放射组学在肝细胞癌微血管侵犯风险分层及预后评估中的表现：一项荟萃分析
Performance of Radiomics in Microvascular Invasion Risk Stratification and Prognostic Assessment in Hepatocellular Carcinoma: A Meta-Analysis.
作者信息
Bodard Sylvain, Liu Yan, Guinebert Sylvain, Kherabi Yousra, Asselah Tarik
机构信息
Service de Radiologie Adulte, Hôpital Universitaire Necker-Enfants Malades, AP-HP Centre, 75015 Paris, France.
Faculté de Médecine, Université Paris Cité, 75007 Paris, France.
出版信息
Cancers (Basel). 2023 Jan 25;15(3):743. doi: 10.3390/cancers15030743.
BACKGROUND
Primary liver cancer is the sixth most commonly diagnosed cancer and the third leading cause of cancer death. Advances in phenomenal imaging are paving the way for application in diagnosis and research. The poor prognosis of advanced HCC warrants a personalized approach. The objective was to assess the value of imaging phenomics for risk stratification and prognostication of HCC.
METHODS
We performed a meta-analysis of manuscripts published to January 2023 on MEDLINE addressing the value of imaging phenomics for HCC risk stratification and prognostication. Publication information for each were collected using a standardized data extraction form.
RESULTS
Twenty-seven articles were analyzed. Our study shows the importance of imaging phenomics in HCC MVI prediction. When the training and validation datasets were analyzed separately by the random-effects model, in the training datasets, radiomics had good MVI prediction (AUC of 0.81 (95% CI 0.76-0.86)). Similar results were found in the validation datasets (AUC of 0.79 (95% CI 0.72-0.85)). Using the fixed effects model, the mean AUC of all datasets was 0.80 (95% CI 0.76-0.84).
CONCLUSIONS
Imaging phenomics is an effective solution to predict microvascular invasion risk, prognosis, and treatment response in patients with HCC.
背景
原发性肝癌是第六大最常被诊断出的癌症，也是癌症死亡的第三大主要原因。显著的成像技术进步为其在诊断和研究中的应用铺平了道路。晚期肝癌预后较差，需要个性化的治疗方法。目的是评估成像表型组学在肝癌风险分层和预后评估中的价值。
方法
我们对截至2023年1月在MEDLINE上发表的关于成像表型组学在肝癌风险分层和预后评估中价值的手稿进行了荟萃分析。使用标准化的数据提取表收集每篇文章的发表信息。
结果
分析了27篇文章。我们的研究表明成像表型组学在肝癌微血管侵犯预测中的重要性。当通过随机效应模型分别分析训练数据集和验证数据集时，在训练数据集中，放射组学对微血管侵犯有良好的预测能力（AUC为0.81（95%CI 0.76 - 0.86））。在验证数据集中也发现了类似结果（AUC为0.79（95%CI 0.72 - 0.85））。使用固定效应模型，所有数据集的平均AUC为0.80（95%CI 0.76 - 0.84）。
结论
成像表型组学是预测肝癌患者微血管侵犯风险、预后和治疗反应的有效方法。
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