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.     

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...     

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 preoperative MRI-based radiomics nomogram outperforms traditional models for prognostic prediction in pancreatic ductal adenocarcinoma

To develop an efficient prognostic model based on preoperative magnetic resonance imaging (MRI) radiomics for patients with pancreatic ductal adenocarcinoma (PDAC), the preoperative MRI data of PDAC patients in two independent centers (defined as development cohort and validation cohort, respectively) were collected retrospectively, and the radiomics features of tumors were then extracted. Based on the optimal radiomics features which were significantly related to overall survival (OS) and progression-free survival (PFS), the score of radiomics signature (Rad-score) was calculated, and its predictive efficiency was evaluated according to the area under receiver operator characteristic curve (AUC). Subsequently, the clinical-radiomics nomogram which incorporated the Rad-score and clinical parameters was developed, and its discrimination, consistency and application value were tested by calibration curve, concordance index (C-index) and decision curve analysis (DCA). Moreover, the predictive value of the clinical-radiomics nomogram was compared with traditional prognostic models. A total of 196 eligible PDAC patients were enrolled in this study. The AUC value of Rad-score for OS and PFS in development cohort was 0.724 and 0.781, respectively, and the value of Rad-score was negatively correlated with PDAC’s prognosis. Moreover, the developed clinical-radiomics nomogram showed great consistency with the C-index for OS and PFS in development cohort was 0.814 and 0.767, respectively. In addition, the DCA demonstrated that the developed nomogram displayed better clinical predictive usefulness than traditional prognostic models. We concluded that the preoperative MRI-based radiomics signature was significantly related to the poor prognosis of PDAC patients, and the developed clinical-radiomics nomogram showed better predictive ability, it might be used for individualized prognostic assessment of preoperative patients with PDAC.     

Development and validation of prognostic nomogram based on log odds of positive lymph nodes for patients with gastric signet ring cell carcinoma

ObjectiveOur aims were to establish novel nomogram models, which directly targeted patients with signet ring cell carcinoma (SRC), for individualized prediction of overall survival (OS) rate and cancer-specific survival (CSS).MethodsWe selected 1,365 SRC patients diagnosed from 2010 to 2015 from Surveillance, Epidemiology and End Results (SEER) database, and then randomly partitioned them into a training cohort and a validation cohort. Independent predicted indicators, which were identified by using univariate testing and multivariate analyses, were used to construct our prognostic nomogram models. Three methods, Harrell concordance index (C-index), receiver operating characteristics (ROC) curve and calibration curve, were used to assess the ability of discrimination and predictive accuracy. Integrated discrimination improvement (IDI), net reclassification improvement (NRI) and decision curve analysis (DCA) were used to assess clinical utility of our nomogram models.ResultsSix independent predicted indicators, age, race, log odds of positive lymph nodes (LODDS), T stage, M stage and tumor size, were associated with OS rate. Nevertheless, only five independent predicted indicators were associated with CSS except race. The developed nomograms based on those independent predicted factors showed reliable discrimination. C-index of our nomogram for OS and CSS was 0.760 and 0.763, which were higher than American Joint Committee on Cancer (AJCC) 8th edition tumor-node-metastasis (TNM) staging system (0.734 and 0.741, respectively). C-index of validation cohort for OS was 0.757 and for CSS was 0.773. The calibration curves also performed good consistency. IDI, NRI and DCA showed the nomograms for both OS and CSS had a comparable clinical utility than the TNM staging system.ConclusionsThe novel nomogram models based on LODDS provided satisfying predictive ability of SRC both in OS and CSS than AJCC 8th edition TNM staging system alone.     

基于列线图构建和验证肝细胞癌伴门静脉癌栓患者肝切除术后的预后评估模型

目的 分析影响肝细胞癌伴门静脉癌栓（PVTT-HCC）患者肝切除术后预后的影响因素,并基于列线图模型构建和验证预后评估模型。方法 本研究为回顾性队列研究,选择2008年1月—2017年11月在本院行肝切除术的PVTT-HCC患者为研究对象,随访截至2021年1月。主要预测结局为1、3、5年总生存率。按照7∶3的比例将患者随机分为训练集和验证集,在训练集中采用Cox比例风险回归分析影响预后的影响,并基于影响因素构建列线图模型。同时在训练集和验证集中采用C-index评价模型的区分度,一致性曲线评估模型的校准度。结果 共231例患者符合纳入排除标准纳入分析,其中训练集162例,验证集69例。Cox比例风险回归模型显示,AFP≥400 μg/L、AST≥40 U/L、ALP≥80 U/L、肿瘤个数>1个及肿瘤包膜不完整是影响预后的危险因素。在训练集中,列线图模型预测1、3、5年总生存率的C-index分别为0.826（95%CI: 0.791~0.861）、0.818（95%CI：0.782~0.854）、0.781（95%CI：0.742~0.820）,在验证集中分别为0.814（95%CI：0.777~0.851）、0.798（95%CI：0.758~0.837）、0.769（95%CI：0.728~0.810）。校正曲线显示列线图模型在训练集和验证集均有较好的校准度。结论 本研究构建的列线图模型可准确预测PVTT-HCC患者的预后。     

Nomogram for Predicting the Overall Survival of Patients With Breast Cancer With Pathologic Nodal Status N3

BackgroundPatients with breast cancer with pathologic N3 (pN3) lymph node status have been proven to have a poor prognosis. This study aimed to establish a nomogram to predict overall survival (OS) in patients with pN3 breast cancer.Materials and MethodsThe eligible patients from the Surveillance, Epidemiology, and End Results (SEER) database were randomly divided into training and validation cohorts. χ² tests and survival curves were performed to define the consistency between these 2 cohorts. Univariate and multivariate logistic regressions were carried out to identify the independent clinicopathologic factors of patients with pN3 breast cancer. A nomogram was developed and validated internally and externally by a calibration curve and compared with the seventh edition American Joint Committee on Cancer TNM staging classification in discrimination ability.ResultsRace, age at diagnosis, marital status, grade, T stage, N stage, breast cancer subtype, surgery, radiotherapy, and chemotherapy were independent predictive factors of OS in pN3 breast cancer. We developed a nomogram to predict 1-, 3-, and 5-year OS and further validated it in both cohorts, demonstrating better prediction capacity in OS than that of the seventh edition American Joint Committee on Cancer TNM staging classification (area under the curve in the receiver operating characteristic curve, 0.745 and 0.611 in the training cohort and 0.768 and 0.624 in the validation cohort, respectively).ConclusionWe have developed and validated the first nomogram for predicting the survival of pN3 breast cancer. This nomogram accurately and reliably predicted the OS of patients with pN3 breast cancer. However, more prognostic factors need to be further explored to improve the nomogram.     

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.     

宫颈癌术后患者列线图预测模型及危险分层系统构建

目的 构建宫颈癌术后患者列线图预测模型,基于列线图个体得分建立危险分层系统。方法 通过搜索美国SEER （Surveillance,Epidemiology,and End Results）数据库中1973—2015年的6 835例宫颈癌术后患者数据构建预测模型,同时选取120例于苏州大学附属第二医院接受宫颈癌手术的患者作为外部验证队列。通过单因素和多因素的Cox回归筛选预后因子并构建列线图,基于列线图模型建立危险分层系统。结果 Cox回归分析显示诊断年龄、人种、组织学分级、T分期、N分期、淋巴结清扫状况、肿瘤大小、肿瘤浸润深度是宫颈癌术后患者的独立预后指标。由此构建的列线图模型的一致性指数在建模队列、内部验证队列和外部验证队列分别为0.824、0.814、0.730,校准曲线显示模型预测效果与实际生存情况基本相符,危险分层系统能区分不同FIGO分期患者的生存情况（均P<0.05）。结论 本研究所建立的列线图模型能有效预测宫颈癌术后患者预后,基于该列线图预测模型的危险分层系统对区分高危患者具有一定临床价值。     

Preoperative nomogram to predict survival following colorectal cancer liver metastasis simultaneous resection

BackgroundSimultaneous resection for patients with synchronous colorectal cancer liver metastases (CRLM) remains an optimal option for the sake of curability. However, few studies so far focus on outcome of this subgroup of patients (who receive simultaneous resection for CRLM). Substantial heterogeneity exists among such patients and more precise categorization is needed preoperatively to identify those who may benefit more from surgery. In this study, we formulated this internally validated scoring system as an option.MethodsClinicopathological and follow-up data of 234 eligible CRLM patients undergoing simultaneous resection from January 2010 to March 2019 in our center were included for analysis. Patients were randomized to either a training or validation cohort. We performed multivariable Cox regression analysis to determine preoperative factors with prognostic significance using data in training cohort, and a nomogram scoring system was thus established. Time-dependent receiver operating characteristic (ROC) curve and calibration plot were adopted to evaluate the predictive power of our risk model.ResultsIn the multivariable Cox regression analysis, five factors including presence of node-positive primary defined by enhanced CT/MR, preoperative CEA level, primary tumor location, tumor grade and number of liver metastases were identified as independent prognostic indicators of overall survival (OS) and adopted to formulate the nomogram. In the training cohort, calibration plot graphically showed good fitness between estimated and actual 1- and 3-year OS. Time-dependent ROC curve by Kaplan-Meier method showed that our nomogram model was superior to widely used Fong’s score in prediction of 1- and 3-year OS (AUC 0.702 vs. 0.591 and 0.848 vs. 0.801 for 1- and 3-year prediction in validation cohort, respectively). Kaplan-Meier curves for patients stratified by the assessment of nomogram showed great discriminability (P<0.001).ConclusionsIn this retrospective analysis we identified several preoperative factors affecting survival of synchronous CRLM patients undergoing simultaneous resection. We also constructed and validated a risk model which showed high accuracy in predicting 1- and 3-year survival after surgery. Our risk model is expected to serve as a predictive tool for CRLM patients receiving simultaneous resection and assist physicians to make treatment decision.     

卵巢癌免疫相关预后模型的构建与验证

目的 构建卵巢癌免疫相关预后模型并初步筛选预后标志物。方法 将癌症基因组图谱（TCGA）中卵巢癌样本按照7∶3的比例随机分为训练集和测试集,GSE26712作为外部验证集。通过limma软件包分析免疫相关的差异基因（IRDEGs）,单因素Cox回归筛选预后相关的IRDEGs,通过LASSO回归和多因素Cox回归构建模型,采用受试者工作特征（ROC）曲线和C-index对模型进行评价。构建列线图模型,通过校准曲线和决策曲线评价列线图的预测性能。 结果 本研究在训练集中成功构建了包含11个基因（C5AR1、CX3CR1、CXCL11、CXCL13、IGF1、IL27RA、NFKBIB、PENK、PI3、PSMC1和PSME3）的预后模型,C-index为0.69,1、3、5年的曲线下面积分别为0.67、0.71和0.75;多因素Cox回归分析显示该风险模型是卵巢癌患者的独立预后因素（HR=2.58, 95%CI=2.15~3.25）。基于风险得分成功构建了列线图模型,其校准曲线拟合良好,决策曲线显示列线图在指导临床决策方面具有积极的净收益。结论 本研究构建的免疫相关预后模型在卵巢癌预后预测中具有良好的效能,其中的相关基因可能是卵巢癌患者免疫治疗的潜在标志物。     

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.     

Validation of a postresection pancreatic adenocarcinoma nomogram for disease-specific survival.

PURPOSE: Nomograms are statistically based tools that provide the overall probability of a specific outcome. They have shown better individual discrimination than the current TNM staging system in numerous patient tumor models. The pancreatic nomogram combines individual clinicopathologic and operative data to predict disease-specific survival at 1, 2, and 3 years from initial resection. A single US institution database was used to test the validity of the pancreatic adenocarcinoma nomogram established at Memorial Sloan-Kettering Cancer Center. PATIENTS AND METHODS: The nomogram was created from a prospective pancreatic adenocarcinoma database that included 555 consecutive patients between October 1983 and April 2000. The nomogram was validated by an external patient cohort from a retrospective pancreatic adenocarcinoma database at Massachusetts General Hospital that included 424 consecutive patients between January 1985 and December 2003. RESULTS: Of the 424 patients, 375 had all variables documented. At last follow-up, 99 patients were alive, with a median follow-up time of 27 months (range, 2 to 151 months). The 1-, 2-, and 3-year disease-specific survival rates were 68% (95% CI, 63% to 72%), 39% (95% CI, 34% to 44%), and 27% (95% CI, 23% to 32%), respectively. The nomogram concordance index was 0.62 compared with 0.59 with the American Joint Committee on Cancer (AJCC) stage (P = .004). This suggests that the nomogram discriminates disease-specific survival better than the AJCC staging system. CONCLUSION: The pancreatic cancer nomogram provides more accurate survival predictions than the AJCC staging system when applied to an external patient cohort. The nomogram may aid in more accurately counseling patients and in better stratifying patients for clinical trials and molecular tumor analysis.     

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.     

基于深度学习算法开发和验证的肝细胞癌预后预测模型：一项大样本队列和外部验证研究

目的 基于深度学习算法开发和验证可评估肝细胞癌（hepatocellular carcinoma,HCC）患者预后的预测模型,并评估其价值。方法 选择2011年1月—2015年12月美国国立癌症研究所的监测、流行病学和最终结果（Surveillance,Epidemiology and Results,SEER）数据库中经病理确诊的HCC患者为训练队列用于构建模型,选择同期在本院经病理确诊的HCC患者为外部验证队列用于评估模型。主要预测结局为1、3、5年总生存率。分别使用深度学习算法DeepSurv、随机生存森林（RFS）、Cox比例风险回归开发模型,使用C-index评价模型的区分度,一致性曲线评估模型的校准度,log-rank检验评估危险分层能力。结果 训练队列9 730例患者和外部验证队列405例患者被纳入分析。在训练队列中,DeepSurv算法1、3、5年的C-index分别为0.85 （95%CI：0.80~0.90）、0.82 （95%CI：0.77~0.89）、0.80 （95%CI：0.73~0.87）,在外部验证队列中分别为0.83 （95%CI：0.78~0.87）、0.79 （95%CI：0.74~0.83）、0.72 （95%CI：0.67~0.77）。无论在训练队列还是外部验证队列中,DeepSurv算法的C-index和校准度均优于RFS、Cox回归和TNM分期（均P<0.05）;log-rank检验显示,DeepSurv算法具有良好的危险分层能力（P<0.001）。结论 基于深度学习算法DeepSurv开发的预测模型可有效预测HCC患者死亡率,且优于常规的算法和预后评价指标。       

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.     

Development and validation of a nomogram for predicting the survival of patients with non-metastatic nasopharyngeal carcinoma after curative treatment

Background:The TNM staging system is far from perfect in predicting the survival of individual cancer patients because only the gross anatomy is considered. The survival rates of the patients who have the same TNM stage disease vary across a wide spectrum. This study aimed to develop a nomogram that incorporates other clinicopatho-logic factors for predicting the overall survival (OS) of non-metastatic nasopharyngeal carcinoma (NPC) patients after curative treatments. Methods:We retrospectively collected the clinical data of 1520 NPC patients who were diagnosed histologically between November 2000 and September 2003. The clinical data of a separate cohort of 464 patients who received intensity-modulated radiation therapy (IMRT) between 2001 and 2010 were also retrieved to examine the extensibil-ity of the model. Cox regression analysis was used to identify the prognostic factors for building the nomogram. The predictive accuracy and discriminative ability were measured using the concordance index (c-index). Results:We identiifed and incorporated 12 independent clinical factors into the nomogram. The calibration curves showed that the prediction of OS was in good agreement with the actual observation in the internal validation set and IMRT cohort. The c-index of the nomogram was statistically higher than that of the 7th edition TNM staging sys-tem for predicting the survival in both the primary cohort (0.69 vs. 0.62) and the IMRT cohort (0.67 vs. 0.63). Conclusion:We developed and validated a novel nomogram that outperformed the TNM staging system in predict-ing the OS of non-metastatic NPC patients who underwent curative therapy.     

胰头癌根治性胰十二指肠切除术后早期复发预测模型的建立与应用

目的 构建可预测胰头癌根治性胰十二指肠切除术后早期复发的列线图模型,并评估其应用价值。方法 本研究为一项回顾性队列研究,选择2017年6月—2019年7月在本院行胰头癌根治性胰十二指肠切除术的患者为研究对象。研究结局为术后早期复发,采用单因素和多因素logistic回归分析早期复发的影响因素,并基于影响因素构建列线图模型。采用受试者工作特征（ROC）曲线下面积（AUC）评估列线图模型的区分度,校准曲线和Hosmer-Lemeshow检验评估校准度,决策曲线评估临床应用价值。结果 共137例患者符合标准纳入最终分析,术后早期复发58例（42.3%）。多因素logistic回归显示,肿瘤大小≥3 cm、术前CA19-9水平>37 U/mL、肿瘤分化程度低分化和淋巴结转移数目>3枚是影响患者术后早期复发的危险因素（均P<0.05）,基于这些因素成功构建了列线图模型,AUC为0.807 （95%CI：0.729~0.885）,校准曲线Hosmer-Lemeshow检验表明模型具有良好的校准度（P=0.569）。决策曲线显示,列线图具有良好的临床应用价值,即预测早期复发概率达到22%时,可采取干预。结论 本研究成功构建可预测胰头癌根治性胰十二指肠切除术后早期复发的列线图模型,有助于临床早期筛选并识别风险患者。     

Clinicopathologic features,treatment, survival,and prognostic factors of combined hepatocellular and cholangiocarcinoma: A nomogram development based on SEER database and validation in multicenter study

PurposeThe aim of the study was to comprehensively understand the combined hepatocellular and cholangiocarcinoma (CHC) and develop a nomogram for prognostic prediction of CHC.MethodsData were collected from the Surveillance, Epidemiology and End Results (SEER) database (year 2004–2014). Propensity-score matching (PSM) was used to match the demographic characteristic of the CHC versus hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). A nomogram model was established to predict the prognosis in terms of cancer specific survival (CSS). The established nomogram was externally validated by a multicenter cohort.ResultsA total of 71,756 patients enrolled in our study including 62,877 HCC patients, 566 CHC patients, and 8303 ICC patients. The CHC, HCC, and ICC are not exactly similar in clinical characteristic. After PSM, the CSS of CHC was better than HCC but comparable to ICC. Tumor size, M stage, surgery, chemotherapy, and surgery were independently prognostic factors of CHC and were included in the establishment of novel nomogram.The c-index of the novel nomogram in SEER training set and multicenter validation was 0.779 and 0.780, respectively, which indicated that the model was with better discrimination power. In addition, decision curve analyses proved the favorable potential clinical effect of the predictive model. Lastly, a risk classification based on nomogram also verified the reliability of the model.ConclusionCHC had better survival than HCC but was comparable to ICC. The nomogram was established based on tumor size, M stage, chemotherapy, surgery, and radiotherapy and well validated by external multicenter cohort.     

Prognostic nomogram for nonresectable pancreatic cancer treated with gemcitabine-based chemotherapy

Background:

A nomogram is progressively being used as a useful predictive tool for cancer prognosis. A nomogram to predict survival in nonresectable pancreatic cancer treated with chemotherapy has not been reported.

Methods:

Using prospectively collected data on patients with nonresectable pancreatic cancer receiving gemcitabine-based chemotherapy at five Japanese hospitals, we derived a predictive nomogram and internally validated it using a concordance index and calibration plots.

Results:

In total, 531 patients were included between June 2001 and February 2013. The American Joint Committee on Cancer (AJCC) TNM stages were III and IV in 204 and 327 patients, respectively. The median survival time of the total cohort was 11.3 months. A nomogram was generated to predict survival probabilities at 6, 12, and 18 months and median survival time, based on the following six variables: age; sex; performance status; tumour size; regional lymph node metastasis; and distant metastasis. The concordance index of the present nomogram was higher than that of the AJCC TNM staging system at 12 months (0.686 vs 0.612). The calibration plots demonstrated good fitness of the nomogram for survival prediction.

Conclusions:

The present nomogram can provide valuable information for tailored decision-making early after the diagnosis of nonresectable pancreatic cancer.