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Machine Learning Model Identifies Patients With Stage IV EGFR+ NSCLC Likely to Benefit from EGFR TKIs

A diagnostic/prognostic study published in JAMA Network Open provided a clinically applicable, end-to-end CT–based deep learning approach which was able to determine which patients with stage IV EGFR variant–positive non–small cell lung cancer (NSCLC) are more likely to benefit from EGFR tyrosine kinase inhibitor (TKI) therapy based on disease course.

Specifically, investigators used a state-of-the-art representation learning framework to build a deep learning semantic prognosis signature from pretherapy CT images obtained from these patients. According to the investigators, an end-to-end deep learning approach to generate CT-based biomarkers such as this may help produce more rapid clinical translation.

“Compared with radiomics, the approach used in this study not only avoids human intervention procedures requiring manual region of interest segmentation and predesigning…

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