Browsing by Author "Dolcetti, Vincenzo"

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    Artificial Intelligence for Thyroid Nodule Characterization : Where Are We Standing?
    (2022-07-10) Sorrenti, Salvatore; Dolcetti, Vincenzo; Radzina, Maija; Bellini, Maria Irene; Frezza, Fabrizio; Munir, Khushboo; Grani, Giorgio; Durante, Cosimo; D'Andrea, Vito; David, Emanuele; Calò, Pietro Giorgio; Lori, Eleonora; Cantisani, Vito; Rīga Stradiņš University
    Machine learning (ML) is an interdisciplinary sector in the subset of artificial intelligence (AI) that creates systems to set up logical connections using algorithms, and thus offers predictions for complex data analysis. In the present review, an up-to-date summary of the current state of the art regarding ML and AI implementation for thyroid nodule ultrasound characterization and cancer is provided, highlighting controversies over AI application as well as possible benefits of ML, such as, for example, training purposes. There is evidence that AI increases diagnostic accuracy and significantly limits inter-observer variability by using standardized mathematical algorithms. It could also be of aid in practice settings with limited sub-specialty expertise, offering a second opinion by means of radiomics and computer-assisted diagnosis. The introduction of AI represents a revolutionary event in thyroid nodule evaluation, but key issues for further implementation include integration with radiologist expertise, impact on workflow and efficiency, and performance monitoring.
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    The role of ceus in the evaluation of thyroid cancer : From diagnosis to local staging
    (2021-09-30) Sorrenti, Salvatore; Dolcetti, Vincenzo; Fresilli, Daniele; Del Gaudio, Giovanni; Pacini, Patrizia; Huang, Pintong; Camponovo, Chiara; Leoncini, Andrea; D’andrea, Vito; Pironi, Daniele; Frattaroli, Fabrizio; Trimboli, Pierpaolo; Radzina, Maija; Cantisani, Vito; Department of Radiology
    Ultrasound often represents the first diagnostic step for thyroid nodule evaluation in clinical practice, but baseline US alone is not always effective enough to achieve thyroid nodule characterization. In the last decades new ultrasound techniques, such as CEUS, have been introduced to evaluate thyroid parenchyma as recommended by EFSUMB guidelines, for use in clinical research field, although its role is not yet clear. Several papers show the potential utility of CEUS in the differential diagnosis of benign and malignant thyroid nodules and in the analysis of lymph node involvement in neoplastic pathology. Therefore, we carried out an evaluation of the literature concerning the role of CEUS in three specific areas: the characterization of the thyroid nodule, the evaluation of minimally invasive treatment and loco‐regional staging of the lymph node in proven thyroid cancer. According to evidence reported, CEUS can also play an operative role in nodular thyroid pathology as it is able to guide ablation procedures on thyroid nodule and metastatic lymph nodes, to assess the radicality of surgery, to evaluate disease relapse at the level of the margins of ablated regions and to monitor the clinical evolution of necrotic areas in immediate post‐treatment setting.