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Journal of Zhejiang University SCIENCE B
ISSN 1673-1581(Print), 1862-1783(Online), Monthly
2023 Vol.24 No.10 P.868-882
Biomarkers of malignant transformation in oral leukoplakia: from bench to bedside
Abstract: Oral leukoplakia is a common precursor lesion of oral squamous cell carcinoma, which indicates a high potential of malignancy. The malignant transformation of oral leukoplakia seriously affects patient survival and quality of life; however, it is difficult to identify oral leukoplakia patients who will develop carcinoma because no biomarker exists to predict malignant transformation for effective clinical management. As a major problem in the field of head and neck pathologies, it is imperative to identify biomarkers of malignant transformation in oral leukoplakia. In this review, we discuss the potential biomarkers of malignant transformation reported in the literature and explore the translational probabilities from bench to bedside. Although no single biomarker has yet been applied in the clinical setting, profiling for genomic instability might be a promising adjunct.
Key words: Oral leukoplakia; Oral squamous cell carcinoma; Malignant transformation; Biomarker; Prognosis
1浙江大学浙江省大数据智能计算重点实验室，中国杭州市，310027
2浙江大学区块链与数据安全全国重点实验室，中国杭州市，310027
3城云科技（中国）有限公司，中国杭州市，310000
摘要：许多主动学习方法假设学习者可便捷地向注释者询问训练数据的完整标注信息。这些方法主要试图通过最小化标注数量降低标注成本。然而，对于许多现实中的分类任务来说，精确标注实例仍然非常昂贵。为降低单次标注行为成本，本文试图解决一种新的主动学习范式，称为具有补标签的主动学习（ALCL）。ALCL学习器只针对样例特定类别提出是或否的问题。在收到标注者答案后，ALCL学习器获得一些有监督实例和更多具有补标签的训练实例，这些补标签仅表示对应标签与该实例无关。。ALCL具有两个挑战性问题：如何选择要查询的实例以及如何从这些补标签和普通标签中提取信息。针对第一个问题，在主动学习范式下提出一种基于不确定性的抽样策略。针对第二个问题，改进了一种已有的ALCL方法，同时适配了我们的抽样策略。在各种数据集上的实验结果验证了本文方法的有效性。
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DOI:
10.1631/jzus.B2200589
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On-line Access:
2023-06-26
Received:
2022-11-20
Revision Accepted:
2022-12-01
Crosschecked:
2023-09-26