Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets

Radiomic features are being increasingly studied for clinical applications. We aimed to assess the agreement among radiomic features when computed by several groups by using different software packages under very tightly controlled conditions, which included standardized feature definitions and common image data sets. Ten sites (9 from the NCI's Quantitative Imaging Network] positron emission tomography–computed tomography working group plus one site from outside that group) participated in this project. Nine common quantitative imaging features were selected for comparison including features that describe morphology, intensity, shape, and texture. The common image data sets were: three 3D digital reference objects (DROs) and 10 patient image scans from the Lung Image Database Consortium data set using a specific lesion in each scan. Each object (DRO or lesion) was accompanied by an already-defined volume of interest, from which the features were calculated. Feature values for each object (DRO or lesion) were reported. The coefficient of variation (CV), expressed as a percentage, was calculated across software packages for each feature on each object. Thirteen sets of results were obtained for the DROs and patient data sets. Five of the 9 features showed excellent agreement with CV < 1%; 1 feature had moderate agreement (CV < 10%), and 3 features had larger variations (CV ≥ 10%) even after attempts at harmonization of feature calculations. This work highlights the value of feature definition standardization as well as the need to further clarify definitions for some features.

Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches

Radiomics describes a broad set of computational methods that extract quantitative features from radiographic images. The resulting features can be used to inform imaging diagnosis, prognosis, and therapy response in oncology. However, major challenges remain for methodologic developments to optimize feature extraction and provide rapid information flow in clinical settings. Equally important, to be clinically useful, predictive radiomic properties must be clearly linked to meaningful biologic characteristics and qualitative imaging properties familiar to radiologists. Here we use a cross-disciplinary approach to highlight studies in radiomics. We review brain tumor radiologic studies (eg, imaging interpretation) through computational models (eg, computer vision and machine learning) that provide novel clinical insights. We outline current quantitative image feature extraction and prediction strategies with different levels of available clinical classes for supporting clinical decision-making. We further discuss machine-learning challenges and data opportunities to advance radiomic studies.

Integration of clinical information and gene expression profiles for prediction of chemo-response for ovarian cancer

Ovarian cancer is the fifth leading cause of cancer death among women in the United States and western Europe. Platinum drugs are the most active agents in epithelial ovarian cancer therapy. In order to improve the prediction of response to platinum-based chemotherapy for advanced-stage ovarian cancers, we describe an integrated model which combines clinical information tumor and treatment information, with gene expression profile. This integrated modeling framework is based on the support vector machine classifier that evaluates the contributions of both clinical and gene expression data. The results show that the integrated model combining clinical information and gene expression profiles improve the prediction accuracy compared to those made by using gene expression predictor alone.

Improving Reliability of Response Prediction to Platinum-Based Therapy by AdaBoost and Multiple Classifiers

It is a challenge to construct a reliable classifier based on microarray gene expression data for prediction of chemotherapy response, because usually only a small number of samples are available and each sample has thousands of gene expressions. This paper uses boosting and bootstrap approaches to improve the reliability of prediction. Specifically, AdaBoost and multiple classifiers based methods are used, in which support vector machines (SVMs) are utilized as the classifiers due to their good generalization ability. We compare the performance of proposed methods with a single SVM classifier system using MAS gene expression dataset in prediction of the response to platinum-based therapy for advanced-stage ovarian cancers. Statistical tests show both of the proposed methods achieve better prediction performance and have good reliability in terms of mean and standard deviation of the prediction performance for different number of selected features.

Comprehensive processing, display and analysis for in vivo MR spectroscopic imaging

Image reconstruction for magnetic resonance spectroscopic imaging (MRSI) requires specialized spatial and spectral data processing methods and benefits from the use of several sources of prior information that are not commonly available, including MRI-derived tissue segmentation, morphological analysis and spectral characteristics of the observed metabolites. In addition, incorporating information obtained from MRI data can enhance the display of low-resolution metabolite images and multiparametric and regional statistical analysis methods can improve detection of altered metabolite distributions. As a result, full MRSI processing and analysis can involve multiple processing steps and several different data types. In this paper, a processing environment is described that integrates and automates these data processing and analysis functions for imaging of proton metabolite distributions in the normal human brain. The capabilities include normalization of metabolite signal intensities and transformation into a common spatial reference frame, thereby allowing the formation of a database of MR-measured human metabolite values as a function of acquisition, spatial and subject parameters. This development is carried out under the MIDAS project (Metabolite Imaging and Data Analysis System), which provides an integrated set of MRI and MRSI processing functions. It is anticipated that further development and distribution of these capabilities will facilitate more widespread use of MRSI for diagnostic imaging, encourage the development of standardized MRSI acquisition, processing and analysis methods and enable improved mapping of metabolite distributions in the human brain.

Recovery of global nonrigid motion: a model-based approach without point correspondences

We present a novel technique for the estimation of global nonrigid motion of an object's boundary without using point correspondences. A complete description of the motion of an object's boundary involves specifying a displacement vector at each point of the boundary. Such a description provides a large amount of information, which needs to be processed further for study of the global characteristics of the deformation. Nonrigid motion can be studied hierarchically in terms of global nonrigid motion and point-by-point local nonrigid motion. The technique presented gives a method for estimating a global affine or polynomial transformation between two object boundaries. The novelty of the technique lies in the fact that it does not use any point correspondences. Our method uses hyperquadric models to model the data and estimate the global deformation. We show that affine or polynomial transformation between two datasets can be recovered from the hyperquadric parameters. The usefulness of the technique is twofold. First, it paves the way for viewing nonrigid motion hierarchically in terms of global and local motion. Second, it can be used as a front end to other motion analysis techniques that assume small motion. For instance, most nonrigid motion analysis algorithms make some assumptions on the type of nonrigid motion (conformal motion, small motion, etc.) that are not always satisfied in practice. When the motion between two datasets is large, our algorithm can be used to estimate the affine transformation (which includes scale and shear) or a polynomial transformation between the two datasets, which can then be used to warp the first dataset closer to the second so as to satisfy the small-motion assumption. We present experimental results with real and synthetic two- and three-dimensional data.

MRI measurement of brain tumor response: comparison of visual metric and automatic segmentation

An automatic magnetic resonance imaging (MRI) multispectral segmentation method and a visual metric are compared for their effectiveness to measure tumor response to therapy. Automatic response measurements are important for multicenter clinical trials. A visual metric such as the product of the largest diameter and the largest perpendicular diameter of the tumor is a standard approach, and is currently used in the Radiation Treatment Oncology Group (RTOG) and the Eastern Cooperative Oncology Group (EGOG) clinical trials. In the standard approach, the tumor response is based on the percentage change in the visual metric and is categorized into cure, partial response, stable disease, or progression. Both visual and automatic methods are applied to six brain tumor cases (gliomas) of varying levels of segmentation difficulty. The analyzed data were serial multispectral MR images, collected using MR contrast enhancement. A fully automatic knowledge guided method (KG) was applied to the MRI multispectral data, while the visual metric was taken from the MRI films using the T1 gadolinium enhanced image, with repeat measurements done by two radiologists and two residents. Tumor measurements from both visual and automatic methods are compared to "ground truth," (GT) i.e., manually segmented tumor. The KG method was found to slightly overestimate tumor volume, but in a consistent manner, and the estimated tumor response compared very well to hand-drawn ground truth with a correlation coefficient of 0.96. In contrast, the visually estimated metric had a large variation between observers, particularly for difficult cases, where the tumor margins are not well delineated. The inter-observer variation for the measurement of the visual metric was only 16%, i.e., observers generally agreed on the lengths of the diameters. However, in 30% of the studied cases no consensus was found for the categorical tumor response measurement, indicating that the categories are very sensitive to variations in the diameter measurements. Moreover, the method failed to correctly identify the response in half of the cases. The data demonstrate that automatic 3D methods are clearly necessary for objective and clinically meaningful assessment of tumor volume in single or multicenter clinical trials.

Automatic tracking of SPAMM grid and the estimation of deformation parameters from cardiac MR images

Presents a new approach for the automatic tracking of SPAMM (Spatial Modulation of Magnetization) grid in cardiac MR images and consequent estimation of deformation parameters. The tracking is utilized to extract grid points from MR images and to establish correspondences between grid points in images taken at consecutive frames. These correspondences are used with a thin plate spline model to establish a mapping from one image to the next. This mapping is then used for motion and deformation estimation. Spatio-temporal tracking of SPAMM grid is achieved by using snakes-active contour models with an associated energy functional. The authors present a minimizing strategy which is suitable for tracking the SPAMM grid. By continuously minimizing their energy functionals, the snakes lock on to and follow the in-slice motion and deformation of the SPAMM grid. The proposed algorithm was tested with excellent results on 123 images (three data sets each a multiple slice 2D, 16 phase Cine study, three data sets each a multiple slice 2D, 13 phase Cine study and three data sets each a multiple slice 2D, 12 phase Cine study).