Prognostic nomogram for predicting long-term survival in bronchopulmonary carcinoid tumor patients receiving resection

Qiao, Liyan; Chen, Qichen; Chen, Jinghua; Wang, Zijing; Wang, Pan; Zhao, Hong; Zhao, Jun

doi:10.21037/atm-21-1929

Cited by 3 publications

(4 citation statements)

References 44 publications

(71 reference statements)

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“…In the prognostic nomogram in this study, age at diag-nosis was a powerful prognostic factor for PCT patients. It is obvious that older age at diagnosis is related to shorter DSS, in accordance with the results displayed in gastrointestinal carcinoid tumors, 32 bronchopulmonary carcinoid tumors, 27 and pNETs. [33][34][35] This may be attributed to how the risk of death increases with age after treatments such as surgery in pNETs.…”

Section: Discussionsupporting

confidence: 87%

“…Nomograms are an efficient and intuitive prognostic predictive model for helping clinicians make personalized clinical decisions. [24][25][26] In recent years, several prognostic nomogram models have been successfully established to predict survival outcomes in bronchopulmonary carcinoids, [27][28][29] gastric neuroendocrine tumors, 30 and pNETs 31 due to their preferable predictive abilities and excellent discrimination. In the prognostic nomogram in this study, age at diag-nosis was a powerful prognostic factor for PCT patients.…”

Section: Discussionmentioning

confidence: 99%

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Epidemiology and Prognostic Nomogram for Predicting Long-Term Disease-Specific Survival in Patients With Pancreatic Carcinoid Tumor

Lin,

Li,

Chen

et al. 2024

Pancreas

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Objectives Pancreatic carcinoid tumor (PCT) is described as a malignant form of carcinoid tumors. However, the epidemiology and prognostic factors for PCT are poorly understood. Materials and Methods The data of 2447 PCT patients were included in this study from the Surveillance, Epidemiology, and End Results database and randomly divided into a training cohort (1959) and a validation cohort (488). The epidemiology of PCT was calculated, and independent prognostic factors were identified to construct a prognostic nomogram for predicting long-term disease-specific survival (DSS) among PCT patients. Results The incidence of PCT increased remarkably from 2000 to 2018. The 1-, 5-, and 10-year DSS rates were 96.4%, 90.3%, and 86.5%, respectively. Age at diagnosis, stage, surgery, radiotherapy, and chemotherapy were identified as independent prognostic factors to construct a prognostic nomogram. The C-indices; area under the receiver operating characteristic curves for predicting 1-, 5-, and 10-year DSS, and calibration plots of the nomogram in both cohorts indicated a high discriminatory accuracy, preferable survival predictive ability, and optimal concordances, respectively. Conclusions The incidence of PCT has increased rapidly since 2000. In addition, we established a practical, effective, and accurate prognostic nomogram for predicting the long-term DSS of PCT patients.

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Section: Discussionsupporting

confidence: 87%

Section: Discussionmentioning

confidence: 99%

Epidemiology and Prognostic Nomogram for Predicting Long-Term Disease-Specific Survival in Patients With Pancreatic Carcinoid Tumor

Lin,

Li,

Chen

et al. 2024

Pancreas

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“…The chart integration predicts the probability of events. In previous studies, nomograms have demonstrated its superior predictive ability to the TNM staging system (9)(10)(11)(12). As far as we know, the current study is the first that developed nomograms based on a large-size number of patients, which was verified by internal and external validation sets to guarantee the reliability of the results.…”

Section: Introductionmentioning

confidence: 60%

Construction and validation of a novel web-based nomogram for patients with lung cancer with bone metastasis: A real-world analysis based on the SEER database

et al. 2022

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PurposePatients with lung cancer with bone metastasis (LCBM) often have a very poor prognosis. The purpose of this study is to characterize the prevalence and associated factors and to develop a prognostic nomogram to predict the overall survival (OS) and cancer-specific survival (CSS) for patients with LCBM using multicenter population-based data.MethodsPatients with LCBM at the time of diagnosis were identified using the Surveillance, Epidemiology, and End Results (SEER) Program database of the National Cancer Institute (NCI) from 2010 to 2015. Multivariable and univariate logistic regression analyses were performed to identify factors associated with all-cause mortality and lung cancer (LC)–specific mortality. The performance of the nomograms was evaluated with the calibration curves, area under the curve (AUC), and decision curve analysis (DCA). Kaplan–Meier analysis and log-rank tests were used to estimate the survival times of patients with LCBM.ResultsWe finally identified 26,367 patients with LCBM who were selected for survival analysis. Multivariate analysis demonstrated age, sex, T stage, N stage, grade, histology, radiation therapy, chemotherapy, primary site, primary surgery, liver metastasis, and brain metastasis as independent predictors for LCBM. The AUC values of the nomogram for the OS prediction were 0.755, 0.746, and 0.775 in the training cohort; 0.757, 0.763, and 0.765 in the internal validation cohort; and 0.769, 0.781, and 0.867 in the external validation cohort. For CSS, the values were 0.753, 0.753, and 0.757 in the training cohort; 0.753, 0.753, and 0.757 in the internal validation cohort; and 0.767, 0.774, and 0.872 in the external validation cohort.ConclusionsOur study constructs a new prognostic model and clearly presents the clinicopathological features and survival analysis of patients with LCBM. The result indicated that the nomograms had favorable discrimination, good consistency, and clinical benefits in patients. In addition, our constructed nomogram prediction models may assist physicians in evaluating individualized prognosis and deciding on treatment for patients.

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“…Kaplan-Meier survival analysis and log-rank test was performed between patients with and without distant metastasis. We randomly divided all patients (N=1,066) into two cohorts, training cohort and validation cohort, with a ratio of 70:30 to establish and validate the nomogram, respectively ( 13 , 14 ). The Pearson’s chi-squared test was used to compare categorical variables, and the Student’s t- test was used to compare quantitative variables between the training cohort and the validation cohort.…”

Section: Methodsmentioning

confidence: 99%

Establishment and validation of an individualized nomogram to predict distant metastasis in chondrosarcoma patients: a population-based study

Hoang¹,

Nguyen²,

Chen³

et al. 2022

Transl Cancer Res TCR

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Background: Distant metastasis is a significant factor influencing chondrosarcoma (CHS) patients' treatment and prognosis. We aimed to establish a consistent and effective nomogram to better predict distant metastases of CHS individuals. Methods: The Surveillance, Epidemiology and End Results (SEER) database was used to obtain the demographics and clinicopathological characteristics of CHS patients from 2010 to 2018. Independent risk factors were identified via univariate and multivariate logistic regressive analysis. A nomogram that predicts metastasis risk was established based on the training cohort, and its accuracy was validated through the validation cohort. The performance of this predictive model was assessed by the receiver operating characteristic (ROC) curve and Harrell's concordance index (C-index). Finally, decision curve analysis (DCA) was conducted to test its clinical reliability.Results: Data of 1,066 patients were extracted, of these, 66 cases (6.19%) were with distant metastasis at initial diagnosis. The following features were shown to be linked to an increased risk of metastasis: highgrade tumor, T3 stage, and large tumor size; whereas unmarried and use of surgery were independent protective factors. Marital status, tumor grade, T stage, use of cancer-directed surgery and tumor size were incorporated to develop the novel nomogram. The ROC curves showed the effectiveness of the nomogram with the high area under the curves, the C-indices were 0.931 and 0.951 in the internal and external validation, respectively. The calibration plots indicated a good consistency and agreement of the nomogram, while the DCA illustrated that the nomogram had favorable potential clinical applicability due to great positive net benefit with wide ranges of the threshold probabilities.Conclusions: This work developed a novel nomogram for predicting distant metastasis in CHS patients, which might assist clinicians to determine the optimal treatment plan by precisely predicting individualized metastatic risk.

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Prognostic nomogram for predicting long-term survival in bronchopulmonary carcinoid tumor patients receiving resection

Cited by 3 publications

References 44 publications

Epidemiology and Prognostic Nomogram for Predicting Long-Term Disease-Specific Survival in Patients With Pancreatic Carcinoid Tumor

Epidemiology and Prognostic Nomogram for Predicting Long-Term Disease-Specific Survival in Patients With Pancreatic Carcinoid Tumor

Construction and validation of a novel web-based nomogram for patients with lung cancer with bone metastasis: A real-world analysis based on the SEER database

Establishment and validation of an individualized nomogram to predict distant metastasis in chondrosarcoma patients: a population-based study

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