人工智能在口腔癌和口腔发育不良中的应用。

IF 3.5 3区 医学 Q3 CELL & TISSUE ENGINEERING Tissue Engineering Part A Pub Date : 2024-10-01 Epub Date: 2024-08-07 DOI:10.1089/ten.TEA.2024.0096
Chi T Viet, Michael Zhang, Neeraja Dharmaraj, Grace Y Li, Alexander T Pearson, Victoria A Manon, Anupama Grandhi, Ke Xu, Bradley E Aouizerat, Simon Young
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引用次数: 0

摘要

口腔鳞状细胞癌(OSCC)是一种非常难以预测的疾病,死亡率极高,过去几十年来,尽管治疗方法和生物标志物取得了进步,提高了其他癌症的存活率,但死亡率却一直没有改变。延误诊断的情况时有发生,导致更多的毁容性治疗和患者的不良预后。临床面临的挑战在于如何识别那些罹患 OSCC 风险最高的患者。口腔上皮发育不良(OED)是 OSCC 的前兆,不同患者的表现差异很大。目前还没有可靠的临床、病理、组织学或分子生物标志物来确定 OED 患者的个体风险。同样,也没有可靠的生物标志物来预测 OSCC 患者的治疗效果或死亡率。本综述旨在重点介绍基于人工智能(AI)的方法在开发OED转化为OSCC的预测性生物标志物或OSCC死亡率和治疗反应的预测性生物标志物方面取得的进展。基于机器学习的生物标志物(如S100A7)在评估OED恶性转化风险方面显示出良好的前景。机器学习增强型多重免疫组化(mIHC)工作流程可检查肿瘤免疫微环境中的免疫细胞模式和组织,从而生成免疫疗法的结果预测。深度学习(DL)是一种基于人工智能的方法,它使用扩展神经网络或具有多层 "隐藏 "模拟神经元的相关架构,将简单的视觉特征组合成复杂的模式。目前正在开发基于 DL 的数字病理学,以评估 OED 和 OSCC 的结果。机器学习与表观基因组学的整合旨在研究疾病的表观遗传修饰,提高我们检测、分类和预测与表观遗传标记相关的结果的能力。总之,这些工具展示了发现和技术方面令人鼓舞的进步,它们可能为解决目前在预测OED转化和OSCC行为方面存在的局限性提供了潜在的解决方案,而这两者都是改善OSCC存活率所必须应对的临床挑战。
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Artificial Intelligence Applications in Oral Cancer and Oral Dysplasia.

Oral squamous cell carcinoma (OSCC) is a highly unpredictable disease with devastating mortality rates that have not changed over the past decades, in the face of advancements in treatments and biomarkers, which have improved survival for other cancers. Delays in diagnosis are frequent, leading to more disfiguring treatments and poor outcomes for patients. The clinical challenge lies in identifying those patients at the highest risk of developing OSCC. Oral epithelial dysplasia (OED) is a precursor of OSCC with highly variable behavior across patients. There is no reliable clinical, pathological, histological, or molecular biomarker to determine individual risk in OED patients. Similarly, there are no robust biomarkers to predict treatment outcomes or mortality in OSCC patients. This review aims to highlight advancements in artificial intelligence (AI)-based methods to develop predictive biomarkers of OED transformation to OSCC or predictive biomarkers of OSCC mortality and treatment response. Biomarkers such as S100A7 demonstrate promising appraisal for the risk of malignant transformation of OED. Machine learning-enhanced multiplex immunohistochemistry workflows examine immune cell patterns and organization within the tumor immune microenvironment to generate outcome predictions in immunotherapy. Deep learning (DL) is an AI-based method using an extended neural network or related architecture with multiple "hidden" layers of simulated neurons to combine simple visual features into complex patterns. DL-based digital pathology is currently being developed to assess OED and OSCC outcomes. The integration of machine learning in epigenomics aims to examine the epigenetic modification of diseases and improve our ability to detect, classify, and predict outcomes associated with epigenetic marks. Collectively, these tools showcase promising advancements in discovery and technology, which may provide a potential solution to addressing the current limitations in predicting OED transformation and OSCC behavior, both of which are clinical challenges that must be addressed in order to improve OSCC survival.

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来源期刊
Tissue Engineering Part A
Tissue Engineering Part A Chemical Engineering-Bioengineering
CiteScore
9.20
自引率
2.40%
发文量
163
审稿时长
3 months
期刊介绍: Tissue Engineering is the preeminent, biomedical journal advancing the field with cutting-edge research and applications that repair or regenerate portions or whole tissues. This multidisciplinary journal brings together the principles of engineering and life sciences in the creation of artificial tissues and regenerative medicine. Tissue Engineering is divided into three parts, providing a central forum for groundbreaking scientific research and developments of clinical applications from leading experts in the field that will enable the functional replacement of tissues.
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