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Pet Image Radiomics Feature Variability in Lung Cancer: Impact of Image Segmentation Publisher



Hosseini SA1 ; Hajianfar G2 ; Shiri I3 ; Zaidi H3, 4, 5, 6
Authors
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Authors Affiliations
  1. 1. Tehran University of Medical Sciences, Department of Medical Physics and Biomedical Engineering, Tehran, Iran
  2. 2. Iran University of Medical Science, Rajaie Cardiovascular Medical and Research Center, Tehran, Iran
  3. 3. Geneva University Hospital, Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva 4, CH-1211, Switzerland
  4. 4. Geneva University, Geneva University Neurocenter, Geneva, CH-1205, Switzerland
  5. 5. University of Groningen, Department of Nuclear Medicine and Molecular Imaging, Netherlands
  6. 6. University Medical Center Groningen, Groningen, RB 9700, Netherlands

Source: 2021 IEEE Nuclear Science Symposium and Medical Imaging Conference Record# NSS/MIC 2021 and 28th International Symposium on Room-Temperature Semiconductor Detectors# RTSD 2022 Published:2021


Abstract

Radiomic features are powerful tools for characterizing intra-tumor heterogeneity and prognostic modeling. Yet, the lack of robustness evaluation poses difficulties in their application across institutions and among patient communities. The current study aimed to examine the impact of different image segmentation techniques on the extracted PET radiomics features in lung cancer. To this end, 120 patients with non-small cell lung cancer were enrolled. Different segmentations were applied to the images, including iterative thresholding using various threshold (30%, 40%, 60% and 80%), Fuzzy C-means (FCM), region growing, and manual delineation. For each of the contours, 841 features belonging to shape and first-, second-, and higher-order statistics features were extracted. The intraclass correlation coefficient (ICC) was calculated to assess features variability and selecting robust features. The results demonstrated that first-order features showed top performance in terms of robustness against various segmentations. In contrast, Gray Level Co-occurrence Matrix (GLCM), Gray Level Dependence Matrix (GLDM), Gray Level Size Zone Matrix (GLSZM), and shape-based parameters achieved poor performance. In addition, wavelate-based LLL set features achieved the highest robustness. The performance of radiomics features in terms of robustness depends on radiomics features set and features family. Hence, the more robust features should be considered in prognostic modeling. © 2021 IEEE.
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