Despina Kontos, PhD

Associate Professor of Radiology

Department of Radiology

Perelman School of Public Health

University of Pennsylvania

Bio:  Dr. Despina Kontos, Ph.D., is an Associate Professor of Radiology and director of the Computational Biomarker Imaging Group (CBIG) in the Center for Biomedical Image Computing and Analytics (CBICA) at the Radiology Department of the University of Pennsylvania. Dr. Kontos received her C.Eng. Diploma in Computer Engineering and Informatics from the University of Patras in Greece and her MSc and Ph.D. degrees in Computer Science from Temple University in Philadelphia. She completed her postdoctoral training in radiologic physics and biostatistics at the University of Pennsylvania. Her research interests focus on investigating the role of quantitative imaging as a predictive biomarker for guiding personalized clinical decisions in cancer screening, prognosis, and treatment. She is leading several research studies, funded both by the NIH/NCI and private foundations, to incorporate novel quantitative multi-modality imaging measures of breast tumor and tissue composition into cancer risk prediction models.

Title:  “Radiomic Biomarkers for Deciphering Tumor Heterogeneity”

Abstract - Breast cancer is a heterogeneous disease, with known inter-tumor and intra-tumor heterogeneity. Established histopathologic prognostic biomarkers generally acquired from a tumor biopsy may be limited by sampling variation. Radiomics is an emerging field with the potential to leverage the whole tumor via non-invasive sampling afforded by medical imaging to extract high throughput, quantitative features for personalized tumor characterization. Identifying imaging phenotypes via radiomics analysis and understanding their relationship with prognostic markers and patient outcomes can allow for a non-invasive assessment of tumor heterogeneity. In this study, we identified and independently validated intrinsic radiomic phenotypes of tumor heterogeneity for invasive breast cancer that have independent prognostic value when predicting 10-year recurrence. The independent and additional prognostic value of imaging heterogeneity phenotypes suggests that radiomic phenotypes can provide a non-invasive characterization of tumor heterogeneity to augment personalized prognosis and treatment.

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Stephanie Shipp, PhD

Deputy Director and Professor

Social and Decision Analytics Division, Biocomplexity Institute

University of Virginia

Abstract Title: ”Ethical Principles for the All Data Revolution”

The data revolution is changing the conduct of research as increasing amounts of internet-based and administrative data become accessible for use. At the same time, the new data landscape has created significant tension around data privacy and confidentiality. To bridge this gap, conversations about ethics, privacy, transparency, and reproducibility need to play a prominent role in both research partnerships and policymaking. At the research level, these conversations must be translated to action. We have created a comprehensive framework that forms the foundation to data science problem solving through defining rigorous, flexible, and iterative processes where learning at each stage informs the other stages. Embedded in this framework is close attention to ethics. The Institutional Review Board structure is well known in parts of academia and industry, but our public and local government partners are not always aware of these processes. The IRB framework could help them think about informed consent and privacy, as well as ethical considerations around the benefits and risks to individuals and communities under study. Through case studies, these principles are demonstrated.

Keywords: confidentiality, ethics, trust but verify, data science framework

Brief Bio: Data scientists have the opportunity to use their skills to influence and improve society, especially vulnerable populations who need champions. Stephanie Shipp enthusiastically works with communities, policy makers and other data scientists who have also taken that challenge to heart.

For more information about the Statistics Seminar Series go here .