Background: This paper offers an assessment of radiomics tools in the evaluation of intrahepatic cholangiocarcinoma. Methods: The PubMed database was searched for papers published in the English language no earlier than October 2022. Results: We found 236 studies, and 37 satisfied our research criteria. Several studies addressed multidisciplinary topics, especially diagnosis, prognosis, response to therapy, and prediction of staging (TNM) or pathomorphological patterns. In this review, we have covered diagnostic tools developed through machine learning, deep learning, and neural network for the recurrence and prediction of biological characteristics. The majority of the studies were retrospective. Conclusions: It is possible to conclude that many performing models have been developed to make differential diagnosis easier for radiologists to predict recurrence and genomic patterns. However, all the studies were retrospective, lacking further external validation in prospective and multicentric cohorts. Furthermore, the radiomics models and the expression of results should be standardized and automatized to be applicable in clinical practice.

Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma / Brunese, Maria Chiara; Fantozzi, Maria Rita; Fusco, Roberta; De Muzio, Federica; Gabelloni, Michela; Danti, Ginevra; Borgheresi, Alessandra; Palumbo, Pierpaolo; Bruno, Federico; Gandolfo, Nicoletta; Giovagnoni, Andrea; Miele, Vittorio; Barile, Antonio; Granata, Vincenza. - In: DIAGNOSTICS. - ISSN 2075-4418. - 13:8(2023), p. 1488. [10.3390/diagnostics13081488]

Update on the Applications of Radiomics in Diagnosis, Staging, and Recurrence of Intrahepatic Cholangiocarcinoma

Borgheresi, Alessandra;Giovagnoni, Andrea;
2023-01-01

Abstract

Background: This paper offers an assessment of radiomics tools in the evaluation of intrahepatic cholangiocarcinoma. Methods: The PubMed database was searched for papers published in the English language no earlier than October 2022. Results: We found 236 studies, and 37 satisfied our research criteria. Several studies addressed multidisciplinary topics, especially diagnosis, prognosis, response to therapy, and prediction of staging (TNM) or pathomorphological patterns. In this review, we have covered diagnostic tools developed through machine learning, deep learning, and neural network for the recurrence and prediction of biological characteristics. The majority of the studies were retrospective. Conclusions: It is possible to conclude that many performing models have been developed to make differential diagnosis easier for radiologists to predict recurrence and genomic patterns. However, all the studies were retrospective, lacking further external validation in prospective and multicentric cohorts. Furthermore, the radiomics models and the expression of results should be standardized and automatized to be applicable in clinical practice.
2023
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/321943
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