利用人类专家和深度学习系统对前列腺癌自发荧光虚拟染色系统进行临床级验证。

IF 7.1 1区 医学 Q1 PATHOLOGY Modern Pathology Pub Date : 2024-07-26 DOI:10.1016/j.modpat.2024.100573
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引用次数: 0

摘要

前列腺腺癌和导管内癌(IDC-P)的组织诊断包括苏木精和伊红(H&E)染色的肿瘤形态格里森分级,以及 PIN-4 染色(CK5/6、P63、AMACR)的免疫组化(IHC)标记。在这项工作中,我们利用高通量高光谱荧光显微镜和人工智能与机器学习创建了一个自动化系统,可从未被染料的前列腺组织中生成虚拟 H&E 和 PIN-4 IHC 染色。我们证明,虚拟染色机模型可以生成适合泌尿生殖系统病理学家诊断的高质量图像。具体来说,我们通过广泛的人工审查和计算分析,利用先前验证过的格里森评分模型和专家小组,在大量测试切片数据集上验证了我们的系统。这项研究扩展了我们之前在自发荧光虚拟染色方面的工作,证明了这项技术在前列腺癌方面的临床实用性,并为数字病理学的定性和定量评估提供了严格的标准。
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Clinical-Grade Validation of an Autofluorescence Virtual Staining System With Human Experts and a Deep Learning System for Prostate Cancer

The tissue diagnosis of adenocarcinoma and intraductal carcinoma of the prostate includes Gleason grading of tumor morphology on the hematoxylin and eosin stain and immunohistochemistry markers on the prostatic intraepithelial neoplasia-4 stain (CK5/6, P63, and AMACR). In this work, we create an automated system for producing both virtual hematoxylin and eosin and prostatic intraepithelial neoplasia-4 immunohistochemistry stains from unstained prostate tissue using a high-throughput hyperspectral fluorescence microscope and artificial intelligence and machine learning. We demonstrate that the virtual stainer models can produce high-quality images suitable for diagnosis by genitourinary pathologists. Specifically, we validate our system through extensive human review and computational analysis, using a previously validated Gleason scoring model, and an expert panel, on a large data set of test slides. This study extends our previous work on virtual staining from autofluorescence, demonstrates the clinical utility of this technology for prostate cancer, and exemplifies a rigorous standard of qualitative and quantitative evaluation for digital pathology.

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来源期刊
Modern Pathology
Modern Pathology 医学-病理学
CiteScore
14.30
自引率
2.70%
发文量
174
审稿时长
18 days
期刊介绍: Modern Pathology, an international journal under the ownership of The United States & Canadian Academy of Pathology (USCAP), serves as an authoritative platform for publishing top-tier clinical and translational research studies in pathology. Original manuscripts are the primary focus of Modern Pathology, complemented by impactful editorials, reviews, and practice guidelines covering all facets of precision diagnostics in human pathology. The journal's scope includes advancements in molecular diagnostics and genomic classifications of diseases, breakthroughs in immune-oncology, computational science, applied bioinformatics, and digital pathology.
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