小肠腺癌患者预后分析及列线图构建

目的分析影响小肠腺癌(small intestinal adenocarcinoma,SBA)患者的预后因素,构建列线图预测SBA肿瘤特异性生存率(cancer-specific survival,CSS),为SBA患者提供合理的CSS预测。方法通过SEER数据库获取2010年至2015年确诊为SBA的2473例患者的临床资料,采用R软件caret包将数据以6∶4的比例随机分为建模组(n=1485)和验证组(n=988),对建模组进行生存分析及多因素Cox回归分析,将所获取的独立预后因素构建列线图,并采用C指数、校准曲线进行内部验证和外部验证。结果SBA患者1年、3年、5年的CSS分别为61.2%、48.2%、46.1%。多因素Cox回归分析表明年龄、肿瘤分化程度、婚姻状态、TNM分期、N分期、淋巴结清扫数目、手术是SBA患者预后的独立危险因素,将这些独立危险因素绘制SBA患者CSS的列线图。建模组和验证组的C指数分别为0.762(95%CI:0.744~0.780)和0.787(95%CI:0.767~0.806),两组校准图的预测曲线与实际曲线具有良好的一致性。结论本研究所建立的SBA患者CSS预后列线图具有良好的预测价值,有助于临床医师对SBA患者的预后进行较为准确的评估,并提供合理的治疗建议。     

乳腺大汗腺癌预后不良的危险因素及预测模型

目的 分析乳腺大汗腺癌预后不良的独立危险因素,构建乳腺大汗腺癌预后的预测模型。方法 从美国国立癌症研究监测、流行病学和最终结果数据库中检索并筛选出乳腺大汗腺癌患者638例,收集其临床资料,通过COX单因素及多因素比例风险回归分析法分析乳腺大汗腺癌预后不良的独立危险因素。基于乳腺大汗腺癌预后不良的独立危险因素构建乳腺大汗腺癌预后的预测列线图,采用一致性指数（C-指数）和校准曲线评价列线图的区分度和一致性。结果 年龄71～82岁、T4分期、M1分期是乳腺大汗腺癌预后不良的独立危险因素（P均<0.05）。基于预后不良的独立危险因素构建了3、5年乳腺大汗腺癌预后的预测列线图。列线图的C-指数为0.762(95%CI为0.728～0.796)。乳腺大汗腺癌预后的预测列线图预测的生存率与患者生存率接近。结论 年龄71～82岁、T4分期、M1分期的乳腺大汗腺癌患者的预后较差。成功构建乳腺大汗腺癌预后的预测模型,且模型的区分度及一致性均较好。     

肝细胞癌患者肝切除术后预后列线图的建立与验证

目的旨在建立和验证预测肝细胞癌肝切除术后患者总生存期(OS)的列线图。方法选取广西医科大学附属肿瘤医院2004年2月-2013年10月肝细胞癌肝切除术患者1013例。随机分为训练队列(n=710)和验证队列(n=303),在训练队列中,通过Cox比例风险模型确定独立危险因素,并构建训练队列的列线图预测1、3、5年的生存率。通过训练队列内部验证与验证队列的外部验证,并采用C指数、受试者工作特征曲线(ROC曲线)以及校准曲线对模型的性能进行评价。连续变量2组间比较采用独立样本t检验。分类变量2组间比较采用χ2检验或Fisher检验。通过Cox比例风险模型进行单因素和多因素分析。结果训练队列中1、3、5年总生存率分别为0. 72、0. 48、0. 34;验证队列中1、3、5年总生存率分别为0. 66、0. 45、0. 32。单因素和多因素分析确定影响肝细胞癌肝切除术后患者OS的危险因素为年龄、肿瘤数目、肿瘤大小、肿瘤包膜、血管侵犯、微卫星灶、AST、AFP(P值均0. 05),并将其构建列线图模型。训练队列中,预测OS的C指数为0. 748[95%可信区间(95%CI):0. 712～0. 784]。1、3、5年生存率的校准曲线显示列线图的预测值和实际观察值结果一致; 1、3、5年生存率ROC曲线下面积分别为0. 81(95%CI:0. 76～0. 87)、0. 82(95%CI:0. 77～0. 88)、0. 79(95%CI:0. 71～0. 88)。在验证队列中,C指数为0. 712 (95%CI:0. 685～0. 739)。1、3、5年生存率的校准曲线显示列线图预测值与实际观察值结果一致。1、3、5年生存率的ROC曲线下面积分别是0. 75(95%CI:0. 71～0. 79)、0. 77(95%CI:0. 73～0. 81)、0. 74(95%CI:0. 68～0. 80)。结论建立的列线图可以有效的预测肝细胞癌肝切除术后患者的OS。     

糖尿病患者维持性血液透析生存分析及预测模型构建

目的:探讨合并糖尿病的维持性血液透析(MHD)患者生存时间的影响因素,构建透析后生存时间的预测列线图。方法:回顾性分析2013年～2016年186例合并糖尿病的MHD患者信息,应用COX模型筛选影响预后的危险因素,并根据COX回归结果构建死亡风险列线图。结果:186例患者1年、2年、3年累积生存率分别为88.7%,74.2%,64.5%。COX回归分析结果显示,糖尿病病程、心血管疾病、血管通路、估算的肾小球滤过率(eGFR)为影响合并糖尿病MHD患者生存时间的独立危险因素,据此建立死亡预测列线图,一致性系数为0.670(95%CI 0.604～0.737)。结论:基于糖尿病病程、心血管疾病、血管通路、eGFR四个因素建立的糖尿病MHD患者透析后生存预测的列线图有助于个体化预测预后,针对性地治疗预后较差的糖尿病透析患者。     

随机生存森林预测M1期老年结直肠癌患者生存

目的 应用随机生存森林算法构建M1期老年结直肠癌(CRC)患者预后预测模型并对预测结果进行评价。方法 收集SEER数据库2010～2015年诊断的6 118例、>60岁M1期CRC患者的临床数据，通过Cox回归分析影响老年CRC远处转移患者预后的相关因素，随机生存森林模型分析预后因素的交互作用。采用一致性指数、校准曲线及预测误差曲线评估预测模型的效能。结果 婚姻状况、分化程度、T分期、N分期、原发灶手术、淋巴结清扫、化疗、CEA状态、骨转移、肝转移、肺转移是M1期结直肠癌患者的独立预后因素，化疗、原发手术部位及T分期之间存在强交互作用。接受手术和化疗(中位OS:22个月)>单纯化疗(中位OS:14个月)>单纯手术(中位OS:7个月)>两者都不接受(中位OS:4个月)。将随机生存森林模型VIMP法筛选的预后因素构建列线图，测试集6个月、1年、3年、5年受试者工作特征(ROC)曲线下面积(AUC)值分别为0.796、0.759、0.736和0.750。校对曲线显示，模型预测的生存率与患者实际的生存率之间具有良好一致性。预测误差曲线显示，RSF-Cox模型预测错误率低...     

肿瘤大小对3114例Ⅱ期结直肠癌患者预后的影响

目的探讨肿瘤大小对Ⅱ期结直肠癌患者预后的影响,并分析其临床应用价值。方法回顾性分析2007年10月至2020年3月在中山大学附属第六医院收治的Ⅱ期结直肠癌患者的临床病理资料。根据现有指南报道的高危因素,将患者分为高危和低危两组。采用Kaplan-Meier法绘制生存曲线,log-rank检验比较患者生存差异,并通过单、多因素Cox回归分析确定影响Ⅱ期结直肠癌患者预后的独立危险因素。基于多因素分析结果,构建列线图预测模型。结果共有3114例患者被纳入分析,其中高危组患者1149例和低危组患者1965例。仅在低危组患者的生存分析中,发现肿瘤≤5 cm比肿瘤>5 cm的患者的5年无病生存率更低(83.1%vs.89.8%),两者比较差异有统计学意义(χ²=6.004,P=0.014)。多因素Cox回归分析显示,肿瘤≤5 cm、年龄>60岁、CEA>5 ng/mL、CA125>35 U/mL及合并术后并发症是影响低危组患者预后的独立危险因素。基于以上五种独立预后因素构建低危组患者的列线图预后预测模型。通过一致性指数(0.631)、受试者工作特征曲线下面积(1年为0.796,3年为0.760和5年为0.654)和校准曲线(与标准曲线拟合较好)对模型进行评估,显示模型预测的准确性较好。结论肿瘤大小是Ⅱ期低危结直肠癌患者的独立预后因素,而与Ⅱ期高危结直肠癌患者的预后无关。本研究构建的列线图预测模型可较准确地预测Ⅱ期低危结直肠癌患者的1年、3年、5年无病生存率。     

胃癌组织中P4HB与GRP78表达的相关性及对患者预后的预测价值

目的:探讨脯氨酰4-羟化酶β多肽(P4HB)和葡萄糖调节蛋白78(GRP78)表达与胃癌临床特征的相关性及对患者预后的预测价值。方法:采用免疫组织化学法分别评估150例胃癌组织样本中P4HB和GRP78蛋白的表达,分析蛋白表达与胃癌临床病理学特征的关联。采用Kaplan-Meier分析比较总生存期(OS)的生存曲线。用单变量和多变量Cox回归模型分析影响OS的潜在预后因素。基于多变量Cox回归模型构建预后列线图,并与TNM分期比较其临床价值。结果:P4HB的表达与患者年龄、Bormann分型、肿瘤浸润深度、淋巴结转移、术后辅助化疗相关,GRP78的表达与肿瘤浸润深度和淋巴结转移相关;两者的表达呈正相关。Kaplan-Meier分析表明,P4HB和GRP78的高单一表达或共表达预示较短的OS,两者的高共表达在术后辅助化疗组,特别是晚期组中预后不良。多因素Cox回归分析确定癌组织分化程度、TNM分期、术后辅助化疗、P4HB与GRP78共表达是OS的独立预后因素。在受试者工作特征曲线和决策曲线分析中,列线图在辨别能力和临床实用性方面优于TNM分期。结论:P4HB与GRP78的表达呈正相关,其...     

新辅助治疗反应对局部进展期直肠癌患者预后的影响

目的探究新辅助治疗反应对局部进展期直肠癌患者远期预后的影响。 方法回顾性收集中国医学科学院肿瘤医院218例接受术前新辅助放化疗的局部进展期直肠癌患者（LARC）的临床病理资料。根据Dowrak/R?del肿瘤退缩分级（TRG）标准将患者分为治疗反应良好（TRG3~4）和治疗反应不佳（TRG0~2）。采用Cox风险比例回归单因素和多因素分析确定无病生存（disease-free survival,DFS）和肿瘤总生存（overall survival,OS）影响因素。采用Kaplan-Meier法绘制生存曲线并利用Log-rank检验比较肿瘤生存差异。 结果本研究纳入患者218例,其中治疗反应良好126例,治疗反应不佳92例。单因素和多因素Cox回归分析确定新辅助治疗反应不佳是DFS（HR=3.85,95%CI：1.40~10.60;P=0.009）和OS（HR=3.81,95%CI：1.02~14.20;P=0.046）的独立危险因素。5年DFS分别为反应良好93.46%,反应不佳65.04%（χ²=28.23,P＜0.001）;5年OS分别为反应良好95.38%,反应不佳78.99%（χ²=18.51,P＜0.001）。 结论新辅助治疗反应是LARC患者DFS和OS的独立预后因素;良好的治疗反应预示着更好的肿瘤学预后,为进一步的临床研究风险分层提供了理论基础。     

超重/肥胖型PCOS患者IVF/ICSI-ET妊娠失败危险因素分析及预测列线图构建

目的 总结超重/肥胖型多囊卵巢综合征（PCOS）患者体外受精/卵胞质内单精子注射—胚胎移植（IVF/ICSI-ET）妊娠失败的危险因素,基于危险因素构建预测超重/肥胖型PCOS患者IVF/ICSI-ET妊娠失败风险列线图（列线图模型）。方法 131例拟行IVF/ICSI-ET的超重/肥胖型PCOS患者,均完成胚胎移植周期,收集其体成分特征变量、实验室检查特征变量及临床病例资料变量等。将131例患者分为训练集91例和验证集40例,采用LASSO回归分析法分析超重/肥胖型PCOS患者IVF/ICSI-ET妊娠失败的独立危险因素。基于独立危险因素,构建列线图模型,分别采用受试者工作特征曲线（ROC）、校准曲线、Hosmer-Lemeshow拟合优度检验评价列线图模型的预测能力、校准度、拟合度,采用临床决策曲线评价列线图模型的临床应用价值。结果 超重/肥胖型PCOS患者IVF/ICSIET妊娠失败的独立危险因素有年龄、身体质量指数、水合率和胰岛素抵抗（λ分别为-0.186、-0.947、-0.064、-0.290）。列线图模型的曲线下面积为0.934,校准曲线及拟合曲线预测列线图模型的校准度、...     

基于SEER数据库构建Ⅱ～Ⅲ期胃癌预后预测模型

目的 胃癌是具有高度异质性特征的一种疾病。本研究旨在根据患者的各种临床和病理相关风险因素构建预后模型,从而更准确地预测Ⅱ～Ⅲ期胃癌患者术后的1年、3年和5年的总生存率(overall survival, OS)。方法 提取SEER数据库中2010年至2015年间确诊的3752例Ⅱ～Ⅲ期胃癌术后病例,并将其随机分为训练队列和验证队列。此外,收集2010年1月至2017年4月就诊于新疆军区总医院的109名Ⅱ～Ⅲ期胃癌术后患者用作外部验证。相关风险因素通过单变量和多变量COX比例风险回归分析来确定,再由这些因素结合构建列线图,通过一致性指数(C-index)、应用受试者工作特征(ROC)曲线下面积(AUC)和校准曲线评估该预测模型的准确性和区分度。此外,根据计算出的列线图总分绘制不同风险分层下的K-M生存曲线。结果 SEER数据库中的3752名患者被随机分配到70%的训练组(n=2628)和30%的内部验证组(n=1124)。年龄、分级、肿瘤大小、放疗、化疗、T分期、N分期、淋巴结比率(LNR)被用于构建列线图。训练组在1、3、5年的ROC曲线下面积(AUC)值分别为0.758、0.728、...     

Clinicopathological features and prognostic nomogram of giant cell carcinoma of the lung: A population-based study

Background

Due to its rarity, the features and prognosis of giant cell carcinoma of the lung (GCCL) are not well defined. The present study aimed to describe the clinicopathological features and prognostic analysis of this rare disease, compare it with lung adenocarcinoma (LAC), further determine the prognostic factors and establish a nomogram.

Methods

Patients diagnosed with GCCL and LAC were identified from the SEER database between 2004 and 2016. The features and survival between GCCL and LAC were compared in the unmatched and matched cohorts after propensity score matching (PSM) analysis. Univariate and multivariate Cox analyses were used to identify the prognostic factors, and a nomogram was constructed. Area under the curve (AUC), C-index, calibration curve and decision curve analysis (DCA) were used to confirm the established nomogram.

Results

A total of 295 patient diagnosed with GCCL and 149 082 patients with LAC were identified. Compared with LAC, patients with GCCL tend to be younger, male, black and have pathological Grade III/IV GCCL, more proportion of AJCC-TNM-IV, T3/T4 and distant metastases. The 1-, 2- and 5-year OS rates of the patients with GCCL were 21.7%, 13.4% and 7.9%, respectively. The median OS and CSS were 3 and 4 months, respectively. Patients with GCCL had significantly shorter OS and CSS than those with LAC in the unmatched and matched cohorts after PSM. Multivariate Cox analysis demonstrated that T, N and M stages and use of chemotherapy and surgery were independent of survival. Furthermore, we constructed a prognostic nomogram for OS and CSS by using independent prognostic factors. The C-index of OS-specific nomogram is 0.78 (0.74–0.81), and the C-index of CSS-specific nomogram is 0.77 (0.73–0.80). The calibration curve and ROC analysis showed good predictive capability of these nomograms. DCA showed that the nomogram had greater clinical practical value in predicting the OS and CSS of GCCL than TNM staging.

Conclusion

GCCL have distinct clinicopathological characteristics and significantly worse clinical outcomes. Prognostic nomograms for overall survival (OS) and CSS were constructed.     

老年与非老年直肠黏液腺癌患者对不同放疗策略的受益可能不同：一项基于SEER数据库的回顾性研究

目的探讨老年与非老年直肠黏液腺癌患者对于新辅助放疗、辅助放疗的受益情况,并分析影响直肠黏液腺癌患者预后的因素。 方法应用美国国家癌症研究所的监测、流行病学和结果数据库（SEER）,收集2000~2016年,病理诊断为直肠黏液腺癌的患者共3 997例,根据年龄分为老年组（≥60岁）和非老年组（<60岁）,分析比较两组接受新辅助放疗联合手术、单纯手术和术后辅助放疗患者的预后情况,对两组患者的三种治疗方式分别进行倾向得分匹配,比较不同治疗方法对预后的影响,应用Kaplan-Meier法分别绘制生存曲线,应用Log-rank检验分析各组生存差异,应用COX比例风险模型分析影响直肠黏液腺癌患者预后的因素。 结果三种治疗方案的总生存率,新辅助放疗总生存率最高,其次为术后放疗,最后为单纯手术组,组间比较差异有统计学意义（χ²=13.117,22.541;P<0.05）。但三种治疗方案的肿瘤特异性生存,仅新辅助放疗显著高于术后放疗（χ²=4.023,P=0.045）。对各种治疗方案进行倾向得分匹配后,老年患者新辅助放疗的总体生存率显著高于单纯手术（χ²=4.874,P=0.027）,非老年患者单纯手术的总体生存率（χ²=5.530,P=0.019）和肿瘤特异性生存率（χ²=4.825,P=0.028）均显著高于术后放疗。高龄（≥60岁）、男性、未化疗和高TNM分期是直肠黏液腺癌患者总生存率较差的影响因素,其HR分别为1.689（95% CI=1.524~1.871）、1.110（95% CI=1.007~1.223）和1.549（95% CI=1.338~1.792）,Ⅱ期HR=2.675（95% CI=1.191~6.008）,Ⅲ期HR=3.617（95% CI=1.612~8.115）,Ⅳ期HR=10.835（95% CI=4.797~24.474）;高龄（≥60岁）、未化疗和高TNM分期是直肠黏液腺癌患者肿瘤特异性生存率较差的影响因素,其HR分别为1.297（95% CI=1.156~1.456）,1.344（95% CI=1.129~1.601）,Ⅲ期HR=6.365（95% CI=1.582~25.614）,Ⅳ期HR=20.957（95% CI=5.189~84.637）。 结论老年直肠黏液腺癌患者可能从新辅助放疗中获益,而对于非老年患者,放疗的预后并不优于单纯手术治疗。     

Nomogram predicting cancer-specific survival in elderly patients with stages I–III colon cancer

Abstract

Aim: This study aims to establish and validate an effective nomogram to predict cancer-specific survival (CSS) in elderly patients with stages I–III colon cancer.

Methods: The data of elderly colon cancer patients with stages I–III were enrolled from the Surveillance, Epidemiology, and End Results database (SEER) between 2010 and 2015. The eligible patients were randomly divided into a training cohort and a validation cohort (ratio 1:1). All predictors of cancer-specific survival were determined by Cox regression. The concordance index (C-index) and calibration curves were used for validation of nomograms. Decision curve analysis (DCA) was performed to evaluate the clinical net benefit of the nomogram.

Results: Cox hazard analysis in the training cohort indicated that grade, tumor stage, node stage, colectomy, and CEA were independent predictors of CSS. Nomogram was constructed based on these predictors. The C-index of nomograms for CSS was 0.728 (95%CI: 0.7133–0.7427), and were superior to that of AJCC TNM Stage (C-index: 0.625, 95%CI: 0.6093–0.6406). The calibration curves showed satisfactory consistency between actual observation and nomogram-predicted CSS probabilities. The validation cohort demonstrated similar results. The DCA showed high net benefit of nomogram in a clinical context. The population was divided into three groups based on the scores of the nomogram, and the survival analysis showed that this prognostic stratification was statistically significant (p?<?0.01).

Conclusion: The nomograms showed significant accuracy in predicting 1-, 3-, and 5-year CSS in elderly patients with stages I–III colon cancer and may be helpful inpatient counseling clinical decision guidance.     

Prognostic model for predicting overall and cancer-specific survival among patients with superficial spreading melanoma: A SEER based study

Skin malignant melanoma is one of the most aggressive skin tumors. Superficial spreading melanoma (SSM) is the most common histological type, which can originate from different body skin sites, and some patients can still accumulate regional lymph nodes and even have distant metastasis in some cases. This study used the relevant data from the monitoring, epidemiology and results database of the National Cancer Institute database to study the overall survival (OS) and cancer-specific survival (CSS) of SSM patients and established an SSM nomogram to evaluate the prognosis of patients. A total of 13,922 patients were collected from the monitoring, epidemiology and results database of the National Cancer Institute and randomly divided into a training cohort (8353 cases) and a validation cohort (5569 cases). Univariate and multivariate Cox regression analysis were used to determine prognostic factors, and these factors were used to construct OS and CSS nomograms for patients with SSM. Finally, the discrimination and consistency of the nomogram model were evaluated by the consistency index (C-index), area under the curve (AUC) and calibration curve. Multivariate Cox regression analysis suggested that age, sex, tumor site, the American joint committee on cancer T stage and the first primary melanoma were independent predictors of OS and CSS in patients with SSM and that the American joint committee on cancer N stage was also an independent predictor of CSS in patients with SSM. Based on the above prognostic factors, this study constructed a predictive model. The C-index of the model OS and CSS for this training cohort was 0.805 [95% CI: 0.793–0.817] and 0.896 [95% CI: 0.878–0.913], respectively. The AUC values for 1-, 3-, and 5-year OS were 0.822, 0.820, and 0.821, respectively, and the AUC values for CSS were 0.914, 0.922, and 0.893, respectively. The data indicated that both nomograms showed better predictive accuracy. The calibration curves of the training cohort and the validation cohort were in good agreement. The nomogram has superior predictive performance in predicting 1-, 3-, and 5-year OS and CSS prognosis in patients with SSM and can provide a reference for individualized treatment and clinical counseling of SSM.     

Clinicopathological features and prognostic nomogram of giant cell carcinoma of the lung: A population‐based study

BackgroundDue to its rarity, the features and prognosis of giant cell carcinoma of the lung (GCCL) are not well defined. The present study aimed to describe the clinicopathological features and prognostic analysis of this rare disease, compare it with lung adenocarcinoma (LAC), further determine the prognostic factors and establish a nomogram.MethodsPatients diagnosed with GCCL and LAC were identified from the SEER database between 2004 and 2016. The features and survival between GCCL and LAC were compared in the unmatched and matched cohorts after propensity score matching (PSM) analysis. Univariate and multivariate Cox analyses were used to identify the prognostic factors, and a nomogram was constructed. Area under the curve (AUC), C‐index, calibration curve and decision curve analysis (DCA) were used to confirm the established nomogram.ResultsA total of 295 patient diagnosed with GCCL and 149 082 patients with LAC were identified. Compared with LAC, patients with GCCL tend to be younger, male, black and have pathological Grade III/IV GCCL, more proportion of AJCC‐TNM‐IV, T3/T4 and distant metastases. The 1‐, 2‐ and 5‐year OS rates of the patients with GCCL were 21.7%, 13.4% and 7.9%, respectively. The median OS and CSS were 3 and 4 months, respectively. Patients with GCCL had significantly shorter OS and CSS than those with LAC in the unmatched and matched cohorts after PSM. Multivariate Cox analysis demonstrated that T, N and M stages and use of chemotherapy and surgery were independent of survival. Furthermore, we constructed a prognostic nomogram for OS and CSS by using independent prognostic factors. The C‐index of OS‐specific nomogram is 0.78 (0.74–0.81), and the C‐index of CSS‐specific nomogram is 0.77 (0.73–0.80). The calibration curve and ROC analysis showed good predictive capability of these nomograms. DCA showed that the nomogram had greater clinical practical value in predicting the OS and CSS of GCCL than TNM staging.ConclusionGCCL have distinct clinicopathological characteristics and significantly worse clinical outcomes. Prognostic nomograms for overall survival (OS) and CSS were constructed.     

Construction,validation and,visualization of a web-based nomogram for predicting the overall survival and cancer-specific survival of leiomyosarcoma patients with lung metastasis

BackgroundThis study sought to assess the prognostic factors for leiomyosarcoma (LMS) patients with lung metastasis and construct web-based nomograms to predict overall survival (OS) and cancer-specific survival (CSS).MethodPatients diagnosed with LMS combined with lung metastasis between 2010 and 2016 were identified in the Surveillance, Epidemiology, and End Results (SEER) database. The patients were randomly divided into a training set and a testing set. The X-tile analysis provides the best age and tumor size cut-off point, and changes continuous variables into categorical variables. The independent prognostic factors were determined by Cox regression analysis, and 2 nomograms were established. Receiver operating characteristic curves and calibration curves were used to evaluate the nomograms. Based on the nomograms, 2 web-based nomograms were established.ResultsTwo hundred and twenty-eight cases were included in the OS nomogram construction, and were randomly divided into a training set (n=160) and a validation set (n=68). Age, T stage, bone metastasis, surgery, chemotherapy, marital status, tumor size, and tumor site were found to be correlated with OS. One hundred and eighty-three cases were enrolled in the CSS nomogram construction, and randomly divided into a training set (n=129) and a validation set (n=54). Age, bone metastasis, surgery, chemotherapy, tumor size, and tumor site were found to be correlated with CSS. Two nomograms were established to predict OS and CSS. In the training set, the areas under the curve of the nomogram for predicting 1-, 2-, and 3-year OS were 0.783, 0.830, and 0.832, respectively, and those for predicting 1-, 2-, and 3-year CSS were 0.889, 0.777, and 0.884, respectively. Two web-based nomograms were established to predict OS (https://wenn23.shinyapps.io/lmslmosapp/), and CSS (https://wenn23.shinyapps.io/lmslmcssapp/).ConclusionThe developed web-based nomogram is a useful tool for accurately analyzing the prognosis of LMS patients with lung metastasis, and could help clinical doctors to make personalized clinical decisions.     

Prognostic nomograms and risk-stratifying systems for predicting survival in patients with resected pT2-4aN0M0 esophageal carcinoma

BackgroundAccording to the National Comprehensive Cancer Network (NCCN) guidelines, surveillance or adjuvant chemoradiation is recommended for patients with completely resected pT2-4aN0M0 esophageal carcinoma (EC). Due to this population’s variant prognosis, we developed novel nomograms to define the high-risk patients who may need closer follow-up or even post-operative therapy.MethodsCases with resected pT2-4aN0M0 EC from the Surveillance, Epidemiology, and End Results (SEER) database and the Sun Yat-sen University Cancer Center (SYSUCC) were enrolled in the study. The SEER database cases were randomly assigned into the training cohort (SEER-T) and the internal validation cohort (SEER-V). Cases from the SYSUCC served as the external validation cohort (SYSUCC-V). Overall survival (OS) and cancer specific survival (CSS) were compared between groups. Multivariate analyses were applied to identify the prognostic factors. Nomograms and risk-classifying systems were developed. The nomograms’ performances were evaluated by concordance index (C-index), calibration plots and decision curve analysis (DCA).ResultsA total of 2,441 eligible EC cases (SEER-T, n=839; SEER-V, n=279; SYSUCC-V, n=1,323) were included. Age, sex, chemotherapy, lymph node harvested (LNH) and T stage were identified as the independent predictors for CSS. Regarding OS, it also included the prognostic factor of histology. Nomograms were formulated. For CSS, the C-index was 0.68 [95% confidence interval (CI): 0.66–0.71], 0.67 (95% CI: 0.63–0.71) and 0.61 (95% CI: 0.59–0.63) for the SEER-T, SEER-V, and SYSUCC-V, respectively. For OS, the C-index was 0.69 (95% CI: 0.66–0.72), 0.64 (95% CI: 0.59–0.69) and 0.62 (95% CI: 0.61–0.63) for the SEER-T, SEER-V, and SYSUCC-V, respectively. The calibration curves and DCA showed good performances of the nomograms. In further analyses, risk-classification systems stratified pT2-4aN0M0 EC into low-risk and high-risk subgroup. The OS and CSS curves of these 2 subgroups, in the full analysis set or stratified by TNM stage, histology, T stage and LNH categories, showed significant distinctions.ConclusionsThe novel prognostic nomograms and risk-stratifying systems which separated resected pT2-4aN0M0 esophageal carcinoma patients into the low-risk and high-risk prognostic groups were developed. It may help clinicians estimate individual survival and develop individualized treatment strategies.     

Clinicopathological characteristics and prognosis of colon cancer with lung metastasis without liver metastasis: A large population-based analysis

Distant metastasis explains the high mortality rate of colon cancer, in which lung metastasis without liver metastasis (LuM) is a rare subtype. This study is aimed to identify risk factors of LuM and LLM (lung metastasis with liver metastasis) from colon cancer, and to analyze the prognosis of patients with LuM by creating a nomogram. Patients’ information were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Multivariable logistic regression analysis was used to determine the risk factors for LuM and LLM. Prognostic factors for cancer-specific survival (CSS) and overall survival (OS) were identified by multivariate Cox proportional hazards regression and nomogram models were established to predict CSS and OS. Multivariate logistic regression analysis showed that blacks, splenic flexure of colon tumor, tumor size >5 cm, T4, N3, and higher lymph node positive rate were associated with the occurrence of LuM. Meanwhile, age >65 years old, female, splenic flexure of colon, higher lymph node positive rate, and brain metastasis were independent risk factors for CSS. The C-index of the prediction model for CSS was 0.719 (95% CI: 0.691–0.747). In addition, age, primary site, tumor size, differentiation grade, N stage, and bone metastasis were significantly different between LuM and LLM. The nomograms we created were effective in predicting the survival of individuals. Furthermore, patients with LuM and LLM from colon cancer might require different follow-up intervals and examinations.     

Identification of a clinical web-based nomogram to predict overall survival in elderly retroperitoneal sarcoma patients: A population-based study

The purpose of this study was to develop a web-based nomogram and risk stratification system to predict overall survival (OS) in elderly patients with retroperitoneal sarcoma (RPS). Elderly patients diagnosed with RPS between 2004 and 2015 were identified in the Surveillance, Epidemiology, and End Results (SEER) database. We used univariate and multivariate Cox analysis to identify independent prognostic factors. We plotted the nomogram for predicting the OS of elderly RPS patients at 1, 3, and 5 years by integrating independent prognostic factors. The nomograms were subsequently validated by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). By calculating the Nomogram score for each patient, we build a risk stratification model to evaluate the survival benefit of elderly RPS patients. A total of 722 elderly RPS patients were included in our study. The nomogram includes 5 clinicopathological variables as independent prognostic factors: age, histological subtype, grade, metastasis status, and surgery. Through the validation, we found that the nomogram has excellent prediction performance. Then web-based nomograms were established. We performed a web-based nomogram and a risk stratification model to assess the prognosis of elderly RPS patients, which are essential for prognostic clustering and decision-making about treatment.