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）预后不良，从诊断到治疗需要采取多模式、多学科的综合策略以实现最佳疗效。利用腹部影像技术进行无创术前诊断在临床上仍具挑战性。鉴于肝内胆管癌与肝细胞癌及转移性肝恶性肿瘤对局部治疗和全身治疗的反应存在差异，精准诊断至关重要。用于影像分析的深度学习模型已成为腹部放射科医生极具前景的辅助工具，有望提升肝内胆管癌的精准检测与诊断水平。过去五年间，多项研究提出了稳健的深度学习模型，其诊断准确性可与不同经验水平的放射科医生相媲美甚至更优。近期研究进一步拓展了深度学习在肝内胆管癌诊疗其他领域的应用，包括组织病理学诊断、病理特征预测、术前生存期预测以及全身治疗反应的前瞻性评估。本文系统评述了深度学习在肝内胆管癌诊疗中日益扩展的研究进展，剖析当前成果并展望未来研究方向。我们全面整合了深度学习模型在肝内胆管癌研究中的性能表现与局限性，指出关键挑战，为临床医生提供转化医学参考依据。
Deep Learning to Enhance Diagnosis and Management of Intrahepatic Cholangiocarcinoma
肿瘤(癌症)患者之家