Information Sciences in Imaging at Stanford (ISIS)  
Information Sciences in Imaging

Our MissionISIS logo

Our mission is to advance the clinical and basic sciences in radiology, while improving our understanding of biology and the manifestations of disease, by pioneering methods
in the information sciences that integrate imaging, clinical and molecular data.

Our Vision

Our vision is that we gain new knowledge from imaging examinations by integrating and analyzing them with related clinical and molecular data. ISIS aims to achieve this goal by exploring the full spectrum of information-intensive activities in imaging (e.g., image management, storage, retrieval, processing, analysis, understanding, visualization, navigation, interpretation, reporting, and communications) and in non-imaging domains (e.g., pathology, molecular and genetic markers, family history, prior medical reports, and clinical outcomes). Critical to the ISIS mission is the development of core capability to collect annotated imaging, clinical and molecular data, and to integrate them by creating databases that encode and the relationships among them. Through these efforts, we believe that ISIS will improve the diagnostic and treatment planning value of images and lead to personalized, less-invasive approaches to early detection and treatment while, at the same time, improving our understanding of human biology and disease.

ISIS News

Launch of New Decision Tool for Breast Cancer:
The decision support tool is designed for joint use by women with BRCA mutations and their health care providers to guide management of cancer risks. Visit the Decision Tool website.

RSNA awards:
Two ISIS exhibits recieved "Cum Laude" honors at the 2011 RSNA meeting:
caBIG® Annotation and Image Markup (AIM) Version 4.0

An Open Sourced Web-based Application for Viewing, Annotating, and Capturing Quantitative Data in Medical Images

Download the RSNA refresher course:
"Quantitative Image Analysis for Image Retrieval, Decision Support, and Knowledge Discovery" given by Sandy Napel

Read the recent article by David Paik, et al, in Science and Translational Medicine: Survival and Death Signals Can Predict Tumor Response to Therapy After Oncogene Inactivation

 

Recently Awarded Grants

Tools for Linking and Mining Image and Genomic Data in Non-Small Cell Lung Cancer
co-PIs: Sandy Napel, Sylvia Plevritis
(NIH: 1R01CA16025101, 09/2011)

Clinically Relevant Regulatory Networks in the Lung Tumor Microenvironment
PI: Sylvia Plevritis
(NIH: 1U01CA154960-01, 09/2011)

Stanford Medicine Resources:

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