Benign breast disease and breast cancer risk in African women: a case-control study.

PURPOSE: To examine the association between benign breast disease (BBD) and breast cancer (BC) in a heterogeneous population of African women. METHODS: BC cases and controls were enrolled in three sub-Saharan African countries, Nigeria, Cameroon, and Uganda, between 1998 and 2018. Multivariable logistic regression was used to test the association between BBD and BC. Risk factors dually associated with BBD and BC were selected. Using a parametric mediation analysis model, we assessed if selected BC risk factors were mediated by BBD. RESULTS: Of 6,274 participants, 55.6% (3,478) were breast cancer cases. 360 (5.7%) self-reported BBD. Fibroadenoma (46.8%) was the most commonly reported BBD. Women with a self-reported history of BBD had greater odds of developing BC than those without (adjusted odds ratio [aOR] 1.47, 95% CI 1.13-1.91). Biopsy-confirmed BBD was associated with BC (aOR 2.25, 95% CI 1.26-4.02). BBD did not significantly mediate the effects of any of the selected BC risk factors. CONCLUSIONS: In this study, BBD was associated with BC and did not significantly mediate the effects of selected BC risk factors.

Common genetic variants influence human subcortical brain structures.

The highly complex structure of the human brain is strongly shaped by genetic influences. Subcortical brain regions form circuits with cortical areas to coordinate movement, learning, memory and motivation, and altered circuits can lead to abnormal behaviour and disease. To investigate how common genetic variants affect the structure of these brain regions, here we conduct genome-wide association studies of the volumes of seven subcortical regions and the intracranial volume derived from magnetic resonance images of 30,717 individuals from 50 cohorts. We identify five novel genetic variants influencing the volumes of the putamen and caudate nucleus. We also find stronger evidence for three loci with previously established influences on hippocampal volume and intracranial volume. These variants show specific volumetric effects on brain structures rather than global effects across structures. The strongest effects were found for the putamen, where a novel intergenic locus with replicable influence on volume (rs945270; P = 1.08 × 10(-33); 0.52% variance explained) showed evidence of altering the expression of the KTN1 gene in both brain and blood tissue. Variants influencing putamen volume clustered near developmental genes that regulate apoptosis, axon guidance and vesicle transport. Identification of these genetic variants provides insight into the causes of variability in human brain development, and may help to determine mechanisms of neuropsychiatric dysfunction.

Polygenic Architecture of Human Neuroanatomical Diversity.

We analyzed the genomic architecture of neuroanatomical diversity using magnetic resonance imaging and single nucleotide polymorphism (SNP) data from >26 000 individuals from the UK Biobank project and 5 other projects that had previously participated in the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) consortium. Our results confirm the polygenic architecture of neuroanatomical diversity, with SNPs capturing from 40% to 54% of regional brain volume variance. Chromosomal length correlated with the amount of phenotypic variance captured, r ~ 0.64 on average, suggesting that at a global scale causal variants are homogeneously distributed across the genome. At a local scale, SNPs within genes (~51%) captured ~1.5 times more genetic variance than the rest, and SNPs with low minor allele frequency (MAF) captured less variance than the rest: the 40% of SNPs with MAF <5% captured

Progression of White Matter Disease and Cortical Thinning Are Not Related in Older Community-Dwelling Subjects.

BACKGROUND AND PURPOSE: We assessed cross-sectional and longitudinal relationships between whole brain white matter hyperintensity (WMH) volume and regional cortical thickness. METHODS: We measured WMH volume and regional cortical thickness on magnetic resonance imaging at ≈73 and ≈76 years in 351 community-dwelling subjects from the Lothian Birth Cohort 1936. We used multiple linear regression to calculate cross-sectional and longitudinal associations between regional cortical thickness and WMH volume controlling for age, sex, Mini Mental State Examination, education, intelligence quotient at age 11, and vascular risk factors. RESULTS: We found cross-sectional associations between WMH volume and cortical thickness within and surrounding the Sylvian fissure at 73 and 76 years (rho=-0.276, Q=0.004). However, we found no significant longitudinal associations between (1) baseline WMH volume and change in cortical thickness; (2) baseline cortical thickness and change in WMH volume; or (3) change in WMH volume and change in cortical thickness. CONCLUSIONS: Our results show that WMH volume and cortical thinning both worsen with age and are associated cross-sectionally within and surrounding the Sylvian fissure. However, changes in WMH volume and cortical thinning from 73 to 76 years are not associated longitudinally in these relatively healthy older subjects. The underlying cause(s) of WMH growth and cortical thinning have yet to be fully determined.

Volumetric and Correlational Implications of Brain Parcellation Method Selection: A 3-Way Comparison in the Frontal Lobes.

OBJECTIVE: The aims of this study were to compare distinct brain frontal lobe parcellation methods across 90 brain magnetic resonance imaging scans and examine their associations with cognition in older age. METHODS: Three parcellation methods (Manual, FreeSurfer, and Stereology) were applied to T1-weighted magnetic resonance imaging of 90 older men, aged ∼ 73 years. A measure of general fluid intelligence (gf) associated with dorsolateral frontal regions was also derived from a contemporaneous psychological test battery. RESULTS: Despite highly discordant raw volumes for the same nominal regions, Manual and FreeSurfer (but not Stereology) left dorsolateral measures were significantly correlated with gf (r > 0.22), whereas orbital and inferior lateral volumes were not, consistent with the hypothesized frontal localization of gf. CONCLUSIONS: Individual differences in specific frontal lobe brain volumes--variously measured--show consistent associations with cognitive ability in older age. Importantly, differences in parcellation protocol for some regions that may impact the outcome of brain-cognition analyses are discussed.

Brain volumetric changes and cognitive ageing during the eighth decade of life.

Later-life changes in brain tissue volumes--decreases in the volume of healthy grey and white matter and increases in the volume of white matter hyperintensities (WMH)--are strong candidates to explain some of the variation in ageing-related cognitive decline. We assessed fluid intelligence, memory, processing speed, and brain volumes (from structural MRI) at mean age 73 years, and at mean age 76 in a narrow-age sample of older individuals (n = 657 with brain volumetric data at the initial wave, n = 465 at follow-up). We used latent variable modeling to extract error-free cognitive levels and slopes. Initial levels of cognitive ability were predictive of subsequent brain tissue volume changes. Initial brain volumes were not predictive of subsequent cognitive changes. Brain volume changes, especially increases in WMH, were associated with declines in each of the cognitive abilities. All statistically significant results were modest in size (absolute r-values ranged from 0.114 to 0.334). These results build a comprehensive picture of macrostructural brain volume changes and declines in important cognitive faculties during the eighth decade of life.

Circulating inflammatory markers are associated with magnetic resonance imaging-visible perivascular spaces but not directly with white matter hyperintensities.

BACKGROUND AND PURPOSE: White matter hyperintensities (WMH) and perivascular spaces (PVS) are features of small vessel disease, found jointly on MRI of older people. Inflammation is a prominent pathological feature of small vessel disease. We examined the association between inflammation, PVS, and WMH in the Lothian Birth Cohort 1936 (N=634). METHODS: We measured plasma fibrinogen, C-reactive protein, and interleukin-6 and rated PVS in 3 brain regions. We measured WMH volumetrically and visually using the Fazekas scale. We derived latent variables for PVS, WMH, and Inflammation from measured PVS, WMH, and inflammation markers and modelled associations using structural equation modelling. RESULTS: After accounting for age, sex, stroke, and vascular risk factors, PVS were significantly associated with WMH (ß=0.47; P<0.0001); Inflammation was weakly but significantly associated with PVS (ß=0.12; P=0.048), but not with WMH (ß=0.02; P=NS). CONCLUSIONS: Circulating inflammatory markers are weakly associated with MR-visible PVS, but not directly with WMH. Longitudinal studies should examine whether visible PVS predate WMH progression and whether inflammation modulators can prevent small vessel disease.

Differentiation of calcified regions and iron deposits in the ageing brain on conventional structural MR images.

PURPOSE: In the human brain, minerals such as iron and calcium accumulate increasingly with age. They typically appear hypointense on T2*-weighted MRI sequences. This study aims to explore the differentiation and association between calcified regions and noncalcified iron deposits on clinical brain MRI in elderly, otherwise healthy subjects. MATERIALS AND METHODS: Mineral deposits were segmented on co-registered T1- and T2*-weighted sequences from 100 1.5 Tesla MRI datasets of community-dwelling individuals in their 70s. To differentiate calcified regions from noncalcified iron deposits we developed a method based on their appearance on T1-weighted images, which was validated with a purpose-designed phantom. Joint T1- and T2*-weighted intensity histograms were constructed to measure the similarity between the calcified and noncalcified iron deposits using a Euclidean distance based metric. RESULTS: We found distinct distributions for calcified regions and noncalcified iron deposits in the cumulative joint T1- and T2*-weighted intensity histograms across all subjects (correlations ranging from 0.02 to 0.86; mean = 0.26 ± 0.16; t = 16.93; P < 0.001) consistent with differences in iron and calcium signal in the phantom. The mean volumes of affected tissue per subject for calcified and noncalcified deposits were 236.74 ± 309.70 mm(3) and 283.76 ± 581.51 mm(3); respectively. There was a positive association between the mineral depositions (ß = 0.32, P < 0.005), consistent with existing literature reports. CONCLUSION: Calcified mineral deposits and noncalcified iron deposits can be distinguished from each other by signal intensity changes on conventional 1.5T T1-weighted MRI and are significantly associated in brains of elderly, otherwise healthy subjects.

A histogram-based similarity measure for quantitative magnetic resonance imaging: application in acute mild traumatic brain injury.

OBJECTIVES: The most commonly used summary metric in neuroimaging is the mean value, but this pays little attention to the shape of the data distribution and can therefore be insensitive to subtle changes that alter the data distribution. METHODS: We propose a distributional-based metric called the normalized histogram similarity measure (HSM) for characterization of quantitative images. We applied HSM to quantitative magnetic resonance imaging T1 relaxation data of 44 patients with mild traumatic brain injury and compared with data of 43 age-matched controls. RESULTS: Significant differences were found between the patients and the controls in 8 gray matter regions using the HSM whereas in only 1 gray matter region based on the mean values. CONCLUSIONS: Our results show that HSM is more sensitive than the standard mean values in detecting brain tissue changes. Future studies on brain tissue properties using quantitative magnetic resonance imaging should consider the use of HSM to properly capture any tissue changes.

Close correlation between quantitative and qualitative assessments of white matter lesions.

BACKGROUND: White matter lesions (WML) increase with age and are associated with stroke, cognitive decline and dementia. They can be visually rated or computationally assessed. METHODS: We compared WML Fazekas visual rating scores and volumes, determined using a validated multispectral image-fusion technique, in Magnetic Resonance Imaging from 672 participants of the Lothian Birth Cohort 1936 and sought explanations for subjects in whom the correlation (Spearman's ρ) between the total Fazekas score (summed deep and periventricular ratings, 0-6) and WML volume did not concur (z-score difference >1). Infarcts were identified separately. RESULTS: The median WML Fazekas score was 2 [inter-quartile range (IQR): 2], median WML volume 7.7 ml (IQR: 13.6 ml) and median infarct volume (n = 95) 0.98 ml. Score and volume were highly correlated (Spearman's ρ = 0.78, p < 0.001). Infarcts did not alter the correlation. Minor discordance occurred in 94/672 (14%) subjects, most with total Fazekas score of 1 (n = 20, WML volume = 4.5-14.8 ml) or 2 (n = 50, WML volume = 0.1-34.4 ml). The main reasons were: subtle WML identified visually but omitted from the volume; prominent ventricular caps but thin body lining giving a periventricular score of 1/2 but large WML volume, and small deep focal lesions which increase the score disproportionally when beginning to coalesce with little change in WML volume. CONCLUSIONS: WML rating scores and volumes provide near-equivalent estimates of WML burden, therefore either can be used depending on research circumstances. Even closer agreement could result from improved computational detection of subtle WML and modified visual ratings to differentiate prominent ventricular caps from thin periventricular linings, and small non-coalescent from early coalescent deep WML.

Brain atrophy associations with white matter lesions in the ageing brain: the Lothian Birth Cohort 1936.

OBJECTIVE: Cerebral atrophy and white matter lesions (WMLs) are common in older people with common risk factors, but it is unclear if they are related. We investigated whether and to what degree they are related in deep and superficial structures using both volumetric and visual ratings. METHODS: The intracranial, total brain tissue (TBV), cerebrospinal fluid (CSF), ventricular superficial subarachnoid space (SSS), grey matter, normal-appearing white matter, WMLs, and combined CSF, venous sinuses and dural volumes were measured. WMLs were also rated using the Fazekas scale. RESULTS: Amongst 672 adults (mean age 73 ± 1 years), WMLs were associated with global brain atrophy (TBV, ß = -0.43 mm(3), P < 0.01) and specifically with deep (ventricular enlargement, ß = 0.10 mm(3), P = 0.03) rather than superficial (SSS, ß = 0.09 mm(3), P = 0.55) atrophy. A 1 mm(3) increase in WML volume was associated with a 0.43 mm(3) decrease in TBV and 0.10 mm(3) increase in ventricular volume. WMLs were associated with combined CSF + Venous Sinuses + Meninges volumes, but not CSF volume alone. Some of the associations were attenuated after correcting for vascular risk factors. The associations were similar for visually scored WMLs. CONCLUSION: WMLs are associated with brain atrophy, primarily with deep brain structures. Measures of brain atrophy should include all intracranial structures when assessing brain shrinkage.

Assessing the performance of atlas-based prefrontal brain parcellation in an aging cohort.

OBJECTIVE: It is unclear whether atlas-based parcellation is suitable in aging cohorts because age-related brain changes confound the performance of automatic methods. We assessed atlas-based parcellation of the prefrontal lobe in an aging population using visual assessment and volumetric and spatial concordance. METHODS: We used an atlas-based approach to parcellate brain MR images of 90 non-demented healthy adults, aged 72.7 ± 0.7 years, and assessed performance. RESULTS: Volumetric assessment showed that both single-atlas- and multi-atlas-based methods performed acceptably (intraclass correlation coefficient [ICC], 0.74-0.76). Spatial overlap measurements showed that multi-atlas (dice coefficient [DC], 0.84) offered an improvement over the single-atlas (DC, 0.75-0.78) approach. Visual assessment also showed that multi-atlas outperformed single atlas and identified an additional postprocessing step of cerebrospinal fluid removal, enhancing concordance (intraclass correlation coefficient, 0.86; DC, 0.89). CONCLUSIONS: Atlas-based parcellation performed reasonably well in the aging population. Rigorous performance assessment aided method refinement and emphasizes the importance of age matching and postprocessing. Further work is required in more varied subjects.

External Evaluation of a Mammography-based Deep Learning Model for Predicting Breast Cancer in an Ethnically Diverse Population.

Purpose: To externally evaluate a mammography-based deep learning (DL) model (Mirai) in a high-risk racially diverse population and compare its performance with other mammographic measures. Materials and Methods: A total of 6435 screening mammograms in 2096 female patients (median age, 56.4 years ± 11.2 [SD]) enrolled in a hospital-based case-control study from 2006 to 2020 were retrospectively evaluated. Pathologically confirmed breast cancer was the primary outcome. Mirai scores were the primary predictors. Breast density and Breast Imaging Reporting and Data System (BI-RADS) assessment categories were comparative predictors. Performance was evaluated using area under the receiver operating characteristic curve (AUC) and concordance index analyses. Results: Mirai achieved 1- and 5-year AUCs of 0.71 (95% CI: 0.68, 0.74) and 0.65 (95% CI: 0.64, 0.67), respectively. One-year AUCs for nondense versus dense breasts were 0.72 versus 0.58 (P = .10). There was no evidence of a difference in near-term discrimination performance between BI-RADS and Mirai (1-year AUC, 0.73 vs 0.68; P = .34). For longer-term prediction (2-5 years), Mirai outperformed BI-RADS assessment (5-year AUC, 0.63 vs 0.54; P < .001). Using only images of the unaffected breast reduced the discriminatory performance of the DL model (P < .001 at all time points), suggesting that its predictions are likely dependent on the detection of ipsilateral premalignant patterns. Conclusion: A mammography DL model showed good performance in a high-risk external dataset enriched for African American patients, benign breast disease, and BRCA mutation carriers, and study findings suggest that the model performance is likely driven by the detection of precancerous changes.Keywords: Breast, Cancer, Computer Applications, Convolutional Neural Network, Deep Learning Algorithms, Informatics, Epidemiology, Machine Learning, Mammography, Oncology, Radiomics Supplemental material is available for this article. © RSNA, 2023See also commentary by Kontos and Kalpathy-Cramer in this issue.

Benign breast disease and breast cancer risk in African women: A case-control study.

Purpose: To examine the association between benign breast disease (BBD) and breast cancer (BC) in a heterogeneous population of African women. Methods: BC cases and matched controls were enrolled in three sub-Saharan African countries, Nigeria Cameroon, and Uganda, between 1998-2018. Multivariable logistic regression was used to test the association between BBD and BC. Risk factors dually associated with BBD and BC were selected. Using a parametric mediation analysis model, we assessed if selected BC risk factors were mediated by BBD. Results: Of 6418 participants, 55.7% (3572) were breast cancer cases. 360 (5.7%) self-reported BBD. Fibroadenoma (46.8%) was the most reported BBD. Women with a self-reported history of BBD had greater odds of developing BC than those without (adjusted odds ratio [aOR] = 1.47, 95% CI: 1.13-1.91). Biopsy-confirmed BBD was associated with BC (aOR = 3.11, 95% CI: 1.78-5.44). BBD did not significantly mediate the effects of any of the selected BC risk factors. Conclusions: In this study, BBD was associated with BC and did not significantly mediate the effects of selected BC risk factors.

Sleep quality, perivascular spaces and brain health markers in ageing - A longitudinal study in the Lothian Birth Cohort 1936.

BACKGROUND: Sleep is thought to play a major role in brain health and general wellbeing. However, few longitudinal studies have explored the relationship between sleep habits and imaging markers of brain health, particularly markers of brain waste clearance such as perivascular spaces (PVS), of neurodegeneration such as brain atrophy, and of vascular disease, such as white matter hyperintensities (WMH). We explore these associations using data collected over 6 years from a birth cohort of older community-dwelling adults in their 70s. METHOD: We analysed brain MRI data from ages 73, 76 and 79 years, and self-reported sleep duration, sleep quality and vascular risk factors from community-dwelling participants in the Lothian Birth Cohort 1936 (LBC1936) study. We calculated sleep efficiency (at age 76), quantified PVS burden (at age 73), and WMH and brain volumes (age 73 to 79), calculated the white matter damage metric, and used structural equation modelling (SEM) to explore associations and potential causative pathways between indicators related to brain waste cleaning (i.e., sleep and PVS burden), brain and WMH volume changes during the 8th decade of life. RESULTS: Lower sleep efficiency was associated with a reduction in normal-appearing white matter (NAWM) volume (ß = 0.204, P = 0.009) from ages 73 to 79, but not concurrent volume (i.e. age 76). Increased daytime sleep correlated with less night-time sleep (r = -0.20, P < 0.001), and with increasing white matter damage metric (ß = -0.122, P = 0.018) and faster WMH growth (ß = 0.116, P = 0.026). Shorter night-time sleep duration was associated with steeper 6-year reduction of NAWM volumes (ß = 0.160, P = 0.011). High burden of PVS at age 73 (volume, count, and visual scores), was associated with faster deterioration in white matter: reduction of NAWM volume (ß = -0.16, P = 0.012) and increasing white matter damage metric (ß = 0.37, P < 0.001) between ages 73 and 79. On SEM, centrum semiovale PVS burden mediated 5% of the associations between sleep parameters and brain changes. CONCLUSION: Sleep impairments, and higher PVS burden, a marker of impaired waste clearance, were associated with faster loss of healthy white matter and increasing WMH in the 8th decade of life. A small percentage of the effect of sleep in white matter health was mediated by the burden of PVS consistent with the proposed role for sleep in brain waste clearance.

Comparative study of standard space and real space analysis of quantitative MR brain data.

PURPOSE: To compare the robustness of region of interest (ROI) analysis of magnetic resonance imaging (MRI) brain data in real space with analysis in standard space and to test the hypothesis that standard space image analysis introduces more partial volume effect errors compared to analysis of the same dataset in real space. MATERIALS AND METHODS: Twenty healthy adults with no history or evidence of neurological diseases were recruited; high-resolution T(1)-weighted, quantitative T(1), and B(0) field-map measurements were collected. Algorithms were implemented to perform analysis in real and standard space and used to apply a simple standard ROI template to quantitative T(1) datasets. Regional relaxation values and histograms for both gray and white matter tissues classes were then extracted and compared. RESULTS: Regional mean T(1) values for both gray and white matter were significantly lower using real space compared to standard space analysis. Additionally, regional T(1) histograms were more compact in real space, with smaller right-sided tails indicating lower partial volume errors compared to standard space analysis. CONCLUSION: Standard space analysis of quantitative MRI brain data introduces more partial volume effect errors biasing the analysis of quantitative data compared to analysis of the same dataset in real space.

Sleep and brain morphological changes in the eighth decade of life.

OBJECTIVE: Sleep is important for brain health. We analysed associations between usual sleep habits and magnetic resonance imaging (MRI) markers of neurodegeneration (brain atrophy), vascular damage (white matter hyperintensities, WMH) and waste clearance (perivascular spaces, PVS) in older community-dwelling adults. METHOD: We collected self-reported usual sleep duration, quality and medical histories from the Lothian Birth Cohort 1936 (LBC1936) age 76 years and performed brain MRI. We calculated sleep efficiency, measured WMH and brain volumes, quantified PVS, and assessed associations between sleep measures and brain markers in multivariate models adjusted for demographic and medical history variables. RESULTS: In 457 subjects (53% males, mean age 76 ± 0.65 years), we found: brain and white matter loss with increased weekend daytime sleep (ß = -0.114, P = 0.03; ß = -0.122, P = 0.007 respectively), white matter loss with less efficient sleep (ß = 0.132, P = 0.011) and PVS increased with interrupted sleep (OR 1.84 95% CI, P = 0.025). CONCLUSION: Cross-sectional associations of sleep parameters with brain atrophy and more PVS suggest adverse relationships between usual sleep habits and brain health in older people that should be evaluated longitudinally.

MR imaging in Duchenne muscular dystrophy: quantification of T1-weighted signal, contrast uptake, and the effects of exercise.

PURPOSE: To quantify the differences between normal and corticosteroid-treated Duchenne muscular dystrophy (DMD) lower limb muscle using signal intensity measurements on T(1)-weighted and gadolinium contrast-enhanced images and by measurement of muscle T(2) values, and to investigate the effect of exercise. MATERIALS AND METHODS: Eleven ambulant boys with DMD were imaged at 3 Tesla (T(1)-weighted, gadolinium enhancement and T(2) measurement) before stepping exercise and again (gadolinium, T(2) measurement) 4 days later and were compared with five healthy controls imaged 4 days before and after stepping exercise. Muscle region-of-interest signal intensities were referenced to external oil and gadolinium phantoms. RESULTS: DMD thigh muscle T(2) values were significantly higher than normal values with the exception of the gracilis muscle. Eight of nine muscles studied showed a significant increase in T(1)-w signal intensity in DMD as compared to normal muscle, suggestive of increased fat infiltration in DMD muscle. In the DMD boys, an exercise effect (increased contrast enhancement) was only seen for the tibialis anterior muscle. CONCLUSION: Referenced signal intensity measurements may be used to quantify differences between dystrophic and normal muscle without T(1) mapping. Stepping exercise does not have a large impact on subsequent MR imaging of dystrophic muscle.

Identification of the presence of ischaemic stroke lesions by means of texture analysis on brain magnetic resonance images.

BACKGROUND: The differential quantification of brain atrophy, white matter hyperintensities (WMH) and stroke lesions is important in studies of stroke and dementia. However, the presence of stroke lesions is usually overlooked by automatic neuroimage processing methods and the-state-of-the-art deep learning schemes, which lack sufficient annotated data. We explore the use of radiomics in identifying whether a brain magnetic resonance imaging (MRI) scan belongs to an individual that had a stroke or not. MATERIALS AND METHODS: We used 1800 3D sets of MRI data from three prospective studies: one of stroke mechanisms and two of cognitive ageing, evaluated 114 textural features in WMH, cerebrospinal fluid, deep grey and normal-appearing white matter, and attempted to classify the scans using a random forest and support vector machine classifiers with and without feature selection. We evaluated the discriminatory power of each feature independently in each population and corrected the result against Type 1 errors. We also evaluated the influence of clinical parameters in the classification results. RESULTS: Subtypes of ischaemic strokes (i.e. lacunar vs. cortical) cannot be discerned using radiomics, but the presence of a stroke-type lesion can be ascertained with accuracies ranging from 0.7 < AUC < 0.83. Feature selection, tissue type, stroke subtype and MRI sequence did not seem to determine the classification results. From all clinical variables evaluated, age correlated with the proportion of images classified correctly using either different or the same descriptors (Pearson r = 0.31 and 0.39 respectively, p < 0.001). CONCLUSIONS: Texture features in conventionally automatically segmented tissues may help in the identification of the presence of previous stroke lesions on an MRI scan, and should be taken into account in transfer learning strategies of the-state-of-the-art deep learning schemes.