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Prediction of the Gleason Score of Prostate Cancer Patients Using 68Ga-Psma-Pet/Ct Radiomic Models Publisher



Vosoughi Z1 ; Emami F2 ; Vosoughi H3 ; Hajianfar G4 ; Hamzian N1 ; Geramifar P3 ; Zaidi H4, 5, 6, 7
Authors
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Authors Affiliations
  1. 1. Department of Medical Physics, School of Medicine, Shahid Sadoughi University of Medical Sciences, Shohada Gomnam Blv, Yazd, Iran
  2. 2. Nuclear Medicine and Molecular Imaging Department, Imam Reza International University, Razavi Hospital, Mashhad, Iran
  3. 3. Research Center for Nuclear Medicine, Tehran University of Medical Science, Tehran, Iran
  4. 4. Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, CH-1211, Switzerland
  5. 5. Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, 9700 RB, Netherlands
  6. 6. Department of Nuclear Medicine, University of Southern Denmark, Odense, DK-500, Denmark
  7. 7. University Research and Innovation Center, Obuda University, Budapest, Hungary

Source: Journal of Medical and Biological Engineering Published:2024


Abstract

Purpose: To predict Gleason Score (GS) using radiomic features from 68Ga-PSMA-PET/CT images in primary prostate cancer. Methods: 138 patients undergoing 68Ga-PSMA-PET/CT imaging were categorized based on GS, with GS above 4 + 3 as malignant and under 3 + 4 as benign tumors. radiomic features were extracted from tumors’ volume of interest in both PET and CT images, using Feature Elimination with cross-validation. Fusion features were generated by combining features at the feature level; average of features (PET/CTAveFea) or concatenated features (PET/CTConFea). The performance of various models was compared using area under the curve, sensitivity and specificity. Wilcoxon test and F1-score test were used to find the best model. Predictive models were developed for CT-only, PET-only, and PET/CT feature-level fusion models. Results: Random Forest achieved the highest accuracy on CT with 0.74 ± 0.01 AUCMean, 0.75 ± 0.07 sensitivity, and 0.62 ± 0.08 specificity. Logistic regression (LR) exhibited the best predictive performance on PET images with 0.74 ± 0.05 AUCMean, 0.7 ± 0.13 sensitivity, and 0.78 ± 0.14 specificity. The best predictive PET/CTAveFea was achieved by LR, resulting in 0.72 ± 0.07 AUCMean, 0.74 ± 0.12 sensitivity, and 0.63 ± 0.02 specificity. In the case of PET/CTConFea, LR showed the best predictive performance with 0.78 ± 0.08 AUCMean, 0.81 ± 0.09 sensitivity, and 0.66 ± 0.15 specificity. Conclusion: The results demonstrated that radiomic models derived from 68Ga-PSMA-PET/CT images could differentiate between benign and malignant tumors based on GS. © Taiwanese Society of Biomedical Engineering 2024.
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