Zerunian Marta, Polidori Tiziano, Palmeri Federica, Nardacci Stefano, Del Gaudio Antonella, Masci Benedetta, Tremamunno Giuseppe, Polici Michela, De Santis Domenico, Pucciarelli Francesco, Laghi Andrea, Caruso Damiano
Department of Medical Surgical Sciences and Translational Medicine, Sapienza-University of Rome, Radiology Unit-Sant'Andrea University Hospital, 00189 Rome, Italy.
PhD School in Translational Medicine and Oncology, Department of Medical and Surgical Sciences and Translational Medicine, Faculty of Medicine and Psychology, Sapienza University of Rome, 00189 Rome, Italy.
Diagnostics (Basel). 2025 Jan 10;15(2):148. doi: 10.3390/diagnostics15020148.
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.
胆管癌（CCA）是一种恶性胆道系统肿瘤，是继肝细胞癌之后第二常见的原发性肝脏肿瘤。无论类型和位置如何，CCA的预后仍然极差，完整的手术切除仍然是唯一的治愈性治疗选择；然而，由于CCA发病隐匿且进展迅速，大多数患者在初次诊断时已处于晚期，只有30%至60%的CCA患者适合手术。医学成像的最新创新与放射组学和人工智能（AI）的应用相结合，可以改善这些肿瘤的早期检测、特征描述和治疗前分期，指导临床医生制定个性化治疗策略。本综述的目的是概述如何通过放射组学并借助AI分析CCA的放射学特征，以用于多种不同目的，如鉴别诊断、预测淋巴结转移、定义预后分组以及预测早期复发。放射组学与AI的结合具有巨大潜力。不过，其在实践中的有效性仍有待前瞻性多中心研究验证，以便开发标准化的放射组学模型。
