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Taebum Lee

As seen in: Nature, MDPI, medRxiv
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Articles

Generalizing AI-driven Assessment of Immunohistochemistry across Immunostains and Cancer Types: A Universal Immunohistochemistry Analyzer

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By Biagio Brattoli, Taebum Lee, Wonkyung Jung, Mohammad Mostafavi
| medRxiv
Biagio Brattoli, Mohammad Mostafavi, Taebum Lee, Jeongun Ryu, Seonwook Park, Jongchan Park, Sergio Pereira, Seunghwan Shin, Donggeun Yoo, Siraj M. Ali, Kyunghyun Paeng, Chan-Young Ock, and Soo Ick Cho are employees of Lunit and/or have stock/stock options in Lunit.
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Inflamed immune phenotype predicts favorable clinical outcomes of immune checkpoint inhibitor therapy across multiple cancer types

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By Jeanne Shen, Yoon-La Choi, Taebum Lee, Hyojin Kim
| bmj.com
Discussion Here, we present an automated deep learning model, Lunit SCOPE IO, which classifies the immune phenotype of the TME on H&E-stained WSI using TIL distribution and density analysis. We demonstrate, for the first time, the ability of an AI model which runs on routine H&E-stained WSI of pre-ICI treatment FFPE tumor samples to predict ICI clinical outcomes across a broad range of solid tumor types, using a real-world multicenter cohort of 1,806 ICI-treated patients.
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Synergistic Approach of Antibody‐Photosensitizer Conjugate Independent of KRAS‐mutation and its Downstream Blockade Pathway in Colorectal Cancer

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By Minji Ahn, Taebum Lee, Kyoung Sub Kim, Sanghee Lee
| pericles.pericles-prod.literatumonline.com
Here, we present a novel approach to improve the efficacy of antibody-drug conjugates (ADC) by integrating antibody-mediated immunotherapy and photodynamic therapy (PDT) in a combination therapy system utilizing an antibody-photosensitizer conjugate (APC) platform based on a poloxamer polymer linker.
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