Validation of the automated method VIENA: an accurate, precise, and robust measure of ventricular enlargement.

BACKGROUND: In many retrospective studies and large clinical trials, high-resolution, good-contrast 3DT1 images are unavailable, hampering detailed analysis of brain atrophy. Ventricular enlargement then provides a sensitive indirect measure of ongoing central brain atrophy. Validated automated methods are required that can reliably measure ventricular enlargement and are robust across magnetic resonance (MR) image types. AIM: To validate the automated method VIENA for measuring the percentage ventricular volume change (PVVC) between two scans. MATERIALS AND METHODS: Accuracy was assessed using four image types, acquired in 15 elderly patients (five with Alzheimer's disease, five with mild cognitive impairment, and five cognitively normal elderly) and 58 patients with multiple sclerosis (MS), by comparing PVVC values from VIENA to manual outlining. Precision was assessed from data with three imaging time points per MS patient, by measuring the difference between the direct (one-step) and indirect (two-step) measurement of ventricular volume change between the first and last time points. The stringent concordance correlation coefficient (CCC) was used to quantify absolute agreement. RESULTS: CCC of VIENA with manual measurement was 0.84, indicating good absolute agreement. The median absolute difference between two-step and one-step measurement with VIENA was 1.01%, while CCC was 0.98. Neither initial ventricular volume nor ventricular volume change affected performance of the method. DISCUSSION: VIENA has good accuracy and good precision across four image types. VIENA therefore provides a useful fully automated method for measuring ventricular volume change in large datasets. CONCLUSION: VIENA is a robust, accurate, and precise method for measuring ventricular volume change.

Brain atrophy and lesion load predict long term disability in multiple sclerosis.

OBJECTIVE: To determine whether brain atrophy and lesion volumes predict subsequent 10 year clinical evolution in multiple sclerosis (MS). DESIGN: From eight MAGNIMS (MAGNetic resonance Imaging in MS) centres, we retrospectively included 261 MS patients with MR imaging at baseline and after 1-2 years, and Expanded Disability Status Scale (EDSS) scoring at baseline and after 10 years. Annualised whole brain atrophy, central brain atrophy rates and T2 lesion volumes were calculated. Patients were categorised by baseline diagnosis as primary progressive MS (n=77), clinically isolated syndromes (n=18), relapsing-remitting MS (n=97) and secondary progressive MS (n=69). Relapse onset patients were classified as minimally impaired (EDSS=0-3.5, n=111) or moderately impaired (EDSS=4-6, n=55) according to their baseline disability (and regardless of disease type). Linear regression models tested whether whole brain and central atrophy, lesion volumes at baseline, follow-up and lesion volume change predicted 10 year EDSS and MS Severity Scale scores. RESULTS: In the whole patient group, whole brain and central atrophy predicted EDSS at 10 years, corrected for imaging protocol, baseline EDSS and disease modifying treatment. The combined model with central atrophy and lesion volume change as MRI predictors predicted 10 year EDSS with R(2)=0.74 in the whole group and R(2)=0.72 in the relapse onset group. In subgroups, central atrophy was predictive in the minimally impaired relapse onset patients (R(2)=0.68), lesion volumes in moderately impaired relapse onset patients (R(2)=0.21) and whole brain atrophy in primary progressive MS (R(2)=0.34). CONCLUSIONS: This large multicentre study points to the complementary predictive value of atrophy and lesion volumes for predicting long term disability in MS.

Automated classification of hyperlucency, fibrosis, ground glass, solid, and focal lesions in high-resolution CT of the lung.

An automatic method for textural analysis of complete HRCT lung slices is presented. The system performs classification of regions of interest (ROIs) into one of six classes: normal, hyperlucency, fibrosis, ground glass, solid, and focal. We propose a novel method of automatically generating ROIs that contain homogeneous texture. The use of such regions rather than square regions is shown to improve performance of the automated system. Furthermore, the use of two different, previously published, feature sets is investigated. Both feature sets are shown to yield similar results. Classification performance of the complete system is characterized by ROC curves for each of the classes of abnormality and compared to a total of three expert readings by two experienced radiologists. The different types of abnormality can be automatically distinguished with areas under the ROC curve that range from 0.74 (focal) to 0.95 (solid). The kappa statistics for intraobserver agreement, interobserver agreement, and computer versus observer agreement were 0.70, 0.53+/-0.02, and 0.40+/-0.03, respectively. The question whether or not a class of abnormality was present in a slice could be answered by the computer system with an accuracy comparable to that of radiologists.

Computer-aided diagnosis in high resolution CT of the lungs.

A computer-aided diagnosis (CAD) system is presented to automatically distinguish normal from abnormal tissue in high-resolution CT chest scans acquired during daily clinical practice. From high-resolution computed tomography scans of 116 patients, 657 regions of interest are extracted that are to be classified as displaying either normal or abnormal lung tissue. A principled texture analysis approach is used, extracting features to describe local image structure by means of a multi-scale filter bank. The use of various classifiers and feature subsets is compared and results are evaluated with ROC analysis. Performance of the system is shown to approach that of two expert radiologists in diagnosing the local regions of interest, with an area under the ROC curve of 0.862 for the CAD scheme versus 0.877 and 0.893 for the radiologists.