| Autor: |
Saltz J; Biomedical Informatics Department, Stony Brook University, Stony Brook, NY., Almeida J; Biomedical Informatics Department, Stony Brook University, Stony Brook, NY., Gao Y; Biomedical Informatics Department, Stony Brook University, Stony Brook, NY., Sharma A; Biomedical Informatics Department, Emory University, Atlanta, GA., Bremer E; Biomedical Informatics Department, Stony Brook University, Stony Brook, NY., DiPrima T; Biomedical Informatics Department, Stony Brook University, Stony Brook, NY., Saltz M; Department of Radiology, Stony Brook University, Stony Brook, NY., Kalpathy-Cramer J; Department of Radiology, Massachusetts General Hospital, Boston, MA., Kurc T; Biomedical Informatics Department, Stony Brook University, Stony Brook, NY.; Scientific Data Group, Oak Ridge National Laboratory, Oak Ridge, TN. |
| Abstrakt: |
Cancer is a complex multifactorial disease state and the ability to anticipate and steer treatment results will require information synthesis across multiple scales from the host to the molecular level. Radiomics and Pathomics, where image features are extracted from routine diagnostic Radiology and Pathology studies, are also evolving as valuable diagnostic and prognostic indicators in cancer. This information explosion provides new opportunities for integrated, multi-scale investigation of cancer, but also mandates a need to build systematic and integrated approaches to manage, query and mine combined Radiomics and Pathomics data. In this paper, we describe a suite of tools and web-based applications towards building a comprehensive framework to support the generation, management and interrogation of large volumes of Radiomics and Pathomics feature sets and the investigation of correlations between image features, molecular data, and clinical outcome. |
