Chemical transesterification of coconut and corn oils using different metal hydroxides as catalysts to determine the chemical and physiochemical changes to the oils.

Background: The transesterification of butteroil has been shown to alter its lipid chemistry and thus alter the crystallization of the fat. The reaction kinetics and resulting crystallization of the butteroil differ depending on the nature of the catalyst used. Modeling the reaction with vegetable oils is a simpler method for the analysis of resulting products to understand the chemical and physiochemical changes that occur based on catalyst selection.

Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: Preliminary findings from a multi-institutional study.

Purpose: To evaluate in a multi-institutional study whether radiomic features useful for prostate cancer (PCa) detection from 3 Tesla (T) multi-parametric MRI (mpMRI) in the transition zone (TZ) differ from those in the peripheral zone (PZ).

Materials And Methods: 3T mpMRI, including T2-weighted (T2w), apparent diffusion coefficient (ADC) maps, and dynamic contrast-enhanced MRI (DCE-MRI), were retrospectively obtained from 80 patients at three institutions. This study was approved by the institutional review board of each participating institution.

Texture-based Quantification of Centrilobular Emphysema and Centrilobular Nodularity in Longitudinal CT Scans of Current and Former Smokers.

Rationale And Objectives: The effect of smoking cessation on centrilobular emphysema (CLE) and centrilobular nodularity (CN), two manifestations of smoking-related lung injury on computed tomography (CT) images, has not been clarified. The objective of this study is to leverage texture analysis to investigate differences in extent of CLE and CN between current and former smokers.

Materials And Methods: Chest CT scans from 350 current smokers, 401 former smokers, and 25 control subjects were obtained from the multicenter COPDGene Study, a Health Insurance Portability and Accountability Act-compliant study approved by the institutional review board of each participating clinical study center.

Patient-specific pharmacokinetic parameter estimation on dynamic contrast-enhanced MRI of prostate: Preliminary evaluation of a novel AIF-free estimation method.

Purpose: To develop and evaluate a prostate-based method (PBM) for estimating pharmacokinetic parameters on dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) by leveraging inherent differences in pharmacokinetic characteristics between the peripheral zone (PZ) and transition zone (TZ).

Materials And Methods: This retrospective study, approved by the Institutional Review Board, included 40 patients who underwent a multiparametric 3T MRI examination and subsequent radical prostatectomy. A two-step PBM for estimating pharmacokinetic parameters exploited the inherent differences in pharmacokinetic characteristics associated with the TZ and PZ.

Feature Importance in Nonlinear Embeddings (FINE): Applications in Digital Pathology.

Quantitative histomorphometry (QH) refers to the process of computationally modeling disease appearance on digital pathology images by extracting hundreds of image features and using them to predict disease presence or outcome. Since constructing a robust and interpretable classifier is challenging in a high dimensional feature space, dimensionality reduction (DR) is often implemented prior to classifier construction. However, when DR is performed it can be challenging to quantify the contribution of each of the original features to the final classification result.

Selecting features with group-sparse nonnegative supervised canonical correlation analysis: multimodal prostate cancer prognosis.

This paper presents Group-sparse Nonnegative supervised Canonical Correlation Analysis (GNCCA), a novel methodology for identifying discriminative features from multiple feature views. Existing correlation-based methods do not guarantee positive correlations of the selected features and often need a pre-feature selection step to reduce redundant features on each feature view. The new GNCCA approach attempts to overcome these issues by incorporating (1) a nonnegativity constraint that guarantees positive correlations in the reduced representation and (2) a group-sparsity constraint that allows for simultaneous between- and within- view feature selection.

Novel PCA-VIP scheme for ranking MRI protocols and identifying computer-extracted MRI measurements associated with central gland and peripheral zone prostate tumors.

Purpose: To identify computer-extracted features for central gland and peripheral zone prostate cancer localization on multiparametric magnetic resonance imaging (MRI).

Materials And Methods: Preoperative T2-weighted (T2w), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) MRI were acquired from 23 men with confirmed prostate cancer. Following radical prostatectomy, the cancer extent was delineated by a pathologist on ex vivo histology and mapped to MRI by nonlinear registration of histology and corresponding MRI slices.

Variable importance in nonlinear kernels (VINK): classification of digitized histopathology.

Quantitative histomorphometry is the process of modeling appearance of disease morphology on digitized histopathology images via image-based features (e.g., texture, graphs).

Automated texture-based quantification of centrilobular nodularity and centrilobular emphysema in chest CT images.

Rationale And Objectives: Characterization of smoking-related lung disease typically consists of visual assessment of chest computed tomographic (CT) images for the presence and extent of emphysema and centrilobular nodularity (CN). Quantitative analysis of emphysema and CN may improve the accuracy, reproducibility, and efficiency of chest CT scoring. The purpose of this study was to develop a fully automated texture-based system for the detection and quantification of centrilobular emphysema (CLE) and CN in chest CT images.