0000000000061965

AUTHOR

Antonio Vento

showing 2 related works from this author

Theragnostic Use of Radiolabelled Dota-Peptides in Meningioma: From Clinical Demand to Future Applications.

2019

Meningiomas account for approximately 30% of all new diagnoses of intracranial masses. The 2016 World Health Organization’s (WHO) classification currently represents the clinical standard for meningioma’s grading and prognostic stratification. However, watchful waiting is frequently the chosen treatment option, although this means the absence of a certain histological diagnosis. Consequently, MRI (or less frequently CT) brain imaging currently represents the unique available tool to define diagnosis, grading, and treatment planning in many cases. Nonetheless, these neuroimaging modalities show some limitations, particularly in the evaluation of skull base lesions. The emerging evidence supp…

Cancer Researchmedicine.medical_specialtypositron emission tomographymedicine.medical_treatmentReviewlcsh:RC254-282meningioma030218 nuclear medicine & medical imagingMeningioma03 medical and health sciences0302 clinical medicineNeuroimagingFunctional neuroimagingmedicineotorhinolaryngologic diseasesMedical diagnosisRadiation treatment planningGrading (tumors)neoplasmsMeningioma; Neuroimaging; Positron emission tomography; Radionuclide therapy; Somatostatin receptorneuroimagingbusiness.industryradionuclide therapylcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogensmedicine.diseasesomatostatin receptorOncologymeningioma; somatostatin receptor; neuroimaging; positron emission tomography; radionuclide therapy030220 oncology & carcinogenesisRadionuclide therapyRadiologybusinessWatchful waitingCancers
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Choline PET/CT Features to Predict Survival Outcome in High Risk Prostate Cancer Restaging: A Preliminary Machine-Learning Radiomics Study

2020

Background Radiomic features are increasingly utilized to evaluate tumor heterogeneity in PET imaging but to date its role has not been investigated for Cho-PET in prostate cancer. The potential application of radiomics features analysis using a machine-learning radiomics algorithm was evaluated to select 18F-Cho PET/CT imaging features to predict disease progression in PCa. Methods We retrospectively analyzed high-risk PCa patients who underwent restaging 18F-Cho PET/CT from November 2013 to May 2018. 18F-Cho PET/CT studies and related structures containing volumetric segmentations were imported in the "CGITA" toolbox to extract imaging features from each lesion. A Machine-learning model h…

Malemedicine.medical_specialtyn artificial intelligence model demonstrated to be feasible and able to select a panel of 18F-Cho PET/CT features with valuable association with PCa patients' outcome.business.industryProstatic NeoplasmsFeature selectionPet imagingCholine pet ctmedicine.diseaseTumor heterogeneitySurvival outcomeCholineMachine LearningProstate cancerRadiomicsFeature (computer vision)Artificial IntelligencePositron Emission Tomography Computed TomographyMedicineHumansRadiology Nuclear Medicine and imagingRadiologybusinessRetrospective Studies
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