The Cognitive Framework Behind Modern Neuropathology.

IF 3.7 3区 医学 Q2 MEDICAL LABORATORY TECHNOLOGY
José Javier Otero
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引用次数: 0

Abstract

Context: In 2021 the World Health Organization distributed a new classification of central nervous system tumors that incorporated modern testing modalities in the diagnosis. Although universally accepted as a scientifically superior system, this schema has created controversy because its deployment globally is challenging in the best of circumstances and impossible in resource-poor health care ecosystems. Compounding this problem is the significant challenge that neuropathologists with expertise in central nervous system tumors are rare.

Objective: To demonstrate diagnostic use of simple unsupervised machine learning techniques using publicly available data sets. I also discuss some potential solutions to the deployment of neuropathology classification in health care ecosystems burdened by this classification schema.

Data sources: The Cancer Genome Atlas RNA sequencing data from low-grade and high-grade gliomas.

Conclusions: Methylation-based classification will be unable to solve all diagnostic problems in neuropathology. Information theory quantifications generate focused workflows in pathology, resulting in prevention of ordering unnecessary tests and identifying biomarkers that facilitate diagnosis.

现代神经病理学背后的认知框架。
背景:2021 年,世界卫生组织发布了新的中枢神经系统肿瘤分类法,将现代检测模式纳入诊断。尽管该分类法被普遍认为是科学上更优越的系统,但却引发了争议,因为在最好的情况下,在全球范围内采用该分类法具有挑战性,而在资源匮乏的医疗保健生态系统中则是不可能的。使这一问题更加复杂的是,具有中枢神经系统肿瘤专业知识的神经病理学家非常罕见:目的:利用公开数据集展示简单的无监督机器学习技术在诊断中的应用。我还将讨论一些潜在的解决方案,以便在受这种分类模式影响的医疗保健生态系统中部署神经病理学分类:癌症基因组图谱》(Cancer Genome Atlas)中低级别和高级别胶质瘤的 RNA 测序数据:基于甲基化的分类无法解决神经病理学中的所有诊断问题。信息论量化可生成病理学中重点突出的工作流程,从而避免开具不必要的检验单,并确定有助于诊断的生物标记物。
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来源期刊
CiteScore
9.20
自引率
2.20%
发文量
369
审稿时长
3-8 weeks
期刊介绍: Welcome to the website of the Archives of Pathology & Laboratory Medicine (APLM). This monthly, peer-reviewed journal of the College of American Pathologists offers global reach and highest measured readership among pathology journals. Published since 1926, ARCHIVES was voted in 2009 the only pathology journal among the top 100 most influential journals of the past 100 years by the BioMedical and Life Sciences Division of the Special Libraries Association. Online access to the full-text and PDF files of APLM articles is free.
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