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루닛은 증거에 기반한 AI를 개발합니다.
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REFERENCE LIBRARY
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Journal of Clinical Oncology
Lancet Digital Health
JAMA Oncology
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European Journal of Cancer
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Publications
Oncology
Artificial intelligence (AI) -powered spatial analysis of tumor-infiltrating lymphocytes (TIL) for prediction of prognosis in stage II-III resectable colon cancer.
AUTHOR
Yoojoo Lim et al.
PUBLICATION
ASCO GI (2023)
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Oncology
Artificial intelligence (AI)-powered immune phenotyping to predict outcomes of immuno-oncology (IO)-based regimens in hepatocellular carcinoma (HCC).
AUTHOR
Hongjae Chon et al.
PUBLICATION
ASCO GI (2023)
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Oncology
Tumor-infiltrating lymphocyte enrichment predicted by CT radiomics analysis is associated with clinical outcomes of immune checkpoint inhibitors in non-small cell lung cancer
AUTHOR
Changhee Park et al.
PUBLICATION
Frontiers in Immunology (2022)
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Breast
Abstract : Longitudinal Changes of Contralateral Breast Mammographic Artificial Intelligence Algorithms Score in Ductal Carcinoma In Situ patient with Tamoxifen
AUTHOR
Changjin Lim et al.
PUBLICATION
SABCS (2022)
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Breast
Abstract : Accuracy of AI cancer localisation by breast density
AUTHOR
Muzna Nanaa et al.
PUBLICATION
RSNA (2022)
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Breast
Abstract : Investigating a stand-alone AI system prompt accuracy for interval cancer detection
AUTHOR
Sarah Hickman et al.
PUBLICATION
RSNA (2022)
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Oncology
Abstract : NT-I7, a long-acting IL-7, plus pembrolizumab favors CD8 T-cell infiltration in liver metastases of heavily pre-treated, immunologically cold, MSS-colorectal and pancreatic cancer
AUTHOR
Aung Naing et al.
PUBLICATION
SITC (2022)
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Oncology
Abstract : Treatment-related adverse event (TRAE)-related discontinuation rate of immunotherapy alone (IO-only) compared to immunotherapy combined with chemotherapy (chemo-IO): a meta-analysis
AUTHOR
Seunghwan Shin et al.
PUBLICATION
SITC (2022)
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Oncology
Abstract : Spatial analysis of tumor-infiltrating lymphocytes (TILs) based on HER2 expression across multiple cancer types
AUTHOR
Sukjun Kim et al.
PUBLICATION
SITC (2022)
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Chest
Evaluation of chest X-ray with automated interpretation algorithms for mass tuberculosis screening in prisons: A cross-sectional study
AUTHOR
Thiego RamonSoares et al.
PUBLICATION
The Lancet Regional Health - Americas (2023)
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