Clinical Report: Prognostic Framework for Lung Adenocarcinoma with Brain Metastases

Overview

This study developed a prognostic model integrating radiomics and clinical factors to enhance survival predictions for lung adenocarcinoma patients with brain metastases.

Background

Lung cancer with brain metastasis significantly impairs survival, necessitating accurate prognostic tools. The study addresses the high postoperative recurrence rates and the limited efficacy of current diagnostic and therapeutic options. Integrating radiomics with clinical data offers a method to improve prognostic accuracy and patient stratification.

Data Highlights

Key Findings

• EGFR mutation status, number of brain metastases, and Lung-molGPA score are independent prognostic determinants.
• The radiomics model achieved AUCs of 0.862 in the training set and 0.829 in the test set.
• The combined nomogram showed AUC values of 0.904 and 0.874 in the training and test sets, respectively.
• The integration of radiomics with clinical parameters enhances prognostic accuracy.

Clinical Implications

The findings indicate that incorporating radiomics into clinical practice may enhance survival predictions for lung adenocarcinoma patients with brain metastases.

Conclusion

The study presents a combined radiomics-clinical model to improve prognostic accuracy in lung adenocarcinoma patients with brain metastases.

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4. Treatment for Brain Metastases: ASCO-SNO-ASTRO Guideline - PMC
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8. Lorlatinib Versus Crizotinib in Patients With Advanced ALK-Positive Non–Small Cell Lung Cancer: 5-Year Outcomes From the Phase III CROWN Study | Journal of Clinical Oncology
9. External validation of the lung-molGPA to predict survival in patients treated with stereotactic radiotherapy for brain metastases of non-small cell lung cancer - ScienceDirect