calculated the changes during treatment. X-tile software was used to determine the optimal cut-off value. COX regression, Lasso method, and Boruta method were used to variable selection and model establishment. Using the bootstrap internal validation method, concordance index (C-index), calibration plot, and Kaplan-Meier curves were used to evaluate the model. To test the prognostic value of the model, we have also evaluated the performance of the nomogram against a conventional tumor metastasis staging system (TNM). Multivariable COX regression analysis demonstrated that clinical staging, delta lymphocyte, delta monocyte, delta albumin, delta platelet-to-lymphocyte ratio and delta systemic immune inflammation index were related to the PFS of NPC patients. The C-index of the model was 0.712, and the calibration curve indicated that the model had good consistency. The C-index of the TNM staging system was 0.597, which was considerably lower compared to our model (P = 0.015). We demonstrated the predictive value of dynamic changes in inflammatory and nutritional indices for the prognosis of NPC by successfully establishing and validating a prognostic model for predicting 1- and 3-year PFS after comprehensive treatment in patients with stage III-IVA NPC.

Identification of a coagulation-related gene signature for predicting prognosis in recurrent glioma.

Recurrent gliomas rapidly progress and have high mortality and poor prognosis in the central nervous system. Therefore, further investigation of prognostic and therapeutic markers is needed. The mRNA expression, clinical data, and coagulation-related genes (CRGs) associated with recurrent glioma were obtained and calculated from the Chinese Glioma Genome Atlas and Kyoto Encyclopedia of Genes and Genomes databases. The significant CRGs were calculated by Weighted gene co-expression network analysis and PPI network. A prediction model was constructed using the least absolute shrinkage and selection operator regression analysis. Recurrent gliomas were stratified into high and low-risk groups based on the median risk score (RS). The Kaplan-Meier curve was used to analyze the difference in overall survival (OS) between these groups, while the receiver operating characteristic (ROC) curve was used to evaluate the accuracy of the gene model at 1-, 3-, and 5-years. Clinical factors, including age, gender, MGMT methylation status, radiotherapy, chemotherapy, and RS, were included in the univariate and multivariate regression analysis. A prognostic nomogram and calibration curve were established based on these factors. Overall, seven CRGs associated with the prognosis were identified, including BTK, ITGB1, GNAI3, CFH, LYN, CFI, and F3. OS and survival rates were lower in the high-risk group compared with the low-risk group. The ROC curve demonstrated the area under the curve values >0.65 at 1-, 3-, and 5-years, confirming the reliability of the prognostic model. The univariate regression analysis indicated that tumor grade (grades 2, 3, and 4), histopathology (GBM or not), chemotherapy, IDH mutation, and 1p19q co-deletion status were independent prognostic indicators. Univariate and multivariate regression analyses indicated that RS was an independent prognostic factor for patients with recurrent glioma. Immune analysis revealed low infiltration of resting dendritic cells and high expression of programmed death receptor 1 in the high-risk group. This study comprehensively investigated the correlation between CRGs and recurrent glioma prognosis, offering crucial insights for further research into glioma recurrence mechanisms and treatment strategies.

Prognosis analysis and nomogram for predicting lateral lymph node metastasis in Medullary Thyroid Microcarcinoma.

Currently, the incidence rate of Medullary Thyroid Microcarcinoma (micro-MTC) has an increasing trend, but the incidence of LNM and prognosis were still ambiguous. We analyzed the status of neck LNM of micro-MTC patients and created a prognostic nomogram to predict the probability of lateral lymph node metastasis (LLNM) for clinical practice. This is a retrospective study included patients with micro-MTC from SEER database for the period from 2004 to 2017 and patients from our medical center for the period from 2011 to 2019. A nomogram was constructed and the accuracy and clinical practicability were separately tested by Harrell's C-indexes, calibration plots, Receiver operating characteristic curve (ROC) and decision curve analyses (DCA). A total of 413 patients with micro-MTC from SEER database and 64 patients with micro-MTC from our department enrolled in the study. There were 16.0% and 9.4% cases in SEER database and 39.1% and 25.0% cases in our department appeared LNM and LLNM, respectively. Besides, a nomogram was constructed to assess the incidence of LLNM with good C-index, which was 0.850 in training cohort and 0.856 in validation cohort. The results of the area under the curve (AUC) were 0.830 in training cohort, 0.801 in validation cohort and 0.832 in external testing cohort, respectively. A relatively high rate of LLNM than expected was found, which should be emphasized. The prediction model could facilitate clinicians to assess the probability of LLNM and make a personalized treatment strategy.

Prognostic value of serum complement cleavage factor Bb in idiopathic membranous nephropathy and establishment of nomogram model

Complement activation is involved in idiopathic membranous nephropathy (IMN). We aimed to investigate the relationship of serum complement cleavage factor Bb with IMN progression, and to establish a model for early prediction of kidney outcomes. We measured serum factor Bb in a retrospective cohort of 449 IMN patients at the time of kidney biopsy. Cox regression analysis showed that higher levels of serum factor Bb were independently associated with IMN progression event defined as end-stage renal disease or ≥ 40% decline in estimated glomerular filtration rate. Patients in the middle and highest tertiles of serum factor Bb had respectively 2.1-, and 2.6-fold higher risk for disease progression compared with those in the lowest tertile. We developed an optimized prognostic nomogram model incorporating age, log serum anti-PLA2R antibody, log serum Bb, proteinuria and tubular atrophy/interstitial fibrosis. The model demonstrated good predictive ability with a concordance index of 0.77 (bootstrap-corrected of 0.76) for predicting 3-, and 5-year kidney survival. Calibration curves and decision curve analysis confirmed the model’s good calibration and clinical utility. Our findings suggest that serum factor Bb may serve as an essential prognostic indicator of IMN. The novel nomogram model may offer important guidance on the management of this patient population.

Prognostic nomograms for locally advanced cervical cancer based on the SEER database: Integrating Cox regression and competing risk analysis.

Locally advanced cervical carcinoma (LACC) remains a significant global health challenge owing to its high recurrence rates and poor outcomes, despite current treatments. This study aimed to develop a comprehensive risk stratification model for LACC by integrating Cox regression and competing risk analyses. This was done to improve clinical decision making. We analyzed data from 3428 patients with LACC registered in the Surveillance, Epidemiology, and End Results program and diagnosed them between 2010 and 2015. Cox regression and competing risk analyses were used to identify the prognostic factors. We constructed and validated nomograms for overall survival (OS) and disease-specific survival (DSS). Multivariate Cox regression identified key prognostic factors for OS, including advanced International Federation of Gynecology and Obstetrics stage, age, marital status, ethnicity, and tumor size. Notably, International Federation of Gynecology and Obstetrics stages IIIA, IIIB, and IVA had hazard ratios of 2.227, 2.451, and 4.852, respectively, significantly increasing the mortality risk compared to stage IB2. Ethnic disparities were evident, with African Americans facing a 39.8% higher risk than Caucasians did. Competing risk analyses confirmed the significance of these factors in DSS, particularly tumor size. Our nomogram demonstrated high predictive accuracy, with area under the curve values ranging from 0.706 to 0.784 for DSS and 0.717 to 0.781 for OS. Calibration plots and decision curve analyses further validated the clinical utility of this nomogram. We present effective nomograms for LACC risk stratification that incorporate multiple prognostic factors. These models provide a refined approach for individualized patient management and have the potential to significantly enhance therapeutic strategies for LACC.

Liquid-Liquid Phase Separation in the Prognosis of Lung Adenocarcinoma: An Integrated Analysis.

Lung adenocarcinoma (LUAD) is a highly lethal malignancy. Liquid-Liquid Phase Separation (LLPS) plays a crucial role in targeted therapies for lung cancer and in the progression of lung squamous cell carcinoma. However, the role of LLPS in the progression and prognosis of LUAD remains insufficiently explored. This study employed a multi-step approach to identify LLPS prognosis-related genes in LUAD. First, differential analysis, univariate Cox regression analysis, Random Survival For-est (RSF) method, and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regres-sion analysis were utilized to identify five LLPS prognosis-related genes. Subsequently, LASSO Cox regression was performed to establish a prognostic score termed the LLPS-related progno-sis score (LPRS). Comprehensive analyses were then conducted based on the LPRS, including survival analysis, clinical feature analysis, functional enrichment analysis, and tumor microenvi-ronment assessment. The LPRS was integrated with additional clinicopathological factors to de-velop a prognostic nomogram for LUAD patients. Immunohistochemical validation was per-formed on clinical tissue samples to further validate the findings. Finally, the relationship be-tween KRT6A, one of the identified genes, and epidermal growth factor receptor (EGFR) muta-tions was investigated. The LPRS was established using five LLPS-related genes: IGF2BP1, KRT6A, LDHA, PKP2, and PLK1. Higher LPRS was closely associated with poor survival outcomes, gender, progression-free survival (PFS), and advanced TNM stage. Furthermore, LPRS emerged as an independent prognostic factor for LUAD. A nomogram integrating LPRS, TNM stage, and age demonstrated remarkable predictive accuracy for prognosis among patients with LUAD. LLPS likely influences LUAD prognosis through the activity of IGF2BP1, KRT6A, LDHA, PKP2, and PLK1. KRT6A exhibits significant upregulation in LUAD, particularly in patients with EGFR mutations. This study introduces a novel LPRS model that demonstrates high accuracy in pre-dicting the clinical prognosis of LUAD. Moreover, the findings suggest that KRT6A may play a critical role in the LLPS-mediated malignant progression of LUAD.

Development and validation of a tumor size-stratified prognostic nomogram for patients with gastric signet ring cell carcinoma.

Gastric signet ring cell carcinoma (GSRC) is a rare malignancy without a commonly acknowledged prognostic assessment and treatment system. This study aimed to determine the optimal cut-off value of tumor size (TS), and construct a prognostic nomogram in combination with other independent prognostic factors (PFs) to predict 3year and 5year overall survival (OS) in GSRC patients. From the Surveillance, Epidemiology, and End Results (SEER) database, this study collected 4744 patients diagnosed with GSRC. These patients were randomized into a training cohort (n = 2320,) and a validation cohort (n = 1142). A restricted cubic spline (RCS) was used to determine the cut-off value for TS, and univariate and multivariate Cox regression analyses were performed in the training cohort to identified significant predictors. A prognostic nomogram was constructed to predict OS at 3 and 5years. Concordance index (C index), receiver operating characteristics curve (ROC curve), area under curve (AUC), and calibration curve were used to test the predictive accuracy of the model. A non-linear relationship was observed between TS and the risk of OS in GSRC, with TS thresholds at 4.4cm and 9.6cm. Survival was significantly lower in GSRC patients with TS > 4.4cm. Age, marriage, chemotherapy, surgery, TS, SEER stage, regional lymph node status, and total number were independent predictors of OS. The C index in the training cohort was 0.748, and the AUC values for both 3- and 5-year OS were higher than 0.80. Similar results were observed in the validation cohort. In addition, the calibration curves showed good agreement between the predicted 3year and 5year OS and the actual OS. TS is a key prognostic factor for patients with GSRC, and patients with a TS of 4.4-9.6cm and > 9.6cm may have a poorer prognosis than those with a TS of < 4.4cm. The TS-stratified nomogram we constructed and validated has favorable accuracy and calibration precision, and may be helpful in predicting the survival rate of patients.

Establishment and Validation of a Prognostic Nomogram for Predicting Postoperative Overall Survival in Advanced Stage III-IV Colorectal Cancer Patients.

Most colorectal cancer (CRC) patients are at an advanced stage when they are first diagnosed. Risk factors for predicting overall survival (OS) in advanced stage CRC patients are crucial, and constructing a prognostic nomogram model is a scientific method for survival analysis. A total of 2956 advanced stage CRC patients were randomised into training and validation groups at a 7:3 ratio. Univariate and multivariate Cox proportional hazards regression analyses were used to screen risk factors for OS and subsequently construct a prognostic nomogram model for predicting 1-, 3-, 5-, 8- and 10-year OS of advanced stage CRC patients. The performance of the model was demonstrated by the area under the curve (AUC) values, calibration curves and decision curve analysis (DCA). Kaplan-Meier curves were used to plot the survival probabilities for different strata of each risk factor. There was no statistically significant difference (p > 0.05) in the 32 clinical variables between patients in the training and validation groups. Univariate and multivariate Cox proportional hazards regression analyses demonstrated that age, location, TNM, chemotherapy, liver metastasis, lung metastasis, MSH6, CEA, CA199, CA125 and CA724 were risk factors for OS. We estimated the AUC values for the nomogram model to predict 1-, 3-, 5-, 8- and 10-year OS, which in the training group were 0.826 (95% CI: 0.807-0.845), 0.836 (0.819-0.853), 0.839 (0.820-0.859), 0.835 (0.809-0.862) and 0.825 (0.779-0.870) respectively; in the validation group, the corresponding AUC values were 0.819 (0.786-0.852), 0.831 (0.804-0.858), 0.830 (0.799-0.861), 0.815 (0.774-0.857) and 0.802 (0.723-0.882) respectively. Finally, the 1-, 3-, 5-, 8- and 10-year OS rates for advanced stage CRC patients were 73.4 (71.8-75.0), 49.5 (47.8-51.4), 43.3 (41.5-45.2), 40.1 (38.1-41.9) and 38.6 (36.6-40.8) respectively. We constructed and validated an original nomogram for predicting the postoperative OS of advanced stage CRC patients, which can help facilitates physicians to accurately assess the individual survival of postoperative patients and identify high-risk patients.

Two-Year Outcomes and Biomarker Analysis of Locally Advanced Gastric and Gastroesophageal Junction Adenocarcinoma After Neoadjuvant Chemotherapy and Immunotherapy from the Phase II WuhanUHGI001 Trial.

This study reports the 2-year outcomes and biomarker analysis results of patients with locally advanced gastric and gastroesophageal junction (G/GEJ) adenocarcinoma who received neoadjuvant chemotherapy and immunotherapy in a phase II WuhanUHGI001 trial. Eligible patients with cT3/4aN+M0 locally advanced G/GEJ adenocarcinoma were screened, enrolled, and treated with 3 cycles of neoadjuvant tislelizumab and SOX followed by D2 gastrectomy and another 5 cycles of postoperative adjuvant SOX. The primary endpoint was major pathological response. Of the 49 included patients, 24 (49.0%) achieved major pathological response and 13 (26.5%) achieved pathological complete response. During a median follow-up of 26.8 months, the 2-year progression-free survival (PFS) and overall survival (OS) rates were 69.4% and 81.2%, respectively. Grade 3-4 adverse events occurred in six patients (12.2%) during the neoadjuvant period, eight patients (17.0%) during the postoperative period, and seven patients (15.2%) during the adjuvant period. Biomarker analysis revealed that the pathological complete response showed no association with 2-year PFS and OS. Major pathological response showed a potentially strong association with improved 2-year PFS and OS rates. In addition, preoperative circulating tumor cells combined with pathological responses are helpful in prognosis assessment. In addition, our results showed that T downstaging, lymphocyte-to-monocyte ratio, and CD3+ T cells were independent factors that affect PFS. The signet ring cell component (SRCC), T downstaging, and neutrophil-to-lymphocyte ratio were independent factors affecting OS. Prognostic nomograms of PFS and OS constructed based on the multivariate Cox regression results demonstrated suitable calibration and discrimination ability. Neoadjuvant tislelizumab plus SOX exhibits promising efficacy and acceptable toxicity in patients with locally advanced G/GEJ adenocarcinoma. In addition, our study established a prognostic risk signature and nomograms based on clinicopathological characteristics, which can accurately predict patient outcomes and aid in personalized treatment planning.

Identification and Experimental Validation of Prognostic miRNA Signature and Ferroptosis-Related Key Genes in Cervical Squamous Cell Carcinoma.

This study aimed to investigate the prognostic value of miRNAs and ferroptosis-related genes in cervical squamous cell carcinoma. We mined data from public databases for differentially expressed miRNAs, ferroptosis-related genes, and clinical parameters and constructed a prognostic risk model. The predictive performance of the model was evaluated using survival and receiver operating characteristic curve analyses. We combined the clinicopathological features to construct a nomogram and evaluated its efficacy using calibration and clinical decision curves. The correlation between miRNA characteristics, risk score, and the tumor microenvironment was also studied. Next, consensus and key genes were screened, and their biological functions were analyzed using KEGG, GO, GSEA, and drug sensitivity analysis. Finally, the expression of miRNAs and key genes was detected using qRT-PCR and western blotting to verify the prediction results. Seven miRNA signatures (miR-100-3p, miR-301a-5p, miR-331-3p, miR-425-5p, miR-502-3p, miR-505-5p, and miR-629-3p) were generated, and prognostic risk and nomogram models were successfully constructed. These models exhibited good accuracy. miRNA signatures correlated with the tumor microenvironment. Twelve consensus genes and three key genes (SLC2A1, ANO6, and TXNIP) were screened and their biofunctional diversity was identified using various analytical methods. qRT-PCR and western blotting were used to verify the expression of miR-301a-5p, miR-505-5p, SLC2A1, and TXNIP in cervical squamous carcinoma. The results were consistent with those of bioinformatics analyses. Seven miRNAs may serve as prognostic biomarkers of cervical squamous cell carcinoma. SLC2A1, ANO6, and TXNIP are associated with cervical squamous cell carcinoma and may serve as ferroptosis-related markers of the disease.

Survival Benefits of Postoperative Chemotherapy in Patients With Colorectal Mucinous Adenocarcinoma: An Analysis Utilizing Propensity Score Matching From the Surveillance, Epidemiology, and End Results Database.

This study aimed to investigate the characteristics of patients with colorectal mucinous adenocarcinoma (MAC) who benefit from postoperative chemotherapy (POCT) and to develop effective postoperative survival nomograms for predicting overall survival (OS) in colorectal MAC patients. Data of colorectal MAC patients who underwent surgery from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2020 were collected. Patients were grouped based on POCT, and intergroup analysis was performed using 1:1 propensity score matching (PSM). Kaplan-Meier (K-M) curves were used to compare the prognosis between the 2 groups. Cox analysis was employed to identify factors associated with OS in patients with colorectal MAC who underwent POCT. The variance inflation factor (VIF) and bilateral stepwise regression were used to determine factors included in the model. Additionally, a nomogram was constructed to predict postoperative survival outcomes for patients. The discriminative ability of the nomograms was evaluated using the C-index and calibration curve analysis, the decision curve analysis (DCA) assessed the clinical utility of the nomogram, and the receiver operating characteristic (ROC) curve evaluated the nomograms' performance. This study encompassed 6829 patients with colorectal MAC, among whom 2258 received POCT, and 4571 did not. Whether pre or post PSM, patients in the POCT group consistently exhibited a superior median OS compared to those in the postoperative non-chemotherapy group (P < .0001). For colorectal MAC patients undergoing POCT, OS was correlated with factors such as patient age, carcinoembryonic antigen levels, tumor deposits, perineural invasion (PNI), lymph node examination count, T staging, and Grade staging. Notably, a significant chemotherapy advantage was observed in patients without perineural invasion, those with lymph node examination counts exceeding 12, and patients with moderately differentiated tumors. The overall colorectal MAC patient postoperative OS predictive nomogram demonstrated a C-index of .74, with a calibration curve near the diagonal and a DCA curve indicating positive net benefits. In comparison to TNM staging, the ROC curves of the nomogram at 1 year, 3 years, and 5 years demonstrated superior predictive capabilities (AUC: .80 vs .71, .78 vs .71, .77 vs .70). This study revealed the characteristics of colorectal MAC patients who benefit from POCT and established effective prognostic nomograms, which can aid clinicians in designing personalized treatment plans for individual patients and promote precision medicine.

Prognostic nomogram model based on quantitative metrics of subregions surrounding residual cavity in glioblastoma patients

BackgroundThe hyperintensity area surrounding the residual cavity on postoperative fluid-attenuated inversion recovery (FLAIR) image is a potential site for glioblastoma (GBM) recurrence. This study aimed to develop a nomogram using quantitative metrics from subregions of this area, prior to chemoradiotherapy (CRT), to predict early GBM recurrence.MethodsAdult patients with GBM diagnosed between October 2018 and October 2022 were retrospectively analyzed. Quantitative metrics, including the mean, maximum, minimum, median values, and standard deviation of FLAIR signal intensity (SI) (measured using 3D-Slicer software), were extracted from the following subregions surrounding the residual cavity on post-contrast T1-weighted (CE-T1WI)-FLAIR fusion images: the enhancing region (ER), non-enhancing region (NER), and combined ER + NER. Independent prognostic factors were identified using Cox regression and least absolute shrinkage and selection operator (LASSO) analyses and were incorporated into the prediction nomogram model. The model’s performance was evaluated using the C-index, calibration curves, and decision curves.ResultsA total of 129 adult GBM patients were enrolled and randomly assigned to a training (n = 90) and a validation cohorts (n = 39) in a 7:3 ratio. Sixty-nine patients experienced postoperative recurrence. Cox regression analysis identified subventricular zone involvement, the median FLAIR intensity in the ER, the rFLAIR (relative FLAIR intensity compared to the contralateral normal region) of ER + NER, and corpus callosum involvement as independent prognostic factors. For predicting recurrence within 1 year after surgery, the nomogram model had a C-index of 0.733 in the training cohort and 0.746 in the validation cohort. Based on the nomogram score, post-operative GBM patients could be stratified into high- and low-risk for recurrence.ConclusionsNomogram models which based on quantitative metrics from FLAIR hyperintensity subregions may serve as potential markers for assessing GBM recurrence risk. This approach could enhance clinical decision-making and provide an alternative method for recurrence estimation in GBM patients.

Prognostic nomogram models for elderly patients with differentiated thyroid carcinoma: A population-based study.

This study aimed to develop and validate a prognostic model for elderly patients with differentiated thyroid carcinoma (DTC) based on various demographic and clinical parameters in order to accurately predict patient outcomes. Patients who were diagnosed with DTC and were over 55 years old between 2010 and 2019 were identified from the Surveillance, Epidemiology, and End Results database. The patients were then randomly divided into a training set and a validation set in a 7:3 ratio, and patients from our center were included as an external validation group. Univariate and multivariate Cox proportional hazards regression analyses were performed to identify independent prognostic factors, which were then utilized to develop nomograms for predicting the prognosis. The discriminative ability of the nomograms was evaluated using the concordance index, and the calibration was assessed using calibration plots. The clinical usefulness and benefits of the predictive models were determined through decision curve analysis. The findings of the stepwise Cox regression analysis revealed that several variables, including age, marital status, sex, multifocality, T stage, N stage, and M stage, were significantly associated with overall survival in elderly patients with DTC. Additionally, age, tumor size, multifocality, T stage, N stage, and M stage were identified as the primary determinants of cancer specific survival in elderly patients with DTC. Using these predictors, nomograms were constructed to estimate the probability of overall survival and cancer specific survival. The nomograms demonstrated a high level of predictive accuracy, as evidenced by the concordance index, and the calibration plots indicated that the predicted outcomes were consistent with the actual outcomes. Furthermore, the decision curve analysis demonstrated that the nomograms provided substantial clinical net benefit, indicating their utility in clinical practice.

A Novel Prognostic Nomogram Based on TIGIT and NKG2A Can Predict Relapse-Free Survival of Hepatocellular Carcinoma After Hepatectomy.

Hepatocellular carcinoma (HCC) is a major global health concern, and emerging evidence suggests that TIGIT and NKG2A are potential immune checkpoints with implications for HCC progression. This study aimed to evaluate the prognostic significance of TIGIT and NKG2A expression in HCC patients who underwent radical liver resection. We conducted a retrospective analysis of 144 HCC patients who underwent radical liver resection. TIGIT and NKG2A expression levels were assessed using the immunoreactive score. Cox proportional hazards models were utilized to analyze the association between TIGIT/NKG2A expression and clinical characteristics, relapse-free survival (RFS), and overall survival (OS). Prognostic models for OS and RFS was developed and validated using concordance index and calibration curves. Additionally, the random forest algorithm was employed to identify independent risk factors for OS and RFS and their correlation with predicted survival. TIGIT and NKG2A expression were identified as independent risk factors for RFS, while TIGIT expression alone significantly impacted OS. The prognostic models showed good discriminative ability, with concordance indices exceeding 0.7 for predicting 1-, 3-, and 5-year OS or RFS. Calibration curves confirmed the reliability of the nomograms for OS and RFS prediction. The areas under the ROC curve consistently exceeded 0.7 for predicting OS and RFS. Elevated levels of TIGIT and NKG2A expression were associated with diminished RFS, highlighting their importance as prognostic factors. Our study establishes the prognostic significance of TIGIT and NKG2A expression in predicting OS and RFS following radical liver resection for HCC patients. The developed prognostic models incorporating TIGIT and NKG2A expression hold promise for improving risk stratification and clinical management of HCC patients.

Nomograms combining clinical factors and apparent diffusion coefficient to predict downstaging and progression-free survival after concurrent chemoradiotherapy in patients with cervical cancer.

Concurrent chemoradiotherapy (CCRT) is used as the primary treatment modality for currently limited cervical cancer and lacks non-invasive quantitative parameters to assess clinical outcomes of treatment for cervical cancer treatment. To develop nomograms based on clinical prognostic factors and apparent diffusion coefficient (ADC) in predicting downstaging and progression-free survival (PFS) after CCRT for cervical cancer. X-tile was used to calculate the optimal threshold for ΔADCmean(%) for prognostic stratification. Kaplan-Meier curves were used to calculate the difference in PFS between high- and low-risk groups. Univariate and multivariate Cox proportional risk regression models were used to identify clinical and radiological risk factors for prognosis and construct a prognostic nomogram model. ΔADCmean(%) was significantly correlated with tumor downstaging; the area under the receiver operating characteristic curve (AUC) was 0.868. X-tile showed that the optimal threshold for ΔADCmean(%) to diagnose prognosis was 40.8. Kaplan-Meier curves showed that the low-risk population in the training group had significantly longer PFS within 3 years (P < 0.001). Multivariate Cox regression showed that ΔADC (%) is independent risk factor for PFS. The C-index of ΔADC(%) predicting 3-year PFS in the training set is 0.761 and the C-index of the nomogram model is 0.862. ΔADCmean(%) is a non-invasive biomarker for predicting tumor downstaging in cervical cancer after CCRT. The nomograms based on ΔADCmean(%) predict PFS of patients with cervical cancer with moderate accuracy.

Prognostic Nomogram for Predicting Survival in Asian Patients With Small-Cell Lung Cancer: A Comprehensive Population-Based Study and External Verification.

The incidence of small cell lung cancer (SCLC) among Asian patients is on the rise. Nevertheless, there remains a deficiency in precise prognostic models tailored to the specific needs of this patient population. It is imperative to develop a novel nomogram aimed at forecasting the prognosis of Asian SCLC patients. The SEER database supplied data on 661 Asian SCLC patients, who were then divided into training and internal validation sets through a random selection process. In addition, we identified 212 patients from a Chinese medical institution for the purpose of creating an external validation cohort. To forecast survival, we employed both univariate and multivariate analyses. The performance of our nomogram was assessed through calibration plots, the concordance index (C-index), and decision curve analysis (DCA). Five independent prognostic factors were determined and integrated into the nomogram. C-index values for the training and internal validation cohorts were 0.774 (95% confidence interval [CI] = 0.751-0.797) and 0.731 (95%CI = 0.690-0.772), respectively. In the external validation cohort, the C-index is 0.712 (95% CI = 0.655-0.7692). Calibration curves demonstrated highly accurate predictions. When compared to the AJCC staging system, our model exhibited improved net benefits in DCA. Furthermore, the risk stratification system effectively differentiated patients with varying survival risks. We have created a novel nomogram for predicting the survival of Asian patients with SCLC. This nomogram has been subjected to external validation and has shown its superiority over the conventional TNM staging system. It offers a more precise and reliable means of forecasting the prognosis of Asian SCLC patients.

A practical nomogram and risk stratification system for predicting in-hospital mortality of patients with non-acute myocardial infarction related cardiogenic shock

Abstract Background The morbidity and mortality of Cardiogenic Shock(CS) remain high despite the advance in management these years1.As acute myocardial infarction (AMI) is considered to be the most common reason for cardiogenic shock,Non-AMI related CS is excluded from the majority of CS studies and remains largely understudied2. Considering its inherent complexity and hemodynamically multiplicity, it is needed to focusing on these patients to identify specific risk factors, which may be conducive to the promotion of critically CS patients with early decision and timely intervention. Purpose In this study,we sought to investigate the variables which are predictive of in hospital mortality in non AMI related CS patients, developed and validated a practical nomogram for risk quantification. Methods 1298 patients and 548 patients with cardiogenic shock from the MIMIC-IV (version 1.0) and MIMIC-III(version1.4) databaes were included in the study after excluding acute myocardial infarction. We used in-hospital death and 30 day survival as the end points. Lasson and logistic regression analysis were used to identify statistically significant predictors which were finally involved in the nomogram. The predictive performance of the nomogram was evaluated by Harrell’s concordance index (C-index) and calibration after comparing with commonly used ICU scores for in-hospital death.Lastly, we plotted Kaplan-Meier curves of three subgroup classified by the identified cutoff values of the nomogram to further assess the discriminative ability of the prognosis. Results The in-hospital mortality of patients with non-AMI related CS is about 36.6%.After the analysis of regression, age, heart rate, wbc, albumin, lactic acid, GCS score, urine volume, using vasopressor were identified as the most critical factors for the in-hospital death. Based on the these results, a nomogram for predicting in-hospital death was established.The calibration plot also shows good consistency between nomogram prediction and actual in-hospital mortality.In addition, compared with other scores, the AUC value of nomogram was 0.806 (95% CI = 0.799-0.831) in the training queue, 0.814 (95% CI = 0.771-0.852) in the internal validation queue and 0.730 (95% CI = 0.690-0.767) in the external validation set, which was significantly higher than other commonly used ICU scoring systems(SAPSII, APSIII and SOFA).Also, three risk subgroups were created using two boundary points (303 and 339) of the nomogram score and the survival curve shows that there were significant differences in the 30 day survival of each subgroup. Conclusion A prognostic nomogram and risk stratification system of non-AMI related CS were established and verified, which can provide a practical tool for individualized clinical management of patients.Workflow of the study.Nomogram for non-AMI related CS patients

Identification and validation of a prognostic model based on three TLS-Related genes in oral squamous cell carcinoma

BackgroundThe tertiary lymphoid structures (TLSs) have an immunomodulatory function and have a positive impact on the survival outcomes of patients with oral squamous cell carcinoma (OSCC). However, there is a lack of standard approaches for quantifying TLSs and prognostic models using TLS-related genes (TLSRGs). These limitations limit the widespread use of TLSs in clinical practice.MethodsA convolutional neural network was used to automatically detect and quantify TLSs in HE-stained whole slide images. By employing bioinformatics and diverse statistical methods, this research created a prognostic model using TCGA cohorts and explored the connection between this model and immune infiltration. The expression levels of three TLSRGs in clinical specimens were detected by immunohistochemistry. To facilitate the assessment of individual prognostic outcomes, we further constructed a nomogram based on the risk score and other clinical factors.ResultsTLSs were found to be an independent predictor of both overall survival (OS) and disease-free survival in OSCC patients. A larger proportion of the TLS area represented a better prognosis. After analysis, we identified 69 differentially expressed TLSRGs and selected three pivotal TLSRGs to construct the risk score model. This model emerged as a standalone predictor for OS and exhibited close associations with CD4 + T cells, CD8 + T cells, and macrophages. Immunohistochemistry revealed high expression levels of CCR7 and CXCR5 in TLS + OSCC samples, while CD86 was highly expressed in TLS- OSCC samples. The nomogram demonstrates excellent predictive ability for overall survival in OSCC patients.ConclusionsThis is the first prognostic nomogram based on TLSRGs, that can effectively predict survival outcomes and contribute to individual treatment strategies for OSCC patients.

Development and validation of a clinical and laboratory-based nomogram to predict mortality in patients with severe fever with thrombocytopenia syndrome

BackgroundSevere fever with thrombocytopenia syndrome (SFTS) is an emerging global infectious disease with a high mortality rate. Clinicians lack a convenient tool for early identification of critically ill SFTS patients. The aim of this study was to construct a simple and accurate nomogarm to predict the prognosis of SFTS patients.MethodsWe retrospectively analyzed the clinical data of 372 SFTS patients collected between May 2015 and June 2023, which were divided 7:3 into a training set and an internal validation set. We used LASSO regression to select predictor variables and multivariable logistic regression to identify independent predictor variables. Prognostic nomograms for SFTS were constructed based on these factors and analysed for concordance index, calibration curves and area under the curve (AUC) to determine the predictive accuracy and consistency of the model.ResultsIn the training set, LASSO and multivariate logistic regression analyses showed that age, SFTSV RNA, maximum body temperature, pancreatitis, gastrointestinal bleeding, pulmonary fungal infection (PFI), BUN, and PT were independent risk factors for death in SFTS patients. There was a strong correlation between neurological symptoms and mortality (P < 0.001, OR = 108.92). Excluding neurological symptoms, nomograms constructed based on the other eight variables had AUCs of 0.937 and 0.943 for the training and validation sets, respectively. Furthermore, we found that age, gastrointestinal bleeding, PFI, bacteraemia, SFTSV RNA, platelets, and PT were the independent risk factors for neurological symptoms, with SFTSV RNA having the highest diagnostic value (AUC = 0.785).ConclusionsThe nomogram constructed on the basis of eight common clinical variables can easily and accurately predict the prognosis of SFTS patients. Moreover, the diagnostic value of neurological symptoms far exceeded that of other predictors, and SFTSV RNA was the strongest independent risk factor for neurological symptoms, but these need to be further verified by external data.