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

Zixiao Lu | Zendy; Siwen Xu | Zendy; Wei Shao | Zendy; Yi Wu | Zendy; Jie Zhang | Zendy; Zhi Han | Zendy; Qianjin Feng | Zendy; Kun Huang | Zendy

Open Access

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

Author(s) -

Zixiao Lu,

Siwen Xu,

Wei Shao,

Yi Wu,

Jie Zhang,

Zhi Han,

Qianjin Feng,

Kun Huang

Publication year - 2020

Publication title -

jco clinical cancer informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.188

H-Index - 12

ISSN - 2473-4276

DOI - 10.1200/cci.19.00126

Subject(s) - breast cancer , tumor infiltrating lymphocytes , estrogen receptor , oncology , immunotherapy , cancer , histopathology , medicine , biology , cancer research , pathology

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, fully automatic assessment of TILs on WSIs currently remains a great challenge because of the heterogeneity and large size of WSIs. We present an automatic pipeline based on a cascade-training U-net to generate high-resolution TIL maps on WSIs.

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