A Novel Nomogram for Predicting Cancer-Specific Survival in Women with Uterine Sarcoma: A Large Population-Based Study

Abstract Background: Uterine Sarcoma (US) is a rare malignant uterine tumor in women with aggressive behavior and rapid progression. The purpose of this study was to perform a comprehensive nomogram to predict the cancer-specific survival (CSS) for US based on the Surveillance, Epidemiology, and End Results (SEER) database.Methods: Retrospetive population-based study was conducted using the data of patients with US between 2010 and 2015 from SEER database. They were randomly divided into a training cohort and a validation cohort in a 7-to-3 ratio. Multivariate Cox analysis was performed to identify independent prognostic factors. Subsequently, nomogram was established to predict the patients’ CSS. The discrimination and calibration of the nomogram were evaluated by concordance index (C-index) and the area under the curve (AUC). Finally, the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), calibration plotting, and decision-curve analysis (DCA) were used to evaluate the benefits of the new prediction model.Results: A total of 3861 patients with US were included in our study. As revealed in multivariate Cox analysis, age at diagnosis, race, marital status, insurance record, tumor size, pathology grade, histological type, SEER stage, AJCC stage, surgery status, radiotherapy status, and chemotherapy status were found to be independent prognositic factors. In our nomogram, pathology grade has the highest risk on CSS in US, followed by age at diagnosis, then surgery status. Comparing to the AJCC staging system, the new nomogram showed better predictive discrimination with higher C-index in both training and validation cohort (0.796 and 0.767 vs0.706 and 0.713, respectively) . Furthermore, AUC value, calibration plotting, NRI, IDI, and DCA also demonstrated better performance than the traditional system.Conclusion: Our study validated the first comprehensive nomogram for US which could provide more accurately and individualized survival predictions for US patients in clinical practice.

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A Novel Nomogram for Predicting Cancer-specific Survival in Women With Uterine Sarcoma: a Large Population-based Study

10.21203/rs.3.rs-630236/v1 ◽

2021 ◽

Author(s):

Yuan-jie Li ◽

Jun Lyu ◽

Chen Li ◽

Hai-rong He ◽

Jin-feng Wang ◽

...

Keyword(s):

Validation Cohort ◽

Large Population ◽

Area Under The Curve ◽

Staging System ◽

Population Based ◽

Uterine Sarcoma ◽

Cancer Specific Survival ◽

Traditional System ◽

Net Reclassification Improvement ◽

Cox Analysis

Abstract Background: To perform a comprehensive nomogram to predict the cancer-specific survival (CSS) for uterine sarcoma (US) based on the Surveillance, Epidemiology, and End Results (SEER) database.Methods: A total of 3861 patients with US between 2010 and 2015 were identified in this study. They were randomly divided into a training cohort (n = 2702) and a validation cohort (n = 1159) in a 7-to-3 ratio by R software. We performed multivariate Cox analysis to select predictive variables and identify independent prognostic factors. Then, the discrimination and calibration of the nomogram were evaluated by concordance index (C-index) and the area under the curve (AUC). Finally, the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), calibration plotting, and decision-curve analysis (DCA) were used to evaluate the benefits of the new prediction model.Results: We have established a nomogram to predict 1-, 3-, and 5-year CSS for US patients. In this nomogram, pathology grade has the highest risk on CSS in US, followed by age at diagnosis, then surgery status. Comparing to the AJCC staging system, the nomogram showed better predictive discrimination with higher C-index in both training and validation cohort (0.796 and 0.767 vs0.706 and 0.713, respectively) . Furthermore, AUC value, calibration plotting, NRI, IDI, and DCA also demonstrated better performance than the traditional system.Conclusion: Our study validated the first comprehensive nomogram for US which could provide more accurately and individualized survival predictions for US patients in clinical practice.

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A Nomogram Predicting Overall and Cancer-Specific Survival of Patients with Primary Bone Lymphoma: A Large Population-Based Study

BioMed Research International ◽

10.1155/2020/4235939 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

He-Hui Wang ◽

Ke-Na Dai ◽

A-Bing Li

Keyword(s):

Validation Cohort ◽

Large Population ◽

Population Based ◽

Cancer Specific Survival ◽

Population Based Study ◽

Training Cohort ◽

Primary Bone Lymphoma ◽

Primary Bone ◽

Actual Survival ◽

Bone Lymphoma

We aimed to develop a nomogram for evaluating the overall survival (OS) and cancer-specific survival (CSS) in patients with primary bone lymphoma (PBL). Patients diagnosed with PBL between 2007 and 2016 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. All patients were randomly allocated to the training cohort and validation cohort (2 : 1). The nomogram was developed by the training cohort and validated by the validation cohort using the concordance index (C-index), calibration plots, and decision curve analyses (DCAs). The C-index for CSS and OS prediction in the training cohort were 0.76 and 0.77, respectively; in the validation cohort, they were 0.76 and 0.79, respectively. The calibration curve showed good consistency between nomogram prediction and actual survival. The DCA indicated obvious net benefits of the new predictive model. The nomogram showed favorable applicability and accuracy, and it will be a reliable tool for predicting OS and CSS in patients with PBL.

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44P A large population-based study on large cell neuroendocrine lung cancer: A SEER database analysis

Journal of Thoracic Oncology ◽

10.1016/s1556-0864(18)30324-1 ◽

2018 ◽

Vol 13 (4) ◽

pp. S24-S25

Author(s):

X. Fu ◽

H. Zhang ◽

Y. Yang ◽

M. Liu ◽

J. Lu ◽

...

Keyword(s):

Lung Cancer ◽

Large Population ◽

Large Cell ◽

Population Based ◽

Seer Database ◽

Database Analysis ◽

Population Based Study

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The Prevalence and associated Factors for Liver Metastases, Development and Prognosis in newly diagnosed Epithelial Ovarian Cancer: A large Population-Based Study from the SEER Database

Journal of Cancer ◽

10.7150/jca.40590 ◽

2020 ◽

Vol 11 (16) ◽

pp. 4861-4869

Author(s):

Iftikhar Hussain ◽

Jiaqin Xu ◽

Kui Deng ◽

Noor-ul-Amin ◽

Ce Wang ◽

...

Keyword(s):

Ovarian Cancer ◽

Liver Metastases ◽

Epithelial Ovarian Cancer ◽

Associated Factors ◽

Large Population ◽

Population Based ◽

Seer Database ◽

Newly Diagnosed ◽

Population Based Study

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Conditional Survival After Surgical Resection of Appendiceal Tumors: a Population-based Study

10.21203/rs.3.rs-403708/v1 ◽

2021 ◽

Author(s):

Shutao Zhao ◽

Chang Lu ◽

Junan Li ◽

Chao Zhang ◽

Xudong Wang

Keyword(s):

Prognostic Factors ◽

Cox Regression ◽

Population Based ◽

Conditional Survival ◽

Seer Database ◽

Cancer Specific Survival ◽

Population Based Study ◽

Factors Affecting ◽

Probability Of Survival ◽

N Stage

Abstract Background: This study aimed to evaluate the conditional survival (CS) of appendiceal tumors (ATs) after surgery.Methods: A total of 3,031 patients with ATs who underwent surgery were included in the Surveillance Epidemiology and End Results (SEER) database from 2004 to 2016. A multivariate Cox regression model was used to analyze the prognostic factors affecting overall survival (OS) and cancer-specific survival (CSS). CS was used to calculate the probability of survival for another 3 years after the patient had survived x years. The formulas were COS3 = OS (x + 3) /OS (x), and CCS3 = CSS (x + 3)/CSS (x).Results: The 1-year, 3-year, and 5-year OSs for all patients were 95.6%, 83.3%, and 73.9%, respectively, while the 1-year, 3-year, and 5-year CSSs were 97.0%, 87.1%, and 79.9%, respectively. Age, grade, histology, N stage, carcinoembryonic antigen (CEA), and radiation were independent prognostic factors for OS and CSS. For patients that survived for 1 year, 3 years, and 5 years, their COS3s were 81.7%, 83.9%, and 87.0%, respectively. The CCS3s were 85.5%, 88.3%, and 92.0% respectively. In patients with poor clinicopathological factors, COS3 and CCS3 increased significantly, and the survival gap between OS and COS3, CSS and CCS3 was more obvious.Conclusions: CS for appendiceal tumors were dynamic and increased over time, especially in patients with poor prognosis.

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Analysis of clinical characteristics and prognosis with cervical adenosquamous carcinoma: a large population-based study

Future Oncology ◽

10.2217/fon-2020-1156 ◽

2021 ◽

Author(s):

Yanan Ma ◽

Aimei Zhao ◽

Jinjuan Zhang ◽

Sumei Wang ◽

Jiandong Zhang

Keyword(s):

Overall Survival ◽

Regression Models ◽

Clinical Characteristics ◽

Cox Regression ◽

Adenosquamous Carcinoma ◽

Large Population ◽

Population Based ◽

Cancer Specific Survival ◽

Population Based Study ◽

Good Consistency

Objective: The target of this work was to analyze the clinical characteristics and construct nomograms to predict prognosis in patients with cervical adenosquamous carcinoma (ASC). Methods: A total of 788 ASC patients were tracked in the Surveillance, Epidemiology and End Results database. We compared the clinical characteristics and prognostic factors of ASC. Cox regression models were established, and nomograms constructed and verified. Results: ASC patients have lower age levels and higher histological grades than patients with squamous cell carcinoma. Nomograms were constructed with good consistency and feasibility in clinical practice. The C-indices for overall survival and cancer-specific survival were 0.783 and 0.787, respectively. Conclusion: ASC patients have unique clinicopathological and prognostic characteristics. Nomograms were successfully constructed and verified.

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