Construction and Verification of Nomogram Model for Lung Adenocarcinoma With ≤ 5 Bone-Only Metastases Basing on Hematology Markers

An Accelerated Failure Time Model to Predict Cause-Specific Survival and Prognostic Factors of Lung and Bronchus Cancer Patients with at Least Bone or Brain Metastases: Development and Internal Validation Using a SEER-Based Study

BACKGROUND

This study addresses the significant challenge of low survival rates in patients with cause-specific lung cancer accompanied by bone or brain metastases. Recognizing the critical need for an effective predictive model, the research aims to establish survival prediction models using both parametric and non-parametric approaches.

METHODS

Clinical data from lung cancer patients with at least one bone or brain metastasis between 2000 and 2020 from the SEER database were utilized. Four models were constructed: Cox proportional hazard, Weibull accelerated failure time (AFT), log-normal AFT, and Zografos-Balakrishnan log-normal (ZBLN). Independent prognostic factors for cause-specific survival were identified, and model fit was evaluated using Akaike's and Bayesian information criteria. Internal validation assessed predictive accuracy and discriminability through the Harriel Concordance Index (C-index) and calibration plots.

RESULTS

A total of 20,412 patients were included, with 14,290 (70%) as the training cohort and 6122 (30%) validation. Independent prognostic factors selected for the study were age, race, sex, primary tumor site, disease grade, total malignant tumor in situ, metastases, treatment modality, and histology. Among the accelerated failure time (AFT) models considered, the ZBLN distribution exhibited the most robust model fit for the 3- and 5-year survival, as evidenced by the lowest values of Akaike's information criterion of 6322 and 79,396, and the Bayesian information criterion of 63,495 and 79,396, respectively. This outperformed other AFT and Cox models (AIC = [156,891, 211,125]; BIC = [158,848, 211,287]). Regarding predictive accuracy, the ZBLN AFT model achieved the highest concordance C-index (0.682, 0.667), a better performance than the Cox model (0.669, 0.643). The calibration curves of the ZBLN AFT model demonstrated a high degree of concordance between actual and predicted values. All variables considered in this study demonstrated significance at the 0.05 level for the ZBLN AFT model. However, differences emerged in the significant variations in survival times between subgroups. The study revealed that patients with only bone metastases have a higher chance of survival compared to only brain and those with bone and brain metastases.

CONCLUSIONS

The study highlights the underutilized but accurate nature of the accelerated failure time model in predicting lung cancer survival and identifying prognostic factors. These findings have implications for individualized clinical decisions, indicating the potential for screening and professional care of lung cancer patients with at least one bone or brain metastasis in the future.

Construction and validation of a novel prognostic nomogram for predicting overall survival in lung adenocarcinoma patients with different patterns of metastasis

OBJECTIVE

Metastasis of lung cancer is an important factor affecting survival. The present study proposed to establish and verify a nomogram for predicting overall survival (OS) in lung adenocarcinoma (LUAD) patients with different patterns of metastasis.

METHODS

A total of 9727 patients diagnosed with metastatic LUAD patients from 2010 to 2015 were enrolled based on surveillance, epidemiology and end results (SEER) Database and then randomly divided into training and validation cohorts, and 136 patients in our Cancer Center were enrolled as the external validation cohort. Univariate and multivariate analyses were performed to evaluate the prognostic impact on OS. A prognostic nomogram was constructed and evaluated by C-index, calibration curve, decision curve analysis (DCA), and risk stratification system.

RESULTS

Ultimately, 6809 and 2918 patients diagnosed with metastatic LUAD in the training and validation cohorts were enrolled in the study, respectively. A male sex, a later T and N stage, a larger tumor size, treatment including no surgery, no chemotherapy and no radiotherapy, metastasis sites were found to be independent risk factors in LUAD patients for worse OS, and then incorporated into the nomogram. The frequency of bone metastasis was the highest, and in single site metastasis, the prognosis of liver metastasis was the worst. Two-site metastasis is more common than three-site and four-site metastasis, and co-metastasis eventually leads to a worse survival outcome. The C-index value of nomogram for predicting OS were 0.798, 0.703 and 0.698 in the internal training, validation and external validation cohorts, separately. The calibration curves for the 6-months, 1-year and 2-year showed significant agreement between nomogram models and actual observations. The DCA curves indicated nomogram was more beneficial than the AJCC TNM stage. Patients were further divided into low-risk and high-risk groups according to nomogram predicted scores and developed a survival risk classification system.

CONCLUSIONS

Our prognostic nomogram is expected to be an accurate and individualized clinical predictive tool for predicting OS in LUAD patients with different patterns of metastasis.