Radiomic phenotypes of the background lung parenchyma from [18]F-FDG PET/CT images can augment tumor radiomics and clinical factors in predicting response after surgical resection of tumors in patients with non-small cell lung cancer

Andrew William Chen; Ohm Patel; Eric A. Cohen; Leonid Roshkovan; Despina Kontos

doi:10.1117/12.3006944

3 April 2024 Radiomic phenotypes of the background lung parenchyma from [18]F-FDG PET/CT images can augment tumor radiomics and clinical factors in predicting response after surgical resection of tumors in patients with non-small cell lung cancer

Andrew William Chen, Ohm Patel, Eric A. Cohen, Leonid Roshkovan, Despina Kontos

Author Affiliations +

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 129272R (2024) https://doi.org/10.1117/12.3006944
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

In this study we investigate the novel approach of using radiomic phenotypes from the lung parenchyma and tumor region of PET/CT images in non-small cell lung cancer (NSCLC) patients to predict overall survival (OS) and progression free survival (PFS) after tumor resection. We used 144 publicly available fluorodeoxyglucose ([18]F-FDG) PET/CT images from The Cancer Imaging Archive (TCIA) NSCLC Radiogenomics dataset. We extract features using the cancer phenomics imaging toolkit (CaPTk) to extract radiomic features from each of four image source regions: PET imaging of the tumor, PET imaging of the non-tumor lung parenchyma, CT imaging of the tumor, and CT of the parenchyma. Using each of the four sets of features, we independently clustered patients into phenotypes using unsupervised hierarchical clustering. The four phenotyping schemes individually, together, and in combination with clinical variables were assessed for association with time to OS and PFS via Cox proportional-hazards modeling, assessing covariate association via the log-rank p-value and model predictive performance via the C statistic. The clinical variables divided high from low hazard groups with p ≤ 0.05 for OS (p = 0.002, C = 0.62) but not for PFS (p = 0.098, C = 0.58). For PFS, radiomic phenotype derived from PET lung parenchyma performed better than the clinical variables both alone (p = 0.014, C = 0.59) and in conjunction with the clinical variables (p = 0.014, C = 0.62). Radiomic phenotypes from the lung parenchyma of PET/CT images can improve outcomes prediction for PFS after tumor resection in patients with NSCLC. Radiomic phenotypes from the non-cancerous parenchyma may derive prognostic value by detecting differences in the tissue linked to the biology of recurrence.

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

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Andrew William Chen, Ohm Patel, Eric A. Cohen, Leonid Roshkovan, and Despina Kontos "Radiomic phenotypes of the background lung parenchyma from [18]F-FDG PET/CT images can augment tumor radiomics and clinical factors in predicting response after surgical resection of tumors in patients with non-small cell lung cancer", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 129272R (3 April 2024); https://doi.org/10.1117/12.3006944

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