A novel model with nutrition-related parameters for predicting overall survival of cancer patients

Abstract Background Increasing evidence indicates that nutritional status could influence the survival of cancer patients. This study aims to develop and validate a nomogram with nutrition-related parameters for predicting the overall survival of cancer patients.Patients and Methods 8,749 patients from the multicentre cohort study in China were included as the primary cohort to develop the nomogram, and 696 of these patients were recruited as a validation cohort. Patients nutritional status were assessed using the PG-SGA. LASSO regression models and Cox regression analysis were used for factor selection and nomogram development. The nomogram was then evaluated for its effectiveness in discrimination, calibration, and clinical usefulness by the C-index, calibration curves, and Decision Curve Analysis. Kaplan-Meier survival curves were used to compare the survival rate.Results Seven independent prognostic factors were identified and integrated into the nomogram. The C-index was 0.73 (95% CI, 0.72 to 0.74) and 0.77 (95% CI, 0.74 to 0.81) for the primary cohort and validation cohort, which were both higher than 0.59 (95% CI, 0.58 to 0.61) of the TNM staging system. DCA demonstrated that the nomogram was higher than the TNM staging system and the TNM staging system combined with PG-SGA. Significantly median overall survival differences were found by stratifying patients into different risk groups (score <18.5 and ≥18.5) for each TNM category (all Ps < 0.001).Conclusion Our study screened out seven independent prognostic factors and successfully generated an easy-to-use nomogram, validated and shown a better predictive validity for the overall survival of cancer patients.

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Prognostic nomogram to predict overall survival for patients with perihilar cholangiocarcinoma: A population-based study of SEER database (Preprint)

10.2196/preprints.26118 ◽

2020 ◽

Author(s):

Chendong Wang

Keyword(s):

Overall Survival ◽

Prognostic Factors ◽

Staging System ◽

Tnm Staging ◽

Survival Outcome ◽

Perihilar Cholangiocarcinoma ◽

Cox Regression Analysis ◽

Calibration Curves ◽

Tnm Staging System ◽

Net Benefits

BACKGROUND Perihilar cholangiocarcinoma (pCCA) is a highly aggressive malignancy with poor prognosis. Accurate prediction is of great significance for patients’ survival outcome. OBJECTIVE The present study aimed to propose a prognostic nomogram for predicting the overall survival (OS) for patients with pCCA. METHODS We conducted a retrospective analysis in a total of 940 patients enrolled from the Surveillance, Epidemiology, and End Results (SEER) program and developed a nomogram based on the prognostic factors identified from the cox regression analysis. Concordance index (C-index), risk group stratification and calibration curves were adopted to test the discrimination and calibration ability of the nomogram with bootstrap method. Decision curves were also plotted to evaluate net benefits in clinical use against TNM staging system. RESULTS On the basis of multivariate analysis, five independent prognostic factors including age, summary stage, surgery, chemotherapy, together with radiation were selected and entered into the nomogram model. The C-index of the model was significantly higher than TNM system in the training set (0.703 vs 0.572, P<0.001), which was also proved in the validation set (0.718 vs 0.588, P<0.001). The calibration curves for 1-, 2-, and 3-year OS probabilities exhibited good agreements between the nomogram-predicted and the actual observation. Decision curves displayed that the nomogram obtained more net benefits than TNM staging system in clinical context. The OS curves of two distinct risk groups stratified by nomogram-predicted survival outcome illustrated statistical difference. CONCLUSIONS We established and validated an easy-to-use prognostic nomogram, which can provide more accurate individualized prediction and assistance in decision making for pCCA patients.

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Prognostic nomogram to predict overall survival for patients with perihilar cholangiocarcinoma: A population-based study of SEER database

10.21203/rs.3.rs-40400/v1 ◽

2020 ◽

Author(s):

Chendong Wang ◽

Huanyu Gong ◽

Zhiyuan Zhang ◽

Danzhou Fang ◽

Huiqun Wu

Keyword(s):

Overall Survival ◽

Prognostic Factors ◽

Staging System ◽

Tnm Staging ◽

Survival Outcome ◽

Perihilar Cholangiocarcinoma ◽

Cox Regression Analysis ◽

Calibration Curves ◽

Tnm Staging System ◽

Net Benefits

Abstract Background Perihilar cholangiocarcinoma (pCCA) is a highly aggressive malignancy with poor prognosis. Accurate prediction is of great significance for patients’ survival outcome. The present study aimed to propose a prognostic nomogram for predicting the overall survival (OS) for patients with pCCA. Methods We conducted a retrospective analysis in a total of 940 patients enrolled from the Surveillance, Epidemiology, and End Results (SEER) program and developed a nomogram based on the prognostic factors identified from the cox regression analysis. Concordance index (C-index), risk group stratification and calibration curves were adopted to test the discrimination and calibration ability of the nomogram with bootstrap method. Decision curves were also plotted to evaluate net benefits in clinical use against TNM staging system. Results On the basis of multivariate analysis, five independent prognostic factors including age, summary stage, surgery, chemotherapy, together with radiation were selected and entered into the nomogram model. The C-index of the model was significantly higher than TNM system in the training set (0.703 vs 0.572, P<0.001), which was also proved in the validation set (0.718 vs 0.588, p<0.001). The calibration curves for 1-, 2-, and 3-year OS probabilities exhibited good agreements between the nomogram-predicted and the actual observation. Decision curves displayed that the nomogram obtained more net benefits than TNM staging system in clinical context. The OS curves of two distinct risk groups stratified by nomogram-predicted survival outcome illustrated statistical difference. Conclusions We established and validated an easy-to-use prognostic nomogram, which can provide more accurate individualized prediction and assistance in decision making for pCCA patients.

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Prognostic nomogram to predict the overall survival of patients with early-onset colorectal cancer: a population-based analysis

International Journal of Colorectal Disease ◽

10.1007/s00384-021-03992-w ◽

2021 ◽

Author(s):

Junxian Wu ◽

Linbin Lu ◽

Hong Chen ◽

Yihong Lin ◽

Huanlin Zhang ◽

...

Keyword(s):

Colorectal Cancer ◽

Overall Survival ◽

Prognostic Factors ◽

Early Onset ◽

Clinical Decision Making ◽

Staging System ◽

Tnm Staging ◽

Discriminative Ability ◽

Tnm Staging System ◽

Early Onset Colorectal Cancer

Abstract Purpose The present study aimed to identify independent clinicopathological and socio-economic prognostic factors associated with overall survival of early-onset colorectal cancer (EO-CRC) patients and then establish and validate a prognostic nomogram for patients with EO-CRC. Methods Eligible patients with EO-CRC diagnosed from 2010 to 2017 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into a training cohort and a testing cohort. Independent prognostic factors were obtained using univariate and multivariate Cox analyses and were used to establish a nomogram for predicting 3- and 5-year overall survival (OS). The discriminative ability and calibration of the nomogram were assessed using C-index values, AUC values, and calibration plots. Results In total, 5585 patients with EO-CRC were involved in the study. Based on the univariate and multivariate analyses, 15 independent prognostic factors were assembled into the nomogram to predict 3- and 5-year OS. The nomogram showed favorable discriminatory ability as indicated by the C-index (0.840, 95% CI 0.827–0.850), and the 3- and 5-year AUC values (0.868 and 0.84869 respectively). Calibration plots indicated optimal agreement between the nomogram-predicted survival and the actual observed survival. The results remained reproducible in the testing cohort. The C-index of the nomogram was higher than that of the TNM staging system (0.840 vs 0.804, P < 0.001). Conclusion A novel prognostic nomogram for EO-CRC patients based on independent clinicopathological and socio-economic factors was developed, which was superior to the TNM staging system. The nomogram could facilitate postoperative individual prognosis prediction and clinical decision-making.

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Development and validation of a prognostic nomogram for predicting overall survival in patients with primary bladder sarcoma: a SEER-based retrospective study

BMC Urology ◽

10.1186/s12894-021-00929-x ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Shijie Li ◽

Xuefeng Liu ◽

Xiaonan Chen

Keyword(s):

Overall Survival ◽

Risk Stratification ◽

Cox Regression ◽

Staging System ◽

Tnm Staging ◽

Calibration Curves ◽

Tnm Staging System ◽

Primary Bladder ◽

Stratification System ◽

Risk Stratification System

Abstract Background Primary bladder sarcoma (PBS) is a rare malignant tumor of the bladder with a poor prognosis, and its disease course is inadequately understood. Therefore, our study aimed to establish a prognostic model to determine individualized prognosis of patients with PBS. Patients and Methods Data of 866 patients with PBS, registered from 1973 to 2015, were extracted from the surveillance, epidemiology, and end result (SEER) database. The patients included were randomly split into a training (n = 608) and a validation set (n = 258). Univariate and multivariate Cox regression analyses were employed to identify the important independent prognostic factors. A nomogram was then established to predict overall survival (OS). Using calibration curves, receiver operating characteristic curves, concordance index (C-index), decision curve analysis (DCA), net reclassification improvement (NRI) and integrated discrimination improvement (IDI), the performance of the nomogram was internally validated. We compared the nomogram with the TNM staging system. The application of the risk stratification system was tested using Kaplan–Meier survival analysis. Results Age at diagnosis, T-stage, N-stage, M-stage, and tumor size were identified as independent predictors of OS. C-index of the training cohort were 0.675, 0.670, 0.671 for 1-, 3- and 5-year OS, respectively. And that in the validation cohort were 0.701, 0.684, 0.679, respectively. Calibration curves also showed great prediction accuracy. In comparison with TNM staging system, improved net benefits in DCA, evaluated NRI and IDI were obtained. The risk stratification system can significantly distinguish the patients with different survival risk. Conclusion A prognostic nomogram was developed and validated in the present study to predict the prognosis of the PBS patients. It may assist clinicians in evaluating the risk factors of patients and formulating an optimal individualized treatment strategy.

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Comparing prognostic values of the 7th and 8th editions of the American Joint Committee on Cancer TNM staging system for gastric cancer

The International Journal of Biological Markers ◽

10.1177/1724600819891585 ◽

2019 ◽

Vol 35 (1) ◽

pp. 26-32 ◽

Cited By ~ 3

Author(s):

Ming-hua Zhu ◽

Ke-cheng Zhang ◽

Ze-long Yang ◽

Zhi Qiao ◽

Lin Chen

Keyword(s):

Gastric Cancer ◽

Overall Survival ◽

Cancer Patients ◽

Staging System ◽

Tnm Staging ◽

American Joint Committee ◽

Tnm Staging System ◽

Staging Systems ◽

Gastric Cancer Patients ◽

8Th Edition

Background and Aim: Our aim was to compare the prognostic value of the American Joint Committee on Cancer (AJCC) 7th and 8th editions staging systems for patients with gastric cancer in China. Methods: A total of 1326 gastric cancer patients diagnosed between 2008 and 2012 were included. The discriminative ability of the AJCC 8th and 7th editions was compared using the Harrell’s concordance index (C-index). Results: There are two main modifications in the 8th edition. (i) pN3 staging was divided into pN3a and pN3b. The gastric cancer patients with pN3a experienced significantly better overall survival compared with those with pN3b (5-year overall survival: 34.5% vs. 15.6%, P < 0.001) (stratified by pT: pT3: 5-year overall survival: 33.9% vs. 13.2%, P < 0.001; pT4a: 32.8% vs. 16.9%, P = 0.056; pT4b: 17.0% vs. 11.1%, P = 0.048). (ii) Subgroup staging adjustments. The subgroup staging adjustments (T3N3bM0 (IIIB→IIIC), T4aN3aM0 (IIIC→IIIB), T4bN0M0 (IIIB→IIIA), and T4bN2M0 (IIIC→IIIB)) resulted in more gastric cancer patients being accurately staged. Furthermore, the C-index value of the 8th edition tumor node metastasis (TNM) staging system was significantly higher than that of the AJCC 7th TNM staging system to predict the survival of gastric cancer patients (0.701 vs. 0.685, P < 0.001). Conclusions: The 8th edition of the TNM staging system is superior to the 7th edition staging system for prediction of survival of gastric cancer patients in China.

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A Nomogram Based on Aspartate Aminotransferase/Alanine Aminotransferase (AST/ALT) Ratio to Predict Prognosis After Surgery in Gastric Cancer Patients

Cancer Control ◽

10.1177/1073274820954458 ◽

2020 ◽

Vol 27 (1) ◽

pp. 107327482095445

Author(s):

Linfang Li ◽

Qiuyao Zeng ◽

Ning Xue ◽

Miantao Wu ◽

Yaqing Liang ◽

...

Keyword(s):

Gastric Cancer ◽

Aspartate Aminotransferase ◽

Alanine Aminotransferase ◽

Cox Regression ◽

Staging System ◽

Tnm Staging ◽

Study Cohort ◽

Cox Regression Analysis ◽

Tnm Staging System ◽

Resectable Gastric Cancer

Introduction: Using the TMN classification alone to predict survival in patients with gastric cancer has certain limitations, we conducted this study was to develop an effective nomogram based on aspartate aminotransferase/alanine aminotransferase (AST/ALT) ratio to predict overall survival (OS) in surgically treated gastric cancer. Methods: we retrospectively analyzed 190 cases of gastric cancer and used Cox regression analysis to identify the significant prognostic factors for OS in patients with resectable gastric cancer. The predictive accuracy of nomogram was assessed using a calibration plot, concordance index (C-index) and decision curve. This was then compared with a traditional TNM staging system. Based on the total points (TPS) by nomogram, we further divided patients into different risk groups. Results: multivariate analysis of the entire cohort revealed that independent risk factors for survival were age, clinical stage and AST/ALT ratio, which were entered then into the nomogram. The calibration curve for the probability of OS showed that the nomogram-based predictions were in good agreement with actual observations. Additionally, the C-index of the established nomogram for predicting OS had a superior discrimination power compared to the TNM staging system [0.794 (95% CI: 0.749-0.839) vs 0.730 (95% CI: 0.688-0.772), p < 0.05]. Decision curve also demonstrated that the nomogram was better than the TNM staging system. Based on TPS of the nomogram, we further subdivided the study cohort into 3 groups including low risk (TPS ≤ 158), middle risk (158 < TPS ≤ 188) and high risk (TPS > 188) categories. The differences in OS rate were significant among the groups. Conclusion: the established nomogram is associated with a more accurate prognostic prediction for individual patients with resectable gastric cancer.

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A nomogram for predicting overall survival in patients with Merkel cell carcinoma: A population-based analysis

10.21203/rs.3.rs-1052951/v1 ◽

2021 ◽

Author(s):

Miao Wang ◽

Ye Qiu ◽

Fang Tang ◽

Yi-Xin Liu ◽

Yi Li ◽

...

Keyword(s):

Overall Survival ◽

Prognostic Factors ◽

Cell Carcinoma ◽

Merkel Cell Carcinoma ◽

Cox Regression ◽

Predictive Accuracy ◽

Characteristic Curve ◽

Staging System ◽

Merkel Cell ◽

Cox Regression Analysis

Abstract Background: Merkel cell carcinoma (MCC) is a rare neuroendocrine skin cancer with increasing incidence and poor prognosis. We sought to develop and validate a nomogram to estimate overall survival (OS) of MCC patients. Methods: 1863 MCC patients between 2010-2015 from the Surveillance, Epidemiology and End Results (SEER) database were randomly divided into the training and validation cohort. Independent prognostic factors determined by Cox regression analysis in the training cohort were used to establish a nomogram. We evaluated prognostic performance using the concordance index (C-index), area under receiver operating characteristic curve (AUC) and calibration curves. Decision curve analysis (DCA), net reclassification index (NRI) and integrated discrimination improvement (IDI) were used to compared the the nomogram’s clinical utility with that of the staging system.Results: eight independent prognostic factors were incorporated in the nomogram. The C-index of the nomogram was 0.744, which was superior to the C-index of AJCC TNM Stage (0.659). The AUC was greater than 0.75 and the calibration plots of this model exhibited good performance. Additionally, the positive NRI and IDI of nomogram versus the staging system illustrated that the nomogram had better predictive accuracy than the staging system (P<0.001) and the DCA showed great clinical usefulness of the nomogram. MCC patients were perfectly classified into three risk groups by the nomogram, showing better discrimination than the staging system.Conclusions: We developed and validated a nomogram to assist clinicians in evaluating prognosis of MCC patients.

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A nomogram predicting overall survival in patients with non-metastatic pancreatic head adenocarcinoma after surgery: a population-based study

BMC Cancer ◽

10.1186/s12885-021-08250-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Wenbo Zou ◽

Zizheng Wang ◽

Fei Wang ◽

Gong Zhang ◽

Rong Liu

Keyword(s):

Overall Survival ◽

Prognostic Factors ◽

Cox Regression ◽

Predictive Accuracy ◽

Pancreatic Head ◽

Staging System ◽

Seer Database ◽

Cox Regression Analysis ◽

Net Reclassification Improvement ◽

Calibration Curves

Abstract Background Pancreatic head adenocarcinoma (PHAC), a malignant tumour, has a very poor prognosis, and the existing prognostic tools lack good predictive power. This study aimed to develop a better nomogram to predict overall survival after resection of non-metastatic PHAC. Methods Patients with non-metastatic PHAC were collected from the Surveillance, Epidemiology, and End Results (SEER) database and divided randomly into training and validation cohorts at a ratio of 7:3. Cox regression analysis was used to screen prognostic factors and construct the nomogram. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were calculated to evaluate the performance of the model. The predictive accuracy and clinical benefits of the nomogram were validated using the area under the curve (AUC), calibration curves, and decision curve analysis (DCA). Results From 2010 to 2016, 6419 patients with non-metastatic PHAC who underwent surgery were collected from the SEER database. A model including T stage, N stage, grade, radiotherapy, and chemotherapy was constructed. The concordance index of the nomogram was 0.676, and the AUCs of the model assessing survival at multiple timepoints within 60 months were significantly higher than those of the American Joint Committee on Cancer (AJCC) 8th staging system in the training cohort. Calibration curves showed that the nomogram had ability to predict the actual survival. The NRI, IDI, and DCA curves also indicated that our nomogram had higher predictive capability and clinical utility than the AJCC staging system. Conclusions Our nomogram has an ability to predict overall survival after resection of non-metastatic PHAC and includes prognostic factors that are easy to obtain in clinical practice. It would help assist clinicians to conduct personalized medicine.

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A nomogram based on pretreatment levels of serum bilirubin and total bile acid levels predicts survival in colorectal cancer patients

BMC Cancer ◽

10.1186/s12885-021-07805-9 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Yinghao Cao ◽

Shenghe Deng ◽

Lizhao Yan ◽

Junnan Gu ◽

Jia Yang ◽

...

Keyword(s):

Colorectal Cancer ◽

Prognostic Factors ◽

Bile Acid ◽

Bile Acids ◽

Cancer Patients ◽

Serum Bilirubin ◽

Cox Regression ◽

Total Bile Acid ◽

Cox Regression Analysis ◽

Colorectal Cancer Patients

Abstract Background Serum bilirubin and total bile acid (TBA) levels have been reported to be strongly associated with the risk and prognosis of certain cancers. Here, we aimed to investigate the effects of pretreatment levels of serum bilirubin and bile acids on the prognosis of patients with colorectal cancer (CRC). Methods A retrospective cohort of 1474 patients with CRC who underwent surgical resection between January 2015 and December 2017 was included in the study. Survival analysis was used to evaluate the predictive value of pretreatment levels of bilirubin and bile acids. X-Tile software was used to identify optimal cut-off values for total bilirubin (TBIL), direct bilirubin (DBIL) and TBA in terms of overall survival (OS) and disease-free survival (DFS). Results DBIL, TBIL, and TBA were validated as significant prognostic factors by univariate Cox regression analysis for both 3-year OS and DFS. Multivariate Cox regression analyses confirmed that high DBIL, TBIL and TBA levels were independent prognostic factors for both OS (HR: 0.435, 95% CI: 0.299–0.637, P < 0.001; HR: 0.436, 95% CI: 0.329–0.578, P < 0.001; HR: 0.206, 95% CI: 0.124–0.341, P < 0.001, respectively) and DFS (HR: 0.583, 95% CI: 0.391–0.871, P = 0.008; HR:0.437,95% CI: 0.292–0.655, P <0.001; HR: 0.634, 95% CI: 0.465–0.865, P = 0.004, respectively). In addition, nomograms for OS and DFS were established according to all significant factors, and the c-indexes were 0.819 (95% CI: 0.806–0.832) and 0.835 (95% CI: 0.822–0.849), respectively. Conclusions TBIL, DBIL and TBA levels are independent prognostic factors in colorectal cancer patients. The nomograms based on OS and DFS can be used as a practical model for evaluating the prognosis of CRC patients.

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Incidence, Survival, and Associated Factors Estimation in Osteosarcoma Patients with Lung Metastasis: A Single-center Experience of 11 Years in Tianjin, China

10.21203/rs.3.rs-1071152/v1 ◽

2021 ◽

Author(s):

Chao Zhang ◽

Haixiao Wu ◽

Guijun Xu ◽

Wenjuan Ma ◽

Lisha Qi ◽

...

Keyword(s):

Risk Factors ◽

Overall Survival ◽

Regression Analysis ◽

Prognostic Factors ◽

Lung Metastasis ◽

Associated Factors ◽

Cox Regression ◽

Cancer Center ◽

Single Center ◽

Cox Regression Analysis

Abstract Background: Osteosarcoma is the most common primary malignant bone tumor. The current study was conducted to describe the general condition of patients with primary osteosarcoma in a single cancer center in Tianjin, China and to investigate the associated factors in osteosarcoma patients with lung metastasis. Methods: From February 2009 to October 2020, patients from Tianjin Medical University Cancer Institute and Hospital, China were retrospectively analyzed. The Kaplan–Meier method was used to evaluate the overall survival of osteosarcoma patients. Prognostic factors of patients with osteosarcoma were identified by the Cox proportional hazard regression analysis. Risk factor of lung metastasis in osteosarcoma were investigated by the logistic regression model. Results: A total of 203 patients were involved and 150 patients were successfully followed up for survival status. The 5-year survival rate of osteo-sarcoma patients was 70.0%. Surgery, bone and lung metastasis were the significant prognostic factors in multivariable Cox regression analysis. Twenty-one (10.3%) patients showed lung metastasis at the diagnosis of osteosarcoma and 67 (33%) lung metastases during the later course. T3 stage (OR=11.415, 95%CI 1.362-95.677, P=0.025) and synchronous bone metastasis (OR=6.437, 95%CI 1.69-24.51, P=0.006) were risk factors of synchronous lung metastasis occurrence. Good necrosis (≥90%, OR=0.097, 95%CI 0.028-0.332, P=0.000) and elevated Ki-67 (≥50%, OR=4.529, 95%CI 1.241-16.524, P=0.022) were proved to be significantly associated with metachronous lung metastasis occurrence. Conclusion: The overall survival, prognostic factors and risk factors for lung metastasis in this single center provided insight about osteosarcoma management.

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