Typing less common ovarian tumors: A training tool based on a pattern-based algorithm applied to a set of 20 virtual slides.
Details
Serval ID
serval:BIB_DFF6D9160249
Type
Inproceedings: an article in a conference proceedings.
Publication sub-type
Abstract (Abstract): shot summary in a article that contain essentials elements presented during a scientific conference, lecture or from a poster.
Collection
Publications
Institution
Title
Typing less common ovarian tumors: A training tool based on a pattern-based algorithm applied to a set of 20 virtual slides.
Title of the conference
12th European Congress on Digital Pathology
Address
Paris, France; 18-21 June 2014
Publication state
Published
Issued date
2014
Language
english
Abstract
Context: Ovarian tumors (OT) typing is a competency expected from pathologists, with significant clinical implications. OT however come in numerous different types, some rather rare, with the consequence of few opportunities for practice in some departments.
Aim: Our aim was to design a tool for pathologists to train in less common OT typing.
Method and Results: Representative slides of 20 less common OT were scanned (Nano Zoomer Digital Hamamatsu®) and the diagnostic algorithm proposed by Young and Scully applied to each case (Young RH and Scully RE, Seminars in Diagnostic Pathology 2001, 18: 161-235) to include: recognition of morphological pattern(s);
shortlisting of differential diagnosis; proposition of relevant immunohistochemical markers. The next steps of this project will be: evaluation of the tool in several post-graduate training centers in Europe and Québec; improvement of its design based on evaluation results; diffusion to a larger public.
Discussion: In clinical medicine, solving many cases is recognized as of utmost importance for a novice to become an expert. This project relies on the virtual slides technology to provide pathologists with a learning tool aimed at increasing their skills in OT typing. After due evaluation, this model might be extended to other
uncommon tumors.
Aim: Our aim was to design a tool for pathologists to train in less common OT typing.
Method and Results: Representative slides of 20 less common OT were scanned (Nano Zoomer Digital Hamamatsu®) and the diagnostic algorithm proposed by Young and Scully applied to each case (Young RH and Scully RE, Seminars in Diagnostic Pathology 2001, 18: 161-235) to include: recognition of morphological pattern(s);
shortlisting of differential diagnosis; proposition of relevant immunohistochemical markers. The next steps of this project will be: evaluation of the tool in several post-graduate training centers in Europe and Québec; improvement of its design based on evaluation results; diffusion to a larger public.
Discussion: In clinical medicine, solving many cases is recognized as of utmost importance for a novice to become an expert. This project relies on the virtual slides technology to provide pathologists with a learning tool aimed at increasing their skills in OT typing. After due evaluation, this model might be extended to other
uncommon tumors.
Create date
14/07/2014 10:35
Last modification date
20/08/2019 17:04
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