RESUMEN
HER2 is an important prognostic and predictive biomarker in breast cancer. Its detection makes it possible to define which patients will benefit from a targeted treatment. While assessment of HER2 status by immunohistochemistry in positive vs negative categories is well implemented and reproducible, the introduction of a new "HER2-low" category could raise some concerns about its scoring and reproducibility. We herein described the current HER2 testing methods and the application of innovative machine learning techniques to improve these determinations, as well as the main challenges and opportunities related to the implementation of digital pathology in the up-and-coming AI era.
Asunto(s)
Inteligencia Artificial , Neoplasias de la Mama/química , Neoplasias de la Mama/diagnóstico , Receptor ErbB-2/análisis , Algoritmos , Neoplasias de la Mama/genética , Diagnóstico por Computador , Femenino , Amplificación de Genes , Genes erbB-2 , Humanos , Aprendizaje Automático , Guías de Práctica Clínica como Asunto , Reproducibilidad de los ResultadosRESUMEN
In case of a new breast symptom or an abnormal result of breast imaging, some women have a problem finding a quick answer to allay their anxiety. The Institut Sainte-Catherine in Avignon has set up a new form of accelerated disease management through the opening of a new dedicated consultation called SOS SEIN 84. We present the result of a prospective quality study of our first new patients.