Artificial Intelligence and Radiomics in Cholangiocarcinoma: A Comprehensive Review

Artificial Intelligence and Radiomics in Cholangiocarcinoma: A Comprehensive Review

Cholangiocarcinoma (CCA) is a malignant biliary system tumor and the second most common primary hepatic neoplasm, following hepatocellular carcinoma. CCA still has an extremely high unfavorable prognosis, regardless of type and location, and complete surgical resection remains the only curative ther...

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Main Authors: Marta Zerunian, Tiziano Polidori, Federica Palmeri, Stefano Nardacci, Antonella Del Gaudio, Benedetta Masci, Giuseppe Tremamunno, Michela Polici, Domenico De Santis, Francesco Pucciarelli, Andrea Laghi, Damiano Caruso
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Diagnostics
Subjects:
cholangiocarcinoma
radiomics
artificial intelligence
deep learning
ultrasonography
computed tomography
Online Access:https://www.mdpi.com/2075-4418/15/2/148
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Summary:Cholangiocarcinoma (CCA) is a malignant biliary system tumor and the second most common primary hepatic neoplasm, following hepatocellular carcinoma. CCA still has an extremely high unfavorable prognosis, regardless of type and location, and complete surgical resection remains the only curative therapeutic option; however, due to the underhanded onset and rapid progression of CCA, most patients present with advanced stages at first diagnosis, with only 30 to 60% of CCA patients eligible for surgery. Recent innovations in medical imaging combined with the use of radiomics and artificial intelligence (AI) can lead to improvements in the early detection, characterization, and pre-treatment staging of these tumors, guiding clinicians to make personalized therapeutic strategies. The aim of this review is to provide an overview of how radiological features of CCA can be analyzed through radiomics and with the help of AI for many different purposes, such as differential diagnosis, the prediction of lymph node metastasis, the defining of prognostic groups, and the prediction of early recurrence. The combination of radiomics with AI has immense potential. Still, its effectiveness in practice is yet to be validated by prospective multicentric studies that would allow for the development of standardized radiomics models.
ISSN:2075-4418

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