Pingfu Fu, PhD

Professor
Department of Population and Quantitative Health Sciences
School of Medicine
Member
Developmental Therapeutics Program
Case Comprehensive Cancer Center

With broad expertise in mathematics, statistics/biostatistics, and computer science, Dr. Pingfu Fu has worked extensively in cancer research. He has been involved in projects on lung, breast, and prostate cancer and leukemia, among others, and in other medical areas including dermatology and HIV/AIDS.

The majority of Dr. Fu’s work is collaborative, interdisciplinary research, in which he advises researchers on study design, best practices, and statistical methods to analyze complex data from pre-clinical and clinical studies. He has a specific interest in survival data analysis, and teaches an annual course in the subject. In 2010 he was named “professor of the year” by the Department of Epidemiology and Biostatistics (now PQHS).

Teaching Information

Courses Taught

Survival Data Analytics
Independent Study

Research Information

Research Interests

  • Survival analysis
  • Study design
  • Measurements with limits of detection
  • Tree-based methods
  • Problem of separation
  • Clinical trials
  • Novel applications of statistical methods to medical research, especially to cancer and HIV/AIDS

Professional Memberships

American Statistical Association
American Mathematical Society
American Cancer Society
Case Comprehensive Cancer Center Clinical Trials Protocol Development and Review Committee
Data Safety and Toxicity Committee

Publications

View Fu's publications

  1. Chiu, M, Lipka, MB, Bhateja, P, Fu, P, Dowlati, A. A detailed smoking history and determination of MYC status predict response to checkpoint inhibitors in advanced non-small cell lung cancer. Transl Lung Cancer Res 2020; 9 (1): 55-60. PubMed PMID:32206553 PubMed Central PMC7082292.
  2. Bhargava, HK, Leo, P, Elliott, R, Janowczyk, A, Whitney, J, Gupta, S, Fu, P, Yamoah, K, Khani, F, Robinson, BD, Rebbeck, TR, Feldman, M, Lal, P, Madabhushi, A. Computationally Derived Image Signature of Stromal Morphology Is Prognostic of Prostate Cancer Recurrence Following Prostatectomy in African American Patients. Clin. Cancer Res. 2020; : . PubMed PMID:32139401 .
  3. Vaidya, P, Bera, K, Gupta, A, Wang, X, Corredor, G, Fu, P, Beig, N, Prasanna, P, Patil, P, Velu, P, Rajiah, P, Gilkeson, R, Feldman, M, Choi, H, Velcheti, V, Madabhushi, A. CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in Stage I, II resectable Non-Small Cell Lung Cancer: a retrospective multi-cohort study for outcome prediction. Lancet Digit Health 2020; 2 (3): e116-e128. PubMed PMID:32123864 PubMed Central PMC7051021.
  4. Shukla, S, Srivastava, JK, Shankar, E, Kanwal, R, Nawab, A, Sharma, H, Bhaskaran, N, Ponsky, LE, Fu, P, MacLennan, GT, Gupta, S. Oxidative Stress and Antioxidant Status in High-Risk Prostate Cancer Subjects. Diagnostics (Basel) 2020; 10 (3): . PubMed PMID:32120827 .
  5. Khorrami, M, Bera, K, Leo, P, Vaidya, P, Patil, P, Thawani, R, Velu, P, Rajiah, P, Alilou, M, Choi, H, Feldman, MD, Gilkeson, RC, Linden, P, Fu, P, Pass, H, Velcheti, V, Madabhushi, A. Stable and discriminating radiomic predictor of recurrence in early stage non-small cell lung cancer: Multi-site study. Lung Cancer 2020; 142 : 90-97. PubMed PMID:32120229 .
  6. Khorrami, M, Khunger, M, Zagouras, A, Patil, P, Thawani, R, Bera, K, Rajiah, P, Fu, P, Velcheti, V, Madabhushi, A. Combination of Peri- and Intratumoral Radiomic Features on Baseline CT Scans Predicts Response to Chemotherapy in Lung Adenocarcinoma. Radiol Artif Intell 2019; 1 (2): e180012. PubMed PMID:32076657 PubMed Central PMC6515986.
  7. Zeng, G, Chen, Z, Fu, P. Temporal Pattern of Co-Development of Internalizing and Externalizing Problem Behaviors: An Application of Bivariate Mixed-Effects Models. Rev Recent Clin Trials 2020; 15 (1): 60-69. PubMed PMID:31746303 .
  8. Khorrami, M, Prasanna, P, Gupta, A, Patil, P, Velu, PD, Thawani, R, Corredor, G, Alilou, M, Bera, K, Fu, P, Feldman, M, Velcheti, V, Madabhushi, A. Changes in CT Radiomic Features Associated with Lymphocyte Distribution Predict Overall Survival and Response to Immunotherapy in Non-Small Cell Lung Cancer. Cancer Immunol Res 2020; 8 (1): 108-119. PubMed PMID:31719058 .
  9. Khorrami, M, Jain, P, Bera, K, Alilou, M, Thawani, R, Patil, P, Ahmad, U, Murthy, S, Stephans, K, Fu, P, Velcheti, V, Madabhushi, A. Corrigendum to "Predicting pathologic response to neoadjuvant chemoradiation in resectable stage III non-small cell lung cancer patients using computed tomography radiomic features" [Lung Cancer 135 (September) (2019) 1-9]. Lung Cancer 2019; 136 : 156. PubMed PMID:31564290 .
  10. Khorrami, M, Jain, P, Bera, K, Alilou, M, Thawani, R, Patil, P, Ahmad, U, Murthy, S, Stephans, K, Fu, P, Velcheti, V, Madabhushi, A. Predicting pathologic response to neoadjuvant chemoradiation in resectable stage III non-small cell lung cancer patients using computed tomography radiomic features. Lung Cancer 2019; 135 : 1-9. PubMed PMID:31446979 PubMed Central PMC6711393.

Publishing impact 

Metrics from Web of Science/publons and Scopus/SciVal: 

  • H-index: 33
  • Total publications: 126
  • Total citations: 3,895
  • Publications in top-tier journals: 45%  
  • Collaborative publishing national/international: 82%/18% 

Editorial roles:

  • Reviews on Recent Clinical Trials, regional editor

Education

Ph.D.
Biostatistics
Case Western Reserve University
2001
M.S.
Statistics
Case Western Reserve University
1996
M.S.
Mathematics
Xiangtan University
1988
B.S.
Mathematics
Jiangxi Normal University
1984

Additional Information

View Pingfu Fu's personal Website

Contributions to science:

  • Applied new methods to various clinical studies in order to tackle mathematical and statistical problems in the area of stochastic processes
  • Made new collaborative observations concerning public health issues such as different types of cancers and some viruses
  • Resolved several issues concerning statistical science in the area of tree-based models, study design, missing value issues, and problem of separation

Active grants:

  • NIH/NCI P30: Case Comprehensive Cancer Center Support Grant
  • IIR: Overcoming stromal-specific immune escape mechanisms by targeted immunotherapy
  • DOD/CDMRP W81: MRI-pathology correlation for image analytics-based treatment outcome assessment and margin planning in rectal cancers
  • NIH/NCI R01: Computerized histologic image predictor of cancer outcome
  • NIH R01: Phenotypes and mechanisms of urinary Incontinence in obesity/pre-type 2 diabetes
  • NIH/NCI R01: Computerized histologic risk predator for early stage lung cancers
  • NIH/NCI R01: Quantitative histomorphometric risk classifier in HPV + oropharyngeal carcinoma
  • NIH/NCI R21: Identification and targeting of chemotherapy refractory small cell lung cancer
  • DoD PC17: Targeting metabolic pathways in overcoming drug resistance in metastatic castrate-resistant prostate cancer
  • DoD LCRP IITRA LC17: LunIRiS: Novel CT-derived radiomic biomarkers for lung nodule characterization in the screening
  • NIH/NCI R01: Functional image and molecular marker to individualize adaptive radiation doses

Student and mentee totals, over CWRU career:

  • Master’s: 200
  • PhD:  2