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Predicting Immunohistochemical Biomarkers of Breast Cancer Using 18F-Fdg Pet/Ct Radiomics: A Multicenter Study Publisher



Faraji S1 ; Emami F2 ; Vosoughi Z3 ; Hajianfar G4 ; Naseri S1 ; Samimi R5 ; Vosoughi H6 ; Geramifar P6 ; Zaidi H4, 7, 8, 9
Authors
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Authors Affiliations
  1. 1. Department of Medical Physics, Mashhad University of Medical Science, Mashhad, Iran
  2. 2. Nuclear Medicine and Molecular Imaging Department, Imam Reza International University, Razavi Hospital, Mashhad, Iran
  3. 3. Department of Medical Physics, School of Medicine, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
  4. 4. Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4, 1211, Switzerland
  5. 5. Khatam PET-CT Center, Khatam Hospital, Tehran, Iran
  6. 6. Research Center for Nuclear Medicine, Tehran University of Medical Science, Shariati Hospital, North Kargar Ave.1411713135, Tehran, Iran
  7. 7. Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, 9700 RB, Netherlands
  8. 8. Department of Nuclear Medicine, University of Southern Denmark, Odense, 500, Denmark
  9. 9. University Research and Innovation Center, Obuda University, Budapest, Hungary

Source: Journal of Medical and Biological Engineering Published:2024


Abstract

Purpose: This study aimed at predicting four important immunohistochemical biomarkers, including estrogen receptor, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki67 (cell proliferation rate index)) in breast cancer using radiomic features derived from multicentric 18F-FDG PET/CT images. Methods: Sixty-two patients with locally advanced breast cancer who underwent 18F-FDG PET/CT imaging before any treatment were included. Three different PET/CT scanner models were used to acquire the images. After tumor segmentation, radiomic features from PET and CT images were extracted using the Python PyRadiomics package. Fusion features were created at the feature level, including concatenation (Con) and averaging (Avg). Combat was applied for features harmonization. The area under the curve (AUC), sensitivity, and specificity were used to evaluate the performance of predictive models. Results: Random Forest (RF) model in Con features with mean AUC of 0.69 ± 0.11, Support Vector Machine (SVC) model in radiomic features from CT with a mean AUC of 0.74 ± 0.02 were outstanding in predicting ER and PR, respectively. The best models for predicting HER2 were RF and SVC using CT images, with mean AUC of 0.72 ± 0.04 and 0.73 ± 0.03. Respectively. In addition, Ki67 was predicted on radiomic features derived from PET images by RF and SVC models with mean AUC of 0.8 ± 0.09 and 0.83 ± 0.03, respectively. Conclusion: Machine learning classifiers based on PET, CT, and PET/CT radiomic features could be correlated with the immunohistochemical biomarkers in breast cancer. © Taiwanese Society of Biomedical Engineering 2024.
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