Quantitative analysis has tremendous but mostly unrealized potential in healthcare to support objective and accurate interpretation of the clinical imaging. In 2008, the National Cancer Institute began building the Quantitative Imaging Network (QIN) initiative with the goal of advancing quantitative imaging in the context of personalized therapy and evaluation of treatment response. Computerized analysis is an important component contributing to reproducibility and efficiency of the quantitative imaging techniques. The success of quantitative imaging is contingent on robust analysis methods and software tools to bring these methods from bench to bedside. 3D Slicer is a free open source software application for medical image computing. As a clinical research tool, 3D Slicer is similar to a radiology workstation that supports versatile visualizations but also provides advanced functionality such as automated segmentation and registration for a variety of application domains. Unlike a typical radiology workstation, 3D Slicer is free and is not tied to specific hardware. As a programming platform, 3D Slicer facilitates translation and evaluation of the new quantitative methods by allowing the biomedical researcher to focus on the implementation of the algorithm, and providing abstractions for the common tasks of data communication, visualization and user interface development. Compared to other tools that provide aspects of this functionality, 3D Slicer is fully open source and can be readily extended and redistributed. In addition, 3D Slicer is designed to facilitate the development of new functionality in the form of 3D Slicer extensions.In this paper, we present an overview of 3D Slicer as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications. To illustrate the utility of the platform in the scope of QIN, we discuss several use cases of 3D Slicer by the existing QIN teams,

Significance This study demonstrates that antiangiogenic therapy increases tumor blood perfusion in a subset of newly diagnosed glioblastoma patients, and that it is these patients who survive longer when this expensive and potentially toxic therapy is combined with standard radiation and chemotherapy. This study provides fresh insights into the selection of glioblastoma patients most likely to benefit from antiangiogenic treatments.

Early imaging or blood biomarkers of tumor response are desperately needed to customize antiangiogenic therapy for cancer patients. Anti-vascular endothelial growth factor (VEGF) therapy can ''normalize'' brain tumor vasculature by decreasing vessel diameter and permeability, and thinning the abnormally thick basement membrane. We hypothesized that the extent of vascular normalization will be predictive of outcome of anti-VEGF therapy in glioblastoma. We used advanced magnetic resonance imaging methods to monitor vascular parameters and treatment response in 31 recurrent glioblastoma patients enrolled in a phase II trial of cediranib, an oral pan-VEGF receptor tyrosine kinase inhibitor. We evaluated the correlation between clinical outcome and magnetic resonance imaging-measured changes in vascular permeability/flow (i.e., K trans ) and in microvessel volume, and the change of circulating collagen IV levels, all after a single dose of cediranib. Here, we show that evaluation of biomarkers as early as after one day of anti-VEGF therapy with cediranib is predictive of response in patients with recurrent glioblastoma. Changes in K trans , microvessel volume, and circulating collagen IV correlated with duration of overall survival and/or progression-free survival (P < 0.05). When we combined these three parameters into a ''vascular normalization index,'' we found that it closely associated with overall survival (R = 0.54; P = 0.004) and progression-free survival (R = 0.6; P = 0.001). The vascular normalization index described here should be validated in randomized clinical trials. [Cancer Res 2009;69(13):5296-300]