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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MDPI AG
2025-01-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/15/2/148 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Marta Zerunian |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1c754cd61da0452bafa1d60fd670662d |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-1c754cd61da0452bafa1d60fd670662d2025-01-24T13:28:54ZengMDPI AGDiagnostics2075-44182025-01-0115214810.3390/diagnostics15020148Artificial Intelligence and Radiomics in Cholangiocarcinoma: A Comprehensive ReviewMarta Zerunian0Tiziano Polidori1Federica Palmeri2Stefano Nardacci3Antonella Del Gaudio4Benedetta Masci5Giuseppe Tremamunno6Michela Polici7Domenico De Santis8Francesco Pucciarelli9Andrea Laghi10Damiano Caruso11Department of Medical Surgical Sciences and Translational Medicine, Sapienza–University of Rome, Radiology Unit–Sant’Andrea University Hospital, 00189 Rome, ItalyDepartment of Medical Surgical Sciences and Translational Medicine, Sapienza–University of Rome, Radiology Unit–Sant’Andrea University Hospital, 00189 Rome, ItalyDepartment of Medical Surgical Sciences and Translational Medicine, Sapienza–University of Rome, Radiology Unit–Sant’Andrea University Hospital, 00189 Rome, ItalyDepartment of Medical Surgical Sciences and Translational Medicine, Sapienza–University of Rome, Radiology Unit–Sant’Andrea University Hospital, 00189 Rome, ItalyDepartment of Medical Surgical Sciences and Translational Medicine, Sapienza–University of Rome, Radiology Unit–Sant’Andrea University Hospital, 00189 Rome, ItalyDepartment of Medical Surgical Sciences and Translational Medicine, Sapienza–University of Rome, Radiology Unit–Sant’Andrea University Hospital, 00189 Rome, ItalyDepartment of Medical Surgical Sciences and Translational Medicine, Sapienza–University of Rome, Radiology Unit–Sant’Andrea University Hospital, 00189 Rome, ItalyDepartment of Medical Surgical Sciences and Translational Medicine, Sapienza–University of Rome, Radiology Unit–Sant’Andrea University Hospital, 00189 Rome, ItalyDepartment of Medical Surgical Sciences and Translational Medicine, Sapienza–University of Rome, Radiology Unit–Sant’Andrea University Hospital, 00189 Rome, ItalyDepartment of Medical Surgical Sciences and Translational Medicine, Sapienza–University of Rome, Radiology Unit–Sant’Andrea University Hospital, 00189 Rome, ItalyDepartment of Medical Surgical Sciences and Translational Medicine, Sapienza–University of Rome, Radiology Unit–Sant’Andrea University Hospital, 00189 Rome, ItalyDepartment of Medical Surgical Sciences and Translational Medicine, Sapienza–University of Rome, Radiology Unit–Sant’Andrea University Hospital, 00189 Rome, ItalyCholangiocarcinoma (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.https://www.mdpi.com/2075-4418/15/2/148cholangiocarcinomaradiomicsartificial intelligencedeep learningultrasonographycomputed tomography |
| spellingShingle | 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 Artificial Intelligence and Radiomics in Cholangiocarcinoma: A Comprehensive Review Diagnostics cholangiocarcinoma radiomics artificial intelligence deep learning ultrasonography computed tomography |
| title | Artificial Intelligence and Radiomics in Cholangiocarcinoma: A Comprehensive Review |
| title_full | Artificial Intelligence and Radiomics in Cholangiocarcinoma: A Comprehensive Review |
| title_fullStr | Artificial Intelligence and Radiomics in Cholangiocarcinoma: A Comprehensive Review |
| title_full_unstemmed | Artificial Intelligence and Radiomics in Cholangiocarcinoma: A Comprehensive Review |
| title_short | Artificial Intelligence and Radiomics in Cholangiocarcinoma: A Comprehensive Review |
| title_sort | artificial intelligence and radiomics in cholangiocarcinoma a comprehensive review |
| topic | cholangiocarcinoma radiomics artificial intelligence deep learning ultrasonography computed tomography |
| url | https://www.mdpi.com/2075-4418/15/2/148 |
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