CoINcIDE: A framework for discovery of patient subtypes across multiple datasets.

Patient disease subtypes have the potential to transform personalized medicine. However, many patient subtypes derived from unsupervised clustering analyses on high-dimensional datasets are not replicable across multiple datasets, limiting their clinical utility. We present CoINcIDE, a novel methodological framework for the discovery of patient subtypes across multiple datasets that requires no between-dataset transformations.

Database integration of 4923 publicly-available samples of breast cancer molecular and clinical data.

We outline a paradigm for meta-microarray database creation and integration with clinical variables. We use as our implementation example a breast cancer database linking RNA expression measurements (by microarray) and clinical variables, such as survival metrics and tumor size. Such an endeavor involves integrating across different microarray datasets as well as clinical parameters.

Temporal sampling requirements for reference region modeling of DCE-MRI data in human breast cancer.

Purpose: To assess the temporal sampling requirements needed for quantitative analysis of dynamic contrast-enhanced MRI (DCE-MRI) data with a reference region (RR) model in human breast cancer.

Materials And Methods: Simulations were used to study errors in pharmacokinetic parameters (K(trans) and v(e)) estimated by the RR model using six DCE-MRI acquisitions over a range of pharmacokinetic parameter values, arterial input functions, and temporal samplings. DCE-MRI data were acquired on 12 breast cancer patients and parameters were estimated using the native resolution data (16.