Non-small cell lung cancer (NSCLC) accounts for approximately 85% of lung cancer patients. Recurrence rate for NSCLC patients is 30%-50% with a significant risk of mortality. Predicting recurrence can lead to personalized target therapy. Prediction models using radiomic features extracted from CT images have been developed but have not shown optimal performance. Gabor filters are linear filters that improve the performance of texture classification. This work shows how Gabor features improve performance in models used to predict recurrence in NSCLC patients.
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