Survival Nomogram for Patients with Initially Diagnosed Metastatic Non-Small-Cell Lung Cancer: A SEER-Based Study

Wang, Yu; Pang, Zhaofei; Chen, Xiaowei; Bie, Fenglong; Wang, Yadong; Wang, Guanghui; Liu, Qi; Du, Jiajun

doi:10.2217/fon-2019-0007

Cited by 22 publications

(29 citation statements)

References 31 publications

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“…In our new established model, it is clear that AJCC stage and surgery have a significant impact on the total score used for predicting the outcomes of elderly SCC patients. These findings were similar with previous reports, it was observed that metastatic location and pathological grading were crucial independent predictors for OS in NSCLC [26]. Some studies discovered that chemotherapy, metastasis and surgery made the larger contribution to the prognosis for distantly metastatic non-small-cell lung cancer [27,28].…”

Section: Discussionsupporting

confidence: 92%

A prognostic model for elderly patients with squamous non-small cell lung cancer: a population-based study

Chen

Gao

et al. 2020

J Transl Med

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Background Squamous cell carcinoma (SCC) is a main pathological type of non-small cell lung cancer. It is common among elderly patients with poor prognosis. We aimed to establish an accurate nomogram to predict survival for elderly patients (≥ 60 years old) with SCC based on the Surveillance, Epidemiology, and End Results (SEER) database. Methods The gerontal patients diagnosed with SCC from 2010 to 2015 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. The independent prognostic factors were identified using multivariate Cox proportional hazards regression analysis, which were utilized to conduct a nomogram for predicting survival. The novel nomogram was evaluated by Concordance index (C-index), calibration curves, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). Results 32,474 elderly SCC patients were included in the analysis, who were randomly assigned to training cohort (n = 22,732) and validation cohort (n = 9742). The following factors were contained in the final prognostic model: age, sex, race, marital status, tumor site, AJCC stage, surgery, radiation and chemotherapy. Compared to AJCC stage, the novel nomogram exhibited better performance: C-index (training group: 0.789 vs. 0.730, validation group: 0.791 vs. 0.733), the areas under the receiver operating characteristic curve of the training set (1-year AUC: 0.846 vs. 0.791, 3-year AUC: 0.860 vs. 0.801, 5-year AUC: 0.859 vs. 0.794) and the validation set (1-year AUC: 0.846 vs. 0.793, 3-year AUC: 0.863 vs. 0.806, 5-year AUC: 0.866 vs. 0.801), and the 1-, 3- and 5-year calibration plots. Additionally, the NRI and IDI and 1-, 3- and 5-year DCA curves all confirmed that the nomogram was a great prognosis tool. Conclusions We constructed a novel nomogram that could be practical and helpful for precise evaluation of elderly SCC patient prognosis, thus helping clinicians in determining the appropriate therapy strategies for individual SCC patients.

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Section: Discussionsupporting

confidence: 92%

A prognostic model for elderly patients with squamous non-small cell lung cancer: a population-based study

Chen

Gao

et al. 2020

J Transl Med

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“…Our study found that the overall incidence of UTUC decreased slightly between 1988 and 2015, which may be due to in the article by Munoz et al used 1980 US standard population (21,526,796) as a standardized, while we used the 2000 US standard population (26,787,544) as a standardized, resulting in a lower overall incidence than Munoz et al We speculate that the difference between our results and Raman et al due to the impact of different databases used.…”

Section: Discussioncontrasting

confidence: 56%

“…[20] The nomogram is a visual tool for predicting prognosis based on multiple variables. [21] The model integrates a variety of prognostic factors and can make more preparations for evaluating the survival possibility of individual patients. [22] At present, many cancer nomograms have been developed and…”

Section: Discussionmentioning

confidence: 99%

Trends of Incidence and Prognosis of Upper Tract Urothelial Carcinoma

Mao

Li³

et al. 2020

Preprint

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Backgrud The purpose of this study was to investigate trends of incidence of upper tract urothelial carcinoma (UTUC) patients and establish reliable and practical nomogram based on significant clinical factors to predict the overall survival (OS) and cancer-specific survival (CSS) of UTUC patients.Methods The Surveillance, Epidemiology, and End Results (SEER) database extracts UTUC patients between 1988 and 2015. Incidence was calculated using joinpoint regression software, and trends were quantified by annual percentage change (APC). A nomogram was constructed using R software to predict the OS and CSS probabilities of a single patient. Results From 1988 to 2015, the incidence of UTUC showed a downward trend (1988: 1.57/100,000 to 2015: 1.51/100,000; APC=-0.1). After stratification according to sex, age and primary site, we found that the incidence of UTUC patients in male, 70+ years and renal pelvis were higher than those of female, <60 years,60-69 years and ureter UTUC patients. The nomogram based on age, sex, marital status, race, origin, primary site, histological type, grade, SEER stage, surgery, radiotherapy and chemotherapy variables showed good OS and CSS accuracy (OS: C-index=0.701, AUC=0.738; CSS: C-index=0.729, AUC=0.689). In addition, the calibration curves showed a good consistency between the predicted and actual 3-, 5- and 10-year OS and CSS rates of the nomogram.Conclusion In the past 30 years, the incidence of UTUC showed a general downward trend, and the prognostic nomogram we established can provide a personalized risk assessment for the survival of UTUC patients.

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“…According to our study, tumor size is a significant factor affecting the patient prognosis, which has not been recognized previously [11][12][13]. T stage roughly classifies tumor size or depth of invasion, but it cannot reflect the specific character of NSCLC patients with BM and accurately predict the prognosis, because the tumor size of the same period varies greatly [23][24][25].…”

Section: Discussionmentioning

confidence: 63%

“…More importantly, many other factors should be considered and involved in predicting the prognosis of patients [9,10]. Survival prediction models designed for lung cancer with BM specifically have been reported previously [11][12][13]. However, the performance of these models is barely satisfactory as these models are based on the simple Cox regression model but not established as a survival prediction model for NSCLC with BM particularly.…”

Section: Introductionmentioning

confidence: 99%

An Artificial Intelligence Model for Predicting 1-Year Survival of Bone Metastases in Non-Small-Cell Lung Cancer Patients Based on XGBoost Algorithm

Huang

Chi

et al. 2020

BioMed Research International

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Non-small-cell lung cancer (NSCLC) patients often develop bone metastases (BM), and the overall survival for these patients is usually perishing. However, a model with high accuracy for predicting the survival of NSCLC with BM is still lacking. Here, we aimed to establish a model based on artificial intelligence for predicting the 1-year survival rate of NSCLC with BM by using extreme gradient boosting (XGBoost), a large-scale machine learning algorithm. We selected NSCLC patients with BM between 2010 and 2015 from the Surveillance, Epidemiology, and End Results database. In total, 5973 cases were enrolled and divided into the training (n=4183) and validation (n=1790) sets. XGBoost, random forest, support vector machine, and logistic algorithms were used to generate predictive models. Receiver operating characteristic curves were used to evaluate and compare the predictive performance of each model. The parameters including tumor size, age, race, sex, primary site, histological subtype, grade, laterality, T stage, N stage, surgery, radiotherapy, chemotherapy, distant metastases to other sites (lung, brain, and liver), and marital status were selected to construct all predictive models. The XGBoost model had a better performance in both training and validation sets as compared with other models in terms of accuracy. Our data suggested that the XGBoost model is the most precise and personalized tool for predicting the 1-year survival rate for NSCLC patients with BM. This model can help the clinicians to design more rational and effective therapeutic strategies.

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Survival Nomogram for Patients with Initially Diagnosed Metastatic Non-Small-Cell Lung Cancer: A SEER-Based Study

Cited by 22 publications

References 31 publications

A prognostic model for elderly patients with squamous non-small cell lung cancer: a population-based study

A prognostic model for elderly patients with squamous non-small cell lung cancer: a population-based study

Trends of Incidence and Prognosis of Upper Tract Urothelial Carcinoma

An Artificial Intelligence Model for Predicting 1-Year Survival of Bone Metastases in Non-Small-Cell Lung Cancer Patients Based on XGBoost Algorithm

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