Routine blood test markers for predicting liver disease post HBV infection: precision pathology and pattern recognition.

IF 2.2 Q2 MEDICINE, GENERAL & INTERNAL Diagnosis Pub Date : 2023-09-20 eCollection Date: 2023-11-01 DOI:10.1515/dx-2023-0078
Busayo I Ajuwon, Katrina Roper, Alice Richardson, Brett A Lidbury
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Abstract

Background: Early stages of hepatitis B virus (HBV) infection usually involve inflammation of the liver. Patients with chronic infection have an increased risk of progressive liver fibrosis, cirrhosis, and life-threatening clinical complications of end-stage hepatocellular carcinoma (HCC).

Content: Early diagnosis of hepatic fibrosis and timely clinical management are critical to controlling disease progression and decreasing the burden of end-stage liver cancer. Fibrosis staging, through its current gold standard, liver biopsy, improves patient outcomes, but the clinical procedure is invasive with unpleasant post-procedural complications. Routine blood test markers offer promising diagnostic potential for early detection of liver disease without biopsy. There is a plethora of candidate routine blood test markers that have gone through phases of biomarker validation and have shown great promise, but their current limitations include a predictive ability that is limited to only a few stages of fibrosis. However, the advent of machine learning, notably pattern recognition, presents an opportunity to refine blood-based non-invasive models of hepatic fibrosis in the future.

Summary: In this review, we highlight the current landscape of routine blood-based non-invasive models of hepatic fibrosis, and appraise the potential application of machine learning (pattern recognition) algorithms to refining these models and optimising clinical predictions of HBV-associated liver disease.

Outlook: Machine learning via pattern recognition algorithms takes data analytics to a new realm, and offers the opportunity for enhanced multi-marker fibrosis stage prediction using pathology profile that leverages information across patient routine blood tests.

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预测HBV感染后肝病的血常规标志物:精确病理学和模式识别。
背景:乙型肝炎病毒(HBV)感染的早期通常涉及肝脏炎症。慢性感染患者发生进行性肝纤维化、肝硬化和危及生命的终末期肝细胞癌(HCC)临床并发症的风险增加。内容:肝纤维化的早期诊断和及时的临床治疗对于控制疾病进展和减轻终末期癌症负担至关重要。通过目前的金标准肝活检,纤维化分期可以改善患者的预后,但临床程序具有侵入性,术后并发症令人不快。常规血液检测标志物为无需活检的肝脏疾病早期检测提供了有希望的诊断潜力。有很多候选的常规血液检测标志物已经经历了生物标志物验证阶段,并显示出了巨大的前景,但它们目前的局限性包括仅限于纤维化的几个阶段的预测能力。然而,机器学习,尤其是模式识别的出现,为未来完善基于血液的肝纤维化非侵入性模型提供了机会。摘要:在这篇综述中,我们强调了基于常规血液的肝纤维化非侵入性模型的现状,并评估了机器学习(模式识别)算法在完善这些模型和优化HBV相关肝病临床预测方面的潜在应用。展望:通过模式识别算法进行的机器学习将数据分析带到了一个新的领域,并为利用患者常规血液测试信息的病理学档案增强多标志物纤维化阶段预测提供了机会。
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来源期刊
Diagnosis
Diagnosis MEDICINE, GENERAL & INTERNAL-
CiteScore
7.20
自引率
5.70%
发文量
41
期刊介绍: Diagnosis focuses on how diagnosis can be advanced, how it is taught, and how and why it can fail, leading to diagnostic errors. The journal welcomes both fundamental and applied works, improvement initiatives, opinions, and debates to encourage new thinking on improving this critical aspect of healthcare quality.  Topics: -Factors that promote diagnostic quality and safety -Clinical reasoning -Diagnostic errors in medicine -The factors that contribute to diagnostic error: human factors, cognitive issues, and system-related breakdowns -Improving the value of diagnosis – eliminating waste and unnecessary testing -How culture and removing blame promote awareness of diagnostic errors -Training and education related to clinical reasoning and diagnostic skills -Advances in laboratory testing and imaging that improve diagnostic capability -Local, national and international initiatives to reduce diagnostic error
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