Genetic variants for head size share genes and pathways with cancer.

The size of the human head is highly heritable, but genetic drivers of its variation within the general population remain unmapped. We perform a genome-wide association study on head size (N = 80,890) and identify 67 genetic loci, of which 50 are novel. Neuroimaging studies show that 17 variants affect specific brain areas, but most have widespread effects. Gene set enrichment is observed for various cancers and the p53, Wnt, and ErbB signaling pathways. Genes harboring lead variants are enriched for macrocephaly syndrome genes (37-fold) and high-fidelity cancer genes (9-fold), which is not seen for human height variants. Head size variants are also near genes preferentially expressed in intermediate progenitor cells, neural cells linked to evolutionary brain expansion. Our results indicate that genes regulating early brain and cranial growth incline to neoplasia later in life, irrespective of height. This warrants investigation of clinical implications of the link between head size and cancer.

Integrative Diagnostics: The Time Is Now-A Report From the International Society for Strategic Studies in Radiology.

Enormous recent progress in diagnostic testing can enable more accurate diagnosis and improved clinical outcomes. Yet these tests are increasingly challenging and frustrating; the volume and diversity of results may overwhelm the diagnostic acumen of even the most dedicated and experienced clinician. Because they are gathered and processed within the "silo" of each diagnostic discipline, diagnostic data are fragmented, and the electronic health record does little to synthesize new and existing data into usable information. Therefore, despite great promise, diagnoses may still be incorrect, delayed, or never made. Integrative diagnostics represents a vision for the future, wherein diagnostic data, together with clinical data from the electronic health record, are aggregated and contextualized by informatics tools to direct clinical action. Integrative diagnostics has the potential to identify correct therapies more quickly, modify treatment when appropriate, and terminate treatment when not effective, ultimately decreasing morbidity, improving outcomes, and avoiding unnecessary costs. Radiology, laboratory medicine, and pathology already play major roles in medical diagnostics. Our specialties can increase the value of our examinations by taking a holistic approach to their selection, interpretation, and application to the patient's care pathway. We have the means and rationale to incorporate integrative diagnostics into our specialties and guide its implementation in clinical practice.

DEEPMIR: a deep neural network for differential detection of cerebral microbleeds and iron deposits in MRI.

Lobar cerebral microbleeds (CMBs) and localized non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Particularly, CMBs are small lesions and require multiple neuroimaging modalities for accurate detection. Quantitative susceptibility mapping (QSM) derived from in vivo magnetic resonance imaging (MRI) is necessary to differentiate between iron content and mineralization. We set out to develop a deep learning-based segmentation method suitable for segmenting both CMBs and iron deposits. We included a convenience sample of 24 participants from the MESA cohort and used T2-weighted images, susceptibility weighted imaging (SWI), and QSM to segment the two types of lesions. We developed a protocol for simultaneous manual annotation of CMBs and non-hemorrhage iron deposits in the basal ganglia. This manual annotation was then used to train a deep convolution neural network (CNN). Specifically, we adapted the U-Net model with a higher number of resolution layers to be able to detect small lesions such as CMBs from standard resolution MRI. We tested different combinations of the three modalities to determine the most informative data sources for the detection tasks. In the detection of CMBs using single class and multiclass models, we achieved an average sensitivity and precision of between 0.84-0.88 and 0.40-0.59, respectively. The same framework detected non-hemorrhage iron deposits with an average sensitivity and precision of about 0.75-0.81 and 0.62-0.75, respectively. Our results showed that deep learning could automate the detection of small vessel disease lesions and including multimodal MR data (particularly QSM) can improve the detection of CMB and non-hemorrhage iron deposits with sensitivity and precision that is compatible with use in large-scale research studies.

Cigarette smoking and gray matter brain volumes in middle age adults: the CARDIA Brain MRI sub-study.

Cigarette smoking has been associated with dementia and dementia-related brain changes, notably gray matter (GM) volume atrophy. These associations are thought to reflect the co-morbidity of smoking and vascular, respiratory, and substance use/psychological conditions. However, the extent and localization of the smoking-GM relationship and the degree to which vascular, respiratory, and substance use/psychological factors influence this relationship remain unclear. In the Coronary Artery Risk Development in Young Adults CARDIA cohort (n = 698; 52% women; 40% black participants; age = 50.3 (SD = 3.5)), we examined the associations of smoking status with total GM volume and GM volume of brain regions linked to neurocognitive and addiction disorders. Linear regression models were used to adjust for vascular, respiratory, and substance use/psychological factors and to examine whether they modify the smoking-GM relationship. Compared to never-smokers, current smokers had smaller total GM volume (-8.86 cm3 (95%CI = -13.44, -4.29). Adjustment for substance use/psychological - but not vascular or respiratory - factors substantially attenuated this association (coefficients = -5.54 (95% CI = -10.32, -0.76); -8.33 (95% CI = -12.94, -3.72); -7.69 (95% CI = -6.95, -4.21), respectively). There was an interaction between smoking and alcohol use such that among alcohol non-users, smoking was not related to GM volumes and among alcohol users, those who currently smoked had -12 cm3 smaller total GM, specifically in the frontal and temporal lobes, amygdala, cingulate, and insula. Results suggest a large-magnitude association between smoking and smaller GM volume at middle age, accounting for vascular, respiratory, and substance use/psychological factors, and that the association was strongest in alcohol users. Regions suggested to be most vulnerable are those where cognition and addiction processes overlap.

Emerging Applications of Artificial Intelligence in Neuro-Oncology.

Due to the exponential growth of computational algorithms, artificial intelligence (AI) methods are poised to improve the precision of diagnostic and therapeutic methods in medicine. The field of radiomics in neuro-oncology has been and will likely continue to be at the forefront of this revolution. A variety of AI methods applied to conventional and advanced neuro-oncology MRI data can already delineate infiltrating margins of diffuse gliomas, differentiate pseudoprogression from true progression, and predict recurrence and survival better than methods used in daily clinical practice. Radiogenomics will also advance our understanding of cancer biology, allowing noninvasive sampling of the molecular environment with high spatial resolution and providing a systems-level understanding of underlying heterogeneous cellular and molecular processes. By providing in vivo markers of spatial and molecular heterogeneity, these AI-based radiomic and radiogenomic tools have the potential to stratify patients into more precise initial diagnostic and therapeutic pathways and enable better dynamic treatment monitoring in this era of personalized medicine. Although substantial challenges remain, radiologic practice is set to change considerably as AI technology is further developed and validated for clinical use.

Cigarette smoking and cerebral blood flow in a cohort of middle-aged adults.

Cigarette smoking is often associated with dementia. This association is thought to be mediated by hypoperfusion; however, how smoking behavior relates to cerebral blood flow (CBF) remains unclear. Using data from the Coronary Artery Risk Development in Young Adults (CARDIA) cohort (mean age = 50; n = 522), we examined the association between smoking behavior (status, cumulative pack-years, age at smoking initiation, and years since cessation) and CBF (arterial spin labeling) in brain lobes and regions linked to dementia. We used adjusted linear regression models and tested whether associations differed between current and former-smokers. Compared to never-smokers, former-smokers had lower CBF in the parietal and occipital lobes, cuneus, precuneus, putamen, and insula; in contrast, current-smokers did not have lower CBF. The relationship between pack-years and CBF was different between current and former-smokers (p for interaction < 0.05): Among current-smokers, higher pack-years were associated with higher occipital, temporal, cuneus, putamen, insula, hippocampus, and caudate CBF; former-smokers had lower caudate CBF with increasing pack-years. Results show links between smoking and CBF at middle-age in regions implicated in cognitive and compulsive/addictive processes. Differences between current and former smoking suggest that distinct pathological and/or compensatory mechanisms may be involved depending on the timing and history of smoking exposure.

Relationships between cerebral structure and cognitive function in African Americans with type 2 diabetes.

BACKGROUND: Relationships between cognitive function and brain structure remain poorly defined in African Americans with type 2 diabetes. METHODS: Cognitive testing and cerebral magnetic resonance imaging in African Americans from the Diabetes Heart Study Memory IN Diabetes (n = 480) and Action to Control Cardiovascular Risk in Diabetes MIND (n = 104) studies were examined for associations. Cerebral gray matter volume (GMV), white matter volume (WMV) and white matter lesion volume (WMLV) and cognitive performance (Mini-mental State Exam [MMSE and 3MSE], Digit Symbol Coding (DSC), Stroop test, and Rey Auditory Verbal Learning Test) were recorded. Multivariable models adjusted for age, sex, BMI, scanner, intracranial volume, education, diabetes duration, HbA1c, LDL-cholesterol, smoking, hypertension and cardiovascular disease assessed associations between cognitive tests and brain volumes by study and meta-analysis. RESULTS: Mean(SD) participant age was 60.1(7.9) years, diabetes duration 12.1(7.7) years, and HbA1c 8.3(1.7)%. In the fully-adjusted meta-analysis, lower GMV associated with poorer global performance on MMSE/3MSE (ß̂ = 7.1 × 10-3, SE 2.4 × 10-3, p = 3.6 × 10-3), higher WMLV associated with poorer performance on DSC (ß̂ = -3 × 10-2, SE 6.4 × 10-3, p = 5.2 × 10-5) and higher WMV associated with poorer MMSE/3MSE performance (ß̂ = -7.1 × 10-3, SE = 2.4 × 10-3, p = 3.6 × 10-3). CONCLUSIONS: In African Americans with diabetes, smaller GMV and increased WMLV associated with poorer performance on tests of global cognitive and executive function. These data suggest that WML burden and gray matter atrophy associate with cognitive performance independent of diabetes-related factors in this population.

Associations of Early Kidney Disease With Brain Magnetic Resonance Imaging and Cognitive Function in African Americans With Type 2 Diabetes Mellitus.

BACKGROUND: Relationships between early kidney disease, neurocognitive function, and brain anatomy are poorly defined in African Americans with type 2 diabetes mellitus (T2DM). STUDY DESIGN: Cross-sectional associations were assessed between cerebral anatomy and cognitive performance with estimated glomerular filtration rate (eGFR) and urine albumin-creatinine ratio (UACR) in African Americans with T2DM. SETTING & PARTICIPANTS: African Americans with cognitive testing and cerebral magnetic resonance imaging (MRI) in the African American-Diabetes Heart Study Memory in Diabetes (AA-DHS MIND; n=512; 480 with MRI) and Action to Control Cardiovascular Risk in Diabetes (ACCORD) MIND (n=484; 104 with MRI) studies. PREDICTORS: eGFR (CKD-EPI creatinine equation), spot UACR. MEASUREMENTS: MRI-based cerebral white matter volume (WMV), gray matter volume (GMV), and white matter lesion volume; cognitive performance (Mini-Mental State Examination, Digit Symbol Coding, Stroop Test, and Rey Auditory Verbal Learning Test). Multivariable models adjusted for age, sex, body mass index, scanner, intracranial volume, education, diabetes duration, hemoglobin A1c concentration, low-density lipoprotein cholesterol concentration, smoking, hypertension, and cardiovascular disease were used to test for associations between kidney phenotypes and the brain in each study; a meta-analysis was performed. RESULTS: Mean participant age was 60.1±7.9 (SD) years; diabetes duration, 12.1±7.7 years; hemoglobin A1c concentration, 8.3%±1.7%; eGFR, 88.7±21.6mL/min/1.73m2; and UACR, 119.2±336.4mg/g. In the fully adjusted meta-analysis, higher GMV associated with lower UACR (P<0.05), with a trend toward association with higher eGFR. Higher white matter lesion volume was associated with higher UACR (P<0.05) and lower eGFR (P<0.001). WMV was not associated with either kidney parameter. Higher UACR was associated with lower Digit Symbol Coding performance (P<0.001) and a trend toward association with higher Stroop interference; eGFR was not associated with cognitive tests. LIMITATIONS: Cross-sectional; single UACR measurement. CONCLUSIONS: In African Americans with T2DM, mildly high UACR and mildly low eGFR were associated with smaller GMV and increased white matter lesion volume. UACR was associated with poorer processing speed and working memory.

Science to Practice: Can Focused Ultrasound Disruption of the Blood-Brain Barrier Improve Malignant Brain Tumor Treatment Outcomes?

A relatively novel combination treatment for malignant brain tumors that includes focused ultrasound has been shown to improve tumor response and treatment outcome in a preclinical mouse model. This method directly addresses the great need for better treatments for this lethal disease. However, there are substantial technologic hurdles that must be addressed before clinical efficacy will be known. Unfortunately, few of these questions can be readily answered outside the clinical environment. Although it would be enormously challenging, a phase I clinical trial seems feasible and needed to determine the clinical value of this technique.

APOL1 renal-risk variants associate with reduced cerebral white matter lesion volume and increased gray matter volume.

To assess apolipoprotein L1 gene (APOL1) renal-risk-variant effects on the brain, magnetic resonance imaging (MRI)-based cerebral volumes and cognitive function were assessed in 517 African American-Diabetes Heart Study (AA-DHS) Memory IN Diabetes (MIND) and 2568 hypertensive African American Systolic Blood Pressure Intervention Trial (SPRINT) participants without diabetes. Within these cohorts, 483 and 197 had cerebral MRI, respectively. AA-DHS participants were characterized as follows: 60.9% female, mean age of 58.6 years, diabetes duration 13.1 years, estimated glomerular filtration rate of 88.2 ml/min/1.73 m(2), and a median spot urine albumin to creatinine ratio of 10.0 mg/g. In additive genetic models adjusting for age, sex, ancestry, scanner, intracranial volume, body mass index, hemoglobin A1c, statins, nephropathy, smoking, hypertension, and cardiovascular disease, APOL1 renal-risk-variants were positively associated with gray matter volume (ß = 3.4 × 10(-3)) and negatively associated with white matter lesion volume (ß = -0.303) (an indicator of cerebral small vessel disease) and cerebrospinal fluid volume (ß= -30707) (all significant), but not with white matter volume or cognitive function. Significant associations corresponding to adjusted effect sizes (ß/SE) were observed with gray matter volume (0.16) and white matter lesion volume (-0.208), but not with cerebrospinal fluid volume (-0.251). Meta-analysis results with SPRINT Memory and Cognition in Decreased Hypertension (MIND) participants who had cerebral MRI were confirmatory. Thus, APOL1 renal-risk-variants are associated with larger gray matter volume and lower white matter lesion volume suggesting lower intracranial small vessel disease.

Cardiorespiratory fitness and brain volume and white matter integrity: The CARDIA Study.

OBJECTIVE: We hypothesized that greater cardiorespiratory fitness is associated with lower odds of having unfavorable brain MRI findings. METHODS: We studied 565 healthy, middle-aged, black and white men and women in the CARDIA (Coronary Artery Risk Development in Young Adults) Study. The fitness measure was symptom-limited maximal treadmill test duration (Maxdur); brain MRI was measured 5 years later. Brain MRI measures were analyzed as means and as proportions below the 15th percentile (above the 85th percentile for white matter abnormal tissue volume). RESULTS: Per 1-minute-higher Maxdur, the odds ratio for having less whole brain volume was 0.85 (p = 0.04) and for having low white matter integrity was 0.80 (p = 0.02), adjusted for age, race, sex, clinic, body mass index, smoking, alcohol, diet, physical activity, education, blood pressure, diabetes, total cholesterol, and lung function (plus intracranial volume for white matter integrity). No significant associations were observed between Maxdur and abnormal tissue volume or blood flow in white matter. Findings were similar for associations with continuous brain MRI measures. CONCLUSIONS: Greater physical fitness was associated with more brain volume and greater white matter integrity measured 5 years later in middle-aged adults.

Vascular risk factors, cerebrovascular reactivity, and the default-mode brain network.

Cumulating evidence from epidemiologic studies implicates cardiovascular health and cerebrovascular function in several brain diseases in late life. We examined vascular risk factors with respect to a cerebrovascular measure of brain functioning in subjects in mid-life, which could represent a marker of brain changes in later life. Breath-hold functional MRI (fMRI) was performed in 541 women and men (mean age 50.4 years) from the Coronary Artery Risk Development in Young Adults (CARDIA) Brain MRI sub-study. Cerebrovascular reactivity (CVR) was quantified as percentage change in blood-oxygen level dependent (BOLD) signal in activated voxels, which was mapped to a common brain template and log-transformed. Mean CVR was calculated for anatomic regions underlying the default-mode network (DMN) - a network implicated in AD and other brain disorders - in addition to areas considered to be relatively spared in the disease (e.g. occipital lobe), which were utilized as reference regions. Mean CVR was significantly reduced in the posterior cingulate/precuneus (ß=-0.063, 95% CI: -0.106, -0.020), anterior cingulate (ß=-0.055, 95% CI: -0.101, -0.010), and medial frontal lobe (ß=-0.050, 95% CI: -0.092, -0.008) relative to mean CVR in the occipital lobe, after adjustment for age, sex, race, education, and smoking status, in subjects with pre-hypertension/hypertension compared to normotensive subjects. By contrast, mean CVR was lower, but not significantly, in the inferior parietal lobe (ß=-0.024, 95% CI: -0.062, 0.014) and the hippocampus (ß=-0.006, 95% CI: -0.062, 0.050) relative to mean CVR in the occipital lobe. Similar results were observed in subjects with diabetes and dyslipidemia compared to those without these conditions, though the differences were non-significant. Reduced CVR may represent diminished vascular functionality for the DMN for individuals with prehypertension/hypertension in mid-life, and may serve as a preclinical marker for brain dysfunction in later life.

Vascular factors and multiple measures of early brain health: CARDIA brain MRI study.

OBJECTIVE: To identify early changes in brain structure and function that are associated with cardiovascular risk factors (CVRF). DESIGN: Cross-sectional brain Magnetic Resonance I (MRI) study. SETTING: Community based cohort in three U.S. sites. PARTICIPANTS: A Caucasian and African-American sub-sample (n= 680; mean age 50.3 yrs) attending the 25 year follow-up exam of the Coronary Artery Risk Development in Young Adults Study. PRIMARY AND SECONDARY OUTCOMES: 3T brain MR images processed for quantitative estimates of: total brain (TBV) and abnormal white matter (AWM) volume; white matter fractional anisotropy (WM-FA); and gray matter cerebral blood flow (GM-CBF). Total intracranial volume is TBV plus cerebral spinal fluid (TICV). A Global Cognitive Function (GCF) score was derived from tests of speed, memory and executive function. RESULTS: Adjusting for TICV and demographic factors, current smoking was significantly associated with lower GM-CBF and TBV, and more AWM (all <0.05); SA with lower GM-CBF, WM-FA and TBV (p=0.01); increasing BMI with decreasing GM-CBF (p<0003); hypertension with lower GM-CBF, WM-FA, and TBV and higher AWM (all <0.05); and diabetes with lower TBV (p=0.007). The GCS was lower as TBV decreased, AWM increased, and WM-FA (all p<0.01). CONCLUSION: In middle age adults, CVRF are associated with brain health, reflected in MRI measures of structure and perfusion, and cognitive functioning. These findings suggest markers of mid-life cardiovascular and brain health should be considered as indication for early intervention and future risk of late-life cerebrovascular disease and dementia.

Multiethnic genome-wide association study of cerebral white matter hyperintensities on MRI.

BACKGROUND: The burden of cerebral white matter hyperintensities (WMH) is associated with an increased risk of stroke, dementia, and death. WMH are highly heritable, but their genetic underpinnings are incompletely characterized. To identify novel genetic variants influencing WMH burden, we conducted a meta-analysis of multiethnic genome-wide association studies. METHODS AND RESULTS: We included 21 079 middle-aged to elderly individuals from 29 population-based cohorts, who were free of dementia and stroke and were of European (n=17 936), African (n=1943), Hispanic (n=795), and Asian (n=405) descent. WMH burden was quantified on MRI either by a validated automated segmentation method or a validated visual grading scale. Genotype data in each study were imputed to the 1000 Genomes reference. Within each ethnic group, we investigated the relationship between each single-nucleotide polymorphism and WMH burden using a linear regression model adjusted for age, sex, intracranial volume, and principal components of ancestry. A meta-analysis was conducted for each ethnicity separately and for the combined sample. In the European descent samples, we confirmed a previously known locus on chr17q25 (P=2.7×10(-19)) and identified novel loci on chr10q24 (P=1.6×10(-9)) and chr2p21 (P=4.4×10(-8)). In the multiethnic meta-analysis, we identified 2 additional loci, on chr1q22 (P=2.0×10(-8)) and chr2p16 (P=1.5×10(-8)). The novel loci contained genes that have been implicated in Alzheimer disease (chr2p21 and chr10q24), intracerebral hemorrhage (chr1q22), neuroinflammatory diseases (chr2p21), and glioma (chr10q24 and chr2p16). CONCLUSIONS: We identified 4 novel genetic loci that implicate inflammatory and glial proliferative pathways in the development of WMH in addition to previously proposed ischemic mechanisms.

Web modules on professionalism and ethics.

Health care disciplines have always held resolutely to a commitment to professionalism and high ethical standards. With the present emphasis on public accountability, professionalism and ethics are receiving enhanced attention in health care education and practice. A challenge for radiologists, radiation oncologists, and medical physicists is to define the scope and depth of knowledge about professionalism and ethics that are necessary for the practice of the disciplines. A further challenge is to develop accessible educational materials that encompass this required knowledge. About 2 years ago, the ABR Foundation decided to address these challenges through the development of an ethics and professionalism curriculum and production of a series of Web-based educational modules that follow the curriculum. Six organizations agreed initially to contribute financially to construction of the curriculum and modules and were later joined by a seventh. The curriculum was developed by the ABR Foundation and included in a request for proposals that was widely distributed. Teams of authors for each of 10 modules were selected from respondents to the request for proposals. As the modules were developed, they were reviewed in 3 successive stages, including peer review by members of the ACR Committee on Professionalism and the RSNA-ACR Task Force on an Ethics Curriculum. After revisions were prepared in response to the reviews, the modules were translated into a format compatible with the e-learning platform on which they are mounted. The modules are now available to all who wish to study them.

Science to practice: is t2* enough to assess oxygenation?

As initially reported by Ogawa et al (1), the magnetic resonance (MR) imaging T2* blood oxygen level­dependent (BOLD) signal is sensitive to blood oxygen concentration; however, this signal is also sensitive to a number of other normal and abnormal tissue features. As a result, T2* imaging alone cannot be used to accurately measure vascular oxygenation, much less tissue oxygenation. However, with separate MR imaging measurements of other tissue factors influencing T2*, it might be possible to noninvasively image local tissue oxygen. Such a capability could be of great clinical importance, not only in patients with hypoxic or ischemic disease states, but also in patients with other pathologic conditions that have abnormal respiratory metabolism, such as cancer.

A uniform approach to modeling risk factor relationships for ischemic lesion prevalence and extent: the Women's Health Initiative Magnetic Resonance Imaging study.

BACKGROUND: Both the prevalence and extent of brain magnetic resonance imaging (MRI) abnormalities are related to risk factors for dementia. Typically these associations have been explored separately, but an integrated modeling approach would allow the separate relationships to be consistently described and contrasted. METHODS: Region-specific measures of ischemic lesion volumes were obtained from standardized brain MRI from 1,403 women enrolled in the Women's Health Initiative hormone therapy trials. Mixed-effects mixed-distribution models were fitted to explore jointly the relationships that the region-specific prevalence of ischemic lesions and region-specific ischemic lesion volumes had with risk factors and scores from tests of cognitive function. RESULTS: Women with greater probabilities (prevalence) of having ischemic lesions in brain regions also tended to have larger volumes (extent) of ischemic lesions within the affected regions (p < 0.001). Across the 5 regions included in analyses (frontal, limbic, occipital, parietal and temporal), prevalence and extent varied (p < 0.001). Each was increased among women who were older, had hypertension or who had previously been classified as cognitively impaired (p < 0.01). Additionally, extent was significantly increased among women with a history of smoking (p = 0.02). Cognitive function tests were more strongly related to the extent than prevalence of ischemic lesions and relationships varied among cognitive domains (p < 0.001). CONCLUSIONS: Mixed-effects mixed-distribution models provide a coherent basis for examining relationships involving the prevalence and extent of ischemic brain lesions. Across the cohort and regions we examined, relationships with risk factors and cognitive function appeared to be stronger for extent than for prevalence.

Measuring brain lesion progression with a supervised tissue classification system.

Brain lesions, especially White Matter Lesions (WMLs), are associated with cardiac and vascular disease, but also with normal aging. Quantitative analysis of WML in large clinical trials is becoming more and more important. In this paper, we present a computer-assisted WML segmentation method, based on local features extracted from conventional multi-parametric Magnetic Resonance Imaging (MRI) sequences. A framework for preprocessing the temporal data by jointly equalizing histograms reduces the spatial and temporal variance of data, thereby improving the longitudinal stability of such measurements and hence the estimate of lesion progression. A Support Vector Machine (SVM) classifier trained on expert-defined WML's is applied for lesion segmentation on each scan using the AdaBoost algorithm. Validation on a population of 23 patients from 3 different imaging sites with follow-up studies and WMLs of varying sizes, shapes and locations tests the robustness and accuracy of the proposed segmentation method, compared to the manual segmentation results from an experienced neuroradiologist. The results show that our CAD-system achieves consistent lesion segmentation in the 4D data facilitating the disease monitoring.