Urraro Fabrizio, Pacella Giulia, Giordano Nicoletta, Spiezia Salvatore, Balestrucci Giovanni, Caiazzo Corrado, Russo Claudio, Cappabianca Salvatore, Costa Gianluca
Department of Life Sciences, Health and Health Professions, Link Campus University, 00165 Rome, Italy.
Department of Medicine and Health Sciences "V. Tiberio", 86100 Campobasso, Italy.
J Clin Med. 2025 Jul 28;14(15):5326. doi: 10.3390/jcm14155326.
Post-hepatectomy liver failure (PHLF) is the most worrisome complication after a major hepatectomy and is the leading cause of postoperative mortality. The most important predictor of PHLF is the future liver remnant (FLR), the volume of the liver that will remain after the hepatectomy, representing a major concern for hepatobiliary surgeons, radiologists, and patients. Therefore, an accurate preoperative assessment of the FLR and the prediction of PHLF are crucial to minimize risks and enhance patient outcomes. Recent radiomics and deep learning models show potential in predicting PHLF and the FLR by integrating imaging and clinical data. However, most studies lack external validation and methodological homogeneity and rely on small, single-center cohorts. This review outlines current CT-based approaches for surgical risk stratification and key limitations hindering clinical translation. A literature analysis was performed on the PubMed Dataset. We reviewed original articles using the subsequent keywords: [(Artificial intelligence OR radiomics OR machine learning OR deep learning OR neural network OR texture analysis) AND liver resection AND CT]. Of 153 pertinent papers found, we underlined papers about the prediction of PHLF and about the FLR. Models were built according to machine learning (ML) and deep learning (DL) automatic algorithms. Radiomics models seem reliable and applicable to clinical practice in the preoperative prediction of PHLF and the FLR in patients undergoing major liver surgery. Further studies are required to achieve larger validation cohorts.
肝切除术后肝衰竭（PHLF）是大型肝切除术后最令人担忧的并发症，也是术后死亡的主要原因。PHLF最重要的预测指标是未来肝脏残余量（FLR），即肝切除术后剩余的肝脏体积，这是肝胆外科医生、放射科医生和患者主要关注的问题。因此，术前准确评估FLR并预测PHLF对于降低风险和改善患者预后至关重要。最近的放射组学和深度学习模型通过整合影像和临床数据，在预测PHLF和FLR方面显示出潜力。然而，大多数研究缺乏外部验证和方法学同质性，且依赖于小型单中心队列。本综述概述了当前基于CT的手术风险分层方法以及阻碍临床转化的关键局限性。我们对PubMed数据集进行了文献分析。我们使用以下关键词检索了原始文章：[（人工智能或放射组学或机器学习或深度学习或神经网络或纹理分析）且肝切除且CT]。在找到的153篇相关论文中，我们重点关注了关于PHLF预测和FLR预测的论文。模型是根据机器学习（ML）和深度学习（DL）自动算法构建的。放射组学模型在术前预测接受大型肝脏手术患者的PHLF和FLR方面似乎可靠且适用于临床实践。需要进一步研究以获得更大的验证队列。
