AIMC Topic: Receptor, ErbB-2

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Deep-Learning-Based Characterization of Tumor-Infiltrating Lymphocytes in Breast Cancers From Histopathology Images and Multiomics Data.

JCO clinical cancer informatics
PURPOSE: Tumor-infiltrating lymphocytes (TILs) and their spatial characterizations on whole-slide images (WSIs) of histopathology sections have become crucial in diagnosis, prognosis, and treatment response prediction for different cancers. However, ...

[Stage Ⅳ Breast Cancer with Difficulty in Initiating Chemotherapy-A Case Report].

Gan to kagaku ryoho. Cancer & chemotherapy
We report a case of breast cancer(T4b[skin], N1, M1[lung], ER-, PR-, HER2 3+)in a 63-year-old woman with liver dysfunction of unknown cause(T-Bil 3.6mg/dL, ALP 3,483 U/L, AST 214 U/L, ALT 320 U/L, g / -GTP 1,943 U/L). Further- more, serum CA19-9(4,67...

Machine learning approaches to decipher hormone and HER2 receptor status phenotypes in breast cancer.

Briefings in bioinformatics
Breast cancer prognosis and administration of therapies are aided by knowledge of hormonal and HER2 receptor status. Breast cancer lacking estrogen receptors, progesterone receptors and HER2 receptors are difficult to treat. Regarding large data repo...

Electronic Health Record Phenotypes for Precision Medicine: Perspectives and Caveats From Treatment of Breast Cancer at a Single Institution.

Clinical and translational science
Precision medicine is at the forefront of biomedical research. Cancer registries provide rich perspectives and electronic health records (EHRs) are commonly utilized to gather additional clinical data elements needed for translational research. Howev...