Nakajo, M., Hirahara, D., Jinguji, M., Ojima, S., Hirahara, M., Tani, A., . . . Yoshiura, T. (2024). Machine learning approach using 18F-FDG-PET-radiomic features and the visibility of right ventricle 18F-FDG uptake for predicting clinical events in patients with cardiac sarcoidosis. Japanese Journal of Radiology, 42(7), 744-753.
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Cita Chicago Style (17a ed.)
Nakajo, Masatoyo, et al. "Machine Learning Approach Using 18F-FDG-PET-radiomic Features and the Visibility of Right Ventricle 18F-FDG Uptake for Predicting Clinical Events in Patients with Cardiac Sarcoidosis."
Japanese Journal of Radiology 42, no. 7 (2024): 744-753.
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Nakajo, Masatoyo, et al. "Machine Learning Approach Using 18F-FDG-PET-radiomic Features and the Visibility of Right Ventricle 18F-FDG Uptake for Predicting Clinical Events in Patients with Cardiac Sarcoidosis."
Japanese Journal of Radiology, vol. 42, no. 7, 2024, pp. 744-753.
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