Atsushi Tani
Kagoshima, 8908544, Japan
Last Updated: 09/26/2026
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Found 36 publications
Machine learning models using 18F-FDG PET/CT radiomics for RAS mutation prediction and prognostic stratification in colorectal cancer.
Journal: The British journal of radiology
Published: January 13, 2026
Machine learning-based prognostic modeling in gallbladder cancer using clinical data and pre-treatment [18F]-FDG-PET-radiomic features.
Journal: Japanese journal of radiology
Published: September 26, 2024
Applying deep learning-based ensemble model to [18F]-FDG-PET-radiomic features for differentiating benign from malignant parotid gland diseases.
Journal: Japanese journal of radiology
Published: July 19, 2024
Usefulness of the Primary Tumor Standardized Uptake Value of Iodine-123 Metaiodobenzylguanidine for Predicting Metastatic Potential in Pheochromocytoma and Paraganglioma.
Journal: Molecular imaging and biology
Published: June 14, 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.
Journal: Japanese journal of radiology
Published: December 20, 2023
The usefulness of machine-learning-based evaluation of clinical and pretreatment 18F-FDG-PET/CT radiomic features for predicting prognosis in patients with laryngeal cancer.
Journal: The British journal of radiology
Published: July 02, 2023
Application of Machine Learning Analyses Using Clinical and [18F]-FDG-PET/CT Radiomic Characteristics to Predict Recurrence in Patients with Breast Cancer.
Journal: Molecular imaging and biology
Published: November 08, 2022
Application of 123I-MIBG myocardial maximum standardized uptake value to characterize cardiac function in patients with pheochromocytoma: comparison with echocardiography.
Journal: Japanese journal of radiology
Published: September 18, 2022
The Usefulness of Machine Learning-Based Evaluation of Clinical and Pretreatment [18F]-FDG-PET/CT Radiomic Features for Predicting Prognosis in Hypopharyngeal Cancer.
Journal: Molecular imaging and biology
Published: April 12, 2022
The efficacy of 18F-FDG-PET-based radiomic and deep-learning features using a machine-learning approach to predict the pathological risk subtypes of thymic epithelial tumors.
Journal: The British journal of radiology
Published: March 21, 2022
Last Updated: 09/26/2026