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

Dr. Huang’s research includes prediction algorithm development using artificial intelligent methods (including machine learning, deep learning, and computer-aided diagnosis), image texture analysis, non-parametric multivariate analysis, statistical methods in clinical trials, and minimum aberration split-plot design. She is the inventor of DeepLR that estimates lung cancer risk and provides optimal screening interval during the follow-up screening visit. more


  • Associate Professor of Oncology

Departments / Divisions

Centers & Institutes



  • Ph.D.; University of Rochester (New York) (2000)

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, Woolson RF, O'Brien PC. A rank-based sample size method for multiple outcomes in clinical trials. Statistics in Medicine. 2008; 27(16):3084-3104. PMID: 18189338, PMCID: PMC3163145

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. 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

Huang P, Illei PB, Franklin W, Wu P-H, Forde PM, Ashrafinia S, Hu C, Khan H, Vadvala HV, Shih I-M, Battafarano RJ, Jacobs MA, Kong X, Lewis J, Yan R, Chen Y, Housseau F, Rahmim A, Fishman EK, Ettinger DS, Pienta KJ, Wirtz D, Brock MV, Lam S, Gabrielson E. Lung Cancer Recurrence Risk Prediction through Integrated Deep Learning Evaluation. Cancers. 2022; 14(17):4150


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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