Construction of a Nomogram After Using Propensity Score Matching to Reveal the Prognostic Benefit of Tumor Resection of Stage IV M1a Nonsmall Cell Lung Cancer Patients

Yin, Guofang; Liao, Yi; Huang, Chengliang; Fan, Xiaoxuan

doi:10.1080/07357907.2020.1753761

Cited by 12 publications

(10 citation statements)

References 35 publications

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“…We found that age at diagnosis, other race, whether to receive surgery, pTNM stage, the grade III of differentiation, whether to receive chemotherapy, and whether to receive radiation were independent prognostic factors. We used propensity score matching to minimize the effect of other variables on CSS (22). After balancing these factors, we suggested that the influence of surgery on survival in patients with stage I-IIA was more precise.…”

Section: Discussionmentioning

confidence: 99%

Surgery and Surgery Approach Affect Survival of Patients With Stage I-IIA Small-Cell Lung Cancer: A Study Based SEER Database by Propensity Score Matching Analysis

et al. 2022

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PurposeThe purpose of this study was to observe the significance of surgery and its approach in stage I-IIA (according to 8th American Joint Committee on Cancer Staging Manual) small-cell lung cancer (SCLC) using the Surveillance, Epidemiology, and End Results (SEER) database.Patients and MethodsA total of 1,421 patients from ages 31 to 93 years who were diagnosed with stage I-IIA SCLC in the SEER database from 2010 to 2015 were analyzed. The 1:1 propensity score matching analysis was used to minimize the effect of selection bias, and 355 pairs of patients' data was performed subsequent statistical analysis. K–M analysis and a Cox proportional hazards model were used to observe the role of surgery and other clinical features in the patients' prognoses on cancer-specific survival (CSS).ResultsOverall, within the whole cohort, the 3- and 5-year CSS rates were 41.0 and 34.0%, respectively. In a Cox regression that adjusted for other clinical features, patients were more likely to benefit from the surgery [hazard ratio (HR) 0.292, 95% confidence interval (CI) 0.237–0.361, P < 0.001]. Unadjusted 5-year cancer-specific survival among those with surgery was 55.0%, compared with 23.0% among those without surgery. In the propensity scored-matched dataset, however, 5-year CSS among those with surgery was 54.0%, compared with 17.0% among those without surgery (HR 0.380, 95%CI 0.315–0.457, P < 0.001). In patients who received surgery, cases with lobectomy had a better 5-year CSS than those without lobectomy (65.0 vs. 39.0%). The lobectomy might be a protective factor for patients who underwent resection in CSS (HR 0.433, 95%CI 0.310–0.604, P < 0.001).ConclusionsWe suggested that the surgery and lobectomy were the independent prognostic as well as the protective factors in stage I-IIA SCLC patients. We recommended that patients with no surgical contraindications receive surgery, preferably, lobectomy.

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Section: Discussionmentioning

confidence: 99%

Surgery and Surgery Approach Affect Survival of Patients With Stage I-IIA Small-Cell Lung Cancer: A Study Based SEER Database by Propensity Score Matching Analysis

et al. 2022

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“…Studies have documented various M1a NSCLC prognostic algorithms that use a few baseline variables. Yin et al 13 established an M1a prognostic nomogram (C‐index for OS: 0.710; C‐index for cancer‐specific survival: 0.723) by propensity score matching to ease the influence of confounding variables. Based on the M1a NSCLC cohort, in which 5,976 patients had not been subject to surgery and 386 individuals underwent surgery, they found that tumor resection provided better prognosis.…”

Section: Discussionmentioning

confidence: 99%

“… 12 In 2020, there was a study created a nomogram focus on M1a NSCLC patients. 13 Several clinical characteristics and clinicopathological variables were included in the nomogram. Nevertheless, the metastatic site, which has been demonstrated to influence overall survival in M1a NSCLC patients 14 and should be considered for predicting individualized prognosis, was not included as a predictor.…”

Section: Introductionmentioning

confidence: 99%

A novel LASSO‐derived prognostic model predicting survival for non‐small cell lung cancer patients with M1a diseases

Chen

et al. 2022

Cancer Medicine

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Introduction:The current American Joint Committee on Cancer (AJCC) M1a staging of non-small cell lung cancer (NSCLC) encompasses a wide disease spectrum, showing diverse prognosis. Methods: Patients who diagnosed in an earlier period formed the training cohort, and those who diagnosed thereafter formed the validation cohort. Kaplan-Meier analysis was performed for the training cohort by dividing the M1a stage into three subgroups: (I) malignant pleural effusion (MPE) or malignant pericardial effusion (MPCE); (II) separate tumor nodules in contralateral lung (STCL); and (III) pleural tumor nodules on the ipsilateral lung (PTIL). Gender, age, histologic, N stage, grade, surgery for primary site, lymphadenectomy, M1a groups, and chemotherapy were selected as independent prognostic factors using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. And a nomogram was constructed using Cox hazard regression analysis. Accuracy and clinical practicability were separately tested by Harrell's concordance index, the receiver operating characteristic (ROC) curve, calibration plots, residual plot, the integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA). Results: The concordance index (0.661 for the training cohort and 0.688 for the validation cohort) and the area under the ROC curve (training cohort: 0.709 for 1-year and 0.727 for 2-year OS prediction; validation cohort: 0.737 for 1-year and 0.734 for 2-year OS prediction) indicated satisfactory discriminative ability of the nomogram. Calibration curve and DCA presented great prognostic accuracy, and clinical applicability. Its prognostic accuracy preceded the AJCC staging with evaluated NRI (1-year: 0.327; 2-year: 0.302) and IDI (1-year: 0.138; 2-year: 0.130). Conclusion: Our study established a nomogram for the prediction of 1-and 2year OS in patients with NSCLC diagnosed with stage M1a, facilitating healthcare How to cite this article: Chen H, Huang C, Ge H, et al. A novel LASSO-derived prognostic model predicting survival for non-small cell lung cancer patients with M1a diseases.

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“…Whether radiotherapy has a beneficial effect on the prognosis of patients is debated. In particular, the reported effects of radiotherapy on prognosis for different stages of lung cancer are inconsistent (30,31).…”

Section: Discussionmentioning

confidence: 99%

The Positive Lymph Node Ratio Predicts Survival in T1−4N1−3M0 Non-Small Cell Lung Cancer: A Nomogram Using the SEER Database

2020

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Background: An increasing number of studies have shown that the positive lymph node ratio (pLNR) can be used to evaluate the prognosis of non-small cell lung cancer (NSCLC) patients. To determine the predictive value of the pLNR, we collected data from the Surveillance, Epidemiology, and End Results (SEER) database and performed a retrospective analysis. Methods: We collected survival and clinical information on patients with T 1−4 N 1−3 M 0 NSCLC diagnosed between 2010 and 2016 from the SEER database and screened them according to inclusion and exclusion criteria. X-tile software was used to obtain the best cutoff value for the pLNR. Then, we randomly divided patients into a training set and a validation set at a ratio of 7:3. Pearson's correlation coefficient, tolerance and the variance inflation factor (VIF) were used to detect collinearity between variables. Univariate and multivariate Cox regression analyses were used to identify significant prognostic factors, and nomograms was constructed to visualize the results. The concordance index (C-index), calibration curves, and decision curve analysis (DCA) were used to assess the predictive ability of the nomogram. We divided the patient scores into four groups according to the interquartile interval and constructed a survival curve using Kaplan-Meier analysis. Results: A total of 6,245 patients were initially enrolled. The best cutoff value for the pLNR was determined to be 0.55. The nomogram contained 13 prognostic factors, including the pLNR. The pLNR was identified as an independent prognostic factor for both overall survival (OS) and cancer-specific survival (CSS). The C-index was 0.703 (95% CI, 0.695-0.711) in the training set and 0.711 (95% CI, 0.699-0.723) in the validation set. The calibration curves and DCA also indicated the good predictability of the nomogram. Risk stratification revealed a statistically significant difference among the four groups of patients divided according to quartiles of risk score. Conclusion: The nomogram containing the pLNR can accurately predict survival in patients with T 1−4 N 1−3 M 0 NSCLC.

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Construction of a Nomogram After Using Propensity Score Matching to Reveal the Prognostic Benefit of Tumor Resection of Stage IV M1a Nonsmall Cell Lung Cancer Patients

Cited by 12 publications

References 35 publications

Surgery and Surgery Approach Affect Survival of Patients With Stage I-IIA Small-Cell Lung Cancer: A Study Based SEER Database by Propensity Score Matching Analysis

Surgery and Surgery Approach Affect Survival of Patients With Stage I-IIA Small-Cell Lung Cancer: A Study Based SEER Database by Propensity Score Matching Analysis

A novel LASSO‐derived prognostic model predicting survival for non‐small cell lung cancer patients with M1a diseases

The Positive Lymph Node Ratio Predicts Survival in T1−4N1−3M0 Non-Small Cell Lung Cancer: A Nomogram Using the SEER Database

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