Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
MR Scientific Marketing, Siemens Healthineers Ltd., Shanghai, China.
J Magn Reson Imaging. 2024 Mar;59(3):767-783. doi: 10.1002/jmri.28982. Epub 2023 Aug 30.
Hepatocellular carcinoma (HCC) is the fifth most common malignancy and the third leading cause of cancer-related death worldwide. HCC exhibits strong inter-tumor heterogeneity, with different biological characteristics closely associated with prognosis. In addition, patients with HCC often distribute at different stages and require diverse treatment options at each stage. Due to the variability in tumor sensitivity to different therapies, determining the optimal treatment approach can be challenging for clinicians prior to treatment. Artificial intelligence (AI) technology, including radiomics and deep learning approaches, has emerged as a unique opportunity to improve the spectrum of HCC clinical care by predicting biological characteristics and prognosis in the medical imaging field. The radiomics approach utilizes handcrafted features derived from specific mathematical formulas to construct various machine-learning models for medical applications. In terms of the deep learning approach, convolutional neural network models are developed to achieve high classification performance based on automatic feature extraction from images. Magnetic resonance imaging offers the advantage of superior tissue resolution and functional information. This comprehensive evaluation plays a vital role in the accurate assessment and effective treatment planning for HCC patients. Recent studies have applied radiomics and deep learning approaches to develop AI-enabled models to improve accuracy in predicting biological characteristics and prognosis, such as microvascular invasion and tumor recurrence. Although AI-enabled models have demonstrated promising potential in HCC with biological characteristics and prognosis prediction with high performance, one of the biggest challenges, interpretability, has hindered their implementation in clinical practice. In the future, continued research is needed to improve the interpretability of AI-enabled models, including aspects such as domain knowledge, novel algorithms, and multi-dimension data sources. Overcoming these challenges would allow AI-enabled models to significantly impact the care provided to HCC patients, ultimately leading to their deployment for clinical use. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.
肝细胞癌 (HCC) 是全球第五大常见恶性肿瘤和第三大癌症相关死亡原因。HCC 表现出强烈的肿瘤间异质性，不同的生物学特征与预后密切相关。此外，HCC 患者常分布在不同的阶段，每个阶段需要不同的治疗选择。由于肿瘤对不同治疗方法的敏感性存在差异，因此在治疗前，临床医生确定最佳治疗方法具有一定挑战性。人工智能 (AI) 技术，包括放射组学和深度学习方法，为改善 HCC 临床治疗范围提供了独特的机会，可在医学成像领域预测生物学特征和预后。放射组学方法利用源自特定数学公式的手工特征来构建各种用于医学应用的机器学习模型。在深度学习方法中，卷积神经网络模型是根据从图像中自动提取的特征来开发的，以实现高分类性能。磁共振成像具有出色的组织分辨率和功能信息优势。这种综合评估在 HCC 患者的准确评估和有效治疗计划中起着至关重要的作用。最近的研究已经应用放射组学和深度学习方法来开发 AI 模型，以提高预测生物学特征和预后（如微血管侵犯和肿瘤复发）的准确性。尽管 AI 模型在预测 HCC 的生物学特征和预后方面表现出了很高的性能，但其中一个最大的挑战，即可解释性，阻碍了它们在临床实践中的应用。未来需要进一步研究以提高 AI 模型的可解释性，包括领域知识、新算法和多维数据源等方面。克服这些挑战将使 AI 模型能够对 HCC 患者的护理产生重大影响，最终实现其临床应用。证据级别：5 技术功效：阶段 2。
