MRI Phenotypes Research Articles

P15.08.A MRI PHENOTYPES OF GLIOBLASTOMAS EARLY AFTER TREATMENT ARE SUGGESTIVE OF OVERALL PATIENT SURVIVAL

Abstract BACKGROUND Distinguishing between true tumor progression (TP) and treatment induced abnormalities (e.g. pseudo-progression (PP) after radiotherapy) early after treatment on conventional MRI scans remains challenging for glioblastomas (GBM). Despite MRI perfusion techniques being promising in differentiating between TP and PP, individual imaging markers have only showed a modest association with tumor progression and overall survival. Therefore, we aimed to establish brain MRI phenotypes of glioblastomas early after treatment by combined analysis of radiological scoring of structural and perfusion tumor characteristics and assessed the relation with recurrence rate and overall-survival time. MATERIAL AND METHODS MR images and clinical data were retrospectively collected from 67 histologically confirmed GBM patients. Non-invasive (arterial spin labeling, ASL) and contrast-based (dynamic susceptibility contrast DSC) perfusion MRI, T2-FLAIR and post-contrast 3D-T1w scans were acquired on a 3T-MR scanner at around 3 months post-radiotherapy. Structural (presence and type of (enhancing) lesion patterns) and perfusion (increase, decrease or iso) MRI markers (MRIm) were visually scored by a neuroradiologist. Additionally, volume and eccentricity of the enhancing and T2-hyperintense areas were calculated.Subsequently, patients were grouped based on their similarity on tumor perfusion and structural brain MRIm by means of hierarchical clustering. These subgroups were then compared using a chi-square test and one way ANOVA. Survival outcome was assessed using a multivariate cox proportional hazard model, both unadjusted and adjusted for age, KPS and extension of resection. Progression at 9 months (when unavailable at 3/6 months) post-radiotherapy and clinical parameters were compared between subgroups using a chi-square test. RESULTS Four subgroups of patients were established with significantly different tumor perfusion and structural radiological markers (p≤0.05) and therefore showed distinct MRI phenotypes of GBM. Subgroup 1 had less aggressive MRIm with mostly nodular enhancing lesions; subgroup 2 had the least aggressive MRIm with mostly patchy enhancing lesions; subgroup 3 showed the most aggressive MRIm and subgroup 4 showed moderately aggressive MRIm. TP and PP did not differ significantly between subgroups (p = 0.775). However, after performing an adjusted and unadjusted cox regression analysis we found that subgroup 1 and 3 were at increased risk of mortality, respectively (subgroup 1 - HR: 2.649 (95% CI: 1.119 - 6.270; subgroup 3 - HR: 2.363 (95% CI: 1.110 - 5.030); p=0.03). CONCLUSION Our study suggests that differences in MRI phenotypes of GBM at 3 months post-radiotherapy can be indicative of overall patient survival. The early prognostic information our method provides might in the future be beneficial for treatment decisions in GBM patients.

Depth of Radiographic Response and Time to Tumor Regrowth Predicts Overall Survival Following Anti-VEGF Therapy in Recurrent Glioblastoma.

Antiangiogenic therapies are known to cause high radiographic response rates due to reduction in vascular permeability resulting in a lower degree of contrast extravasation. In this study, we investigate the prognostic ability for model-derived parameters describing enhancing tumor volumetric dynamics to predict survival in recurrent glioblastoma treated with antiangiogenic therapy. N = 276 patients in two phase II trials were used as training data, including bevacizumab ± irinotecan (NCT00345163) and cabozantinib (NCT00704288), and N = 74 patients in the bevacizumab arm of a phase III trial (NCT02511405) were used for validation. Enhancing volumes were estimated using T1 subtraction maps, and a biexponential model was used to estimate regrowth (g) and regression (d) rates, time to tumor regrowth (TTG), and the depth of response (DpR). Response characteristics were compared to diffusion MR phenotypes previously shown to predict survival. Optimized thresholds occurred at g = 0.07 months-1 (phase II: HR = 0.2579, P = 5 × 10-20; phase III: HR = 0.2197, P = 5 × 10-5); d = 0.11 months-1 (HR = 0.3365, P < 0.0001; HR = 0.3675, P = 0.0113); TTG = 3.8 months (HR = 0.2702, P = 6 × 10-17; HR = 0.2061, P = 2 × 10-5); and DpR = 11.3% (HR = 0.6326, P = 0.0028; HR = 0.4785, P = 0.0206). Multivariable Cox regression controlling for age and baseline tumor volume confirmed these factors as significant predictors of survival. Patients with a favorable pretreatment diffusion MRI phenotype had a significantly longer TTG and slower regrowth. Recurrent glioblastoma patients with a large, durable radiographic response to antiangiogenic agents have significantly longer survival. This information is useful for interpreting activity of antiangiogenic agents in recurrent glioblastoma.

Testing for associations between HbA1c levels, polygenic risk and brain health in UK Biobank (N = 39 283).

To investigate whether continuous HbA1c levels and HbA1c-polygenic risk scores (HbA1c-PRS) are significantly associated with worse brain health independent of type 2 diabetes (T2D) diagnosis (vs. not), by examining brain structure and cognitive test score phenotypes. Using UK Biobank data (n = 39 283), we tested whether HbA1c levels and/or HbA1c-PRS were associated with cognitive test scores and brain imaging phenotypes. We adjusted for confounders of age, sex, Townsend deprivation score, level of education, genotyping chip, eight genetic principal components, smoking, alcohol intake frequency, cholesterol medication, body mass index, T2D and apolipoprotein (APOE) e4 dosage. We found an association between higher HbA1c levels and poorer performance on symbol digit substitution scores (standardized beta [β] = -0.022, P = .001) in the fully adjusted model. We also found an association between higher HbA1c levels and worse brain MRI phenotypes of grey matter (GM; fully-adjusted β = -0.026, P < .001), whole brain volume (β = -0.072, P = .0113) and a general factor of frontal lobe GM (β = -0.022, P < .001) in partially and fully adjusted models. HbA1c-PRS were significantly associated with GM volume in the fully adjusted model (β = -0.010, P = .0113); however, when adjusted for HbA1c levels, the association was not significant. Our findings suggest that measured HbA1c is associated with poorer cognitive health, and that HbA1c-PRS do not add significant information to this.

MRI-based breast cancer radiogenomics using RNA profiling: association with subtypes in a single-center prospective study

BackgroundThere are few prospective studies on the correlations between MRI features and whole RNA-sequencing data in breast cancer according to molecular subtypes. The purpose of our study was to explore the association between genetic profiles and MRI phenotypes of breast cancer and to identify imaging markers that influences the prognosis and treatment according to subtypes.MethodsFrom June 2017 to August 2018, MRIs of 95 women with invasive breast cancer were prospectively analyzed, using the breast imaging-reporting and data system and texture analysis. Whole RNA obtained from surgical specimens was analyzed using next-generation sequencing. The association between MRI features and gene expression profiles was analyzed in the entire tumor and subtypes. Gene networks, enriched functions, and canonical pathways were analyzed using Ingenuity Pathway Analysis. The P value for differential expression was obtained using a parametric F test comparing nested linear models and adjusted for multiple testing by reporting Q value.ResultsIn 95 participants (mean age, 53 years ± 11 [standard deviation]), mass lesion type was associated with upregulation of CCL3L1 (sevenfold) and irregular mass shape was associated with downregulation of MIR421 (sixfold). In estrogen receptor-positive cancer with mass lesion type, CCL3L1 (21-fold), SNHG12 (11-fold), and MIR206 (sevenfold) were upregulated, and MIR597 (265-fold), MIR126 (12-fold), and SOX17 (fivefold) were downregulated. In triple-negative breast cancer with increased standard deviation of texture analysis on precontrast T1-weighted imaging, CLEC3A (23-fold), SRGN (13-fold), HSPG2 (sevenfold), KMT2D (fivefold), and VMP1 (fivefold) were upregulated, and IGLC2 (73-fold) and PRDX4 (sevenfold) were downregulated (all, P < 0.05 and Q < 0.1). Gene network and functional analysis showed that mass type estrogen receptor-positive cancers were associated with cell growth, anti-estrogen resistance, and poor survival.ConclusionMRI characteristics are associated with the different expressions of genes related to metastasis, anti-drug resistance, and prognosis, depending on the molecular subtypes of breast cancer.

Analysis of carotid vulnerable plaque MRI high-risk features and clinical risk factors associated with concomitant acute cerebral infarction

BackgroundThis study aimed to investigate the correlation between the high-risk characteristics of high-resolution MRI carotid vulnerable plaques and the clinical risk factors and concomitant acute cerebral infarction (ACI).MethodsForty-five patients diagnosed with a single vulnerable carotid plaque by MRI were divided into two groups based on whether they had ipsilateral ACI. The clinical risk factors and the observation values or frequency of occurrence of high-risk MRI phenotypes of plaque volume, LRNC, IPH and ulcer were statistically compared between the two groups.ResultsA total of 45 vulnerable carotid artery plaques were found in 45 patients, 23 patients with ACI and 22 patients without ACI. There were no significant differences in age, sex, smoking, serum TC, TG and LDL between the two groups (all P > 0.05), but the ACI group had significantly more patients with hypertension (P < 0.05) and the without ACI group coronary heart disease (P < 0.05). The volume of vulnerable carotid plaque in the group with ACI (1004.19 ± 663.57 mm3) was significantly larger than that in the group without ACI (487.21 ± 238.64 mm3) (P < 0.05). The phenotype of vulnerable carotid artery plaque was 13 cases of LRNC, 8 cases of LRNC + IPH, 5 cases of LRNC + Ulcer, and 19 cases of LRNC + IPH + Ulcer. There was no significant difference in this distribution between the two groups (all P > 0.05) with the exception of LRNC + IPH + Ulcer. The 14 cases of LRNC + IPH + LRNC + IPH + Ulcer (60.87%) in the group with ACI and was significantly greater than the 5 (22.73%) in patients without ACI (P < 0.05).ConclusionIt is preliminarily thought that hypertension is the main clinical risk factor for vulnerable carotid plaques with ACI and the combination of plaque volume with vulnerable carotid plaque and LRNC + IPH + Ulcer is a high-risk factor for complicated ACI. It has high clinical therapeutic value due to the accurate diagnosis of responsible vessels and plaques with high-resolution MRI.

Retrospective Analysis of Factors Associated with the Treatment Outcomes of Intradiscal Platelet-Rich Plasma-Releasate Injection Therapy for Patients with Discogenic Low Back Pain.

Background and Objectives: Recently, the clinical application of platelet-rich plasma (PRP) has gained popularity for the treatment of degenerative disc diseases. However, the regenerative effects and factors associated with treatment outcomes after intradiscal injection of PRP remain unknown. This study aimed to evaluate time-dependent changes in imaging findings related to intervertebral disc (IVD) degeneration and to identify factors associated with the outcomes of PRP injection therapy. Materials and Methods: A retrospective analysis of a previous randomized clinical trial of intradiscal injection of the releasate isolated from PRP (PRPr) in patients with discogenic low back pain (LBP) was performed. Radiographic parameters (segmental angulation and lumbar lordosis) and MRI phenotypes, including Modic changes, disc bulge, and high-intensity zones (HIZs), were evaluated at baseline and 6 and 12 months post-injection. Treatment outcomes were evaluated based on the degree of LBP and LBP-related disability at 12 months post-injection. Results: A total of 15 patients (mean age: 33.9 ± 9.5 years) were included in this study. Radiographic parameters showed no significant changes after the PRPr injection. There were no remarkable changes in the prevalence or type of MRI phenotype. Treatment outcomes were significantly improved after treatment; however, the number of targeted discs and the presence of posterior HIZs at baseline were significantly but negatively associated with treatment outcomes. Conclusions: Intradiscal injection of PRPr significantly improved LBP and LBP-related disability 12 months post-injection; however, patients with multiple target lesions or posterior HIZs at baseline were significantly associated with poor treatment outcomes.

Telomere length and brain imaging phenotypes in UK Biobank.

Telomeres form protective caps at the ends of chromosomes, and their attrition is a marker of biological aging. Short telomeres are associated with an increased risk of neurological and psychiatric disorders including dementia. The mechanism underlying this risk is unclear, and may involve brain structure and function. However, the relationship between telomere length and neuroimaging markers is poorly characterized. Here we show that leucocyte telomere length (LTL) is associated with multi-modal MRI phenotypes in 31,661 UK Biobank participants. Longer LTL is associated with: i) larger global and subcortical grey matter volumes including the hippocampus, ii) lower T1-weighted grey-white tissue contrast in sensory cortices, iii) white-matter microstructure measures in corpus callosum and association fibres, iv) lower volume of white matter hyperintensities, and v) lower basal ganglia iron. Longer LTL was protective against certain related clinical manifestations, namely all-cause dementia (HR 0.93, 95% CI: 0.91-0.96), but not stroke or Parkinson's disease. LTL is associated with multiple MRI endophenotypes of neurodegenerative disease, suggesting a pathway by which longer LTL may confer protective against dementia.

MRI phenotypes associated with breast cancer predisposing genetic variants, a multisite review

PurposeExamine MRI phenotypes of breast cancers arising in patients with various pathogenic variants, to assess for imaging trends and associations. MethodMultisite retrospective review evaluated 410 patients from 2001 to 2020 with breast cancer and a predisposing pathogenic variant who underwent breast MRI at time of cancer diagnosis. Dominant malignant lesion features were reported, including lesion type (mass versus non-mass enhancement), size, shape, margin, internal enhancement pattern, plus other features. Kruskal-Wallis test, Fisher’s exact test, and pairwise comparisons performed comparing imaging manifestations for the most frequent genetic results. ResultsBRCA1 (29.5 %) and BRCA2 (25.9 %) variants were most common, followed by CHEK2 (16.6 %), ATM (8.0 %), and PALB2 (6.3 %), with significant associated differences in race/ethnicity (p = 0.040), age at cancer diagnosis (p = 0.005), tumor shapes (p = 0.001), margins (p < 0.001), grade (p < 0.001), internal enhancement pattern (rim enhancement) (p < 0.001), kinetics (washout) (p < 0.001), and presence of necrosis (p < 0.001). CHEK2 and ATM tumors were often lower grade with spiculated margins (CHEK2: 47.1 %, ATM: 45.5 %), rarely exhibiting washout or tumor necrosis (p < 0.001), and were mostly comprised of luminal molecular subtypes (CHEK2: 88.2 %, ATM: 90.9 %). BRCA1 tumors had the highest proportions with round shape (31.4 %), circumscribed margins (24.0 %), rim enhancement (24.0 %), washout (58.7 %), and necrosis (19.8 %), with 47.9 % comprised of triple negative subtype. Bilateral mastectomy was performed in higher proportions of patients with BRCA1 (84.3 %) and BRCA2 (75.5 %) variants compared to others. ConclusionsGenetic and molecular profiles of breast cancers demonstrate reproducible MRI phenotypes.

Abstract PD16-07: Association of MRI morphologic phenotype from unsupervised learning with breast cancer subtypes and treatment response

Abstract Background Breast cancer is a heterogeneous disease and can be categorized into clinically or biologically meaningful subtypes. Predictive models built by MRI biomarkers performed better when they are optimized by breast cancer subtype than models optimized in the full cohort [1]. Functional tumor volume (FTV) measured from breast MRI has been used to assess tumor response to neoadjuvant therapy longitudinally in the I-SPY 2 TRIAL. Tumors show distinct morphological patterns, or phenotypes, on MRI. Previous studies demonstrated that either qualitative or quantitative measurements characterizing these phenotypes may provide additional information about treatment response [2,3]. In this study, we investigated if MRI morphologic phenotypes defined by unsupervised clustering is associated with breast cancer subtype and pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC). Methods A cohort of 990 patients enrolled in the I-SPY 2 TRIAL were included in this retrospective analysis. Patients were randomized to one of nine experimental drug arms or standard NAC, and pCR was assessed at surgery. DCE-MRI data acquired at pretreatment (T0) and early treatment (T1) were analyzed. Four subtypes of breast cancer were defined by immunohistochemistry (IHC) based on hormone receptor (HR) and HER2 status. Radiomic features were extracted by PyRadiomics [4] using FTV masks from DCE-MRI. MRI morphologic phenotypes were determined based on unsupervised hierarchical clustering approach on extracted radiomic shape features plus FTV using Pearson correlation with agglomerative ward linkage. The associations between the unsupervised clusters of radiomic features and FTV with four IHC subtypes and pCR were evaluated using χ2 test of independence. Cramer’s V [5] were computed to measure the strength of association (higher Cramer’s V means stronger association). P-value &amp;lt; 0.05 was considered statistically significant. Results Three clusters were generated by unsupervised hierarchical clustering in a population of 910 patients included in our analysis (80 patients excluded due to missing pCR or DCE-MRIs). At T0, the unsupervised clusters showed statistically significant but weak association with pCR (Cramer’s V = 0.088, p = 0.029), but the association between the clusters and HR/HER2 subtypes did not reach significance (Cramer’s V = 0.055, p = 0.48). The unsupervised clusters based on T1 shape radiomic features showed statistically significant association with both pCR and HR/HER2 subtypes (p &amp;lt; 0.001 for both) with Cramer’s V of 0.231 and 0.154, respectively. Our results showed stronger association between pCR and cancer subtypes with MRI shape radiomic features at T1 than at T0. Various pCR rates were observed in MRI clusters at T1. They were 56%, 36%, and 23% in Cluster 1, 2, 3, respectively. Table 1 shows pCR rates by HR/HER2 subtype in each cluster. In all sub-cohorts, pCR rate was highest in Cluster 1 and lowest in Cluster 3. In HR+/HER2-, the pCR rate in Cluster 1 was 2-fold of the pCR rates in Clusters 2 and 3-fold of Cluster 3. pCR rate was statistically significantly different depending on the MRI clusters in the sub-cohorts except for the HR/HER2+ sub-cohort: HR+/HER2-, p&amp;lt; 0.001; HR+/HER2+, p=0.021; HR-/HER2+, p=0.083; HR-/HER2-, p&amp;lt; 0.001. Conclusion MRI phenotype generated by unsupervised clustering using radiomic shape features at both pretreatment and early-treatment time points was associated with pCR outcome. Stronger association was observed at early-treatment time point. The association differed by subtype, with the strongest observed in HR+/HER2- and triple negative subtypes. Our results suggest that radiomic shape features derived from DCE-MRI may be helpful for early prediction of tumor response to NAC. Citations 1. npj Breast Cancer 6, (2020). 2. Tomography 6, (2020). 3. Annals of Surgical Oncology 20, 3823–3830 (2013). 4. Cancer Research 77, e104–e107 (2017). 5. Korean Stat Soc 42, 323–328 (2013). Table 1. pCR rate by HR/HER2 subtype in each MRI cluster at T1 Citation Format: Nu N. Le, Natsuko Onishi, David C. Newitt, Jessica E. Gibbs, Lisa J. Wilmes, Efstathios Gennatas, Barbara LeStage, Laura J. Esserman, Nola M. Hylton, Wen Li. Association of MRI morphologic phenotype from unsupervised learning with breast cancer subtypes and treatment response [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD16-07.

Deciphering spatially distinct immune microenvironments in glioblastoma using ferumoxytol and gadolinium-enhanced and FLAIR hyperintense MRI phenotypes

Abstract Background MRI with gadolinium (Gd)-contrast agents is used to assess glioblastoma treatment response but does not specifically reveal heterogeneous biology or immune microenvironmental composition. Ferumoxytol (Fe) contrast is an iron nanoparticle that localizes glioblastoma macrophages and microglia. Therefore, we hypothesized that the use of Fe contrast improves upon standard Gd-based T1-weighted and T2/FLAIR analysis by specifically delineating immune processes. Methods In this, HIPAA-compliant institutional review board-approved prospective study, stereotactic biopsy samples were acquired from patients with treatment-naïve and recurrent glioblastoma based on MR imaging phenotypes; Gd and Fe T1 enhancement (Gd+, Fe+) or not (Gd–, Fe–), as well as T2-Flair hyperintensity (FLAIR+, FLAIR–). Analysis of genetic expression was performed with RNA microarrays. Imaging and genomic expression patterns were compared using false discovery rate statistics. Results MR imaging phenotypes defined a variety of immune pathways and Hallmark gene sets. Gene set enrichment analysis demonstrated that Gd+, Fe+, and FLAIR+ features were individually correlated with the same 7 immune process gene sets. Fe+ tissue showed the greatest degree of immune Hallmark gene sets compared to Gd+ or Flair+ tissues and had statistically elevated M2 polarized macrophages, among others. Importantly, the FLAIR+ Gd+ and Fe– imaging phenotypes did not demonstrate expression of immune Hallmark gene sets. Conclusions Our study demonstrates the potential of Fe and Gd-enhanced MRI phenotypes to reveal spatially distinct immune processes within glioblastoma. Fe improves upon the standard of care Gd enhancement by specifically localizing glioblastoma-associated inflammatory processes, providing valuable insights into tumor biology.

MRI phenotypes of glioblastomas early after treatment are suggestive of overall patient survival.

Distinguishing true tumor progression (TP) from treatment-induced abnormalities (eg, pseudo-progression (PP) after radiotherapy) on conventional MRI scans remains challenging in patients with a glioblastoma. We aimed to establish brain MRI phenotypes of glioblastomas early after treatment by combined analysis of structural and perfusion tumor characteristics and assessed the relation with recurrence rate and overall survival time. Structural and perfusion MR images of 67 patients at 3 months post-radiotherapy were visually scored by a neuroradiologist. In total 23 parameters were predefined and used for hierarchical clustering analysis. Progression status was assessed based on the clinical course of each patient 9 months after radiotherapy (or latest available). Multivariable Cox regression models were used to determine the association between the phenotypes, recurrence rate, and overall survival. We established 4 subgroups with significantly different tumor MRI characteristics, representing distinct MRI phenotypes of glioblastomas: TP and PP rates did not differ significantly between subgroups. Regression analysis showed that patients in subgroup 1 (characterized by having mostly small and ellipsoid nodular enhancing lesions with some hyper-perfusion) had a significant association with increased mortality at 9 months (HR: 2.6 (CI: 1.1-6.3); P = .03) with a median survival time of 13 months (compared to 22 months of subgroup 2). Our study suggests that distinct MRI phenotypes of glioblastomas at 3 months post-radiotherapy can be indicative of overall survival, but does not aid in differentiating TP from PP. The early prognostic information our method provides might in the future be informative for prognostication of glioblastoma patients.

Educational attainment, structural brain reserve and Alzheimer's disease: a Mendelian randomization analysis.

Higher educational attainment is observationally associated with lower risk of Alzheimer's disease. However, the biological mechanisms underpinning this association remain unclear. The protective effect of education on Alzheimer's disease may be mediated via increased brain reserve. We used two-sample Mendelian randomization to explore putative causal relationships between educational attainment, structural brain reserve as proxied by MRI phenotypes and Alzheimer's disease. Summary statistics were obtained from genome-wide association studies of educational attainment (n = 1 131 881), late-onset Alzheimer's disease (35 274 cases, 59 163 controls) and 15 measures of grey or white matter macro- or micro-structure derived from structural or diffusion MRI (nmax = 33 211). We conducted univariable Mendelian randomization analyses to investigate bidirectional associations between (i) educational attainment and Alzheimer's disease; (ii) educational attainment and imaging-derived phenotypes; and (iii) imaging-derived phenotypes and Alzheimer's disease. Multivariable Mendelian randomization was used to assess whether brain structure phenotypes mediated the effect of education on Alzheimer's disease risk. Genetically proxied educational attainment was inversely associated with Alzheimer's disease (odds ratio per standard deviation increase in genetically predicted years of schooling = 0.70, 95% confidence interval 0.60, 0.80). There were positive associations between genetically predicted educational attainment and four cortical metrics (standard deviation units change in imaging phenotype per one standard deviation increase in genetically predicted years of schooling): surface area 0.30 (95% confidence interval 0.20, 0.40); volume 0.29 (95% confidence interval 0.20, 0.37); intrinsic curvature 0.18 (95% confidence interval 0.11, 0.25); local gyrification index 0.21 (95% confidence interval 0.11, 0.31)]; and inverse associations with cortical intracellular volume fraction [-0.09 (95% confidence interval -0.15, -0.03)] and white matter hyperintensities volume [-0.14 (95% confidence interval -0.23, -0.05)]. Genetically proxied levels of surface area, cortical volume and intrinsic curvature were positively associated with educational attainment [standard deviation units change in years of schooling per one standard deviation increase in respective genetically predicted imaging phenotype: 0.13 (95% confidence interval 0.10, 0.16); 0.15 (95% confidence interval 0.11, 0.19) and 0.12 (95% confidence interval 0.04, 0.19)]. We found no evidence of associations between genetically predicted imaging-derived phenotypes and Alzheimer's disease. The inverse association of genetically predicted educational attainment with Alzheimer's disease did not attenuate after adjusting for imaging-derived phenotypes in multivariable analyses. Our results provide support for a protective causal effect of educational attainment on Alzheimer's disease risk, as well as potential bidirectional causal relationships between education and brain macro- and micro-structure. However, we did not find evidence that these structural markers affect risk of Alzheimer's disease. The protective effect of education on Alzheimer's disease may be mediated via other measures of brain reserve not included in the present study, or by alternative mechanisms.

Prediction of lumbar disc herniation resorption in symptomatic patients: a prospective, multi-imaging and clinical phenotype study

Symptomatic lumbar disc herniations (LDH) are very common. LDH resorption may occur by a "self-healing" process, however this phenomenon remains poorly understood. By most guidelines, if LDH remains symptomatic after 3 months and conservative management fails, surgical intervention may be an option. The following prospective study aimed to identify determinants that may predict early versus late LDH resorption. Prospective study with patients recruited at a single center. Ninety-three consecutive patients diagnosed with acute symptomatic LDH were included in this study (n=23 early resorption and n=67 late resorption groups) with a mean age of 48.7±11.9 years. Baseline assessment of patient demographics (eg, smoking status, height, weight, etc.), herniation characteristics (eg, the initial level of herniation, the direction of herniation, prevalence of multiple herniations, etc.) and MRI phenotypes (eg, Modic changes, end plate abnormalities, disc degeneration, vertebral body dimensions, etc.) were collected for further analysis. Lumbar MRIs were performed approximately every 3 months for 1 year from time of enrollment to assess disc integrity. All patients were managed similarly. LDH resorption was classified as early (<3 months) or late (>3 months). A prediction model of pretreatment factors was constructed. No significant differences were noted between groups at any time-point (p>.05). Patients in the early resorption group experienced greater percent reduction of disc herniation between MRI-0-MRI-1 (p=.043), reduction of herniation size for total study duration (p=.007), and percent resorption per day compared to the late resorption group (p<.001). Based on multivariate modeling, greater L4 posterior vertebral height (coeff:14.58), greater sacral slope (coeff:0.12), and greater herniated volume (coeff:0.013) at baseline were found to be most predictive of early resorption (p<.05). This is the first comprehensive imaging and clinical phenotypic prospective study, to our knowledge, that has identified distinct determinants for early LDH resorption. Early resorption can occur in 24.7% of LDH patients. We developed a prediction model for early resorption which demonstrated great overall performance according to pretreatment measures of herniation size, L4 posterior body height, and sacral slope. A risk profile is proposed which may aid clinical decision-making and managing patient expectations.

Infant Visual Brain Development and Inherited Genetic Liability in Autism.

Autism spectrum disorder (ASD) is heritable, and younger siblings of ASD probands are at higher likelihood of developing ASD themselves. Prospective MRI studies of siblings report that atypical brain development precedes ASD diagnosis, although the link between brain maturation and genetic factors is unclear. Given that familial recurrence of ASD is predicted by higher levels of ASD traits in the proband, the authors investigated associations between proband ASD traits and brain development among younger siblings. In a sample of 384 proband-sibling pairs (89 pairs concordant for ASD), the authors examined associations between proband ASD traits and sibling brain development at 6, 12, and 24 months in key MRI phenotypes: total cerebral volume, cortical surface area, extra-axial cerebrospinal fluid, occipital cortical surface area, and splenium white matter microstructure. Results from primary analyses led the authors to implement a data-driven approach using functional connectivity MRI at 6 months. Greater levels of proband ASD traits were associated with larger total cerebral volume and surface area and larger surface area and reduced white matter integrity in components of the visual system in siblings who developed ASD. This aligned with weaker functional connectivity between several networks and the visual system among all siblings during infancy. The findings provide evidence that specific early brain MRI phenotypes of ASD reflect quantitative variation in familial ASD traits. Multimodal anatomical and functional convergence on cortical regions, fiber pathways, and functional networks involved in visual processing suggest that inherited liability has a role in shaping the prodromal development of visual circuitry in ASD.

Brain charts for the human lifespan

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data (http://www.brainchart.io/). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.

The role of critical immune genes in brain disorders: insights from neuroimaging immunogenetics.

Genetic variants in the human leukocyte antigen and killer cell immunoglobulin-like receptor regions have been associated with many brain-related diseases, but how they shape brain structure and function remains unclear. To identify the genetic variants in HLA and KIR genes associated with human brain phenotypes, we performed a genetic association study of ∼30 000 European unrelated individuals using brain MRI phenotypes generated by the UK Biobank (UKB). We identified 15 HLA alleles in HLA class I and class II genes significantly associated with at least one brain MRI-based phenotypes (P < 5 × 10−8). These associations converged on several main haplotypes within the HLA. In particular, the human leukocyte antigen alleles within an ancestral haplotype 8.1 were associated with multiple MRI measures, including grey matter volume, cortical thickness (TH) and diffusion MRI (dMRI) metrics. These alleles have been strongly associated with schizophrenia. Additionally, associations were identified between HLA-DRB1*04∼DQA1*03:01∼DQB1*03:02 and isotropic volume fraction of diffusion MRI in multiple white matter tracts. This haplotype has been reported to be associated with Parkinson’s disease. These findings suggest shared genetic associations between brain MRI biomarkers and brain-related diseases. Additionally, we identified 169 associations between the complement component 4 (C4) gene and imaging phenotypes. We found that C4 gene copy number was associated with cortical TH and dMRI metrics. No KIR gene copy numbers were associated with image-derived phenotypes at genome-wide threshold. To address the multiple testing burden in the phenome-wide association study, we performed a multi-trait association analysis using trait-based association test that uses extended Simes procedure and identified MRI image-specific associations. This study contributes to insight into how critical immune genes affect brain-related traits as well as the development of neurological and neuropsychiatric disorders.

Preoperative MRI of breast squamous cell carcinoma: diagnostic value of distinguishing between two subtypes

To retrospectively analyse the clinical and MRI data of primary squamous cell carcinoma (SCC), particularly pure squamous cell carcinoma (PSCC) and mixed squamous cell carcinoma (MSCC). The MRI data and clinicopathological characteristics of 20 patients with histopathologically confirmed SCC of the breast, including eight PSCC patients and 12 MSCC patients, from multiple centres between January 2013 and December 2020 were analysed retrospectively. Nine of 12 patients in the MSCC group showed hyperintensity on T1-weighted imaging (WI), while this feature was not observed in the PSCC group (p=0.001). Most of the PSCC group showed rim enhancement, whereas most of the MSCC group showed heterogeneous enhancement (p=0.007). In addition, there was no significant difference in the thickness of the rim enhancement and the percentage of necrotic components in the tumours between the two types of SCCs of the breast (p=0.545 and p=0.662, respectively). Four patients (4/12) in the MSCC group had sentinel lymph node metastasis, while only one patient (1/8) in the PSCC group showed lymph node metastasis (p=0.603). Metastatic disease occurred in 25% of patients with PSCC and in approximately 41.7% of patients with MSCC. The signal on T1WI and internal enhancement characteristics were the key features for differentiating PSCC and MSCC. Therefore, MRI phenotypes may provide additional information for the pathological classification of breast SCC.

Modic changes and its association with other MRI phenotypes in east Anatolian low back pain patients

Objective Modic changes (MCs) are known to be associated with low back pain (LBP). Literature contains conflicting reports about the prevalence of MCs and other spinal phenotypes among different populations with LBP patients. We aimed to evaluate the prevalence of MCs in the lumbar spine and associated features in Eastern Anatolian chronic LBP patients. Methods The study sample comprised of 786 consecutive patients [(490 female, 296 male), (mean age 39.7; range 20–78)] with a history of low back pain for at least 3 months. Data about MCs involvement, Schmorl’s nodes (SN), disc degeneration (DD), disc displacement, disc height and osteophytes were obtained via MRI. Patients’ demographic characteristics, Oswestry disability index (ODI) and visual analog scale (VAS) scores were assessed using a questionnaire. Results MCs were present in 67.2% (528/786) of the patients. Of all evaluated lumbar-level changes, 86 (6.2%) were Type I, 991 (76.8%) were Type II, 11 (0.8%) were Type III, 47 (6.5%) were Type II/III, 89 (8.5%) were Type I/II, and 29 (1.2%) were Type I/II/III MCs. MCs were significantly associated with severe DD (p < 0.001), disc displacement (p < 0.001), SN (p < 0.001), and osteophytes (p < 0.001). In the multivariate regression analysis, BMI (for both ODI and VAS) and age (only for ODI) were the only independent predictors of clinical severity. Conclusions The present study is the largest cross-sectional study of adult members of the Eastern Anatolian population with chronic LBP. Modic changes were detected in 67.2% of patients with chronic LBP and the prevalence of other phenotypic features differed significantly between MCs and non-MCs disc levels. Nevertheless, the results of the current study do not support a causal relationship of MCs or any MRI changes with clinical symptom severity.

Different cardiovascular risk factors are related to distinct white matter hyperintensity MRI phenotypes in older adults

The underlying mechanisms of the association between cardiovascular risk factors and a higher white matter hyperintensity (WMH) burden are unknown. We investigated the association between cardiovascular risk factors and advanced WMH markers in 155 non-demented older adults (mean age: 71 ± 5 years). The association between cardiovascular risk factors and quantitative MRI-based WMH shape and volume markers were examined using linear regression analysis. Presence of hypertension was associated with a more irregular shape of periventricular/confluent WMH (convexity (B (95 % CI)): −0.12 (−0.22–−0.03); concavity index: 0.06 (0.02–0.11)), but not with total WMH volume (0.22 (−0.15–0.59)). Presence of diabetes was associated with deep WMH volume (0.89 (0.15–1.63)). Body mass index or hyperlipidemia showed no association with WMH markers. In conclusion, different cardiovascular risk factors seem to be related to a distinct pattern of WMH shape markers in non-demented older adults. These findings may suggest that different underlying cardiovascular pathological mechanisms lead to different WMH MRI phenotypes, which may be valuable for early detection of individuals at risk for stroke and dementia.

Radiomic signatures of posterior fossa ependymoma: Molecular subgroups and risk profiles.

The risk profile for posterior fossa ependymoma (EP) depends on surgical and molecular status [Group A (PFA) versus Group B (PFB)]. While subtotal tumor resection is known to confer worse prognosis, MRI-based EP risk-profiling is unexplored. We aimed to apply machine learning strategies to link MRI-based biomarkers of high-risk EP and also to distinguish PFA from PFB. We extracted 1800 quantitative features from presurgical T2-weighted (T2-MRI) and gadolinium-enhanced T1-weighted (T1-MRI) imaging of 157 EP patients. We implemented nested cross-validation to identify features for risk score calculations and apply a Cox model for survival analysis. We conducted additional feature selection for PFA versus PFB and examined performance across three candidate classifiers. For all EP patients with GTR, we identified four T2-MRI-based features and stratified patients into high- and low-risk groups, with 5-year overall survival rates of 62% and 100%, respectively (P < .0001). Among presumed PFA patients with GTR, four T1-MRI and five T2-MRI features predicted divergence of high- and low-risk groups, with 5-year overall survival rates of 62.7% and 96.7%, respectively (P = .002). T1-MRI-based features showed the best performance distinguishing PFA from PFB with an AUC of 0.86. We present machine learning strategies to identify MRI phenotypes that distinguish PFA from PFB, as well as high- and low-risk PFA. We also describe quantitative image predictors of aggressive EP tumors that might assist risk-profiling after surgery. Future studies could examine translating radiomics as an adjunct to EP risk assessment when considering therapy strategies or trial candidacy.