Spyridon (Spyros) Bakas, Ph.D.

Instructor

Spyridon Bakas, Ph.D.Artificial Intelligence in Biomedical Imaging Lab (AIBIL)
Center for Biomedical Image Computing & Analytics (CBICA)
Jointly appointed:
  - Dept of Radiology
  - Dept of Pathology & Laboratory Medicine
Perelman School of Medicine
University of Pennsylvania
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Contact:

Richards Medical Research Laboratories, Floor 7
3700 Hamilton Walk
Philadelphia, PA 19104
Email

My News

Educational Qualifications

Ph.D. in Medical Image Computing & Analysis - Kingston University, London (UK)
M.Sc. in Vision, Imaging & Virtual Environments - University College London (UK)
B.Sc. (Hons) in Computer Science - Kingston University, London (UK)

Research Summary

My research interest focuses on the development and application of computational algorithms in oncological imaging, with the intention of improving the assessment, quantification and diagnosis of cancer in the current clinical practice. Eagerly embracing the concept of personalized/precision medicine, I am also involved in radiogenomic research where correlations between quantitative imaging features and genomic information lead to highly accurate imaging biomarkers, which can enable treatment selection models customized on an individual patient basis. My work so far has spanned across the areas of image segmentation, feature extraction, statistical analysis, motion analysis and machine learning techniques applied in brain magnetic-resonance (MR), and liver contrast-enhanced ultrasound (CEUS), imaging data. The ultimate aim of my research is to contribute towards making diagnostic and treatment decisions more promptly, objectively, and precisely.

Published Material


Funding

Active Funding
  • Principal Investigator
    • The Federated Tumor Segmentation (FeTS) platform: An intuitive tool facilitating secure multi-institutional collaboration (U01CA242871, NIH/NCI/ITCR, PI: Bakas, Spyridon, 07/01/2019 - 06/30/2022)
    • A pilot radiogenomics study evaluating MRI signatures of therapeutically targetable gene expression alterations in human glioblastoma (CTSA/ITMAT/TBIC, PIs: Bakas, Spyridon / Bagley, Stephen, 03/01/2019 - 02/28/2021)
    • Democratization of AI-based Lesion Segmentation (Intramural CBICA seed grant, PI: Bakas, Spyridon, 02/03/2020 - 02/02/2021)
  • Collaborative Funding
    • Imaging Signatures of Genetic Mutations in Glioblastoma Using Machine Learning (R01NS042645, PI: NIH/NINDS, PI: Davatzikos, Christos (Role: Co-I), 12/15/2019 - 11/30/2024)
    • Populating the Penn Immune Health Report to Support Precision Medicine: Immunohistochemical Markers to Predict Response to Checkpoint Blockade in Non-Small Cell Lung Cancer (NSCLC) (Abramson Cancer Center, PI: Thompson, Jeffrey (Role: Co-I), 04/22/2020 - 04/21/2021)
    • Cancer imaging phenomics software suite: application to brain and breast cancer (1U24CA189523, NIH/NCI/ITCR, PI: Davatzikos, Christos, 09/01/2015 - 08/31/2020)
Completed
  • Predicting brain tumor progression via multiparametric image analysis and modeling (R01NS042645, NIH/NINDS, PI: Davatzikos, Christos, 09/01/2014 - 05/31/2020)
  • Refined Personalized Radiotherapy Target Volume Definition using Predictive Recurrence maps in Glioblastoma (Intramural CBICA seed grant, PI: Bakas, Spyridon, 06/01/2018 - 05/31/2019)
  • In vivo surrogate markers of clinically-relevant molecular characteristics of glioblastoma, based on multivariate machine learning and clinically-acquired MRI (CTSA/ITMAT/TBIC, PIs: Bakas, Spyridon / Davatzikos, Christos, 02/01/2017 - 01/31/2019)
  • In vivo predictive models of meningioma progression, (CTSA/ITMAT/TBIC, PIs: Dahmane, Nadia / Grady, Sean / Davatzikos, Christos, 02/01/2017 - 01/31/2019)

Archived News: