Theocharopoulos Charalampos, Theocharopoulos Achilleas, Papadakos Stavros P, Machairas Nikolaos, Pawlik Timothy M
Second Department of Propaedeutic Surgery, Laiko General Hospital, School of Medicine, National Kapodistrian University of Athens, 11527 Athens, Greece.
Department of Electrical and Computer Engineering, National Technical University of Athens, 10682 Athens, Greece.
Cancers (Basel). 2025 May 9;17(10):1604. doi: 10.3390/cancers17101604.
Intrahepatic cholangiocarcinoma (iCCA) is associated with a poor prognosis and necessitates a multimodal, multidisciplinary approach from diagnosis to treatment to achieve optimal outcomes. A noninvasive preoperative diagnosis using abdominal imaging techniques can represent a clinical challenge. Given the differential response of iCCA to localized and systemic therapies compared with hepatocellular carcinoma and secondary hepatic malignancies, an accurate diagnosis is crucial. Deep learning (DL) models for image analysis have emerged as a promising adjunct for the abdominal radiologist, potentially enhancing the accurate detection and diagnosis of iCCA. Over the last five years, several reports have proposed robust DL models, which demonstrate a diagnostic accuracy that is either comparable to or surpasses that of radiologists with varying levels of experience. Recent studies have expanded DL applications into other aspects of iCCA management, including histopathologic diagnosis, the prediction of histopathological features, the preoperative prediction of survival, and the pretreatment prediction of responses to systemic therapy. We herein critically evaluate the expanding body of research on DL applications in the diagnosis and management of iCCA, providing insights into the current progress and future research directions. We comprehensively synthesize the performance and limitations of DL models in iCCA research, identifying key challenges that serve as a translational reference for clinicians.
肝内胆管癌（iCCA）预后较差，从诊断到治疗需要多模式、多学科方法以实现最佳治疗效果。使用腹部成像技术进行非侵入性术前诊断可能是一项临床挑战。鉴于与肝细胞癌和继发性肝恶性肿瘤相比，iCCA对局部和全身治疗的反应不同，准确诊断至关重要。用于图像分析的深度学习（DL）模型已成为腹部放射科医生的一种有前景的辅助工具，有可能提高iCCA的准确检测和诊断。在过去五年中，有几份报告提出了强大的DL模型，这些模型显示出与不同经验水平的放射科医生相当或更高的诊断准确性。最近的研究已将DL应用扩展到iCCA管理的其他方面，包括组织病理学诊断、组织病理学特征预测、术前生存预测以及全身治疗反应的预处理预测。我们在此对DL在iCCA诊断和管理中的应用不断扩展的研究进行批判性评估，深入了解当前进展和未来研究方向。我们全面综合了DL模型在iCCA研究中的性能和局限性，确定了关键挑战，为临床医生提供转化参考。
