Time-dependent Receiver Operating Characteristic Research Articles

A multi-parametric prognostic model based on clinicopathologic features: vessels encapsulating tumor clusters and hepatic plates predict overall survival in hepatocellular carcinoma patients

BackgroundVessels encapsulating tumor clusters (VETC) is a newly described vascular pattern that is distinct from microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). Despite its importance, the current pathological diagnosis report does not include information on VETC and hepatic plates (HP). We aimed to evaluate the prognostic value of integrating VETC and HP (VETC-HP model) in the assessment of HCC.MethodsA total of 1255 HCC patients who underwent radical surgery were classified into training (879 patients) and validation (376 patients) cohorts. Additionally, 37 patients treated with lenvatinib were studied, included 31 patients in high-risk group and 6 patients in low-risk group. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to establish a prognostic model for the training set. Harrell’s concordance index (C-index), time-dependent receiver operating characteristics curve (tdROC), and decision curve analysis were utilized to evaluate our model's performance by comparing it to traditional tumor node metastasis (TNM) staging for individualized prognosis.ResultsA prognostic model, VETC-HP model, based on risk scores for overall survival (OS) was established. The VETC-HP model demonstrated robust performance, with area under the curve (AUC) values of 0.832 and 0.780 for predicting 3- and 5-year OS in the training cohort, and 0.805 and 0.750 in the validation cohort, respectively. The model showed superior prediction accuracy and discrimination power compared to TNM staging, with C-index values of 0.753 and 0.672 for OS and disease-free survival (DFS) in the training cohort, and 0.728 and 0.615 in the validation cohort, respectively, compared to 0.626 and 0.573 for TNM staging in the training cohort, and 0.629 and 0.511 in the validation cohort. Thus, VETC-HP model had higher C-index than TNM stage system(p < 0.01).Furthermore, in the high-risk group, lenvatinib alone appeared to offer less clinical benefit but better disease-free survival time.ConclusionsThe VETC-HP model enhances DFS and OS prediction in HCC compared to traditional TNM staging systems. This model enables personalized temporal survival estimation, potentially improving clinical decision-making in surveillance management and treatment strategies.

A Predictive Model for Graft Failure in Femtosecond Laser-Assisted Penetrating Keratoplasty Among Chinese Patients: A 2-Year Study.

Graft failure is a major challenge in femtosecond laser-assisted penetrating keratoplasty (Fs-PKP). This study focuses on the development and validation of a clinical predictive model aimed at identifying the risk of graft failure in individuals undergoing Fs-PKP in China, offering a tailored approach to improve surgical outcomes. This retrospective cohort study at Nanjing First Hospital involved 238 patients and followed the TRIPOD statement. The cohort was divided into a training set (n = 166) and a validation set (n = 72) in a 7:3 ratio. It analyzed 23 predictor variables related to recipient, donor, and surgical factors, defining graft failure as "visually significant and irreversible corneal stromal edema, haze, or scarring." A comprehensive nomogram was created using univariate and multivariate Cox regression analyses and assessed by concordance index (C-index), time-dependent receiver operating characteristics (ROC) curve, calibration plots, and decision curve analysis (DCA). Five critical risk factors were identified: recipients' history of systemic autoimmune disorders, ocular trauma, prior penetrating keratoplasty (PKP) history, donors' diabetes history, and the endothelial cell density of the donor cornea. The nomogram showed a C-index of 0.72 (95% CI 0.65-0.79) in the training group and 0.66 (95% CI 0.55-0.76) in the validation group, indicating robust predictive accuracy. Time-dependent ROC curves, calibration plots, and DCA consistently validated the model's reliability, predictive power, and clinical utility across both training and validation cohorts. Our study developed and validated a model incorporating five key factors, enhancing preoperative prediction and management for Chinese patients with Fs-PKP graft failure.

Extracellular volume-based scoring system for tracking tumor progression in pancreatic cancer patients receiving intraoperative radiotherapy

ObjectivesTo investigate the value of extracellular volume (ECV) derived from portal-venous phase (PVP) in predicting prognosis in locally advanced pancreatic cancer (LAPC) patients receiving intraoperative radiotherapy (IORT) with initial stable disease (SD) and to construct a risk-scoring system based on ECV and clinical-radiological features.Materials and methodsOne hundred and three patients with LAPC who received IORT demonstrating SD were enrolled and underwent multiphasic contrast-enhanced CT (CECT) before and after IORT. ECV maps were generated from unenhanced and PVP CT images. Clinical and CT imaging features were analyzed. The independent predictors of progression-free survival (PFS) determined by multivariate Cox regression model were used to construct the risk-scoring system. Time-dependent receiver operating characteristic (ROC) curve analysis and the Kaplan–Meier method were used to evaluate the predictive performance of the scoring system.ResultsMultivariable analysis revealed that ECV, rim-enhancement, peripancreatic fat infiltration, and carbohydrate antigen 19-9 (CA19-9) response were significant predictors of PFS (all p < 0.05). Time-dependent ROC of the risk-scoring system showed a satisfactory predictive performance for disease progression with area under the curve (AUC) all above 0.70. High-risk patients (risk score ≥ 2) progress significantly faster than low-risk patients (risk score < 2) (p < 0.001).ConclusionECV derived from PVP of conventional CECT was an independent predictor for progression in LAPC patients assessed as SD after IORT. The scoring system integrating ECV, radiological features, and CA19-9 response can be used as a practical tool for stratifying prognosis in these patients, assisting clinicians in developing an appropriate treatment approach.Critical relevance statementThe scoring system integrating ECV fraction, radiological features, and CA19-9 response can track tumor progression in patients with LAPC receiving IORT, aiding clinicians in choosing individual treatment strategies and improving their prognosis.Key PointsPredicting the progression of LAPC in patients receiving IORT is important.Our ECV-based scoring system can risk stratifying patients with initial SD.Appropriate prognostication can assist clinicians in developing appropriate treatment approaches.Graphical

Time‑dependent ROC curve analysis to determine the predictive capacity of seven clinical scales for mortality in patients with COVID‑19: Study of a hospital cohort with very high mortality.

Clinical data from hospital admissions are typically utilized to determine the prognostic capacity of Coronavirus disease 2019 (COVID-19) indices. However, as disease status and severity markers evolve over time, time-dependent receiver operating characteristic (ROC) curve analysis becomes more appropriate. The present analysis assessed predictive power for death at various time points throughout patient hospitalization. In a cohort study involving 515 hospitalized patients (General Hospital Number 1 of Mexican Social Security Institute, Colima, Mexico from February 2021 to December 2022) with COVID-19, seven severity indices [Pneumonia Severity Index (PSI) PaO2/FiO2 arterial oxygen pressure/fraction of inspired oxygen (Kirby index), the Critical Illness Risk Score (COVID-GRAM), the National Early Warning Score 2 (NEWS-2), the quick Sequential Organ Failure Assessment score (qSOFA), the Fibrosis-4 index (FIB-4) and the Viral Pneumonia Mortality Score (MuLBSTA were evaluated using time-dependent ROC curves. Clinical data were collected at admission and at 2, 4, 6 and 8 days into hospitalization. The study calculated the area under the curve (AUC), sensitivity, specificity, and predictive values for each index at these time points. Mortality was 43.9%. Throughout all time points, NEWS-2 demonstrated the highest predictive power for mortality, as indicated by its AUC values. PSI and COVID-GRAM followed, with predictive power increasing as hospitalization duration progressed. Additionally, NEWS-2 exhibited the highest sensitivity (>96% in all periods) but showed low specificity, which increased from 22.9% at admission to 58.1% by day 8. PSI displayed good predictive capacity from admission to day 6 and excellent predictive power at day 8 and its sensitivity remained >80% throughout all periods, with moderate specificity (70.6-77.3%). COVID-GRAM demonstrated good predictive capacity across all periods, with high sensitivity (84.2-87.3%) but low-to-moderate specificity (61.5-67.6%). The qSOFA index initially had poor predictive power upon admission but improved after 4 days. FIB-4 had a statistically significant predictive capacity in all periods (P=0.001), but with limited clinical value (AUC, 0.639-0.698), and with low sensitivity and specificity. MuLBSTA and IKIRBY exhibited low predictive power at admission and no power after 6 days. In conclusion, in COVID-19 patients with high mortality rates, NEWS-2 and PSI consistently exhibited predictive power for death during hospital stay, with PSI demonstrating the best balance between sensitivity and specificity.

The systemic inflammation response index as a risk factor for hepatic fibrosis and long-term mortality among individuals with metabolic dysfunction-associated steatotic liver disease

Background and aimsThe systemic inflammation response index (SIRI) is associated with various diseases with inflammatory components, but its relationship with the progression of hepatic fibrosis and survival outcomes in patients with metabolic dysfunction-associated steatotic liver disease (MASLD) is still unclear. This study was designed to investigate the potential associations between the SIRI and advanced hepatic fibrosis (AHF) as well as between the SIRI and long-term outcomes in individuals with MASLD. Methods and resultsA prospective cohort study was conducted using data gathered from the National Health and Nutrition Examination Survey (NHANES) spanning from 2005 to 2016. Weighted binary logistic regression, the Cox proportional hazards model, and time-dependent receiver operating characteristic (ROC) analyses were employed to assess the relationships among the SIRI, AHF, and mortality in patients with MASLD. Our study included a total of 5126 patients with MASLD. A higher SIRI was significantly associated with increased odds of AHF (OR 1.55, 95% CI 1.22, 1.96). According to the survival analyses, a higher SIRI was associated with greater all-cause (HR 1.19, 95% CI 1.15, 1.22) and cardiovascular mortality (HR 1.25, 95% CI 1.19, 1.32) after adjustment. The time-dependent ROC analysis indicated that the SIRI had a modest predictive value for discriminating MASLD individuals at higher versus lower mortality risk over 3-year, 5-year, and 10-year follow-up. ConclusionsThe SIRI is a promising tool for identifying MASLD individuals at risk of progressing to AHF and for predicting mortality outcomes.

Triglyceride-inflammation score established on account of random survival forest for predicting survival in patients with nasopharyngeal carcinoma: a retrospective study.

This study aimed to establish an effective prognostic model based on triglyceride and inflammatory markers, including neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR), to predict overall survival (OS) in patients with nasopharyngeal carcinoma (NPC). Additionally, we aimed to explore the interaction and mediation between these biomarkers in their association with OS. A retrospective review was conducted on 259 NPC patients who had blood lipid markers, including triglyceride and total cholesterol, as well as parameters of peripheral blood cells measured before treatment. These patients were followed up for over 5 years, and randomly divided into a training set (n=155) and a validation set (n=104). The triglyceride-inflammation (TI) score was developed using the random survival forest (RSF) algorithm. Subsequently, a nomogram was created. The performance of the prognostic model was measured by the concordance index (C-index), time-dependent receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). The interaction and mediation between the biomarkers were further analyzed. Bioinformatics analysis based on the GEO dataset was used to investigate the association between triglyceride metabolism and immune cell infiltration. The C-index of the TI score was 0.806 in the training set, 0.759 in the validation set, and 0.808 in the entire set. The area under the curve of time-dependent ROC of TI score in predicting survival at 1, 3, and 5 years were 0.741, 0.847, and 0.871 respectively in the training set, and 0.811, 0.837, and 0.758 in the validation set, then 0.771, 0.848, and 0.862 in the entire set, suggesting that TI score had excellent performance in predicting OS in NPC patients. Patients with stage T1-T2 or M0 had significantly lower TI scores, NLR, and PLR, and higher LMR compared to those with stage T3-T3 or M1, respectively. The nomogram, which integrated age, sex, clinical stage, and TI score, demonstrated good clinical usefulness and predictive ability, as evaluated by the DCA. Significant interactions were found between triglyceride and NLR and platelet, but triglyceride did not exhibit any medicating effects in the inflammatory markers. Additionally, NPC tissues with active triglyceride synthesis exhibited high immune cell infiltration. The TI score based on RSF represents a potential prognostic factor for NPC patients, offering convenience and economic advantages. The interaction between triglyceride and NLR may be attributed to the effect of triglyceride metabolism on immune response.

Risk stratification refinements with inclusion of haemodynamic variables at follow-up in patients with pulmonary arterial hypertension.

Haemodynamic variables are prognostic factors in pulmonary arterial hypertension (PAH). However, right heart catheterization (RHC) is not systematically recommended to assess the risk-status during follow-up. This study aimed to assess the added value of haemodynamic variables in prevalent patients to predict the risk of death or lung transplantation according to their risk status assessed by the non-invasive 4 strata model as recommended by the European guidelines. We evaluated incident patients with PAH enrolled in the French PAH Registry between 2009 and 2020 who had a first follow-up RHC. Cox regression identified, in each follow-up risk status, haemodynamic variables significantly associated with transplant-free survival (TFS). Optimal thresholds were determined by time-dependent Receiver-Operating Characteristics. Several multivariable Cox regression models were performed to identify the haemodynamic variables improving the non-invasive risk stratification model. We analysed 1240 incident patients reassessed within a year by RHC. None of haemodynamic variable were significantly associated with TFS among low-risk (n=386) or high-risk (n=71) patients. Among patients at intermediate (-low, n=483, -high, n=300) risk at first follow-up, multivariable models including either stroke volume index (SVi) or mixed venous oxygen saturation (SvO2) were the best. The prognostic performance of refined 6 strata risk stratification model including the non-invasive 4 strata model and SVi>37 mL·m-2 and/or SvO2>65% for patients at intermediate-risk (Area Under the Curve 0.81, c-index 0.74), was better than that of 4 strata model (0.79, p=0.009; c-index 0.72). Cardiopulmonary haemodynamics may improve risk stratification at follow-up in patients at intermediate-risk.

Thoracic versus coronary calcification for atherosclerotic cardiovascular disease events prediction

This study compared the prognostic value of quantified thoracic artery calcium (TAC) including aortic arch on chest CT and coronary artery calcium (CAC) score on ECG-gated cardiac CT.MethodsA total of...

Triglyceride to high-density lipoprotein cholesterol ratio associated with long-term adverse clinical outcomes in patients deferred revascularization following fractional flow reserve

BackgroundGuidelines on coronary intermediate lesions strongly recommend deferred revascularization after detecting a normal fractional flow reserve (FFR). Researches about triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) on cardiovascular diseases has also been well conducted. However, the association of TG/HDL-C and long-term adverse clinical outcomes remains unknown for patients deferred revascularization following FFR.MethodsThis study retrospectively included 374 coronary artery disease (CAD) patients with non-significant coronary lesions diagnosed by coronary angiography (CAG) and FFR. The main outcome measure was the combination of major adverse cardiovascular and cerebrovascular events (MACCEs). All patients were categorized into three subgroups in terms of TG/HDL-C tertiles (T1 < 0.96, 0.96 ≤ T2 < 1.58, T3 ≥ 1.58). Three different Cox regression models were utilized to reveal the association between TG/HDL-C and prevalence of MACCEs.Results47 MACCEs were recorded throughout a median monitoring period of 6.6 years. The Kaplan-Meier survival curves showed a higher MACCEs rate occurred in the higher TG/HDL-C group (5.6% vs. 12.9% vs. 19.4%, log-rank P < 0.01). After adjustment, patients in T3 suffered a 2.6-fold risk compared to the T1 group (T3 vs. T1: HR 2.55, 95% CI 1.05–6.21, P = 0.038; T2 vs. T1: HR 1.71, 95% CI 0.65–4.49, P = 0.075; P for trend = 0.001). The restricted cubic spline (RCS) analysis demonstrated that the HR for MACCEs rose as TG/HDL-C increased. Both the receiver operating characteristic (ROC) and time-dependent ROC proved the excellent predictive ability of TG/HDL-C.ConclusionThe study illustrates that TG/HDL-C correlates with the risk of MACCEs in CAD patients deferred revascularization following FFR. TG/HDL-C could serve as a dependable predictor of cardiovascular events over the long term in this population.

Development and validation of a novel model to predict recurrence-free survival and melanoma-specific survival after sentinel lymph node biopsy in patients with melanoma: an international, retrospective, multicentre analysis

The introduction of adjuvant systemic treatment for patients with high-risk melanomas necessitates accurate staging of disease. However, inconsistencies in outcomes exist between disease stages as defined by the American Joint Committee on Cancer (8th edition). We aimed to develop a tool to predict patient-specific outcomes in people with melanoma rather than grouping patients according to disease stage. Patients older than 13 years with confirmed primary melanoma who underwent sentinel lymph node biopsy (SLNB) between Oct 29, 1997, and Nov 11, 2013, at four European melanoma centres (based in Berlin, Germany; Amsterdam and Rotterdam, the Netherlands; and Warsaw, Poland) were included in the development cohort. Potential predictors of recurrence-free and melanoma-specific survival assessed were sex, age, presence of ulceration, primary tumour location, histological subtype, Breslow thickness, sentinel node status, number of sentinel nodes removed, maximum diameter of the largest sentinel node metastasis, and Dewar classification. A prognostic model and nomogram were developed to predict 5-year recurrence-free survival on a continuous scale in patients with stage pT1b or higher melanomas. This model was also calibrated to predict melanoma-specific survival. Model performance was assessed by discrimination (area under the time-dependent receiver operating characteristics curve [AUC]) and calibration. External validation was done in a cohort of patients with primary melanomas who underwent SLNB between Jan 30, 1997, and Dec 12, 2013, at the Melanoma Institute Australia (Sydney, NSW, Australia). The development cohort consisted of 4071 patients, of whom 2075 (51%) were female and 1996 (49%) were male. 889 (22%) had sentinel node-positive disease and 3182 (78%) had sentinel node-negative disease. The validation cohort comprised 4822 patients, of whom 1965 (41%) were female and 2857 (59%) were male. 891 (18%) had sentinel node-positive disease and 3931 (82%) had sentinel node-negative disease. Median follow-up was 4·8 years (IQR 2·3-7·8) in the development cohort and 5·0 years (2·2-8·9) in the validation cohort. In the development cohort, 5-year recurrence-free survival was 73·5% (95% CI 72·0-75·1) and 5-year melanoma-specific survival was 86·5% (85·3-87·8). In the validation cohort, the corresponding estimates were 66·1% (64·6-67·7) and 83·3% (82·0-84·6), respectively. The final model contained six prognostic factors: sentinel node status, Breslow thickness, presence of ulceration, age at SLNB, primary tumour location, and maximum diameter of the largest sentinel node metastasis. In the development cohort, for the model's prediction of recurrence-free survival, the AUC was 0·80 (95% CI 0·78-0·81); for prediction of melanoma-specific survival, the AUC was 0·81 (0·79-0·84). External validation showed good calibration for both outcomes, with AUCs of 0·73 (0·71-0·75) and 0·76 (0·74-0·78), respectively. Our prediction model and nomogram accurately predicted patient-specific risk probabilities for 5-year recurrence-free and melanoma-specific survival. These tools could have important implications for clinical decision making when considering adjuvant treatments in patients with high-risk melanomas. Erasmus Medical Centre Cancer Institute.

Validation and comparison of prognostic scoring systems in patients with head and neck squamous cell carcinoma treated with nivolumab.

Several scoring systems have been developed to predict prognosis in patients with refractory cancer. We aimed to validate eight scoring systems and determine the best method for predicting the prognosis of head and neck squamous cell carcinoma treated with nivolumab. This multicentre retrospective study involved 154 patients with recurrent and/or metastatic head and neck squamous cell carcinoma treated with nivolumab between 2017 and 2020. Oncological outcomes were assessed according to the scoring systems, including MD Anderson Cancer Center + neutrophil-to-lymphocyte ratio and Hammersmith scores. Objective response, overall survival and progression-free survival were evaluated using logistic regression and Cox proportional hazards analyses. Receiver operating curve analysis was used to calculate the area under the curve and estimate the efficacy of each score. No significant associations were found between the responses and any score. Seven of the eight scoring systems were associated with disease control (odds ratio, 0.26-0.70). Amongst the eight scoring systems, MD Anderson Cancer Center + neutrophil-to-lymphocyte ratio showed the highest area under the curve for predicting response and disease control. Seven scoring systems were prognostic factors for progression-free survival (hazard ratio, 1.22-1.95). All eight scoring systems were prognostic factors for overall survival (hazard ratio, 1.62-3.83). According to the time-dependent receiver operating characteristics analysis for overall survival, the Hammersmith scoring system had the best predictive ability at 3months, and the MD Anderson Cancer Center + neutrophil-to-lymphocyte ratio scoring system had the highest area under the curve between 6 and 24months. MD Anderson Cancer Center + neutrophil-to-lymphocyte ratio and Hammersmith scoring systems were better predictors of prognosis in patients with head and neck squamous cell carcinoma treated with nivolumab.

Comparative analysis of four nutritional scores in predicting adverse outcomes in biopsy-confirmed diabetic kidney Disease.

Malnutrition is associated with adverse outcomes in patients with diabetic kidney disease (DKD). However, it is uncertain which nutritional assessment tools are most effective in predicting the adverse outcomes of DKD. This retrospective study was conducted at a single center and included 367 patients diagnosed with DKD based on biopsy results between August 2009 and December 2018. Four nutritional assessment indices, namely the Prognostic Nutritional Index (PNI), Geriatric Nutritional Risk Index (GNRI), Triglycerides (TG) × Total Cholesterol (TC) × Body Weight (BW) Index (TCBI), and Controlling Nutritional Status (CONUT) score, were selected and calculated. We aimed to assess the association between these nutritional scores and adverse outcomes, including progression to end-stage kidney disease (ESKD), cardiovascular diseases events (CVD), and all-cause mortality. Univariate and multivariate Cox regression analyses, Kaplan-Meier analysis, along with Restricted cubic spline analysis were used to examine the relationship between nutritional scores and adverse outcomes. Furthermore, the area under the curve (AUC) was calculated using time-dependent receiver operating characteristics to determine the predictive value of the four nutritional scores alone and some combinations. Lastly, ordered logistic regression analysis was conducted to explore the correlation between the four nutritional scores and different renal histologic changes. The incidence of ESKD, CVD, and all-cause mortality was significantly higher in patients with DKD who had a lower PNI, lower GNRI, and higher CONUT score. Additionally, The TCBI performed the worst in terms of grading and risk assessment. The PNI offer the highest predictive value for adverse outcomes and a stronger correlation with renal histologic changes compared to other nutritional scores. Patients diagnosed with DKD who have a worse nutritional status are more likely to experience higher rates of adverse outcomes. The PNI might offer more valuable predictive values and a stronger correlation with different renal histologic changes compared to other nutritional scores.

Predictive value of insulin resistance surrogates for the development of diabetes in individuals with baseline normoglycemia: findings from two independent cohort studies in China and Japan

BackgroundInsulin resistance (IR) plays a crucial role in the occurrence and progression of diabetes. This study aimed to evaluate and compare the predictive value of four IR surrogates, including the triglycerides glucose (TyG) index, TyG and body mass index (TyG-BMI), triglycerides/high-density lipoprotein cholesterol (TG/HDL-C) ratio, and the metabolic score for IR (MetS-IR) for diabetes in two large cohorts.MethodsA total of 116,661 adult participants from the China Rich Healthcare Group and 15,464 adult participants from the Japanese NAGALA cohort were included in the study. Multivariable Cox proportional hazards models were used to assess the standardized hazard ratio (HR) of the TyG index, TyG-BMI, TG/HDL-C ratio, and MetS-IR directly associated with diabetes. Receiver operating characteristic (ROC) curve and time-dependent ROC curve analysis were performed to evaluate and compare the predictive value of the four IR surrogates for diabetes.ResultsIn the two independent cohorts, the average follow-up time was 3.1 years in the China cohort, with 2681(2.30%) incident cases of diabetes recorded, and 6.13 years in the Japan cohort, with 373 incident cases (2.41%) of diabetes recorded. After adjusting for potential confounding factors, we found that among the four IR surrogates, TyG-BMI and MetS-IR showed stronger associations with diabetes. The stronger associations persisted even after further stratification by age, sex, hypertension, and obese subgroups. In terms of diabetes prediction, based on ROC analysis, TyG-BMI demonstrated the highest predictive accuracy for diabetes in the Chinese population, while both TyG-BMI and MetS-IR showed the highest predictive accuracy in the Japanese population. The results of further subgroup ROC analysis confirmed the robustness of these findings. Furthermore, the time-dependent ROC results indicated that among the four IR surrogates, MetS-IR exhibited the highest accuracy in predicting future diabetes at various time intervals in the Japanese population.ConclusionOur findings suggest that evaluating TyG-BMI and MetS-IR as IR surrogates may be the most useful for predicting diabetes events and assessing the risk of developing diabetes in East Asian populations.

Non-tumor-related prognostic factors for immunotherapy–chemotherapy or immunotherapy alone as first-line in advanced non-small cell lung cancer (NSCLC)

Besides programmed death ligand 1 (PD-L1) expression, rapid, cost-effective and validated scores or models are critical for the prognosis and prediction of patients received immune checkpoint inhibitors (ICIs). In this retrospective study, 182 patients with NSCLC receiving ICIs from 2015 to 2022 were divided 1:1 into a training cohort and a validation cohort. We identified a score established by three factors and analyzed the prognostic implications by Kaplan–Meier approach (Log rank test) and time-dependent receiver operating characteristic (ROC) analyses. A non-tumor-related score (NTRS) was established that could be used as a prognostic factor (HR 2.260, 95% CI 1.559–3.276, P < 0.001 in training cohort; HR 2.114, 95% CI 1.493–2.994, P < 0.001 in validation cohort) and had a high time-dependent ROC for overall survival (OS) (AUC 0.670–0.782 in training cohort; AUC 0.682–0.841 in validation cohort). PD-L1 (1–49%) and NTRS (score = 0, 1, 2, 3) combination significantly improved the assessment of patients’ OS and progress-free survival (PFS), which was statistically different in training cohorts (P < 0.001 for OS, 0.012 for PFS) and validation cohorts (P = 0.01 for OS, < 0.001 for PFS). The NTRS provided a better assessment of durable clinical benefit (DCB) compared to PD-L1 expression (P = 0.009 vs. 0.232 in training cohort; P = 0.004 vs. 0.434 in validation cohort). NTRS may help improve prognosis stratification of patients receiving ICIs in first-line NSCLC and may be combined with tumor-related parameters.

A four-gene signature predicts overall survival of patients with esophageal adenocarcinoma.

Esophageal adenocarcinoma (EAC) is an aggressive cancer with poor prognosis. Thus, this study aimed to identify a prognostic molecular signature to predict the overall survival (OS) of patients with EAC. The mRNA microarray data sets GSE13898 and GSE26886 were downloaded from the Gene Expression Omnibus (GEO) database. RNA sequencing profile and clinical data of EAC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) between EAC tissues and adjacent non-cancerous tissues were obtained using R software. DEGs associated with prognosis of OS were assessed by univariate Cox analysis, and a prognostic signature was built using stepwise multivariate Cox analysis. Time-dependent receiver operating characteristic (ROC) analysis and stratification analysis were conducted to evaluate its predictive performance. Functional enrichment analysis was performed for genes co-expressed with the signature to explore its biological functions in EAC. A total of 336 genes were identified to be differentially expressed between EAC tissues and adjacent non-cancerous tissues. After univariate and multivariate Cox regression analysis, four genes (ALAD, ABLIM3, IL17RB and IFI6) were screened out to construct a prognostic signature. According to this signature, patients could be assigned into high-risk and low-risk group with significantly different OS (P=4.92e-05<0.0001). Multivariate Cox regression analysis suggested that the four-gene signature served as an independent factor in OS prediction. In the time-dependent ROC analysis, the areas under the curves (AUCs) were 0.804, 0.792 and 0.695 for 1-, 3- and 5-year survival prediction, respectively, suggesting a good performance. Functional enrichment analysis showed that the signature was mainly clustered in cell proliferation related biological processes or pathways. The four-gene signature identified in the current study may be a potential prognostic factor for predicting OS of EAC patients.

Predicting the unpredictable: a robust nomogram for predicting recurrence in patients with ampullary carcinoma

ObjectiveTo screen the risk factors affecting the recurrence risk of patients with ampullary carcinoma (AC)after radical resection, and then to construct a model for risk prediction based on Lasso-Cox regression and visualize it.MethodsClinical data were collected from 162 patients that received pancreaticoduodenectomy treatment in Hebei Provincial Cancer Hospital from January 2011 to January 2022. Lasso regression was used in the training group to screen the risk factors for recurrence. The Lasso-Cox regression and Random Survival Forest (RSF) models were compared using Delong test to determine the optimum model based on the risk factors. Finally, the selected model was validated using clinical data from the validation group.ResultsThe patients were split into two groups, with a 7:3 ratio for training and validation. The variables screened by Lasso regression, such as CA19-9/GGT, AJCC 8th edition TNM staging, Lymph node invasion, Differentiation, Tumor size, CA19-9, Gender, GPR, PLR, Drinking history, and Complications, were used in modeling with the Lasso-Cox regression model (C-index = 0.845) and RSF model (C-index = 0.719) in the training group. According to the Delong test we chose the Lasso-Cox regression model (P = 0.019) and validated its performance with time-dependent receiver operating characteristics curves(tdROC), calibration curves, and decision curve analysis (DCA). The areas under the tdROC curves for 1, 3, and 5 years were 0.855, 0.888, and 0.924 in the training group and 0.841, 0.871, and 0.901 in the validation group, respectively. The calibration curves performed well, as well as the DCA showed higher net returns and a broader range of threshold probabilities using the predictive model. A nomogram visualization is used to display the results of the selected model.ConclusionThe study established a nomogram based on the Lasso-Cox regression model for predicting recurrence in AC patients. Compared to a nomogram built via other methods, this one is more robust and accurate.

Nomogram incorporating prognostic immune-inflammatory-nutritional score for survival prediction in pancreatic cancer: a retrospective study

BackgroundPrognosis prediction for pancreatic cancer has always been difficult in clinical practice because of its high heterogeneity and mortality. The aim of the study was to assess the value of prognostic immune-inflammatory-nutritional (PIIN) score on overall survival (OS) in postoperative patients with pancreatic cancer and to develop a nomogram incorporating PIIN score.MethodsThis study retrospectively analyzed the clinic pathological data of 155 patients with pancreatic cancer who underwent radical surgery. PIIN score was calculated by measuring the fibrinogen (FIB), neutrophil to lymphocyte ratio (NLR), systemic immune-inflammation index (SII), albumin-bilirubin (ALBI) score, and prognostic nutritional index (PNI). Patients were divided into two groups by PIIN score levels over a threshold of 37.2. Univariate and multivariate analysis were performed using the Cox regression analysis model. The time-dependent receiver operating characteristic (ROC) curve was plotted to compare the prognostic values of the scoring systems. Finally, a nomogram based on PIIN score was constructed and validated.ResultsMultivariate regression analysis showed that PIIN score (hazard ratio (HR) = 2.171, 95% confidence interval (CI) = 1.207–3.906, P = 0.010), lymphovascular invasion (HR = 1.663, 95% CI = 1.081–2.557, P = 0.021), poor tumor grade (HR = 2.577, 95% CI = 1.668–3.982, P < 0.001), bad TNM stage (I vs. II: HR = 1.791, 95% CI = 1.103–2.906, P = 0.018; I vs. III: HR = 4.313, 95% CI = 2.365–7.865, P < 0.001) and without adjuvant chemotherapy (HR = 0.552, 95% CI = 0.368–0.829, P = 0.004) were independent risk factors for OS. The time-dependent ROC curves revealed that PIIN score was better than the other scoring systems in predicting survival prognosis. And last, the nomogram established from independent factors such as PIIN score had good predictive power for OS. The ROC curve results showed that the AUC values for 1, 3 and 5 years were 0.826, 0.798 and 0.846, respectively. The calibration plots showed the superior clinical applicability of the nomogram.ConclusionThe nomogram model based on PIIN score can be utilized as one of the prognosis stratifications as well as postoperative follow-up for the development of individual treatment for pancreatic cancer.

Development and validation of a nomogram for predicting cardiovascular mortality risk for diffuse large B-cell lymphoma in children, adolescents, and adults.

This study aimed to construct and validate a nomogram for predicting cardiovascular mortality (CVM) for child, adolescent, and adult patients with diffuse large B-cell lymphoma (DLBCL). Patients with only one primary tumor of DLBCL first diagnosed between 2000 and 2019 in the SEER database were extracted. We used the cumulative incidence function (CIF) to evaluate the cumulative rate of CVM. The outcome of interest was CVM, which was analyzed using a competing risk model, accounting for death due to other causes. The total database was randomly divided into a training cohort and an internal validation cohort at a ratio of 7:3. Adjustments were for demographics, tumor characteristics, and treatment modalities. Nomograms were constructed according to these risk factors to predict CVM risk at 5, 10, and 15 years. Validation included receiver operating characteristic (ROC) curves, time-dependent ROC, C-index, calibration curves, and decision curve analysis. One hundred four thousand six hundred six patients following initial diagnosis of DLBCL were included (58.3% male, median age 64 years, range 0-80, White 83.98%). Among them, 5.02% died of CVM, with a median follow-up time of 61 (31-98) months. Nomograms based on the seven risk factors (age at diagnosis, gender, race, tumor grade, Ann Arbor stage, radiation, chemotherapy) with hazard ratios ranging from 0.19-1.17 showed excellent discrimination, and calibration plots demonstrated satisfactory prediction. The 5-, 10-, and 15-year AUC and C-index of CVM in the training set were 0.716 (0.714-0.718), 0.713 (0.711-0.715), 0.706 (0.704-0.708), 0.731, 0.727, and 0.719; the corresponding figures for the validation set were 0.705 (0.688-0.722), 0.704 (0.689-0.718), 0.707 (0.693-0.722), 0.698, 0.698, and 0.699. Decision curve analysis revealed a clinically beneficial net benefit. We first built the nomogram model for DLBCL patients with satisfactory prediction and excellent discrimination, which might play an essential role in helping physicians enact better treatment strategies at the time of initial diagnosis.

Identification and validation of a 4-extracellular matrix gene signature associated with prognosis and immune infiltration in lung adenocarcinoma

BackgroundThe extracellular matrix (ECM) plays a crucial role in the development and tumor microenvironment of lung adenocarcinoma (LUAD). This study aimed to establish a risk score of ECM-related genes in LUAD and explore the association between the risk score and patient survival as well as immune cell infiltration, somatic mutations, and therapy response. MethodsGene expression data from The Cancer Genome Atlas (TGCA) and eight Gene Expression Omnibus (GEO) databases were used to analyze and identify differentially expressed genes (DEGs). Prognostic ECM-related genes were identified and utilized to formulate a prognostic signature. A nomogram was constructed using TCGA dataset and validated in two GEO datasets. Differences between high- and low-risk patients were analyzed for function enrichment, immune cell infiltration, somatic mutations, and therapy response. Finally, Quantitative real-time PCR (qRT-PCR) was used to detect the mRNA expression of DEGs in LUAD. ResultsA risk score based on four ECM-related genes, ANOS1, CD36, COL11A1, and HMMR, was identified as an independent prognostic factor for overall survival (OS) compared to other clinical variables. Subsequently, a nomogram incorporating the risk score and TNM staging was developed using the TCGA dataset. Internal and external validation of the nomogram, conducted through calibration plots, C-index, time-dependent receiver operating characteristics (ROC), integrated discrimination improvement (IDI), and decision curve analyses (DCA), demonstrated the excellent discriminatory ability and clinical practicability of this nomogram. The risk score correlated with the distribution of function enrichment, immune cell infiltration, and immune checkpoint expression. More somatic mutations occurred in the high-risk group. The risk score also demonstrated a favorable ability to predict immunotherapy response and drug sensitivity. ConclusionA novel signature based on four ECM-related genes is developed to help predict LUAD prognosis. This signature correlates with tumor immune microenvironment and can predict the response to different therapies in LUAD patients.

Construction and validation of a prognostic signature based on seven endoplasmic reticulum stress-related lncRNAs for patients with head and neck squamous cell carcinoma

Endoplasmic reticulum stress (ERS) occurs when misfolded or unfolded proteins accumulate in the endoplasmic reticulum (ER), and it is often observed in tumors, including head and neck squamous cell carcinoma (HNSCC). Relevant studies have demonstrated the prognostic significance of ERS-related long non-coding RNAs (lncRNAs) in various cancers. However, the relationship between ERS and lncRNAs in HNSCC has received limited attention in previous studies. In this study, we aimed to develop an ERS-related lncRNAs prognostic model using correlation analysis, Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) regression analysis based on data from The Cancer Genome Atlas (TCGA) database. The survival and predictive ability of this model were evaluated using Kaplan–Meier analysis and time-dependent receiver operating characteristics (ROC), while nomograms and calibration curves were constructed. Then, functional enrichment analyses, tumor mutation burden (TMB), tumor infiltration of immune cells, single sample Gene Set Enrichment Analysis (ssGSEA), and drug sensitivity analysis were performed. Additionally, we conducted a consensus cluster analysis to compare differences between subtypes of tumors. Finally, we validated the expression of the ERS-related lncRNAs that constructed prognostic risk score model in HNSCC tissues through quantitative real-time PCR (qRT-PCR). We developed a prognostic signature based on seven ERS-related lncRNAs, which showed better predictive performance than other clinicopathological features. The high-risk poor prognosis group had a poorer prognosis in comparison to the low-risk good prognosis. The area under the ROC curve (AUC) predicted by this model for 3-year survival rates of HNSCC patients was 0.805. Enrichment analysis revealed that the differentially expressed genes were primarily enriched in pathways related to immune responses and signal transduction. Low-risk patients had lower TMB, more immune cell infiltrations, and enhanced anti-tumor immunity. Cluster analysis indicated that cluster 3 may have a better prognosis and immunotherapy effect. In addition, the result of qRT-PCR was consistent with our analysis. This prognostic model based on seven ERS-related lncRNAs is a promising tool for risk stratification, survival prediction, and immune cell infiltration status assessment.