Regional white matter atrophy--based classification of multiple sclerosis in cross-sectional and longitudinal data.

BACKGROUND AND PURPOSE: The different clinical subtypes of multiple sclerosis (MS) may reflect underlying differences in affected neuroanatomic regions. Our aim was to analyze the effectiveness of jointly using the inferior subolivary medulla oblongata volume (MOV) and the cross-sectional area of the corpus callosum in distinguishing patients with relapsing-remitting multiple sclerosis (RRMS), secondary-progressive multiple sclerosis (SPMS), and primary-progressive multiple sclerosis (PPMS). MATERIALS AND METHODS: We analyzed a cross-sectional dataset of 64 patients (30 RRMS, 14 SPMS, 20 PPMS) and a separate longitudinal dataset of 25 patients (114 MR imaging examinations). Twelve patients in the longitudinal dataset had converted from RRMS to SPMS. For all images, the MOV and corpus callosum were delineated manually and the corpus callosum was parcellated into 5 segments. Patients from the cross-sectional dataset were classified as RRMS, SPMS, or PPMS by using a decision tree algorithm with the following input features: brain parenchymal fraction, age, disease duration, MOV, total corpus callosum area and areas of 5 segments of the corpus callosum. To test the robustness of the classification technique, we applied the results derived from the cross-sectional analysis to the longitudinal dataset. RESULTS: MOV and central corpus callosum segment area were the 2 features retained by the decision tree. Patients with MOV >0.94 cm(3) were classified as having RRMS. Patients with progressive MS were further subclassified as having SPMS if the central corpus callosum segment area was <55.12 mm(2), and as having PPMS otherwise. In the cross-sectional dataset, 51/64 (80%) patients were correctly classified. For the longitudinal dataset, 88/114 (77%) patient time points were correctly classified as RRMS or SPMS. CONCLUSIONS: Classification techniques revealed differences in affected neuroanatomic regions in subtypes of multiple sclerosis. The combination of central corpus callosum segment area and MOV provides good discrimination among patients with RRMS, SPMS, and PPMS.

Medulla oblongata volume: a biomarker of spinal cord damage and disability in multiple sclerosis.

BACKGROUND AND PURPOSE: While brain MR imaging is routinely performed, the MR imaging assessment of spinal cord pathology in multiple sclerosis (MS) is less frequent in clinical practice. The purpose of this study was to determine whether measurements of medulla oblongata volume (MOV) on routine brain MR imaging could serve as a biomarker of spinal cord damage and disability in MS. MATERIALS AND METHODS: We identified 45 patients with MS with both head and cervical spinal cord MR imaging and 29 age-matched and sex-matched healthy control subjects with head MR imaging. Disability was assessed by the expanded disability status scale (EDSS) and ambulation index (AI). MOV and upper cervical cord volume (UCCV) were manually segmented; semiautomated segmentation was used for brain parenchymal fraction (BPF). These measures were compared between groups, and linear regression models were built to predict disability. RESULTS: In the patients, MOV correlated significantly with UCCV (r = 0.67), BPF (r = 0.45), disease duration (r = -0.64), age (r = -0.47), EDSS score (r = -0.49) and AI (r = -0.52). Volume loss of the medulla oblongata was -0.008 cm(3)/year of age in patients with MS, but no significant linear relationship with age was found for healthy control subjects. The patients had a smaller MOV (mean +/- SD, 1.02 +/- 0.17 cm(3)) than healthy control subjects (1.15 +/- 0.15 cm(3)), though BPF was unable to distinguish between these 2 groups. MOV was smaller in patients with progressive MS (secondary- progressive MS, 0.88 +/- 0.19 cm(3) and primary-progressive MS, 0.95 +/- 0.30 cm(3)) than in patients with relapsing-remitting MS (1.08 +/- 0.15 cm(3)). A model including both MOV and BPF better predicted AI than BPF alone (P = .04). Good reproducibility in MOV measurements was demonstrated for intrarater (intraclass correlation coefficient, 0.97), interrater (0.79), and scan rescan data (0.81). CONCLUSION: MOV is associated with disability in MS and can serve as a biomarker of spinal cord damage.

Segmentation of subtraction images for the measurement of lesion change in multiple sclerosis.

BACKGROUND AND PURPOSE: Lesion volume change (LVC) assessment is essential in monitoring MS progression. LVC is usually measured by independently segmenting serial MR imaging examinations. Subtraction imaging has been proposed for improved visualization and characterization of lesion change. We compare segmentation of subtraction images (SSEG) with serial single time-point conventional segmentation (CSEG) by assessing the LVC relationship to brain atrophy and disease duration, as well as scan-rescan reproducibility and annual rates of lesion accrual. MATERIALS AND METHODS: Pairs of scans were acquired 1.5 to 4.7 years apart in 21 patients with multiple sclerosis (MS). Scan-rescan MR images were acquired within 30 minutes in 10 patients with MS. LVC was measured with CSEG and SSEG after coregistration and normalization. Coefficient of variation (COV) and Bland-Altman analyses estimated method reproducibility. Spearman rank correlations probed associations between LVC and other measures. RESULTS: Atrophy rate and net LVC were associated for SSEG (R = -0.446; P < .05) but not when using CSEG (R = -0.180; P = .421). Disease duration did not show an association with net lesion volume change per year measured by CSEG (R = -0.360; P = .11) but showed an inverse correlation with SSEG-derived measurements (R = -0.508; P < .05). Scan-rescan COV was lower for SSEG (0.98% +/- 1.55%) than for CSEG (8.64% +/- 9.91%). CONCLUSION: SSEG unveiled a relationship between T2 LVC and concomitant brain atrophy and demonstrated significantly higher measurement reproducibility. SSEG, a promising tool providing detailed analysis of subtle alterations in lesion size and intensity, may provide critical outcome measures for clinical trials of novel treatments, and may provide further insight into progression patterns in MS.

The reliability of measuring physical characteristics of spiculated masses on mammography.

The goal of this study was to assess the reliability of measurements of the physical characteristics of spiculated masses on mammography. The images used in this study were obtained from the Digital Database for Screening Mammography. Two experienced radiologists measured the properties of 21 images of spiculated masses. The length and width of all spicules and the major axis of the mass were measured. In addition, the observers counted the total number of spicules. Interobserver and intraobserver variability were evaluated using a hypothesis test for equivalence, the intraclass correlation coefficient (ICC) and Bland-Altman statistics. For an equivalence level of 30% of the mean of the senior radiologist's measurement, equivalence was achieved for the measurements of average spicule length (p<0.01), average spicule width (p = 0.03), the length of the major axis (p<0.01) and for the count of the number of spicules (p<0.01). Similarly, with the ICC analysis technique "excellent" inter-rater agreement was observed for the measurements of average spicule length (ICC = 0.770), the length of the major axis (ICC = 0.801) and for the count of the number of spicules (ICC = 0.780). "Fair to good" agreement was observed for the average spicule width (ICC = 0.561). Equivalence was also demonstrated for intraobserver measurements. Physical properties of spiculated masses can be measured reliably on mammography. The interobserver and intraobserver variability for this task is comparable with that reported for other measurements made on medical images.