AI-derived tumor-infiltrating lymphocytes enhance the prediction of pathologic complete response in early-stage triple-negative breast cancer
Abstract
This repository contains small image tiles of annotations curated from ARTEMIS biopsy slides - de-identified information from 26 patients with breast cancer, used for model development in the paper entitled with 'AI-Derived Tumor-Infiltrating Lymphocytes Enhance the Prediction of Pathologic Complete Response in Early-Stage Triple-Negative Breast Cancer'. These data are provided solely for the purpose of peer review and should not be used for any other purpose prior to the publication of the manuscript.
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Authors: Xiaoxi Pan, Caner Ercan, Zhongya Wang, Roland L. Bassett, Karina B. Pinao Gonzales, GAIANE M. RAUCH, Beatriz E. Adrada, Rosalind P. Candelaria, Debu Tripathy, Jennifer K. Litton, Sharon H. Giordano, Vicente Valero, Banu Arun, Alyson Clayborn, Kiran Virani, Jason White, Qingqing Ding, Aysegul A. Sahin, W. Fraser Symmans, Clinton Yam, Lei Huo, Yinyin Yuan