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.     

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.     

Prognostic impact of lymph node harvest for patients with node-negative esophageal squamous cell carcinoma: a large-scale multicenter study

BackgroundWe examined the association between the number of resected lymph nodes and survival to determine the optimal lymphadenectomy for thoracic esophageal squamous cell carcinoma (ESCC) patients with negative lymph node.MethodsWe included 1,836 patients from Chinese three high-volumed hospitals with corresponding clinicopathological characters such as gender, age, tumor location, tumor grade and TNM stage of patients. The median follow-up of included patients was 45.7 months (range, 1.03–117.3 months). X-Tile plot was used to identify the lowest number of lymphadenectomy. The multivariate model’s construction was in use of parameters with clinical significance for survival and a nomogram based on clinical variable with P<0.05 in Cox regression analysis. Both two models were validated using a cohort extracted from the Surveillance, Epidemiology, and End Results (SEER) 18 registries database between 1975 and 2016 (n=951).ResultsMore lymphadenectomy numbers were significantly associated with better survival in patients both in training cohort [hazard ratio (HR) =0.980; 95% confidence interval (CI): 0.971–0.988; P<0.001] and validation cohort (HR =0.980; 95% CI: 0.968–0.991; P=0.001). Cut-off point analysis determined the lowest number of 9 for thoracic ESCC patients in N0 stage through training cohort (C-index: 0.623; sensitivity: 80.7%; 1 − specificity: 72.5%) when compared with 10 in validation cohort (C-index: 0.643; sensitivity: 78.2%; 1 − specificity: 63.0%). The cut-off points of 9 were examined in training cohort and validated in the divided cohort from validation cohort (all P<0.05). Meanwhile, nomograms for both cohorts were constructed and the calibration curves for both cohorts agreed well with the actual observations in terms of predicting 3- and 5-year survival, respectively.ConclusionsLarger number for lymphadenectomy was associated with better survival in thoracic ESCC patients in N0 stage. Nine was what we got as the lowest number for lymphadenectomy in pN0 ESCC patients through this study, and our result should be confirmed further.     

Prediction of late recurrence after radiofrequency ablation of HBV-related hepatocellular carcinoma with the age-male-albumin-bilirubin-platelets (aMAP) risk score: a multicenter study

BackgroundLong-term survivals of patients with HBV-related hepatocellular carcinoma are limited by the high incidence of tumor recurrence after radiofrequency ablation (RFA), identification of the risk factors and understanding the patterns of recurrence can help to improve the comprehensive management of patients after RFA. Therefore, the purpose of the study is to explore the prognostic value of the age-male-albumin-bilirubin-platelets (aMAP) score in patients with early-stage HBV-related hepatocellular carcinoma (HCC) receiving RFA; investigate the risk factors and patterns of late recurrence (LR); and develop a nomogram to predict recurrence-free survival (RFS).MethodsA retrospective review of HBV-related HCC patients who underwent primary RFA from March 2012 to December 2020 was conducted. The prognostic value of the aMAP score was evaluated in a primary cohort (n=302) and then further validated in an independent validation cohort (n=143). The optimal threshold of aMAP scores was calculated by X-tile 3.6.1 software. A prognostic nomogram was constructed from multivariate analysis and validated in an external validation cohort.ResultsPatients with aMAP scores ≤63.8, 63.8–67.8, and >67.8 were classified into low-, medium-, and high-recurrence risk groups, respectively. The C-index to predict LR was 0.76 (95% CI: 0.700–0.810). The high-risk group was associated with the worst RFS (HR: 5.298; 95% CI, 2.697–10.408; P<0.001) and overall survival (OS) (HR: 2.639; 95% CI, 1.097–6.344; P=0.03) compared with medium- and low-risk groups. The aMAP score, multiple tumors and preoperative HBV DNA level were independent risk factors for LR. The proposed nomogram had excellent performance in predicting LR of HBV-related HCC [C-index: 0.82 (95% CI: 0.772–0.870)].ConclusionsThis study demonstrated that the aMAP score can serve as an objective predictor of LR for HBV-related HCC patients after RFA. The nomogram based on preoperative HBV DNA level, aMAP score, and number of tumors can reliably help clinicians to stratify the recurrence risk of HCC patients after RFA.     

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...     

Development and validation of a nomogram for preoperative prediction of level VII nodal spread in papillary thyroid cancer: Radiologic-pathologic correlation

PurposeTo develop and validate a diagnostic nomogram for preoperative prediction of the level VII nodal spread in papillary thyroid cancer (PTC) by incorporating CT features.MethodsA dataset of 7896 patients experiencing thyroidectomy for thyroid cancer was collected retrospectively from two hospitals, and 300 patients were finally included in this study. The CT features of metastatic LN were extracted with a one by one match of radiologic-pathologic correlation. Multivariable binary logistic regression analysis was used to develop predicting model, and then a nomogram was developed utilizing a primary cohort of 152 patients from hospital #1. The nomogram was validated in external cohort of 62 patients from hospital #2 and an independent cohort of 86 patients from hospital #1. The performance of the nomogram was evaluated with respect to its calibration, discrimination.Results531 LNs from 300 patients were analyzed. 42.6% LNs were > 5 mm in short diameter. A total of 7 selected CT features were significantly associated with LN status (P < 0.05), including nodular enhancement, cystic change, calcification and so on. These features were contained in the prediction nomogram. The model showed good discrimination and good calibration, with a C-index of 0.938 (95% CI, 0.913 to 0.963) and 0. 795 (95% CI, 0. 726 to 0.864) for the primary cohort and the validation cohort, respectively. Decision curve analysis demonstrated that the nomogram was clinically applicable.ConclusionsThis nomogram incorporating pathologically relevant CT features has demonstrated a high diagnostic value for predicting level VII nodal spread in PTC. Our work may help thyroid surgeon to decide whether upper mediastinal lymphadenectomy should be performed, which is associated with thoracotomy or other surgery.     

Development and External Validation of a Clinical Nomogram for Individually Predicting Survival of Metaplastic Breast Cancer

BackgroundFew studies have concerned the prognosis of metaplastic breast cancer (MpBC), a rare and diverse malignancy. A prognostic index estimating the MpBC survival would be attractive in clinical practice.Patients and MethodsWe retrospectively analyzed MpBC patients from the Surveillance, Epidemiology, and End Results (SEER) database. Prognostic factors were identified and the final nomogram was developed to predict the 1-, 3-, or 5-year overall survival (OS). Calibration curves were provided to internally validate the performance of the nomogram and discriminative ability was appraised by concordance index (C-index).ResultsA total of 1017 MpBC patients diagnosed between 2010 and 2015 were assigned into 3:1 as training set (n = 763) and SEER validation set (n = 254). An external validation was performed by an individual set of 94 MpBC patients from National Cancer Center in China from 2010 to 2018. The nomogram finally consisted of 7 independent prognostic factors and presented a good accuracy for predicting the OS with the C-index of 0.77 (95% CI: 0.751-0.786). Interestingly, the nomogram based on the western (including 92.5% non-Asian) SEER validation population (C-index of nomogram: 0.76, 95% CI: 0.737-0.796) also has an optimal discrimination in Asian population (C-index of nomogram: 0.70). The calibration plots of the nomogram predictions were also accurate and corresponded closely with the actual survival rates.ConclusionThis novel nomogram was accurate enough to predict the OS by using readily available clinicopathologic factors in MpBC general population, which could provide individualized recommendations for patients and clinical decisions for physicians.     

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。     

Development of a model to predict pathologic response to chemotherapy in patients with colorectal liver metastases

BackgroundPreoperative chemotherapy has widely been used in colorectal cancer liver metastasis (CRLM). Pathological response to chemotherapy is very important in evaluating tumor biology. However, there is still a lack of a non-invasive and accurate method to evaluate pathological response before surgery.MethodsWe retrospectively analyzed the clinicopathologic data of patients with CRLM who underwent liver resection after preoperative chemotherapy between January 2006 and December 2018. Pathological responses were defined as minor when there are ≥50% remnant viable cells and as major when 0–49% remnant viable cells exist.ResultsA total of 482 patients were included and randomly divided into training (n=241) and validation (n=241) cohorts. The proportion of major pathologic response was similar between the two groups (51.5% and 48.5%). Multivariate analysis determined the disease-free interval (DFI), tumor size, tumor number, and RAS status as independent predictors of major pathologic response to preoperative chemotherapy. The nomogram incorporating these variables showed good concordance statistics in the training cohort (0.746, 95% CI: 0.685–0.807) and validation cohort (0.764, 95% CI: 0.704–0.823). In addition, the nomogram showed good applicability in patients with different characteristics.ConclusionsThe established nomogram model performed well in predicting pathological response in patients with CRLM.     

Coupling radiomics analysis of CT image with diversification of tumor ecosystem: A new insight to overall survival in stage I−III colorectal cancer

ObjectiveThis study aimed to establish a method to predict the overall survival (OS) of patients with stage I−III colorectal cancer (CRC) through coupling radiomics analysis of CT images with the measurement of tumor ecosystem diversification.MethodsWe retrospectively identified 161 consecutive patients with stage I−III CRC who had underwent radical resection as a training cohort. A total of 248 patients were recruited for temporary independent validation as external validation cohort 1, with 103 patients from an external institute as the external validation cohort 2. CT image features to describe tumor spatial heterogeneity leveraging the measurement of diversification of tumor ecosystem, were extracted to build a marker, termed the EcoRad signature. Multivariate Cox regression was used to assess the EcoRad signature, with a prediction model constructed to demonstrate its incremental value to the traditional staging system for OS prediction.ResultsThe EcoRad signature was significantly associated with OS in the training cohort [hazard ratio (HR)=6.670; 95% confidence interval (95% CI): 3.433−12.956; P<0.001), external validation cohort 1 (HR=2.866; 95% CI: 1.646−4.990; P<0.001) and external validation cohort 2 (HR=3.342; 95% CI: 1.289−8.663; P=0.002). Incorporating the EcoRad signature into the prediction model presented a higher prediction ability (P<0.001) with respect to the C-index (0.813, 95% CI: 0.804−0.822 in the training cohort; 0.758, 95% CI: 0.751−0.765 in the external validation cohort 1; and 0.746, 95% CI: 0.722−0.770 in external validation cohort 2), compared with the reference model that only incorporated tumor, node, metastasis (TNM) system, as well as a better calibration, improved reclassification and superior clinical usefulness.ConclusionsThis study establishes a method to measure the spatial heterogeneity of CRC through coupling radiomics analysis with measurement of diversification of the tumor ecosystem, and suggests that this approach could effectively predict OS and could be used as a supplement for risk stratification among stage I−III CRC patients.     

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.     

Prognostic score models for survival of nasopharyngeal carcinoma patients treated with intensity-modulated radiotherapy and chemotherapy

Purpose

To establish accurate prognostic score models to predict survival for patients with nasopharyngeal carcinoma (NPC), treated with intensity-modulated radiotherapy (IMRT) and chemotherapy.

Materials and methods

Six hundred and seventy-five patients with newly diagnosed, nonmetastatic and histologically proven NPC who were treated with IMRT and chemotherapy were analyzed retrospectively. Samples were split randomly into a training set (n = 338) and a test set (n = 337) to analyze. All data from the training set were used to perform an extensive survival analysis and to develop multivariate nomograms based on Cox regression. Data from the test set was used as an external validation set. Risk group stratification was proposed for the nomograms.

Results

The nomograms are able to predict survival with a C-index for external validation of local recurrence-free survival (LRFS; 0.66, 95% CI: 0.58-0.74), distant metastasis-free survival (DMFS; 0.73, 95% CI: 0.66-0.79), and disease-specific survival (DSS; 0.73, 95% CI: 0.67-0.79). The calibration curve for probability of survival showed good agreement between prediction by nomogram and actual observation. The C-index of the nomogram for LRFS, DMFS and DSS were statistically higher than the C-index values of the AJCC seventh edition (P < 0.001). In the test set, the nomogram discrimination was also superior to the AJCC Staging systems (P < 0.001). The stratification in risk groups allows significant distinction between Kaplan-Meier curves for outcome.

Conclusions

Prognostic score models were successfully established and validated to predict LRFS, DMFS, and DSS over a 5-year period after IMRT and chemotherapy, which will be useful for individual treatment.     

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.     

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.     

Impact of metformin on survival outcome in ovarian cancer: a nationwide population-based cohort study

ObjectiveInvestigation of new drugs (INDs) is a tremendously inefficient process in terms of time and cost. Drug repositioning is another method used to investigate potential new agents in well-known drugs. This study assessed the survival impact of metformin medication on ovarian cancer.MethodsA national sample cohort of the Korean National Health Insurance Service Data was analyzed. Cox proportional hazards regression was used to analyzing hazard ratios (HRs) and 95% confidence intervals (CIs) after adjusting for underlying diseases and medications as confounding factors for overall survival (OS) and cancer-specific survival (CSS).ResultsA total of 866 eligible patients were included from among 1,025,340 cohort participants. Among them, 101 (11.7%) were metformin users. No difference in OS was observed between non-users and users. No difference in OS was observed according to age and Charlson Comorbidity Index. Long-term metformin use (≥720 days) was associated with better OS (adjusted HR=0.244; 95% CI=0.090–0.664; p=0.006). A multivariate Cox proportional hazards model showed that long-term metformin use was an independent favorable prognostic factor for OS (HR=0.193; 95% CI=0.070–0.528; p=0.001) but not for CSS (HR=0.599; 95% CI=0.178–2.017; p=0.408).ConclusionLong-term metformin use reduced all-cause mortality, but not CSS in ovarian cancer. Whether metformin itself reduces deaths because of ovarian cancer requires further investigation.     

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.     

Lung Metastasis Probability in Ewing Sarcoma: A Nomogram Based on the SEER Database

Background. Up to now, an accurate nomogram to predict the lung metastasis probability in Ewing sarcoma (ES) at initial diagnosis is lacking. Our objective was to construct and validate a nomogram for the prediction of lung metastasis in ES patients. Methods. A total of 1157 patients with ES from the Surveillance, Epidemiology, and End Results (SEER) database were retrospectively collected. The predictors of lung metastasis were identified via the least absolute shrinkage and selection operator (LASSO) and multivariate logistic analysis. The discrimination and calibration of the nomogram were validated by receiver operating characteristic (ROC) curve and calibration curve. Decision curve analysis (DCA) was used to evaluate the clinical usefulness and net benefits of the prediction model. Results. Factors including age, tumor size, primary site, tumor extension, and other site metastasis were identified as the ultimate predictors for the nomogram. The calibration curves for the training and validation cohorts both revealed good agreement, and the Hosmer–Lemeshow test identified that the model was well fitted (p > 0.05). In addition, the area under the ROC curve (AUC) values in the training and validation cohorts were 0.732 (95% confidence interval, CI: 0.607–0.808) and 0.741 (95% CI: 0.602–0.856), respectively, indicating good predictive discrimination. The DCA showed that when the predictive metastasis probability was between 1% and 90%, the nomogram could provide clinical usefulness and net benefit. Conclusion. The nomogram constructed and validated by us could provide a convenient and effective tool for clinicians that can improve prediction of the probability of lung metastasis in patients with ES at initial diagnosis.     

基于SEER数据库构建1~2枚淋巴结阳性且乳房全切的老年早期乳腺癌患者不同腋窝手术方式的生存预测模型

背景与目的：指南推荐1~2枚前哨淋巴结阳性的保乳并计划行全乳放疗的T1-2期乳腺癌患者可以豁免腋窝淋巴结清扫。探讨1~2枚淋巴结阳性且乳房全切的老年早期乳腺癌患者的预后危险因素,并构建不同腋窝处理手术方式下的生存预测模型。方法：从SEER数据库收集2010—2015年期间65岁及以上、T 1-2 期、1~2枚淋巴结阳性且乳房全切的乳腺癌患者并随机分为验证集和训练集。对训练集进行单因素及多因素COX比例风险回归分析筛选出影响总生存的独立预后因素,利用R软件构建预测患者3年和5年总生存率的列线图,利用一致性指数（C指数）和校正曲线对预测模型进行内部（训练集）和外部（验证集）验证。结果：共纳入4 863例患者,中位随访42个月,训练集（3 647例）和验证集（1 216例）的基线分布符合简单随机分组。将多因素COX回归分析筛选出的年龄、种族、婚姻状态、组织学分级、分子分型、T分期、腋窝手术方式、是否放化疗共9个总生存的独立风险因素（P<0.05）用于构建列线图预测模型。训练集（即内部验证）和验证集（即外部验证）的C指数分别为0.710（95% CI：0.689~0.731）和0.728（95 % CI：0.691~0.765）,两组的校正曲线均靠近45°参考线,表明列线图具有良好的预测能力。结论：本研究构建的列线图预测模型具有良好的预测价值,有利于指导临床对患者进行个体化治疗。     

A prognostic nomogram for predicting the long-term survival outcome of hypopharyngeal squamous cell carcinoma patients after tumour resection to assist the decision-making of postoperative adjuvant treatment

BackgroundSurgery remains the most common therapeutic strategy for Chinese patients with hypopharyngeal squamous cell carcinoma (HSCC), yet the indications for postoperative adjuvant treatment (POAT) remain unclear.MethodsWe retrospectively analysed 385 patients with primary HSCC in our hospital between 2003 and 2014. Patients that received pharyngectomy without POAT were enrolled in this study for developing a nomogram that predicts their survival outcome.ResultsMultivariate analyses showed that the tumour size, oesophagal invasion, extracapsular spread or internal jugular vein adhesion, thyroid gland invasion, and the number of lymph node metastases (≤3 or >3) were significantly correlated to the overall survival (OS) of the patient and were included as risk factors in the nomogram. The C-index was 0.768 (95% CI, 0.719–0.817) in development cohort and 0.767 (95% CI, 0.753–0.781) in validation cohort. A calibration curve was also conducted and was found favourable. The patients were stratified into three groups based on their nomogram scores. In the high-risk group, patients that received POAT had a better OS than those that received only surgery. In the moderate-risk group, POAT did not show any significant association with the OS. However, patients in the low-risk group that received POAT showed a worse OS than those without.ConclusionThe newly-developed nomogram can effectively predict the survival outcome of patients with HSCC. According to the novel stratification criteria created, patients stratified as high-risk could benefit from POAT, while those in the low-risk group are advised not to receive POAT as this correlates with a worse OS.     

基于SEER数据库的炎性乳腺癌生存预测风险模型的建立与验证

目的:建立可预测炎性乳腺癌(inflammatory breast cancer,IBC)生存情况的风险模型.方法:利用监测、流行病学和结果(Surveillance,Epidemiology and End Results,SEER)数据库,筛选2010年至2015年诊断为IBC的病例,通过单因素和Logistic多...