Prediction of recurrence in non-small cell lung cancer (NSCLC) using Gabor and radiomic-feature based models applied to CT image data

Alexandra C. Shiffer; Grace Hyun Kim; Michael McNitt-Gray

doi:10.1117/12.2654457

7 April 2023 Prediction of recurrence in non-small cell lung cancer (NSCLC) using Gabor and radiomic-feature based models applied to CT image data

Alexandra C. Shiffer, Grace Hyun Kim, Michael McNitt-Gray

Author Affiliations +

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 1246509 (2023) https://doi.org/10.1117/12.2654457
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Alexandra C. Shiffer, Grace Hyun Kim, and Michael McNitt-Gray "Prediction of recurrence in non-small cell lung cancer (NSCLC) using Gabor and radiomic-feature based models applied to CT image data", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 1246509 (7 April 2023); https://doi.org/10.1117/12.2654457

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