| Autor: |
Ly, Amy, Garcia, Victor, Blenman, Kim R M, Ehinger, Anna, Elfer, Katherine, Hanna, Matthew G, Li, Xiaoxian, Peeters, Dieter J E, Birmingham, Ryan, Dudgeon, Sarah, Gardecki, Emma, Gupta, Rajarsi, Lennerz, Jochen, Pan, Tony, Saltz, Joel, Wharton, Keith A, Ehinger, Daniel, Acs, Balazs, Dequeker, Elisabeth M C, Salgado, Roberto |
| Předmět: |
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| Zdroj: |
Histopathology; May2024, Vol. 84 Issue 6, p915-923, 9p |
| Abstrakt: |
A growing body of research supports stromal tumour‐infiltrating lymphocyte (TIL) density in breast cancer to be a robust prognostic and predicive biomarker. The gold standard for stromal TIL density quantitation in breast cancer is pathologist visual assessment using haematoxylin and eosin‐stained slides. Artificial intelligence/machine‐learning algorithms are in development to automate the stromal TIL scoring process, and must be validated against a reference standard such as pathologist visual assessment. Visual TIL assessment may suffer from significant interobserver variability. To improve interobserver agreement, regulatory science experts at the US Food and Drug Administration partnered with academic pathologists internationally to create a freely available online continuing medical education (CME) course to train pathologists in assessing breast cancer stromal TILs using an interactive format with expert commentary. Here we describe and provide a user guide to this CME course, whose content was designed to improve pathologist accuracy in scoring breast cancer TILs. We also suggest subsequent steps to translate knowledge into clinical practice with proficiency testing. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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