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Peng Huang, Ph.D.

Photo of Dr. Peng Huang, Ph.D.
  • Associate Professor of Oncology

Background

Dr. Huang received her PhD from University of Rochester in statistics in 2000. She worked at Medical University of South Carolina for 8 years before joining Johns Hopkins University in 2008.

Dr. Huang holds a primary appointment at department of oncology (School of Medicine) and a joint appointment at department of biostatistics (Bloomberg School of Public Heath). She is the primary biostatistics faculty in cancer imaging. She directs the Biostatistics core of ovarian cancer SPORE and previous founded Resource of the In Vivo Cellular and Molecular Imaging Center (ICMIC) and breast cancer SPORE Biostatistics and Bioinformatics Core

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Titles

  • Associate Professor of Oncology

Departments / Divisions

Centers & Institutes

Research & Publications

Research Summary

Dr. Huang's research is in non-parametric machine learning and deep learning methods in prediction algorithm development using multi-dimensional data. She has developed several image texture feature extraction techniques and computer-aided cancer early diagnosis prediction algorithms that have been independently validated in studies of pulmonary nodules, renal masses, hypervascular liver lesions, and pancreatic lesions.

Selected Publications

Huang P, Chen D, Voelkel JO, Minimum aberration two-level split plot design. Technometrics 1998; 40(4): 314-326

Huang P., Tilley B, Woolson R, and Lipsitz S, Adjusting O'Brien's test to control type I error for the generalized nonparametric Behrens-Fisher problem. Biometrics 2005; 61:532-539

Huang P, Ou AH, Piantadosi S, Tan M. Formulating appropriate statistical hypotheses for treatment comparison in clinical trial design and analysis. Contemp Clin Trials. 2014;39(2):294-302. Epub 2014/10/14. doi: 10.1016/j.cct.2014.09.005. PubMed PMID: 25308312; PMCID: PMC4254362

Huang P, Park S, Yan R, Lee J, Chu LC, Cheng TL, Hussien A, Rathmell J, Thomas B, Chen C, Hales R, Steingrimsson J, Ettinger DS, MD, Brock M, Hu P, Fishman EK, Gabrielson E, Lam S. Added Value of Computer-aided CT Image Features for Early Lung Cancer Diagnosis with Small Pulmonary Nodules: A Matched Case-Control Study. Radiology. 2018 Jan;286(1):286-295. Epub 2017 Sep 5. https://doi.org/10.1148/radiol.2017162725 PMID: 28872442

Huang P, Lin CT, Li Y, Tammemagi MC, Brock MV, Atkar-Khattra S, Xu Y, Hu P, Mayo JR, Schmidt H, Gingras M, Pasian S, Stewart L, Tsai S, Seely JM, Manos D, Burrowes P, Bhatia R, Tsao MS, Lam S. Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method. Lancet Digit Health. 2019;1(7):e353-e62. Epub 2020/08/31. doi: 10.1016/S2589-7500(19)30159-1. PubMed PMID: 32864596; PMCID: PMC7450858

Patents

Lung Cancer Prediction
Patent # PCT/US2020039139

Genome-wide Methylation Analysis And Use To Identify Genes Specific To Breast Cancer Hormone Receptor Status And Risk Of Recurrence. International Application No. PCT/US2012/046868

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