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John Kang, MD, PhD

Acting Instructor

Overview

Dr. Kang is a radiation oncologist who specializes in the treatment of lung cancer and thoracic malignancies. He is the University of Washington Department of Radiation Oncology biomedical informatics lead. Dr. Kang practices at UWMC and the SCCA Proton Therapy Center.

Patient Care Philosophy

I approach patients with thoughtful considerations of their unique story and goals. We then work together as a team to understand and navigate the complex, multidisciplinary world of cancer care.

Education and Training

College: Duke University, Durham NC (2007)

Doctorate: PhD at Carnegie Mellon University, Pittsburgh, PA (2013)

Medical Education: MD at University of Pittsburgh, Pittsburgh, PA (2015)

Internship: University of Pittsburgh Medical Center, Pittsburgh, PA (2016)

Residency: University of Rochester, Rochester, NY (2020)

Selected Publications from a Total of 35

Kang J, Morin O, Hong J (2020). “First steps into a larger world: closing the gap between machine learning and clinical cancer care”. JAMA Oncology. doi:10.1001/jamaoncol.2020.4314

Jain A, Aneja S, Fuller CD, Dicker AP, Chung C, Kim E, Kirby JS, Quon H, Lam CJK, Louv WC, Ahern C, Xiao Y, McNutt TR, Housri N, Ennis RD, Kang J, Tang Y, Higley H, Berny-Lang MA, Camphausen KA (2020). “Provider Engagement in Radiation Oncology Data Science: Workshop Report”. JCO Clinical Cancer Informatics. DOI: 10.1200/CCI.20.00051.

Kang J, Coates JT, Strawderman RL, Rosenstein BS, Kerns SLK (2020). “Genomics models in radiotherapy: from mechanistic to machine learning.” Medical Physics. DOI: 10.1002/mp.13751.

Kang J, Thompson R, Aneja S, Lehman C, Trister A, Aneja S, Zou J, Obcemea C, El Naqa I (2020). “NCI Workshop on Artificial Intelligence in Radiation Oncology: Training the Next Generation.” Practical Radiation Oncology. https://doi.org/10.1016/j.prro.2020.06.001.

Milano MT, Mihai A, Kang J, Singh DP, Verma V, Qiu H, Chen Y, Kong FM (2019). “Stereotactic body radiotherapy in patients with multiple lung tumors: a focus on lung dosimetric constraints.” Expert Review of Anticancer Therapy. https://doi.org/10.1080/14737140.2019.1686980

Kang J, Rancati T, Lee S, Oh JH, Kerns SL, Scott JG, Schwartz RS, Kim S, Rosenstein BS (2018). “Machine Learning and Radiogenomics: Lessons Learned and Future Directions.” Frontiers in Oncology. https://doi.org/10.3389/fonc.2018.00228. Included in “Frontiers in Oncology World Cancer Day 2019 Special Edition,” ISBN 9782889457816

Kang J, Doucette C, El Naqa I, Zhang H. “Comparing the Kattan nomogram to a random forest model to predict post-prostatectomy pathology.” ASTRO 60th Annual Meeting. 10/22/18. San Antonio, TX. https://doi.org/10.1016/j.ijrobp.2018.06.173

Kang J, Chowdhry AK, Milano MT (2018). “Long-term CT surveillance after primary lung cancer treatment captures events for all risk groups.” Translation Lung Cancer Research. https://doi.org/10.21037/tlcr.2018.01.10

Kang J, Schwartz RS, Flickinger JC, Beriwal S (2015). “Machine Learning Approaches for Predicting Radiation Therapy Outcomes: A Clinician’s Perspective.” International Journal of Radiation Oncology, Biology, Physics. 93(5), 1127-1135 (2015). https://doi.org/10.1016/j.ijrobp.2015.07.2286

Kang J, Zhang Y, Clump DA, Flickinger JC, Li X, Huq MS. “A free multi-model program for comparing linear-quadratic and non-linear quadratic models in TCP prediction of SABR-treated NSCLC.” ASTRO 56th Annual Meeting. Sept. 14-17, 2014. San Francisco, CA. https://doi.org/10.1016/j.ijrobp.2014.05.2447

Kang J, Steward RL, Kim Y, Schwartz RS, LeDuc PR, Puskar KM (2011). “Response of an Actin Filament Network Model under Cyclic Stretching through a Coarse Grained Monte Carlo Approach.” Journal of Theoretical Biology. https://doi.org/10.1016/j.jtbi.2011.01.011

Cancer Specialties

Lung and Thoracic Cancers

Research Interests

  • Machine learning in health.
  • Natural language processing.
  • Biomedical informatics.

Educational Involvement

  • JCO CCI Reviewer Mentoring Program 2020
  • ASTRO Science Education and Program Development Subcommittee 2020-2021

Professional Involvement

  • JCO Clinical Cancer Informatics editorial board 2019
  • AAPM Task Group for Quality Assurance for Machine Learning-based Clinical Technologies (Machine Learning Subcommittee)9