Shandong Wu, PhD Assistant Professor

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Presentation transcript:

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)

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

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

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)

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

Develop automated computerized algorithms for quantitative radiological imaging analysis

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

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

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