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Predicting the mutation statuses of 2 essential pathogenic genes [epidermal growth factor receptor ( EGFR ) and Kirsten rat sarcoma ( KRAS )] in non-small cell lung cancer (NSCLC) based on CT is valuable for targeted therapy because it is a non-invasive and less costly method. Although deep learning technology has realized substantial computer vision achievements, CT imaging being used to predict gene mutations remains challenging due to small dataset limitations.

Multi-channel multi-task deep learning for predicting EGFR and KRAS mutations of non-small cell lung cancer on CT images