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1.
Performance Analysis of Six Semi-Automated Tumour Delineation Methods on [18F] Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (FDG PET/CT) in Patients with Head and Neck Cancer.
Sensors (Basel)
; 23(18)2023 Sep 18.
Artículo
en Inglés
| MEDLINE | ID: mdl-37766009
2.
Form Factors as Potential Imaging Biomarkers to Differentiate Benign vs. Malignant Lung Lesions on CT Scans.
Sensors (Basel)
; 22(13)2022 Jul 04.
Artículo
en Inglés
| MEDLINE | ID: mdl-35808538
3.
Comparative evaluation of conventional and deep learning methods for semi-automated segmentation of pulmonary nodules on CT.
Quant Imaging Med Surg
; 11(7): 3286-3305, 2021 Jul.
Artículo
en Inglés
| MEDLINE | ID: mdl-34249654
4.
Value of Shape and Texture Features from 18F-FDG PET/CT to Discriminate between Benign and Malignant Solitary Pulmonary Nodules: An Experimental Evaluation.
Diagnostics (Basel)
; 10(9)2020 Sep 15.
Artículo
en Inglés
| MEDLINE | ID: mdl-32942729
5.
Texture Analysis on [18F]FDG PET/CT in Non-Small-Cell Lung Cancer: Correlations Between PET Features, CT Features, and Histological Types.
Mol Imaging Biol
; 21(6): 1200-1209, 2019 12.
Artículo
en Inglés
| MEDLINE | ID: mdl-30847822
6.
Evaluation of Shape and Textural Features from CT as Prognostic Biomarkers in Non-small Cell Lung Cancer.
Anticancer Res
; 38(4): 2155-2160, 2018 04.
Artículo
en Inglés
| MEDLINE | ID: mdl-29599334
7.
Role of Artificial Intelligence Techniques (Automatic Classifiers) in Molecular Imaging Modalities in Neurodegenerative Diseases.
Curr Alzheimer Res
; 14(2): 198-207, 2017.
Artículo
en Inglés
| MEDLINE | ID: mdl-27334942
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