Prognostic nomogram for predicting cancer-specific survival in patients with resected hilar cholangiocarcinoma: a large cohort study

BackgroundThe aim of the study was to establish and validate a novel prognostic nomogram of cancer-specific survival (CSS) in resected hilar cholangiocarcinoma (HCCA) patients.MethodsA training cohort of 536 patients and an internal validation cohort of 270 patients were included in this study. The demographic and clinicopathological variables were extracted from the Surveillance, Epidemiology and End Results (SEER) database. Univariate and multivariate Cox regression analysis were performed in the training cohort, followed by the construction of nomogram for CSS. The performance of the nomogram was assessed by concordance index (C-index) and calibration plots and compared with the American Joint Committee on Cancer (AJCC) staging systems. Decision curve analysis (DCA) was applied to measure the predictive power and clinical value of the nomogram.ResultsThe nomogram incorporating age, tumor size, tumor grade, lymph node ratio (LNR) and T stage parameters was with a C-index of 0.655 in the training cohort, 0.626 in the validation cohort, compared with corresponding 0.631, 0.626 for the AJCC 8th staging system. The calibration curves exhibited excellent agreement between CSS probabilities predicted by nomogram and actual observation in the training cohort and validation cohort. DCA indicated that this nomogram generated substantial clinical value.ConclusionsThe proposed nomogram provided a more accurate prognostic prediction of CSS for individual patients with resected HCCA than the AJCC 8th staging system, which might be served as an effective tool to stratify resected HCCA patients with high risk and facilitate optimizing therapeutic benefit.     

Comparison of 4 lymph node staging systems for the prognostic prediction of esophagogastric junction adenocarcinoma with ≤15 retrieved lymph nodes

BackgroundDirectly applying the 8th American Joint Committee on Cancer (AJCC) Tumor Node Metastasis (TNM) staging system to evaluate the prognosis of patients with esophagogastric junction adenocarcinoma (AEG) might lead to under-staging, when insufficient lymph nodes were retrieved during surgery. The prognostic value of 4 lymph nodes staging systems, 8th AJCC TNM N stage, lymph node ratio (LNR), log odds of positive lymph nodes (LODDS), and negative lymph nodes (NLN), in AEG patients having ≤15 retrieved lymph nodes were compared.Methods869 AEG patients diagnosed between 2004 and 2012 with ≤15 retrieved lymph nodes were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression analyses were conducted to assess the association of cancer-specific survival (CSS) and overall survival (OS) with 8th AJCC TNM N stage, LNR, LODDS, and NLN respectively. Predictive survival ability was assessed and compared using linear trend χ2 score, likelihood ratio (LR) test, Akaike information criterion (AIC), Harrell concordance index (C-index), and Receiver Operative Curve (ROC).ResultsThe N stage, LNR, LODDS, and NLN were all independent prognostic predictors for CSS and OS in multivariate Cox models. Comparatively, LODDS demonstrated higher linear trend χ2 score, LR test score, C-index and integrated area under the curve (iAUC) value, and lower AIC in CSS compared to the other three systems. Moreover, for patients without regional lymph node metastasis, NLN showed higher C-index and lower AIC.ConclusionsLODDS showed better predictive performance than N, LNR, and NLN among patients with node-positive patients while NLN performed better in node-negative patients. A combination of LODDS and NLN has the potential to provide more prognostic information than the current AJCC TNM classification.     

Development and validation of a Surveillance,Epidemiology, and End Results (SEER)-based prognostic nomogram for predicting survival in elderly patients with gastric cancer after surgery

BackgroundElderly gastric cancer (ELGC) remains one of the intensively investigated topics during the last decades. To establish a comprehensive nomogram for effective clinical practice and assessment is of significance. This study is designed to develop a prognostic nomogram for ELGC both in overall survival (OS) and cancer-specific survival (CSS).MethodsThe recruited cases were from the Surveillance, Epidemiology, and End Results (SEER) database and input for the construction of nomogram.ResultsA total of 4,414 individuals were recruited for this study, of which 2,208 were randomly in training group and 2,206 were in validation group. In univariate analysis of OS, significant variables (P<0.05) included age, marital status, grade, American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) stage, bone/brain/liver/lung metastasis and tumor size. In univariate analysis of CSS, significant variables (P<0.05) included age, grade, AJCC TNM stage, bone/brain/liver/lung metastasis and tumor size. In multivariate analysis of OS, sex, age, race, grade, TNM stage, lung metastasis and tumor size were considered as the significant variables and subjected to the establishment of nomogram. In multivariable analysis of CSS, age, grade, TNM, tumor size were considered as the significant variables and input to the establishment of nomogram. Sex, age, race, grade, TNM stage, lung metastasis and tumor size were included for the establishment of nomogram in OS while age, grade, TNM, tumor size were included to the establishment of nomogram in CSS. C-index, decision curve analysis (DCA) and the area under the curve (AUC) showed distinct value of newly established nomogram models. Both OS and CSS nomograms showed higher statistic power over the AJCC stage.ConclusionsThis study established and validated novel nomogram models of OS and CSS for ELGC based on population dataset.     

Construction and validation of a prognostic model for stage IIIC endometrial cancer patients after surgery

BackgroundTo explore the most predictive lymph node (LN) scheme for stage IIIC endometrial cancer (EC) patients after hysterectomy and develop a scheme-based nomogram.MethodsData from 2626 stage IIIC EC patients, diagnosed between 2010 and 2014, were extracted from the Surveillance, Epidemiology, and End Results (SEER) registry. The predictive ability of four LN schemes was assessed using C-index and Akaike information criterion (AIC). A nomogram based on the most predictive LN scheme was constructed and validated. The comparison of the predictive ability between nomogram and FIGO stage was conducted using the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA).ResultsFIGO stage (stage IIIC1/stage IIIC2) was not an independent risk factor for OS in stage IIIC EC patients (P = 0.672) and log odds of positive lymph nodes (LODDS) had the best predictive ability (C-index: 0.742; AIC: 8228.95). A nomogram based on LODDS was constructed and validated, which had a decent C-index of 0.742 (0.723–0.762). The nomogram showed a better predictive ability than that of the FIGO staging system.ConclusionFIGO IIIC1/FIGO IIIC2 could not differentiate the prognosis for stage IIIC EC patients. We developed and validated a nomogram based on LODDS to predict OS for post-operative patients with stage IIIC EC.     

皮肤梭形细胞黑色素瘤患者生存预测模型的构建及验证

背景与目的：梭形细胞黑色素瘤（spindle cell melanoma,SCM）是一种罕见的黑色素瘤类型,有关SCM患者生存预后的研究较少。通过提取公共数据库中的SCM临床信息,构建并验证皮肤SCM患者5和10年癌症特异性生存率（cancer-specific survival,CSS）和总生存率（overall survival,OS）的生存预测模型。方法：从美国国立癌症研究所监测、流行病学和最终结果（Surveillance, Epidemiology, and End Results,SEER）数据库筛选出共1 445例患者,分成建模组（n=1 011）和验证组（n=434）。通过单因素和多因素COX回归分析确定独立预后影响因素,建立列线图预测模型。利用一致性指数（concordance index,C-index）、受试者工作特征（receiver operating characteristic,ROC）曲线和校准曲线评估模型的区分度和准确性,利用决策曲线分析（decision curve analysis,DCA）评估模型的临床实用性。结果：年龄、肿瘤部位、肿瘤厚度、溃疡...     

A novel prognostic nomogram based on microvascular invasion and hematological biomarkers to predict survival outcome for hepatocellular carcinoma patients

PurposeThis study aimed to develop and validate a nomogram for overall survival (OS) prediction in which combine clinical characteristics and hematological biomarkers in patients with hepatocellular carcinoma (HCC).MethodsWe performed a retrospective analysis of 807 HCC patients. All the clinical data of these patients were collected through electronic medical record (EMR). The independent predictive variables were identified by cox regression analysis. We tested the accuracy of the nomograms by discrimination and calibration, and then plotted decision curves to assess the benefits of nomogram-assisted decisions in a clinical context, and compared with the TNM staging systems and microvascular invasion (MVI) on HCC prognosis.ResultsThe primary cohort consisted of 545 patients with clinicopathologically diagnosed with HCC from 2008 to 2013, while 262 patients from 2014 to 2016 in external validation cohort. Variables included in the nomograms were TNM Stage, microvascular invasion (MVI), alpha fetoprotein (AFP), platelet to lymphocyte ratio (PLR) and prothrombin time (PT). The C-index of nomogram was 0.768, which was superior than the C-index of TNM Stage (0.660, P < 0.001) and MVI(0.664, P < 0.001) alone in the primary cohort. In the validation cohort, the models had a C-index of 0.845, and were also statistically higher when compared to C-index values for TNM Stage (0.687, P < 0.001) and MVI(0.684, P < 0.001). Calibration curves showed adequate calibration of predicted and reported OS prediction throughout the range of HCC outcomes. Decision curve analysis demonstrated that the nomogram was clinically useful than the TNM Stage and MVI alone. Moreover, patients were divided into three distinct risk groups for OS by the nomogram: low risk group, middle risk group and a high risk group, respectively.ConclusionThe nomogram presents more accurate and useful prognostic power, which could be used to predict OS for patients with HCC.     

Prognosis prediction model for a special entity of gastric cancer,linitis plastica

BackgroundGastric linitis plastica (GLP) is characteristic by its poor prognosis and highly aggressive characteristics compared with other types of gastric cancer (GC). However, the guidelines have not yet been distinguished between GLP and non-GLP.MethodsA total of 342 eligible patients with GLP identified in the Surveillance, Epidemiology, and End Results (SEER) dataset were randomly divided into training set (n=298) and validation set (n=153). A nomogram would be developed with the constructed predicting model based on the training cohort’s data, and the validation cohort would be used to validate the model. Principal component analysis (PCA) was used to evaluate the differences between groups. Cox regression and LASSO (least absolute shrinkage and selection operator) were used to construct the models. Calibration curve, time-dependent receiver operating characteristic (ROC) curve, concordance index (C-index) and decision curve analysis (DCA) were used to evaluate the predicting performance. Restricted mean survival time (RMST) was used to analyze the curative effect of adjuvant therapy.ResultsFor patients in training cohort, univariable and multivariable Cox analyses showed that age, examined lymph nodes (LN.E), positive lymph nodes (LN.P), lesion size, combined resection, and radiotherapy are independent prognostic factors for overall survival (OS), while chemotherapy can not meet the proportional hazards (PHs) assumption; age, race, lesion size, LN.E, LN.P, combined resection and marital status are independent prognostic factors for cancer-specific survival (CSS). The C-index of the nomogram was 0.678 [95% confidence interval (CI), 0.660–0.696] and 0.673 (95% CI, 0.630–0.716) in the training and validation cohort, respectively. Meanwhile, the C-index of the CSS nomogram was 0.671 (95% CI, 0.653–0.699) and 0.650 (95% CI, 0.601–0.691) in the training and validation cohort for CSS, respectively. Furthermore, the nomogram was well calibrated with satisfactory consistency. RMST analysis further determined that chemotherapy and radiotherapy might be beneficial for improving 1- and 3-year OS and CSS, but not the 5-year CSS.ConclusionsWe developed nomograms to help predict individualized prognosis for GLP patients. The new model might help guide treatment strategies for patients with GLP.     

肺腺癌患者列线图预后模型的构建与验证

目的 基于SEER数据库的大样本数据,构建肺腺癌患者生存预后的列线图预测模型.方法 回顾性分析SEER数据库收集的2010—2015年诊断为肺腺癌患者的临床数据.根据影响肺腺癌患者预后的独立因素,采用Lasso Cox回归分析构建列线图模型.C指数和校准曲线评估列线图的判别和校准能力.使用NRI和DCA曲线评估列线图的...     

Survival after resection of perihilar cholangiocarcinoma—development and external validation of a prognostic nomogram

BackgroundThe objective of this study was to derive and validate a prognostic nomogram to predict disease-specific survival (DSS) after a curative intent resection of perihilar cholangiocarcinoma (PHC).Patients and methodsA nomogram was developed from 173 patients treated at Memorial Sloan Kettering Cancer Center (MSKCC), New York, USA. The nomogram was externally validated in 133 patients treated at the Academic Medical Center (AMC), Amsterdam, The Netherlands. Prognostic accuracy was assessed with concordance estimates and calibration, and compared with the American Joint Committee on Cancer (AJCC) staging system. The nomogram will be available as web-based calculator at mskcc.org/nomograms.ResultsFor all 306 patients, the median overall survival (OS) was 40 months and the median DSS 41 months. Median follow-up for patients alive at last follow-up was 48 months. Lymph node involvement, resection margin status, and tumor differentiation were independent prognostic factors in the derivation cohort (MSKCC). A nomogram with these prognostic factors had a concordance index of 0.73 compared with 0.66 for the AJCC staging system. In the validation cohort (AMC), the concordance index was 0.72, compared with 0.60 for the AJCC staging system. Calibration was good in the derivation cohort; in the validation cohort patients had a better median DSS than predicted by the model.ConclusionsThe proposed nomogram to predict DSS after curative intent resection of PHC had a better prognostic accuracy than the AJCC staging system. Calibration was suboptimal because DSS differed between the two institutions. The nomogram can inform patients and physicians, guide shared decision making for adjuvant therapy, and stratify patients in future randomized, controlled trials.     

基于阴性淋巴结数目的胃印戒细胞癌预后评估模型的建立与验证

目的 探讨阴性淋巴结数目（NLNC）对胃印戒细胞癌（GSRC）患者预后的影响及构建G S R C 患者的预后预测模型。方法 基于SEER数据库收集GSRC患者2101例，随机分为建模组和验证组，检验临床病理特征与GSRC预后的关系。多因素Cox比例风险回归模型分析影响总生存的独立危险因素并建立预后预测模型。一致性指数（C?index）、校准曲线、净分类指数（NRI）、综合判别指数（IDI）和临床决策曲线（DCA）对列线图进行准确性和临床适用性评估。结果 所有患者按照7:3比例划分，建模组1473例，验证组628例。NLNC>10是GSRC患者预后的保护因素（HR=0.578, 95%CI: 0.504~0.662），根据多因素Cox比例风险回归模型筛选的变量建立Nomogram图，建模组和验证组的C-index分别为0.737（95%CI: 0.720~0.753）和0.724（95%CI: 0.699~0.749），区分度良好，校准曲线显示模型的一致性较高。NRI=17.77%，连续NRI=36.34%，IDI=4.2%，表明该模型较传统模型是正向收益，DCA决策曲线远离基准线表明模型临床适用性好。结论 NLNC增加是GSRC患者预后的有利因素。本研究建立的列线图相对准确，可预测GSRC患者的预后。     

Prognostic stratification based on a novel nomogram for left-sided pancreatic adenocarcinoma after surgical resection: a multi-center study

Left-sided pancreatic adenocarcinoma (LPAC) has a poorer prognosis and has some distinct features compared to cancer of pancreatic head. A reliable model to predict the prognosis of LPAC following surgery is needed in clinical practice. Our study included 231 patients with resected LPAC from 3 Chinese pancreatic disease centers. Cox-regression analysis was conducted to identify independent risk factors of LAPC. Then we established a nomogram and performed C-index, receiver operating characteristic curve, calibration plot and decision curve analysis to assess its discrimination and calibration. As a result, CA19-9, surgical margin, tumor differentiation, lymph node metastasis, and postoperative adjuvant chemotherapy were identified as significant prognostic factors. Based on these predictors, a novel nomogram was constructed. The nomogram achieved high C-indexes in the training cohort (0.805) and validation cohort (0.719), which were superior than the AJCC-8 staging system and other nomograms. The area under curve of the nomogram for predicting patients survival at 1-, 2-, and 3-year in training cohort were more than 0.8. Kaplan-Meier survival curve for the subgroups stratified based on the nomogram showed a better separation than the AJCC-8 stage I, II, III, indicating a superior ability of risk stratification for our model. In summary, we constructed a nomogram which showed a better predictive ability for patients’ survival with LPAC after surgical resection than the AJCC staging system and other predictive models. Our model would be helpful to discriminate high-risk LPAC and facilitate clinical decision making.     

A population-based study: how to identify high-risk T1 gastric cancer patients?

In T1 gastric cancer (GC), lymph nodes metastasis (LNM) is considered as a significant prognostic predictor and closely associated with following therapeutic approaches as well as distant metastasis (DM). This study aimed to not only seek risk factors of LNM and DM but also unpack the prognosis in T1 GC patients. We performed a retrospective study enrolling 5547 patients in T1 GC from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression models were produced to recognize independent risk factors of LNM and DM. Cox regression analyses were performed to identify important prognostic factors of overall survival (OS). Cancer-specific cumulative incidence was plotted by cumulative incidence function. Three nomograms of LNM, DM and OS were established and validated by receiver operating characteristic (ROC) and calibration curves to evaluate discrimination and accuracy. Decision curve analysis (DCA), clinical impact curves (CIC) and subgroups based on risk scores were constructed to measure nomograms clinical utility. The area under the curve (AUC) of LNM nomogram and DM nomogram were 0.735 and 0.896, respectively. OS nomogram was constructed and the corresponding C-index was 0.797. In conclusion, our user-friendly nomograms, which aimed to predict LNM, DM and OS in T1 gastric cancer patients, have shown high efficiency of discrimination and accuracy. These useful and visual tools may have advantageous clinical utility to identify high-risk T1 gastric patients and help clinicians to draw up an individual therapeutic strategy.     

Development and validation of a new clinical staging system to predict survival for esophageal squamous cell carcinoma patients: Application of the nomogram

IntroductionSurvival of patients with the same clinical stage varies widely and effective tools to evaluate the prognosis utilizing clinical staging information is lacking. This study aimed to develop a clinical nomogram for predicting survival of patients with Esophageal Squamous Cell Carcinoma (ESCC).Materials and methodsOn the basis of data extracted from the SEER database (training cohort, n = 3375), we identified and integrated significant prognostic factors for nomogram development and internal validation. The model was then subjected to external validation with a separate dataset obtained from Jinling Hospital of Nanjing Medical University (validation cohort, n = 1187). The predictive accuracy and discriminative ability of the nomogram were determined by concordance index (C-index), Akaike information criterion (AIC) and calibration curves. And risk group stratification was performed basing on the nomogram scores.ResultsOn multivariable analysis of the training cohort, seven independent prognostic factors were identified and included into the nomogram. Calibration curves presented good consistency between the nomogram prediction and actual observation for 1-, 3-, and 5-year OS. The AIC value of the nomogram was lower than that of the 8th edition American Joint Committee on Cancer TNM (AJCC) staging system, whereas the C-index of the nomogram was significantly higher than that of the AJCC staging system. The risk groups stratified by CART allowed significant distinction between survival curves within respective clinical TNM categories.ConclusionsThe risk stratification system presented better discriminative ability for survival prediction than current clinical staging system and might help clinicians in decision making.     

Nomogram for the prediction of lymph node metastasis and survival outcomes in rectal neuroendocrine tumour patients undergoing resection

BackgroundThe current study analysed rectal neuroendocrine tumour (RNET) patients undergoing resection to identify predictive factors and construct nomograms for lymph node metastasis, cancer-specific survival (CSS) and overall survival (OS).MethodsRNET patients registered in the Surveillance, Epidemiology, and End Results (SEER) database were included in this study. Multivariable logistic regression analysis was used to investigate the relationships between clinicopathological factors and lymph node metastasis. A multivariate competing risk model was applied to investigate factors independently associated with CSS. Through the Cox regression model, a multivariable analysis of OS was performed. Nomograms were established based on independent predictive factors. Calibration plots, receiver operating characteristic (ROC) curves and Brier scores were used to evaluate the predictive accuracy of the nomograms.ResultsIn this study, 1,253 RNET patients were included for further analysis. Tumour size ≥12 mm (P<0.001), T3/T4 stage (P<0.001) and M1 stage (P=0.001) were independently associated with lymph node metastasis. The performance of the nomogram was acceptable for predicting lymph node metastasis, with an area under the ROC curve (AUC) of 0.937 [95% confidence interval (CI): 0.874–1.000]. Calibration curves and the Hosmer-Lemeshow test revealed desirable model calibration (P=0.99996). The multivariate competing risk model analysis showed that grade II (P=0.017), tumour size ≥12 mm (P=0.007), AJCC TNM stage II (P=0.002), stage III (P<0.001) and stage IV (P<0.001) were significantly associated with worse CSS. In the competing risk nomogram model, the time-dependent AUC revealed good discriminatory ability of the model (time from 1 to 107 months, AUC >0.900), and the Brier score showed good accuracy of the nomogram, which was greater than that of the AJCC TNM stage. Multivariate Cox analysis showed that age >60 years (P=0.002), median income ≥$65,000 (P=0.013), AJCC TNM stage III (P=0.038) and AJCC TNM stage IV (P<0.001) were independently associated with worse OS. In the nomogram for the prediction of OS, the C-statistic was 0.703 (95% CI: 0.615–0.792), which was significantly better than that of the AJCC TNM stage (0.703 vs. 0.607, P=0.009). A calibration plot for the probability of survival demonstrated good calibration.ConclusionsThe present study is the first to establish nomograms with great discrimination and accuracy for the prediction of lymph node metastases, CSS and OS in RNET patients, which can be used to guide treatment decision-making and surveillance.     

A prognostic nomogram for colorectal cancer liver metastases after percutaneous thermal ablation

Purpose: To assess the efficacy of percutaneous thermal ablation in treating colorectal cancer liver metastases (CRCLM), and to propose a prognostic nomogram for overall survival (OS).

Materials and methods: Seventy-one patients with CRCLM undergoing thermal ablation at our institute from 2009 to 2013 were identified and analysed to formulate a prognostic nomogram. The concordance index (C-index) and calibration curve were calculated to evaluate the predictive accuracy of the nomogram. The nomogram was compared with two current prognostic nomograms for patients with CRCLM who had undergone hepatectomy (Kattan) and selective internal radiation therapy (Fendler). Predictive validity was assessed in the validation cohort of 25 patients who had undergone thermal ablation from 2014 to 2016.

Results: The median OS in the primary cohort was 26.4?months, whereas the 1-, 3- and 5-year OS rates were 72.2%, 37.2% and 17%, respectively. The median progression-free survival was 4.2?months. After univariate and multivariate analysis, a prognostic nomogram was formulated based on four predictors, including the number of tumours, maximum diameter of the tumour, CA19–9 level and ablation margin. The C-index of the nomogram was 0.815. Based on the patients of this study, the C-index was significantly higher than that of the Fendler nomogram (C-index, 0.698) and Kattan nomogram (C-index, 0.514, p?Conclusions: Thermal ablation was an effective therapy for CRCLM. Moreover, the nomogram was effective and simple for CRCLM patients undergoing thermal ablation.     

Prognostic performance of different lymph node classification systems in young gastric cancer

BackgroundAccurate staging plays a pivotal role in cancer care. The lymph node (LN) ratio (LNR) and the log odds of positive LNs (LODDS) have been suggested as alternatives to the N staging since the TNM system has the risk of stage migration. The prognostic significance of LNR and LODDS in young patients with gastric cancer (GC) has not been reported. This study aims to investigate the correlations between LNR and LODDS and survival of young patients with GC, and compare the predictive performance of these LN staging methods.MethodsGC patients before the age of 40 from 2004 to 2016 in the Surveillance, Epidemiology and End Results database were enrolled. The prognostic evaluation of the N factor, LNR and LODDS was compared using the time-dependent receiver operating characteristic (ROC) analysis, area under the curve (AUC), C-index and Akaike information criterion (AIC).ResultsMultivariate survival analysis identified that the LNR and LODDS were significantly independent prognostic indicators for overall survival (OS) in young patients with GC and in the subgroups comprised of patients with ≤15 LNs examined. The time-dependent ROC curves of the LNR and LODDS were continuously superior to that of the N factor in predicting OS during the observation period. And the AUCs revealed that the predictive accuracy of the LNR and LODDS was remarkably superior to the N factor at 1 and 3 years (P<0.05). The model incorporating LNR or LODDS had higher C-index and lower AIC when comparing to the model incorporating the N factor.ConclusionsThe LNR and LODDS improve accuracy of survival risk prediction in young patients with GC when comparing to the N factor. These two novel LN classification methods should be considered as alternatives to the N staging for the prognostic prediction of young patients with GC.     

External validation of three lymph node ratio-based nomograms predicting survival using an international cohort of patients with resected pancreatic head ductal adenocarcinoma

IntroductionLymph node ratio (LNR) is an important prognostic factor of survival in patients with pancreatic ductal adenocarcinoma (PDAC). This study aimed to validate three LNR-based nomograms using an international cohort.Materials and methodsConsecutive PDAC patients who underwent upfront pancreatoduodenectomy from six centers (Europe/USA) were collected (2000–2017). Patients with metastases, R2 resection, missing LNR data, and who died within 90 postoperative days were excluded. The updated Amsterdam nomogram, the nomogram by Pu et al., and the nomogram by Li et al. were selected. For the validation, calibration, discrimination capacity, and clinical utility were assessed.ResultsAfter exclusion of 176 patients, 1′113 patients were included. Median overall survival (OS) of the cohort was 23 months (95% CI: 21–25).For the three nomograms, Kaplan-Meier curves showed significant OS diminution with increasing scores (p < 0.01). All nomograms showed good calibration (non-significant Hosmer-Lemeshow tests). For the Amsterdam nomogram, area under the ROC curve (AUROC) for 3-year OS was 0.64 and 0.67 for 5-year OS. Sensitivity and specificity for 3-year OS prediction were 65% and 59%. Regarding the nomogram by Pu et al., AUROC for 3- and 5-year OS were 0.66 and 0.70. Sensitivity and specificity for 3-year OS prediction were 68% and 53%. For the Li nomogram, AUROC for 3- and 5-year OS were 0.67 and 0.71, while sensitivity and specificity for 3-year OS prediction were 63% and 60%.ConclusionThe three nomograms were validated using an international cohort. Those nomograms can be used in clinical practice to evaluate survival after pancreatoduodenectomy for PDAC.     

A novel prognostic nomogram for hepatocellular carcinoma after thermal ablation

It remains impossible to accurately assess the prognosis after thermal ablation in patients with hepatocellular carcinoma (HCC). Our aim was to build a nomogram to predict the survival rate of HCC patients after thermal ablation. We developed and validated a nomogram using data of 959 HCC patients after thermal ablation from two centers. Harrell’s concordance index (C-index), calibration plot and Decision curve analysis (DCA) were used to measure the performance of the nomogram, and we compared it with the Barcelona Clinic Liver Cancer (BCLC) staging system and a previous nomogram. Six variables including age, serum albumin, operation method, risk area, tumor number and early recurrence were selected to construct the nomogram. In the training cohort, internal validation cohort, and external validation cohort, the nomogram all had a higher C-index to predict survival rate than both the BCLC staging system and the previous nomogram (0.736, 0.558 and 0.698, respectively; 0.763, 0.621 and 0.740, respectively; and 0.825, 0.551 and 0.737, respectively). Calibration plots showed a high degree of consistency between prediction and actual observation. Decision curve analysis (DCA) presented that compared with BCLC system and the previous nomogram, our nomogram had the highest net benefit. In all three cohorts, the nomogram could accurately divide patients into three subgroups according to predicted survival risk. A nomogram was developed and validated to predict survival of HCC patients who underwent thermal ablation, which is helpful for prognostic prediction and individual surveillance in clinical practice.     

Nomograms for survival prediction in patients undergoing liver resection for hepatitis B virus related early stage hepatocellular carcinoma

BackgroundThe long-term outcomes of patients who underwent liver resection (LR) for early-stage hepatitis B virus (HBV)-related hepatocellular carcinomas (HCCs) are difficult to predict. This study aimed to develop two nomograms to predict postoperative disease-free survival (DFS) and overall survival (OS), respectively.MethodsData on a primary cohort of 1328 patients who underwent LR for HBV-related HCCs within Milan criteria at the Eastern Hepatobiliary Surgery Hospital (EHBH) from 2000 to 2006 were used to develop the nomograms by the Cox regression analyses. An internal validation cohort of 442 patients operated from 2006 to 2011 at the EHBH and an external validation cohort of 474 patients operated from 2007 to 2009 at the Zhongshan Hospital were used for validation studies. Discrimination and calibration were measured using concordance index (C-index), calibration plots and Kaplan–Meier curves.ResultsThe independent predictors of DFS or OS which included tumour stage factors, biomarker and HBV–DNA level were respectively incorporated into the two nomograms. In the primary cohort, the C-indexes of the models in predicting DFS and OS were 0.76 (95% confidence interval: 0.75–0.78) and 0.79 (0.77–0.81), respectively. The calibration curves fitted well. Both nomograms accurately stratify patients into four distinct incremental prognostic subgroups. The C-indexes of the nomogram for OS prediction was significantly higher than those of the six conventional staging systems (0.65–0.71, all P < 0.001). These results were verified by the internal and external validations.ConclusionThe proposed nomograms showed good prognostication for patients with early HBV-related HCCs after hepatectomy.     

四肢纤维肉瘤患者预后诺模图的开发和验证

目的:开发诺模图来预测原发于四肢纤维肉瘤患者的总体生存率(OS)和癌症特异性生存率(CSS)。方法:根据SEER数据库,收集原发于四肢纤维肉瘤病例。采用Cox比例风险回归模型对病例预后进行分析,获得独立的预测因素。这些独立的预测因子被整合在一起,形成了预测5年和10年OS及CSS的诺模图。使用R软件通过一致性指数(C-index指数）、ROC曲线和校准曲线图来评估诺模图的性能。结果:在OS的单因素和多因素分析中,年龄、病理分级、肿瘤大小和手术被确定为独立的危险因素。 在CSS的单变量和多变量分析中,病理分级、肿瘤大小和肿瘤分期被确定为独立的危险因素。 这些特征均整合在诺模图中以预测5年和10年OS和CSS,C指数分别为0.812和0.857。通过5年和10年OS和CSS的概率的C-index指数和AUG曲线显示,诺模图预测和观察结果之间具有很好的一致性。结论:诺模图可以准确地预测四肢纤维肉瘤患者的OS和CSS,并有助于个性化的预后评估和个性化的临床决策。