Iván Rienda, João Vale, João Pinto, António Polónia, Catarina Eloy
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引用次数: 0
Abstract
The digital transformation of pathology, through automation and computational tools, addresses current challenges in the field. This study evaluates Paige Pan Cancer, a novel artificial intelligence tool based on the Virchow foundation model, designed to flag invasive cancer in haematoxylin and eosin-stained slides from 16 primary tissue types. Using 62 cases from the Ipatimup Pathology Laboratory, we found the tool had a sensitivity of 93.3% and specificity of 87.5% in biopsies, and 94.7% sensitivity and 75.0% specificity in resections. Overall accuracy was 90.3%. Despite some misclassifications, Paige Pan Cancer demonstrates high sensitivity as a multi-organ screening tool in clinical practice.
通过自动化和计算工具，病理学的数字化转型解决了该领域当前的挑战。该研究评估了Paige Pan Cancer，这是一种基于Virchow基础模型的新型人工智能工具，旨在标记来自16种原发组织类型的血红素和伊红染色玻片中的浸润性癌症。使用来自Ipatimup病理实验室的62例病例，我们发现该工具在活检中的敏感性为93.3%，特异性为87.5%，在切除中的敏感性为94.7%，特异性为75.0%。总体准确率为90.3%。尽管有一些错误的分类，但在临床实践中，潘氏癌作为多器官筛查工具具有很高的敏感性。
期刊介绍：
Manuscripts of original studies reinforcing the evidence base of modern diagnostic pathology, using immunocytochemical, molecular and ultrastructural techniques, will be welcomed. In addition, papers on critical evaluation of diagnostic criteria but also broadsheets and guidelines with a solid evidence base will be considered. Consideration will also be given to reports of work in other fields relevant to the understanding of human pathology as well as manuscripts on the application of new methods and techniques in pathology. Submission of purely experimental articles is discouraged but manuscripts on experimental work applicable to diagnostic pathology are welcomed. Biomarker studies are welcomed but need to abide by strict rules (e.g. REMARK) of adequate sample size and relevant marker choice. Single marker studies on limited patient series without validated application will as a rule not be considered. Case reports will only be considered when they provide substantial new information with an impact on understanding disease or diagnostic practice.
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