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.     

预测年轻乳腺癌患者生存率列线图的建立和验证

目的:构建预测年轻乳腺癌患者生存情况的列线图,以期帮助临床诊疗。方法:收集SEER数据库中5 525例年轻乳腺癌患者的临床信息,通过单因素Log-rank检验和多因素Cox生存分析筛选出独立预后因素,用于构建预测患者3、5年总生存率(overall survival,OS)和癌症特异性生存率（cancer special survival,CSS)的列线图,将我院就诊的147例年轻乳腺癌患者作为验证集进行外部验证。结果:单因素和多因素分析结果显示,种族、病理类型、组织学分级、T分期、N分期、M分期、ER状态、HER-2状态、手术方式是与患者OS和CSS相关的独立危险因素,将这些因素纳入并建立预测患者OS和CSS的列线图模型。内部和外部验证结果显示模型具有良好的预测性能。基于建立的OS和CSS列线图模型对患者进行了风险分层,能够准确地将年轻乳腺癌患者分成预后有显著差异的三个风险亚组。结论:本研究构建的预测模型能较为准确的预测年轻乳腺癌患者的预后情况,为临床的诊疗提供科学依据。     

Nomogram prediction for overall survival of patients diagnosed with cervical cancer

Background:Nomograms are predictive tools that are widely used for estimating cancer prognosis. The aim of this study was to develop a nomogram for the prediction of overall survival (OS) in patients diagnosed with cervical cancer.Methods:Cervical cancer databases of two large institutions were analysed. Overall survival was defined as the clinical endpoint and OS probabilities were estimated using the Kaplan-Meier method. Based on the results of survival analyses and previous studies, relevant covariates were identified, a nomogram was constructed and validated using bootstrap cross-validation. Discrimination of the nomogram was quantified with the concordance probability.Results:In total, 528 consecutive patients with invasive cervical cancer, who had all nomogram variables available, were identified. Mean 5-year OS rates for patients with International Federation of Gynecologists and Obstetricians (FIGO) stage IA, IB, II, III, and IV were 99.0%, 88.6%, 65.8%, 58.7%, and 41.5%, respectively. Seventy-six cancer-related deaths were observed during the follow-up period. FIGO stage, tumour size, age, histologic subtype, lymph node ratio, and parametrial involvement were selected as nomogram covariates. The prognostic performance of the model exceeded that of FIGO stage alone and the model's estimated optimism-corrected concordance probability was 0.723, indicating accurate prediction of OS. We present the prediction model as nomogram and provide a web-based risk calculator (http://www.ccc.ac.at/gcu).Conclusion:Based on six easily available parameters, a novel statistical model to predict OS of patients diagnosed with cervical cancer was constructed and validated. The model was implemented in a nomogram and provides accurate prediction of individual patients' prognosis useful for patient counselling and deciding on follow-up strategies.     

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 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.     

术前白蛋白与纤维蛋白原比值对三阴性乳腺癌患者的预后影响和临床意义

目的:探讨术前白蛋白与纤维蛋白原比值（albumin to fibrinogen,AFR）对三阴性乳腺癌患者的预后影响和临床意义。方法:回顾性分析我院自2010年1月至2013年12月间就诊的195例三阴性乳腺癌患者。用受试者工作特性曲线（ROC）确定AFR的最佳临界值。采用卡方检验或Fisher精确检验分析比较计数资料。Kaplan-Meier方法和Log rank方法用于分析生存曲线。单因素和多因素分析（Cox比例风险回归模型）用于评估独立的预后因素。结果:ROC曲线确定AFR最佳临界值为15.00,依此分两组,即低AFR组（AFR＜15.00）和高AFR组（AFR≥15.00）。单因素和多因素分析显示AFR是三阴性乳腺癌DFS（P=0.045,HR:0.627,95%CI:0.397~0.990;P=0.026,HR:0.595,95%CI:0.377~0.940）和OS（P=0.039,HR:0.238,95%CI:0.061~0.927;P=0.001,HR:0.385,95%CI:0.221~0.670）的独立预后因素。高AFR组患者术后中位DFS和OS显著高于低AFR组患者,差异具有显著统计学意义（χ2=8.190,P=0.004;χ2=8.720,P=0.003）。散点图分析显示,AFR与ALB呈显著正相关（R2=0.028,P=0.020）,AFR与FIB呈显著负相关（R2=0.516,P＜0.000 1）。此外,对伴有淋巴管侵犯的患者,高AFR组患者比低AFR组患者术后生存时间长,预后更好。结论:术前AFR是影响患者预后的独立因素。AFR具有操作简单、易于推广、成本低、可重复性好等优点,具有潜在的临床应用价值。     

Adjuvant treatment for the elderly patient with resected gallbladder cancer: a SEER-Medicare analysis

BackgroundIn patients with resected gallbladder cancer (GBC), the role of adjuvant chemotherapy (aCT) remains ill-defined, especially in elderly patients. This study evaluates the value of aCT in elderly patients with GBC and assesses response according to tumor stage.MethodsPatients of ≥65 years of age with resected GBC diagnosed from 2004–2015 were identified using a Surveillance, Epidemiology and End Results (SEER)/Medicare linked database. After propensity score matching, survival of patients treated with aCT was compared to survival of patients who did not receive aCT using Kaplan-Meier and Cox proportional hazards analysis.ResultsOf 2,179 patients with resected GBC, 876 (25%) received aCT. In the full cohort of 810 propensity-score matched patients, survival did not differ between patients treated with aCT (17.6 months ) and without aCT (19.5 months, P=0.7720). Subgroup analysis showed that survival was significantly better after aCT in T3/T4 disease (12.3 vs. 7.2 months, P=0.013). Interaction analysis showed that benefit of aCT was primarily seen in combined T3/T4, node-positive disease (HR 0.612 , P=0.006).ConclusionsIn this large cohort of elderly patients with resected GBC, aCT was not associated with increased survival. However, aCT may provide a survival benefit in T3/4, node-positive disease.     

基于SEER数据库分析胆囊癌肝转移患者预后因素并构建竞争风险列线图

目的:分析影响胆囊癌肝转移患者的预后因素,建立竞争风险列线图从而量化生存差异,提供临床决策。方法:本研究通过回顾性分析SEER数据库2010年至2015年共318例胆囊癌肝转移患者完整临床信息资料,采用单因素和多因素Cox回归分析得到影响患者总生存期的独立预后因素,采用Kaplan-Meier（Log-rank 检验）进行生存分析,并基于独立预后因素绘制竞争风险列线图。结果:318例胆囊癌肝转移患者,采用总生存期单因素和多因素Cox回归分析,结果显示病理分级(HR:1.397,95%CI:1.157~1.687,P<0.001)、手术方式(HR:0.790,95%CI:0.682~0.913,P=0.002)、化疗情况(HR:0.344,95%CI:0.265~0.446,P<0.001)和淋巴结检出数(HR:0.774,95%CI:0.642~0.933,P=0.007)是胆囊癌肝转移患者总生存期独立预后因素。通过将多因素Cox回归分析得到的显著临床病理参数建立列线图来评估影响总生存期的竞争性风险和量化生存差异。计算列线图C-index为7.46,并且0.5年、1年、3年的校正曲线显示列线图有着较好预测能力。结论:肿瘤病理分级、手术方式、淋巴结检出数和化疗情况是胆囊癌肝转移患者独立预后因素。基于列线图显示,化疗情况影响程度的权重值最大。     

甲状腺癌患者术后复发风险预测的列线图模型建立

目的 建立预测甲状腺癌患者术后复发风险的列线图模型.方法 回顾性分析2015年7月—2017年12月因甲状腺癌于沈阳医学院附属中心医院普外科行手术治疗的患者临床资料,筛选甲状腺癌术后复发的独立危险因素,基于筛选结果建立列线图预测模型,并对模型的预测准确性进行内外部验证.结果 Logistic回归分析结果显示,肿瘤大小≥...     

预测去势抵抗性前列腺癌患者总生存期Halabi风险列线图的验证研究

目的 在中国上海及周边地区去势抵抗性前列腺癌（CRPC）患者中验证Halabi风险列线图,预测CRPC患者的总生存期。方法 选择2006年8月至2013年12月在复旦大学附属肿瘤医院被确诊为CRPC的228例患者。预测患者生存率通过Halabi风险列线图计算,模型效应用偏倚和校准分析评估。结果 228例患者中死亡150例,中位总生存期为20.8个月（4～48个月）。Halabi风险列线图的准确度为0.61（95％ CI：0.51～0.73）。Halabi列线图预测的中位总生存期为16.4个月（6～31个月）。预测模型校准结果显示,1年和2年肿瘤特异性生存率被低估。结论 Halabi风险列线图未能准确预测中国上海地区CRPC患者的生存率,仍需要更大样本的研究来验证Halabi风险列线图或者建立新的风险列线图来预测CRPC患者的生存情况。     

A nomogram for predicting low-volume/low-grade prostate cancer: a tool in selecting patients for active surveillance

BACKGROUND: The authors reported previously that assessment of the number of positive biopsy cores, maximum tumor length in a core, Gleason score, and prostate volume in an extended biopsy enhanced the accuracy of predicting low-volume/low-grade prostate cancer. On the basis of those findings, they developed a nomogram to predict the probability of low-volume/low-grade prostate cancer specifically for men with a single positive biopsy core. METHODS: The study cohort comprised 258 men who underwent radical prostatectomy without neoadjuvant therapy. Prostate cancer was diagnosed in only 1 core of an extended biopsy scheme. Low-volume/low-grade cancer was defined as pathologic organ-confined disease and a tumor volume<0.5 cc with no Gleason grade 4 or 5 cancer. Patient age, prostate-specific antigen (PSA) level, prostate volume, PSA density (PSAD), and tumor length in a biopsy core were examined as variables. A fitted multiple logistic regression model was used to establish the nomogram. RESULTS: One hundred thirty-three patients (51.6%) had low-volume/low-grade cancer. To establish the nomogram, age, PSAD, and tumor length were adopted as variables. The fitted model suggested that older age, higher PSAD values, and greater tumor length would reduce the probability of low-volume/low-grade cancer. The nomogram predicted low-volume/low-grade cancer with good discrimination (an area under the receiver operating characteristic curve of 0.727). Calibration of this nomogram showed good predicted probability. CONCLUSIONS: The authors established a nomogram with which to predict low-volume/low-grade cancer in men with 1 positive biopsy core in an extended biopsy scheme, and they recommend this nomogram for use in selecting men for active surveillance.     

External validation of a prognostic nomogram for overall survival in women with uterine leiomyosarcoma

BACKGROUND:

There is no validated system to identify prognostically distinct cohorts of women with uterine leiomyosarcoma (ULMS). By using an independent, pooled, multi‐institutional, international patient cohort, the authors validated a recently proposed ULMS nomogram.

METHODS:

The ULMS nomogram incorporated 7 clinical characteristics (age, tumor size, tumor grade, cervical involvement, locoregional metastases, distant metastases, and mitotic index (per 10 high‐power fields) to predict overall survival (OS) after primary surgery. Independent cohorts from 2 sarcoma centers were included. Eligible women, at minimum, underwent a hysterectomy for primary, locally advanced, or metastatic ULMS and received part of their care at 1 of the centers between 1994 and 2010.

RESULTS:

In total, 187 women with ULMS were identified who met the above criteria described above (median age, 51 years; median tumor size, 9 cm; median mitotic index, 20 per 10 high‐power fields). Tumors generally were high grade (88%), FIGO stage I or II (61%) without cervical involvement (93%) and without locoregional metastases (77%) or distant metastases (83%). The median OS and the 5‐year OS rate were 4.5 years (95% confidence interval, 3.2‐5.3 years) and 46%, respectively; and 65 women (35%) remained alive at last follow‐up. The nomogram concordance index was 0.67(standard error, 0.02), which was as high as the concordance index from the initial cohort used for nomogram development. The concordance between actual OS and nomogram predictions suggests excellent calibration because predictions were within 1% of actual 5‐year OS rates for patients with a predicted 5‐year OS of less than 0.68.

CONCLUSIONS:

The ULMS nomogram was externally validated using independent cohorts. These findings support the international use of the ULMS nomogram prognostic of OS in ULMS. Cancer 2013. © 2012 American Cancer Society.     

Development of a novel combined nomogram model integrating Rad-score,age and ECOG to predict the survival of patients with hepatocellular carcinoma treated by transcatheter arterial chemoembolization

BackgroundLiver cancer is affecting more and more people''s health. Transcatheter arterial chemoembolization (TACE) has become a routine treatment option, but the prognosis of patients is not optimistic. Effectively prediction of prognosis can provide clinicians with an objective basis for patient prognosis and timely adjustment of treatment strategies, thus improving the quality of patient survival. However, the current prediction methods have some limitations. Therefore, this study aims to develop a radiomics nomogram for predicting survival after TACE in patients with advanced hepatocellular carcinoma (HCC).MethodsSeventy advanced HCC patients treated with TACE were enrolled from January 2013 to July 2019. Clinical information included age, sex, and Eastern Cooperative Oncology Group (ECOG) score. Overall survival (OS) was confirmed by postoperative follow-up. Radiomics features were extracted using 3D Slicer (version 4.11.20210226) software, then obtain radiomics signature and calculate radiomics score (Rad-score) for each patient. Univariate and multivariate Cox regression were used to analyze the baseline clinical data of patients and establish clinical models. The obtained radiomics signature was incorporated into the clinical model to establish the radiomics nomogram. The predictive performance and calibration ability of the model were assessed by the area under the receiver operating characteristic (ROC) curve (AUC), C-index, and calibration curve.ResultsThree significant features were selected from 851 radiomics features by the least absolute shrinkage and selection operator (LASSO) Cox regression model to construct the radiomics signature, and were significantly correlated with overall survival (P<0.001). Rad-score, age, and ECOG score were combined to construct a radiomics nomogram. The AUC, sensitivity, and specificity of the radiomics nomogram were 0.801 (95% CI: 0.693–0.909), 0.822 (95% CI: 0.674–0.915), and 0.720 (95% CI: 0.674–0.915), respectively. The C-index of the radiomics nomogram was 0.700 (95% CI: 0.547–0.853). Calibration curves showed better agreement between the predicted and actual probabilities in the radiomics nomogram among the 3 features.ConclusionsThe Rad-score was a strong risk predictor of survival after TACE for HCC patients. The radiomics nomogram might be improved the predictive efficacy of survival after TACE and it may also provide assistance to physicians in making treatment decisions.     

A nomogram for predicting the risk of postoperative recurrence of hepatitis B virus-related hepatocellular carcinoma in patients with high preoperative serum glutamyl transpeptidase

BackgroundRecurrence is a major risk factor affecting the postoperative survival of patients with hepatocellular carcinoma (HCC), especially those with high preoperative serum γ-glutamyl transpeptidase (GGT) levels. This study had the aim of developing a personalized predictive tool to accurately determine the risk of postoperative recurrence of hepatitis B-virus (HBV)-related HCC in patients with high preoperative serum GGT levels.MethodsPatients who underwent curative liver resection of HBV-related HCC and had high preoperative GGT levels were consecutively enrolled between 2008 and 2011. Prognostic indicators for recurrence were determined using Cox regression analysis. A nomogram was then developed and assessed by integrating the independent risk factors into the model.ResultsA total of 603 eligible patients were included. The final nomogram for predicting HCC recurrence in patients with high preoperative GGT levels consisted of five independent prognostic factors: α-fetoprotein (AFP), HBV-DNA, satellite nodules, microvascular invasion, and tumor grade. The C-index of the nomogram for predicting recurrence was 0.759, and validation showed high accuracy and discriminatory.ConclusionsThe predictive nomogram developed and validated in this study performs well in predicting postoperative recurrence of HBV-related HCC in patients with high preoperative GGT levels. It can provide personalized assessments to inform the development of surveillance strategies and allows patients with a high risk of recurrence to be selected for further adjuvant treatment.     

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...