Dataset Information

Development and validation of two prognostic nomograms for predicting survival in patients with non-small cell and small cell lung cancer.

ABSTRACT:

Purpose

This study aimed to construct two prognostic nomograms to predict survival in patients with non-small-cell lung cancer (NSCLC) and small-cell lung cancer (SCLC) using a novel set of clinical parameters.

Patients and methods

Two nomograms were developed, using a retrospective analysis of 5384 NSCLC and 647 SCLC patients seen during a 10-year period at Xiang Ya Affiliated Cancer Hospital (Changsha, China). The patients were randomly divided into training and validation cohorts. Univariate and multivariate analyses were used to identify the prognostic factors needed to establish nomograms for the training cohort. The model was internally validated via bootstrap resampling and externally certified using the validation cohort. Predictive accuracy and discriminatory capability were estimated using concordance index (C-index), calibration curves, and risk group stratification.

Results

The largest contributor to overall survival (OS) prognosis in the NSCLC nomogram was the therapeutic regimen and diagnostic method parameters, and in the SCLC nomogram was the therapeutic regimen and health insurance plan parameters. Calibration curves for the nomogram prediction and the actual observation were in optimal agreement for the 3-year OS and acceptable agreement for the 5-year OS in both training datasets. The C-index was higher for the NSCLC cohort nomogram than for the TNM staging system (0.67 vs. 0.64, P = 0.01) and higher for the SCLC nomogram than for the clinical staging system (limited vs. extensive) (0.60 vs. 0.53, P = 0.12).

Conclusion

Treatment regimen parameter made the largest contribution to OS prognosis in both nomograms, and these nomograms might provide clinicians and patients a simple tool that improves their ability to accurately estimate survival based on individual patient parameters rather than using an averaged predefined treatment regimen.

SUBMITTER: Xiao HF

PROVIDER: S-EPMC5610004 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Publications

Development and validation of two prognostic nomograms for predicting survival in patients with non-small cell and small cell lung cancer.

Xiao Hai-Fan HF Zhang Bai-Hua BH Liao Xian-Zhen XZ Yan Shi-Peng SP Zhu Song-Lin SL Zhou Feng F Zhou Yi-Kai YK

Oncotarget 20170802 38

<h4>Purpose</h4>This study aimed to construct two prognostic nomograms to predict survival in patients with non-small-cell lung cancer (NSCLC) and small-cell lung cancer (SCLC) using a novel set of clinical parameters.<h4>Patients and methods</h4>Two nomograms were developed, using a retrospective analysis of 5384 NSCLC and 647 SCLC patients seen during a 10-year period at Xiang Ya Affiliated Cancer Hospital (Changsha, China). The patients were randomly divided into training and validation cohor ...[more]

PMID: 28969072

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Project description:BackgroundMetastatic non-small cell lung cancer (NSCLC) is mostly seen in older patients and is associated with poor prognosis. There is no reliable method to predict the prognosis of elderly patients (≥60 years old) with metastatic NSCLC. The aim of our study was to develop and validate nomograms which accurately predict survival in this group of patients.MethodsNSCLC patients diagnosed between 2010 and 2015 were all identified from the Surveillance, Epidemiology, and End Results (SEER) database. Nomograms were constructed by significant clinicopathological variables (p < 0.05) selected in multivariate Cox analysis regression.ResultsA total of 9584 patients met the inclusion criteria and were randomly allocated in the training (n = 6712) and validation (n = 2872) cohorts. In training cohort, independent prognostic factors included age, gender, race, grade, tumor site, pathology, T stage, N stage, radiotherapy, surgery, chemotherapy, and metastatic site (p < 0.05) for lung cancer-specific survival (LCSS) and overall survival (OS) were identified by the Cox regression. Nomograms for predicting 1-, 2-, and 3-years LCSS and OS were established and showed excellent predictive performance with a higher C-index than that of the 7th TNM staging system (LCSS: training cohort: 0.712 vs. 0.534; p < 0.001; validation cohort: 0.707 vs. 0.528; p < 0.001; OS: training cohort: 0.713 vs. 0.531; p < 0.001; validation cohort: 0.710 vs. 0.528; p < 0.001). The calibration plots showed good consistency from the predicted to actual survival probabilities both in training cohort and validation cohort. Moreover, the decision curve analysis (DCA) achieved better net clinical benefit compared with TNM staging models.ConclusionsWe established and validated novel nomograms for predicting LCSS and OS in elderly patients with metastatic NSCLC with desirable discrimination and calibration ability. These nomograms could provide personalized risk assessment for these patients and assist in clinical decision.

Project description:PurposeOur purpose was to develop predictive nomograms for overall survival (OS), progression-free survival (PFS), and time-to-progression (TTP) at 5 years in patients with early-stage non-small cell lung cancer (ES-NSCLC) treated with stereotactic ablative radiation therapy (SABR).Methods and materialsThe study cohort included 714 ES-NSCLC patients treated with SABR from 2004-2015 with median follow-up of 59 months, divided into training and testing sets (8:2), with the former used for nomogram development. The least absolute shrinkage and selection operator were initially employed to screen for predictors of OS, PFS, and TTP, and identified predictors were subsequently applied toward Cox proportional hazards regression modeling. Significant predictors (P < .05) on multivariable regression were then used to develop nomograms, which were validated via evaluation of concordance indexes (C-index) and calibration plots. Finally, Kaplan-Meier method and Gray's test were employed to compare and confirm differences in outcomes among various groups and explore prognostic factors associated with local versus distant disease progression.ResultsSignificant predictors of both OS and PFS at 5 years included age, sex, Charlson comorbidity index, diffusing capacity of carbon monoxide, systemic immune-inflammation index, and tumor size (P ≤ .01 for all). Eastern Cooperative Oncology Group performance status predicted for OS as well (P = .01), and both tumor size (P < .01) and minimum biological equivalent dose to 95% of planning target volume (PTV D95 BED10; P < .01) were predictive of TTP. The C-indexes for the OS, PFS, and TTP nomograms were 0.73, 0.68, and 0.60 in the training data set and 0.72, 0.66, and 0.59 in the testing data set, respectively. Tumor size > 2.45 cm and PTV D95 BED10 < 113 Gy were significantly associated with both local and distant progression.ConclusionsThese prognostic nomograms can accurately predict for OS, PFS, and TTP at 5 years after SABR for ES-NSCLC and may thus help identify high-risk patients who could benefit from additional systemic therapy.

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Project description:BackgroundNon-small-cell lung cancer (NSCLC) patients with ipsilateral pleural dissemination are defined as M1a in the eighth of American Joint Committee on Cancer (AJCC) TNM staging. We aimed to build a nomogram to predict lung cancer specific survival (LCSS) of NSCLC patients with ipsilateral pleural dissemination and to compare the impact of primary tumor resection (PTR) on LCSS among patients with different features.MethodsA total of 3,918 NSCLC patients with ipsilateral pleural dissemination were identified from the Surveillance, Epidemiology, and End Results (SEER) database. We selected and integrated significant prognostic factors based on competing risk regression to build a nomogram. The model was subjected to internal validation within SEER cohort and external validation with the cohort of 97 patients from Peking University People's Hospital.ResultsAge (P < 0.001), gender (P = 0.037), T stage (P = 0.002), N stage (P < 0.001), metastasis pattern (P = 0.005), chemotherapy (P < 0.001), and PTR (P < 0.001) were independent prognostic factors. The calibration curves presented a good consistency and the Harrell's C-index of nomogram were 0.682 (95%CI: 0.673-0.691), 0.687 (95%CI: 0.670-0.704) and 0.667 (95%CI: 0.584-0.750) in training, internal, and external validation cohort, respectively. Interaction tests suggested a greater LCSS difference caused by PTR in patients without chemotherapy (P < 0.001).ConclusionsWe developed a nomogram based on competing risk regression to reliably predict prognosis of NSCLC patients with ipsilateral pleural dissemination and validated this nomogram in an external Chinese cohort. This novel nomogram might be a practical tool for clinicians to anticipate the 1-, 3- and 5-year LCSS for NSCLC patients with pleural dissemination. Subgroup analysis indicated that patients without chemotherapy could get more benefit from PTR. In order to assess the role of PTR in the management of M1a patients more accurately, further prospective study would be urgently required.

Dataset Information

Development and validation of two prognostic nomograms for predicting survival in patients with non-small cell and small cell lung cancer.

Purpose

Patients and methods

Results

Conclusion

Publications

Development and validation of two prognostic nomograms for predicting survival in patients with non-small cell and small cell lung cancer.

Similar Datasets

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