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the era of precision medicine, early and accurate detection of EGFR mutations in non-small cell lung cancer (NSCLC) has become vital for guiding targeted therapies. This research introduces a nomogram model combining clinical, computed tomography (CT), and radiomic features integrated with a machine learning (ML) algorithm to predict EGFR mutation status in NSCLC patients. By extracting high-dimensional imaging features and correlating them with key clinical parameters, the model demonstrates promising accuracy and clinical applicability. Radiomics, which converts images into mineable data, allows a non-invasive yet quantitative approach to tumor heterogeneity. The machine learning classifier, trained on these multi-dimensional inputs, outperforms traditional methods and helps oncologists make informed decisions swiftly.


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