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Sang Kyu Lee

Formats
  1. Journal Article (8)
  2. Meeting Abstract (7)
  3. Book Section (2)
  4. Conference Paper (1)
Groups
  1. Medical Physics (18)
  2. Sloan Kettering Institute Postdocs (18)
  3. Population Sciences Research Program (3)
  4. Radiotherapy Physics Service (3)
  5. Radiation Oncology, Department of (2)
  6. Center for Pancreatic Cancer Research (1)

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People
  1. Jung Hun Oh (12)
  2. Joseph Deasy (11)
  3. Ellen Yorke (2)
  4. Lei Zhang (2)
  5. Maria Thor (2)
  6. Alexander Kowalski (1)

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Years
  1. 2024 (1)
  2. 2023 (1)
  3. 2022 (2)
  4. 2021 (3)
  5. 2020 (2)
  6. 2019 (2)

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Publications
  1. Radiotherapy and Oncology (4)
  2. International Journal of Radiation Oncology, Biology, Physics (2)
  3. Journal of Applied Clinical Medical Physics (2)
  4. ACM-BCB '17: Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (1)
  5. Artificial Intelligence in Radiation Oncology (1)
  6. BJR Open (1)

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Publishers
  1. Elsevier Inc. (6)
  2. American College of Medical Physics (2)
  3. American Association of Physicists in Medicine (1)
  4. American Society of Clinical Oncology (1)
  5. Assoc Computing Machinery (1)
  6. British Institute of Radiology (1)

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Results (18)


Citation style sort is year ascending, and then by author or title. Author ascending is default.
  • Single institution retrospective study of tolerance of voluntary deep inspiration breath hold for abdominal radiotherapy
    Meeting Abstract

    Lee, S.; Zeng, C.; Jeong, J.; Tang, G.; Yorke, E. D.; Harris, W.; Williams, V. M.; Crane, C. H.; Santanam, L.
    International Journal of Radiation Oncology, Biology, Physics (2024)

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  • Predicting the germline dependence of hematuria risk in prostate cancer radiotherapy patients
    Journal Article

    Oh, J. H.; Lee, S.; Thor, M.; Rosenstein, B. S.; Tannenbaum, A.; Kerns, S.; Deasy, J. O.
    Radiotherapy and Oncology (2023)

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  • Conventional machine learning methods
    Book Section

    Lee, S.; Naqa, I. E.
    In El Naqa, I.; Murphy, M. J. (Eds.) Machine and Deep Learning in Oncology, Medical Physics and Radiology. 2nd ed (2022)

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  • Using AI to predict radiotherapy toxicity risk based on patient germline genotyping
    Book Section

    Oh, J. H.; Lee, S.; Thor, M.; Deasy, J. O.
    In Mun, S. K.; Dieterich, S. (Eds.) Artificial Intelligence in Radiation Oncology (2022)

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  • Comparison of quantitative and qualitative scoring approaches for radiation-induced pulmonary fibrosis as applied to a preliminary investigation into the efficacy of mesenchymal stem cell delivery methods in a rat model
    Journal Article

    Wang, L. M.; Jung, S.; Serban, M.; Chatterjee, A.; Lee, S.; Jeyaseelan, K.; El Naqa, I.; Seuntjens, J.; Ybarra, N.
    BJR Open (2021)

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  • Failure mode and effect analysis for linear accelerator-based paraspinal stereotactic body radiotherapy
    Journal Article

    Lee, S.; Lovelock, D. M.; Kowalski, A.; Chapman, K.; Foley, R.; Gil, M.; Pastrana, G.; Higginson, D. S.; Yamada, Y.; Zhang, L.; more...
    Journal of Applied Clinical Medical Physics (2021)

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  • Accuracy of surface-guided patient setup for conventional radiotherapy of brain and nasopharynx cancer
    Journal Article

    Lee, S. K.; Huang, S.; Zhang, L.; Ballangrud, A. M.; Aristophanous, M.; Cervino Arriba, L. I.; Li, G.
    Journal of Applied Clinical Medical Physics (2021)

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  • Prediction of breast cancer treatmentā€“induced fatigue by machine learning using genome-wide association data
    Journal Article

    Lee, S.; Deasy, J. O.; Oh, J. H.; Di Meglio, A.; Dumas, A.; Menvielle, G.; Charles, C.; Boyault, S.; Rousseau, M.; Besse, C.; more...
    JNCI Cancer Spectrum (2020)

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  • Machine learning on genome-wide association studies to predict the risk of radiation-associated contralateral breast cancer in the WECARE Study
    Journal Article

    Lee, S.; Liang, X.; Woods, M.; Reiner, A. S.; Concannon, P.; Bernstein, L.; Lynch, C. F.; Boice, J. D.; Deasy, J. O.; Bernstein, J. L.; more...
    PLoS ONE (2020)

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  • Prediction of treatment (tx)-induced fatigue in breast cancer (BC) patients (pts) using machine learning on genome-wide association (GWAS) data in the prospective CANTO cohort
    Meeting Abstract

    Lee, S.; Deasy, J. O.; Oh, J. H.; Di Meglio, A.; Boyault, S.; Rousseau-Tsangaris, M.; Besse, C.; Thomas, E.; Boland-AugeĢ, A.; Cottu, P. H.; more...
    Journal of Clinical Oncology (2019)

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