Clinical Scorecard: Creation and utilization of a prognostic framework incorporating radiomics and artificial intelligence for lung adenocarcinoma patients with brain metastases

At a Glance

Key Highlights

• Development of a combined radiomics-clinical prognostic model
• Independent prognostic factors identified: EGFR mutation status, number of brain metastases, Lung-molGPA score
• AUC values of 0.904 and 0.874 for the combined nomogram in training and test sets, respectively
• Radiomics enhances predictive accuracy for survival outcomes in lung adenocarcinoma patients with brain metastases
• Study emphasizes the need for multidisciplinary approaches in managing LCBM

Guideline-Based Recommendations

Diagnosis

• Use contrast-enhanced cranial MRI or CT for confirmation of brain metastases

Management

• Consider multidisciplinary approaches for treatment selection in LCBM

Monitoring & Follow-up

• Evaluate overall survival as a clinical endpoint

Risks

• High postoperative recurrence rates in lung adenocarcinoma with brain metastasis

Patient & Prescribing Data

Patients with histopathologically confirmed lung adenocarcinoma and brain metastases

Prognostic modeling can guide treatment selection and improve clinical decision-making

Clinical Best Practices

• Incorporate radiomic features into clinical assessments for better prognostic accuracy
• Utilize machine learning approaches to enhance predictive modeling in oncology

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