1. Shandong Wu, PhD Assistant Professor
Director, Intelligent Computing for Clinical Imaging (ICCI) Lab
Technical Director for AI Innovations in Radiology
University of Pittsburgh
Assistant Professor
of Radiology (primary appointment),
of Biomedical Informatics,
of Computer Science & Intelligent Systems,
of Bioengineering,
of Computational Biology,
of Clinical and Translational Science,
of Machine Learning (Carnegie Mellon University adjunct professor)

2. Intelligent Computing for Clinical Imaging (ICCI) Lab
16 trainee members and
15 clinician collaborators:
Computer scientists
Radiologists
Pathologists
Medical oncologists
Surgeons
Biologists
Biostatisticians
Postdocs, students

3. Clinical Applications
Research Interests
Computational Image Analysis
Clinical/biomedical Imaging
AI, Machine Learning, Big Data
Clinical Applications
Genomic / Proteomic Correlations

4. Research Funding (PI, >$4.5 millions)
NIH/NCI R01 (#CA193603) (2015)
NIH/NCI R01 Supplement (#CA S) (2017)
NIH/NCI R01 (#CA218405) (2018)
Pittsburgh Health Data Alliance/UPMC Enterprise - Early commercialization development grant (2018)
Amazon Academic Medicine Machine Learning Award (2019)
RSNA Research Scholar Grant (#RSCH1530) (2015)
UPCI-IPM Pilot Award (#MR ) (2016)
University of Pittsburgh Physician (UPP) Foundation Award (2017)
UPMC CMRF Grant (2014)
Pitt CTSI Biomedical Modeling Pilot Award (2016)
Nvidia Academic Grants (2016, 2017, 2018)
Stanly Marks Research Foundation (2018)

5. Selected Research Projects
Technical development: Methodology
Quantitative & automated imaging analysis, machine/deep learning
Radiomics, Radio-genomics and Radio-proteomics
Big data, AI model interpretation, data quality, AI model safety
Breast cancer imaging: Mammography, MRI, DBT, Ultrasound
Screening: Imaging-derived risk factors (X ray, MRI)
Diagnosis: Benign/maligancy/recall decision-making
Prognosis: Recurrence risk, Pathology marker, Subtype prediction
Treatment / intervention：Imaging-based response biomarkers
Beyond breast caner
Liver carcinoma
Liver transplantation
Pneumatosis intestinalis
Brain tumor / glioma
Lung cancer
Rectal cancer
Prostate cancer
Pancreatic cystic lesion
Pelvic readiograph
Head CT trauma brain injury

6. Develop automated computerized algorithms for quantitative radiological imaging analysis

7. Discover quantitative imaging biomarkers for breast cancer risk assessment, diagnosis, prognosis, and treatment/intervention responses
BPE% Calibration
21.7% ±12.6
Screening negative images
Follow up outcome
Deep learning modeling
MRI predicts distant recurrence risk

8. Investigate linkage between imaging phenotypes and biological underpinnings: radiogenomics, radioproteomics
Radiomic Feature
Volume of
Tumor
Protein
ADAR1
BLC2
ATM
CRAF
Pathway
Biology
pathway 1
pathway 2

9. Study radiomics on a range of diseases for outcome prediction for clinical/translational applications