The diagnosis of hepatocellular carcinoma relies largely on non-invasive imaging, and is well suited for radiomics analysis. Radiomics is an emerging method for quantification of tumor heterogeneity by mathematically analyzing the spatial distribution and relationships of gray levels in medical images. The published studies on radiomics analysis of HCC provide encouraging data demonstrating potential utility for prediction of tumor biology, molecular profiles, post-therapy response, and outcome. The combination of radiomics data and clinical/laboratory information provides added value in many studies. Radiomics is a multi-step process that requires optimization and standardization, the development of semi-automated or automated segmentation methods, robust data quality control, and refinement of algorithms and modeling approaches for high-throughput data analysis. While radiomics remains largely in the research setting, the strong associations of predictive models and nomograms with certain pathologic, molecular, and immune markers with tumor aggressiveness and patient outcomes, provide great potential for clinical applications to inform optimized treatment strategies and patient prognosis.

中文翻译：

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肝细胞癌的放射组学。

肝细胞癌的诊断很大程度上依赖于非侵入性成像，非常适合放射组学分析。放射组学是一种通过数学分析医学图像中灰度级的空间分布和关系来量化肿瘤异质性的新兴方法。已发表的关于 HCC 放射组学分析的研究提供了令人鼓舞的数据，证明了预测肿瘤生物学、分子谱、治疗后反应和结果的潜在效用。放射组学数据和临床/实验室信息的结合在许多研究中提供了附加值。放射组学是一个多步骤过程，需要优化和标准化、半自动或自动分割方法的开发、强大的数据质量控制、以及改进用于高通量数据分析的算法和建模方法。虽然放射组学在很大程度上仍处于研究环境中，但预测模型和列线图与某些病理、分子和免疫标志物与肿瘤侵袭性和患者预后的强关联，为临床应用提供了巨大的潜力，可以为优化治疗策略和患者预后提供信息。