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人工智能(AI)在人类癌症中的临床应用:是时候更新肝细胞癌(HCC)管理中的诊断和预测模型了吗?

Clinical Applications of Artificial Intelligence (AI) in Human Cancer: Is It Time to Update the Diagnostic and Predictive Models in Managing Hepatocellular Carcinoma (HCC)?

作者信息

Romeo Mario, Dallio Marcello, Napolitano Carmine, Basile Claudio, Di Nardo Fiammetta, Vaia Paolo, Iodice Patrizia, Federico Alessandro

机构信息

Hepatogastroenterology Division, Department of Precision Medicine, University of Campania Luigi Vanvitelli, 80138 Naples, Italy.

Oncology Division, Monaldi Hospital, 80131 Naples, Italy.

出版信息

Diagnostics (Basel). 2025 Jan 22;15(3):252. doi: 10.3390/diagnostics15030252.


DOI:10.3390/diagnostics15030252
PMID:39941182
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11817573/
Abstract

In recent years, novel findings have progressively and promisingly supported the potential role of Artificial intelligence (AI) in transforming the management of various neoplasms, including hepatocellular carcinoma (HCC). HCC represents the most common primary liver cancer. Alarmingly, the HCC incidence is dramatically increasing worldwide due to the simultaneous "pandemic" spreading of metabolic dysfunction-associated steatotic liver disease (MASLD). MASLD currently constitutes the leading cause of chronic hepatic damage (steatosis and steatohepatitis), fibrosis, and liver cirrhosis, configuring a scenario where an HCC onset has been reported even in the early disease stage. On the other hand, HCC represents a serious plague, significantly burdening the outcomes of chronic hepatitis B (HBV) and hepatitis C (HCV) virus-infected patients. Despite the recent progress in the management of this cancer, the overall prognosis for advanced-stage HCC patients continues to be poor, suggesting the absolute need to develop personalized healthcare strategies further. In this "cold war", machine learning techniques and neural networks are emerging as weapons, able to identify the patterns and biomarkers that would have normally escaped human observation. Using advanced algorithms, AI can analyze large volumes of clinical data and medical images (including routinely obtained ultrasound data) with an elevated accuracy, facilitating early diagnosis, improving the performance of predictive models, and supporting the multidisciplinary (oncologist, gastroenterologist, surgeon, radiologist) team in opting for the best "tailored" individual treatment. Additionally, AI can significantly contribute to enhancing the effectiveness of metabolomics-radiomics-based models, promoting the identification of specific HCC-pathogenetic molecules as new targets for realizing novel therapeutic regimens. In the era of precision medicine, integrating AI into routine clinical practice appears as a promising frontier, opening new avenues for liver cancer research and treatment.

摘要

近年来,新的研究发现逐渐且令人鼓舞地支持了人工智能(AI)在改变包括肝细胞癌(HCC)在内的各种肿瘤管理方面的潜在作用。HCC是最常见的原发性肝癌。令人担忧的是,由于代谢功能障碍相关脂肪性肝病(MASLD)的同时“大流行”传播,HCC的发病率在全球范围内急剧上升。MASLD目前是慢性肝损伤(脂肪变性和脂肪性肝炎)、纤维化和肝硬化的主要原因,形成了一种即使在疾病早期阶段也有HCC发病报告的情况。另一方面,HCC是一种严重的祸患,给慢性乙型肝炎(HBV)和丙型肝炎(HCV)病毒感染患者的预后带来了沉重负担。尽管在这种癌症的管理方面取得了近期进展,但晚期HCC患者的总体预后仍然很差,这表明绝对需要进一步制定个性化的医疗保健策略。在这场“冷战”中,机器学习技术和神经网络正在作为武器崭露头角,能够识别那些通常会逃过人类观察的模式和生物标志物。通过使用先进算法,AI可以高精度地分析大量临床数据和医学图像(包括常规获取的超声数据),有助于早期诊断,提高预测模型的性能,并支持多学科(肿瘤学家、胃肠病学家、外科医生、放射科医生)团队选择最佳的“量身定制”个体治疗方案。此外,AI可以显著有助于提高基于代谢组学 - 放射组学模型的有效性,促进识别特定的HCC致病分子作为实现新治疗方案的新靶点。在精准医学时代,将AI整合到常规临床实践中似乎是一个充满希望的前沿领域,为肝癌研究和治疗开辟了新途径。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0aaa/11817573/ba41495fda57/diagnostics-15-00252-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0aaa/11817573/ba41495fda57/diagnostics-15-00252-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0aaa/11817573/ba41495fda57/diagnostics-15-00252-g001.jpg

相似文献

[1]
Clinical Applications of Artificial Intelligence (AI) in Human Cancer: Is It Time to Update the Diagnostic and Predictive Models in Managing Hepatocellular Carcinoma (HCC)?

Diagnostics (Basel). 2025-1-22

[2]
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[3]
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[4]
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[6]
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[7]
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[8]
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[9]
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[10]
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Hum Vaccin Immunother. 2025-12

[2]
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[3]
The Role of the Gut-Biliary-Liver Axis in Primary Hepatobiliary Liver Cancers: From Molecular Insights to Clinical Applications.

J Pers Med. 2025-3-24

本文引用的文献

[1]
Discovering novel prognostic biomarkers of hepatocellular carcinoma using eXplainable Artificial Intelligence.

Expert Syst Appl. 2024-10-15

[2]
EASL Clinical Practice Guidelines on the management of hepatocellular carcinoma.

J Hepatol. 2025-2

[3]
Application of a deep learning algorithm for the diagnosis of HCC.

JHEP Rep. 2024-9-16

[4]
Evolving Global Etiology of Hepatocellular Carcinoma (HCC): Insights and Trends for 2024.

J Clin Exp Hepatol. 2025

[5]
Metabolomics Biomarker Discovery to Optimize Hepatocellular Carcinoma Diagnosis: Methodology Integrating AutoML and Explainable Artificial Intelligence.

Diagnostics (Basel). 2024-9-15

[6]
Individualized HCC surveillance using risk stratification scores in advanced fibrosis and cirrhotic HCV patients who achieved SVR: Prospective study.

Aliment Pharmacol Ther. 2025-1

[7]
Artificial intelligence for ultrasonographic detection and diagnosis of hepatocellular carcinoma and cholangiocarcinoma.

Sci Rep. 2024-9-4

[8]
From MASLD to HCC: What's in the middle?

Heliyon. 2024-7-31

[9]
A Machine Learning Model to Predict Risk for Hepatocellular Carcinoma in Patients With Metabolic Dysfunction-Associated Steatotic Liver Disease.

Gastro Hep Adv. 2024-1-23

[10]
Epidemiology of Hepatocellular Carcinoma in India - An Updated Review for 2024.

J Clin Exp Hepatol. 2024

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