shandong wu phd director intelligent computing n.
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Assistant Professor of Radiology ( primary appointment ), of Biomedical Informatics, PowerPoint Presentation
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  1. Shandong Wu, PhD 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. 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 (#CA193603-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 (#MR2014-77613) (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 Screening negative images BPE% Calibration 21.7% ±12.6 MRI predicts distant recurrence risk Deep learning modeling Follow up outcome

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

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