Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence
Motivation: There is currently no reliable tool to predict postoperative recurrence for patients who undergo surgery for brain metastases (BrMs).
Goal(s): This study aimed to develop and externally validate a prognostic model to predict intracranial recurrence and recurrence-free survival (RFS) for lung cancer patients receiving BrM surgery.
Approach: A combined prognostic model-based nomogram was developed by incorporating clinical and structural MRI predictors, radiomics and deep signatures extracted from MR images.
Results: The nomogram predicted accurately for RFS and intracranial recurrence prediction, both in the training and test sets .
Impact: The combined prognostic model-based nomogram can be used as a preoperative tool to predict intracranial recurrence and recurrence-free survival after surgical resection of brain metastases in lung cancer patients.
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