In Vivo Microstructure Imaging in Oropharyngeal Squamous Cell Carcinoma Using the Random Walk With Barriers Model.

Background: Apparent diffusion coefficient is not specifically sensitive to tumor microstructure and therapy-induced cellular changes.

Purpose: To investigate time-dependent diffusion imaging with the short-time-limit random walk with barriers model (STL-RWBM) for quantifying microstructure parameters and early cancer cellular response to therapy.

Study Type: Prospective.

Multi-Site Concordance of Diffusion-Weighted Imaging Quantification for Assessing Prostate Cancer Aggressiveness.

Background: Diffusion-weighted imaging (DWI) is commonly used to detect prostate cancer, and a major clinical challenge is differentiating aggressive from indolent disease.

Purpose: To compare 14 site-specific parametric fitting implementations applied to the same dataset of whole-mount pathologically validated DWI to test the hypothesis that cancer differentiation varies with different fitting algorithms.

Study Type: Prospective.

A Phase 2 Study of Dose-intensified Chemoradiation Using Biologically Based Target Volume Definition in Patients With Newly Diagnosed Glioblastoma.

Purpose: We hypothesized that dose-intensified chemoradiation therapy targeting adversely prognostic hypercellular (TV) and hyperperfused (TV) tumor volumes would improve outcomes in patients with glioblastoma.

Methods And Materials: This single-arm, phase 2 trial enrolled adult patients with newly diagnosed glioblastoma. Patients with a TV/TV >1 cm, identified using high b-value diffusion-weighted magnetic resonance imaging (MRI) and dynamic contrast-enhanced perfusion MRI, were treated over 30 fractions to 75 Gy to the TV/TV with temozolomide.

Tumor image signatures and habitats: a processing pipeline of multimodality metabolic and physiological images.

To create tumor "habitats" from the "signatures" discovered from multimodality metabolic and physiological images, we developed a framework of a processing pipeline. The processing pipeline consists of six major steps: (1) creating superpixels as a spatial unit in a tumor volume; (2) forming a data matrix [Formula: see text] containing all multimodality image parameters at superpixels; (3) forming and clustering a covariance or correlation matrix [Formula: see text] of the image parameters to discover major image "signatures;" (4) clustering the superpixels and organizing the parameter order of the [Formula: see text] matrix according to the one found in step 3; (5) creating "habitats" in the image space from the superpixels associated with the "signatures;" and (6) pooling and clustering a matrix consisting of correlation coefficients of each pair of image parameters from all patients to discover subgroup patterns of the tumors. The pipeline was applied to a dataset of multimodality images in glioblastoma (GBM) first, which consisted of 10 image parameters.

Temporal Feature Extraction from DCE-MRI to Identify Poorly Perfused Subvolumes of Tumors Related to Outcomes of Radiation Therapy in Head and Neck Cancer.

This study aimed to develop an automated model to extract temporal features from DCE-MRI in head-and-neck (HN) cancers to localize significant tumor subvolumes having low blood volume (LBV) for predicting local and regional failure after chemoradiation therapy. Temporal features were extracted from time-intensity curves to build classification model for differentiating voxels with LBV from those with high BV. Support vector machine (SVM) classification was trained on the extracted features for voxel classification.

Literature-based biomedical image classification and retrieval.

Literature-based image informatics techniques are essential for managing the rapidly increasing volume of information in the biomedical domain. Compound figure separation, modality classification, and image retrieval are three related tasks useful for enabling efficient access to the most relevant images contained in the literature. In this article, we describe approaches to these tasks and the evaluation of our methods as part of the 2013 medical track of ImageCLEF.