Evaluating clinical and radiomic features for predicting lung cancer recurrence pre- and post-tumor resection

Wai Lone J. Ho; Nikolai Fetisov; Lawrence O. Hall; Dmitry Goldgof; Matthew B. Schabath

doi:10.1117/12.3006091

2 April 2024 Evaluating clinical and radiomic features for predicting lung cancer recurrence pre- and post-tumor resection

Wai Lone J. Ho, Nikolai Fetisov, Lawrence O. Hall, Dmitry Goldgof, Matthew B. Schabath

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Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 1292623 (2024) https://doi.org/10.1117/12.3006091
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Among patients with early-stage non-small cell lung cancer (NSCLC) undergoing surgical resection, identifying who is at high risk of recurrence can inform clinical guidelines with respect to more aggressive follow-up and/or adjuvant therapy. While predicting recurrence based on pre-surgical resection data is ideal, clinically important pathological features are only evaluated postoperatively. Therefore, we developed two supervised classification models to assess the importance of pre- and post-surgical features for predicting 5-year recurrence. An integrated dataset was generated by combining clinical covariates and radiomic features calculated from pre-surgical computed tomography images. After removing correlated radiomic features, the SHapley Additive exPlanations (SHAP) method and recursive feature elimination (RFE) were used for feature selection. A support vector machine (SVM) classification model was then trained to predict recurrence. We demonstrate that the post-surgical model outperforms the pre-surgical model in predicting lung cancer recurrence, with tumor pathological features and peritumoral radiomic features contributing significantly to the model’s performance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wai Lone J. Ho, Nikolai Fetisov, Lawrence O. Hall, Dmitry Goldgof, and Matthew B. Schabath "Evaluating clinical and radiomic features for predicting lung cancer recurrence pre- and post-tumor resection", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 1292623 (2 April 2024); https://doi.org/10.1117/12.3006091

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