A nomogram model to predict death rate among non-small cell lung cancer (NSCLC) patients with surgery in surveillance, epidemiology, and end results (SEER) database

Jia, Bo; Zheng, Qiwen; Wang, Jingjing; Sun, Hongyan; Zhao, Jun; Wu, Meina; An, Tongtong; Wang, Yuyan; Zhuo, Minglei; Li, Jianjie; Yang, Xue; Zhong, Jia; Chen, Hanxiao; Chi, Yujia; Zhai, Xiaoyu; Wang, Ziping

doi:10.1186/s12885-020-07147-y

Cited by 32 publications

(33 citation statements)

References 21 publications

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“…The significantly higher C-index of the nomogram (in both cohorts) compared with the AJCC staging model indicates the good discrimination ability of the nomogram. This indicated that the model is very precise ( 42 ). In the current study, calibration plots of predictions corresponded well with actual observations indicated by the curve being close to the 45-degree line, verifying the repeatability and reliability of the established nomogram ( 20 , 42 ).…”

Section: Discussionmentioning

confidence: 95%

“…This indicated that the model is very precise ( 42 ). In the current study, calibration plots of predictions corresponded well with actual observations indicated by the curve being close to the 45-degree line, verifying the repeatability and reliability of the established nomogram ( 20 , 42 ). This is the first nomogram constructed to predict the survival of elderly AC patients that we know of.…”

Section: Discussionmentioning

confidence: 95%

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Construction of a Nomogram for Predicting Survival in Elderly Patients With Lung Adenocarcinoma: A Retrospective Cohort Study

et al. 2021

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Elderly patients with non-small-cell lung cancer (NSCLC) exhibit worse reactions to anticancer treatments. Adenocarcinoma (AC) is the predominant histologic subtype of NSCLC, is diverse and heterogeneous, and shows different outcomes and responses to treatment. The aim of this study was to establish a nomogram that includes the important prognostic factors based on the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. We collected 53,694 patients of older than 60 who have been diagnosed with lung AC from the SEER database. Univariate and multivariate Cox regression analyses were used to screen the independent prognostic factors, which were used to construct a nomogram for predicting survival rates in elderly AC patients. The nomogram was evaluated using the concordance index (C-index), calibration curves, net reclassification index (NRI), integrated discrimination improvement (IDI), and decision-curve analysis (DCA). Elderly AC patients were randomly divided into a training cohort and validation cohort. The nomogram model included the following 11 prognostic factors: age, sex, race, marital status, tumor site, histologic grade, American Joint Committee for Cancer (AJCC) stage, surgery status, radiotherapy status, chemotherapy status, and insurance type. The C-indexes of the training and validation cohorts for cancer-specific survival (CSS) (0.832 and 0.832, respectively) based on the nomogram model were higher than those of the AJCC model (0.777 and 0.774, respectively). The CSS discrimination performance as indicated by the AUC was better in the nomogram model than the AJCC model at 1, 3, and 5 years in both the training cohort (0.888 vs. 0.833, 0.887 vs. 0.837, and 0.876 vs. 0.830, respectively) and the validation cohort (0.890 vs. 0.832, 0.883 vs. 0.834, and 0.880 vs. 0.831, respectively). The predicted CSS probabilities showed optimal agreement with the actual observations in nomogram calibration plots. The NRI, IDI, and DCA for the 1-, 3-, and 5-year follow-up examinations verified the clinical usability and practical decision-making effects of the new model. We have developed a reliable nomogram for determining the prognosis of elderly AC patients, which demonstrated excellent discrimination and clinical usability and more accurate prognosis predictions. The nomogram may improve clinical decision-making and prognosis predictions for elderly AC patients.

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Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 95%

Construction of a Nomogram for Predicting Survival in Elderly Patients With Lung Adenocarcinoma: A Retrospective Cohort Study

et al. 2021

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“…20 Our profiling maintains essential insights into PACC and offers valuable knowledge on the morbidity, predictive factors, and survival of patients with PACC, following the analogous statistical methods. 21 In conclusion, our population-based study has demonstrated that PACC is a rare lung adenocarcinoma with an increasing incidence trend, particularly in men. Surgery is related to a better prognosis and should be considered for patients with PACC.…”

Section: Discussionmentioning

confidence: 60%

Section: Discussionmentioning

confidence: 99%

Prognostic factors and survival outcome of primary pulmonary acinar cell carcinoma

Meng

Sun

Wang³

et al. 2021

Thoracic Cancer

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The objective of our study was to investigate the epidemiologic characteristics and prognostic factors in patients with pulmonary acinar cell carcinoma (PACC). Methods: PACC patients diagnosed between 1975 and 2016 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. The trend in PACC incidence was assessed using joinpoint regression software. Overall survival (OS) and disease-specific survival (DSS) were evaluated using the Kaplan-Meier method and logrank test. Univariate and multivariate Cox regression analysis was performed to identify the independent prognostic factors for OS and DSS. Nomograms to predict survival possibilities were constructed based on the identified independent prognostic factors. Results: A total of 2918 patients were identified with PACC. The mean age was 65.2 AE 8.95 years with a female to male of 1.6:1. The incidence of PACC steadily increased by an annual percentage change (APC) of 3.2% (95% CI 2.1-4.4, p < 0.05). Multivariate Cox regression analysis revealed that age, gender, race, stage, grade, tumor size, number of positive lymph nodes, surgery, and chemotherapy were independent prognostic factors for survival. Nomograms specifically for PACC were constructed to predict 1-and 5-year OS and DSS possibility, respectively. The concordance index (C-index) and calibration plots showed the established nomograms had robust and accurate performance. Conclusion: PACC was rare but the incidence has been steadily increasing over the past four decades. Survival has improved in recent years. Surgery or chemotherapy could provide better OS and DSS. The established nomograms specifically for PACC were robust and accurate in predicting 1-and 5-year OS and DSS.

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“…The nomogram is a useful and popular visual graph that illustrates the probability of occurrence of predicted events so as to guide clinical treatments [12][13][14]. Several past studies have reported that nomogram has good risk-prediction ability for post-surgical survival prognostication in patients with malignant tumors [15,16]. However, reports on the use of a nomogram model for predicting the risk of thrombosis are limited [17].…”

Section: Introductionmentioning

confidence: 99%

A nomogram model to predict the venous thromboembolism risk after surgery in patients with gynecological tumors

Wang

Wei

Zhou

et al. 2021

Thrombosis Research

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Venous thromboembolism (VTE) is a common post-surgical complication of gynecological malignant tumors that has serious implications on the prognosis and quality-of-life of patients. However, there exists only a few recognized specific evaluation models for the occurrence of VTE after gynecological malignant tumor surgery. We aimed to establish a nomogram model that could predict the probability of post-surgical VTE in patients with gynecological malignancies. Methods: We collected the clinical information of 673 patients who underwent surgery for gynecological malignant tumor in our hospital between January 2014 and May 2020. To reduce bias from confounding factors between groups, a 1:1 ratio propensity score matching (PSM) method was performed; meanwhile, univariate and multivariate analyses were applied to analyze the risk factors of VTE after surgeries. A nomogram prediction model was accordingly established and internally validated. Results: The predictors contained in the nomogram model included age, D-dimer value, body mass index (BMI), and surgical approach. The C-index of the model was 0.721 (95% confidence interval: 0.644-0.797), with good discrimination and calibration effect. The internally verified C-index value was 0.916. Decision curve analysis confirmed that the nomogram model was clinically useful when the incidence of thrombosis in patients was 10-75%. Conclusions: Considering the risk factors of VTE after surgery for gynecological malignant tumor, a highperformance nomogram model was established and then validated to provide individual risk assessment and guide treatment decisions.

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A nomogram model to predict death rate among non-small cell lung cancer (NSCLC) patients with surgery in surveillance, epidemiology, and end results (SEER) database

Cited by 32 publications

References 21 publications

Construction of a Nomogram for Predicting Survival in Elderly Patients With Lung Adenocarcinoma: A Retrospective Cohort Study

Construction of a Nomogram for Predicting Survival in Elderly Patients With Lung Adenocarcinoma: A Retrospective Cohort Study

Prognostic factors and survival outcome of primary pulmonary acinar cell carcinoma

A nomogram model to predict the venous thromboembolism risk after surgery in patients with gynecological tumors

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