Machine learning-based prediction of 3-month mortality in elderly patients with non-small cell lung cancer and bone metastases.

Elderly patients with non-small cell lung cancer (NSCLC) and bone metastases face a dire prognosis, creating an urgent need for accurate short-term mortality prediction to guide care. To develop and validate machine learning (ML) models for predicting 3-month cancer-specific mortality in patients aged 70 years or older with NSCLC and bone metastases. We analyzed data from 1,773 patients (aged ≥ 70) from the SEER database (2010-2020). The cohort was randomly split into training (70%) and validation (30%) sets. Seven ML algorithms were trained and evaluated using a comprehensive set of performance metrics, including area under the curve (AUC), calibration, and decision curve analysis for clinical utility. The overall 3-month mortality rate was 48.5%. Among the seven models, the logistic regression model demonstrated superior and stable overall performance. It achieved the highest AUC of 0.79 on the validation set and maintained the highest average accuracy (0.78 ± 0.02) across 10-fold cross-validation. Decision curve analysis confirmed its superior net clinical benefit across most threshold probabilities. Key influential predictors identified included the absence of chemotherapy, presence of liver or brain metastases, and a shorter time from diagnosis to treatment initiation. This study developed and validated an interpretable ML-based prediction model that accurately identifies elderly NSCLC patients with bone metastases who are at high risk of early death. The logistic regression model, selected as the optimal tool, can assist clinicians in making individualized decisions, potentially guiding more aggressive supportive care for high-risk patients and definitive treatments for those with a better prognosis. However, this model was developed and internally validated using a single SEER cohort; external validation in independent datasets is required before clinical application.