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. 2024 Jul 2;15(14):4612-4622.
doi: 10.7150/jca.95784. eCollection 2024.

Establishing and Validating a novel Prognostic Model in the Initial Diagnosis of Non-small Cell Lung Cancer with Bone Metastases

Bin Li  1 , Deying Su  2 , Xiaoyan Wen  3 , Miaomiao Jia  4 , Ning Xue  4 , Shulin Chen  2   5 , Chaoju Lou  1
Affiliations

Establishing and Validating a novel Prognostic Model in the Initial Diagnosis of Non-small Cell Lung Cancer with Bone Metastases

Bin Li et al. J Cancer. .

Abstract

Background: The aim of this research is to establish and validate a prognostic model for predicting prognosis in non-small cell lung cancer (NSCLC) patients with bone metastases. Methods: Overall, 176 NSCLC patients with bone metastases were retrospectively evaluated in the research. We employed the LASSO-Cox regression method to select the candidate indicators for predicting the prognosis among NSCLC patients complicated with bone metastases. We employed the receiver operating characteristic curve (ROC) and the concordance index (C-index) to assess the discriminative ability. Results: Based on the LASSO-Cox regression analysis, 9 candidate indicators were screened to build the prognostic model. The prognostic model had a higher C-index in the training cohort (0.738, 95% CI: 0.680-0.796) and the validation cohort (0.660, 95% CI: 0.566-0.754) than the advanced lung cancer inflammation index (ALI). Furthermore, the AUCs of the 1-, 2-, and 3-year OS predictions for the prognostic model were higher than ALI in both cohorts. Kaplan-Meier curves and the estimated restricted mean survival time (RMST) values showed that the patients in the low-risk subgroup had the lower probabilities of cancer-specific mortality than high-risk subgroup. Conclusions: The prognostic model could provide clinicians with precise information and facilitate individualized treatment for patients with bone metastases.

Keywords: LASSO-Cox regression; NSCLC; bone metastases; prognostic model.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interest exists.

Figures

Figure 1
Figure 1
LASSO-Cox regression for potential predictors selection (A). Tenfold cross-validation for prognostic model establishment (B). Radar chart of the indicators in the prognostic model (C).
Figure 2
Figure 2
Time-dependent C-index of ALI and prognostic model in the training cohort (A) and the validation cohort (B).
Figure 3
Figure 3
ROC curves of the prognostic model and ALI for 1-, 2-, and 3-year OS in the training cohort (A, B), and the validation cohort (C, D).
Figure 4
Figure 4
Nomogram for NSCLC patients with bone meta metastasis in the training cohort (A) and the validation cohort (C). Calibration curves for predicting OS in the nomogram in the two cohorts (B, D).
Figure 5
Figure 5
Kaplan-Meier curves for high-risk (red) and low-risk groups (blue) in the training cohort (A) and the validation cohort (B). Estimate of restricted mean survival time (red area) and the restricted mean time lost (blue area) in high-risk group and low-risk group for the training cohort (C) and the validation cohort (D).
Figure 6
Figure 6
Heatmap was generated by clustering of 9 features across identified NSCLC patients with bone metastasis in the training cohort (A) and the validation cohort (B), respectively.
Figure 7
Figure 7
The correlations between the prognostic model, NLR PLR, NLR, NRI, SII, CAR, PNI, and ALI in the training cohort (A) and the validation cohort (B).
See this image and copyright information in PMC

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