Spatial assessments in texture analysis: what the radiologist needs to know

To date, studies investigating radiomics-based predictive models have tended to err on the side of data-driven or exploratory analysis of many thousands of extracted features. In particular, spatial assessments of texture have proven to be especially adept at assessing for features of intratumoral heterogeneity in oncologic imaging, which likewise may correspond with tumor biology and behavior. These spatial assessments can be generally classified as spatial filters, which detect areas of rapid change within the grayscale in order to enhance edges and/or textures within an image, or neighborhood-based methods, which quantify gray-level differences of neighboring pixels/voxels within a set distance. Given the high dimensionality of radiomics datasets, data dimensionality reduction methods have been proposed in an attempt to optimize model performance in machine learning studies; however, it should be noted that these approaches should only be applied to training data in order to avoid information leakage and model overfitting. While area under the curve of the receiver operating characteristic is perhaps the most commonly reported assessment of model performance, it is prone to overestimation when output classifications are unbalanced. In such cases, confusion matrices may be additionally reported, whereby diagnostic cut points for model predicted probability may hold more clinical significance to clinical colleagues with respect to related forms of diagnostic testing

A cross-sectional study to test equivalence of low- versus intermediate-flip angle dynamic susceptibility contrast MRI measures of relative cerebral blood volume in patients with high-grade gliomas at 1.5 Tesla field strength

Introduction1.5 Tesla (1.5T) remain a significant field strength for brain imaging worldwide. Recent computer simulations and clinical studies at 3T MRI have suggested that dynamic susceptibility contrast (DSC) MRI using a 30° flip angle (“low-FA”) with model-based leakage correction and no gadolinium-based contrast agent (GBCA) preload provides equivalent relative cerebral blood volume (rCBV) measurements to the reference-standard acquisition using a single-dose GBCA preload with a 60° flip angle (“intermediate-FA”) and model-based leakage correction. However, it remains unclear whether this holds true at 1.5T. The purpose of this study was to test this at 1.5T in human high-grade glioma (HGG) patients.MethodsThis was a single-institution cross-sectional study of patients who had undergone 1.5T MRI for HGG. DSC-MRI consisted of gradient-echo echo-planar imaging (GRE-EPI) with a low-FA without preload (30°/P-); this then subsequently served as a preload for the standard intermediate-FA acquisition (60°/P+). Both normalized (nrCBV) and standardized relative cerebral blood volumes (srCBV) were calculated using model-based leakage correction (C+) with IBNeuro™ software. Whole-enhancing lesion mean and median nrCBV and srCBV from the low- and intermediate-FA methods were compared using the Pearson’s, Spearman’s and intraclass correlation coefficients (ICC).ResultsTwenty-three HGG patients composing a total of 31 scans were analyzed. The Pearson and Spearman correlations and ICCs between the 30°/P-/C+ and 60°/P+/C+ acquisitions demonstrated high correlations for both mean and median nrCBV and srCBV.ConclusionOur study provides preliminary evidence that for HGG patients at 1.5T MRI, a low FA, no preload DSC-MRI acquisition can be an appealing alternative to the reference standard higher FA acquisition that utilizes a preload

Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images

Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI), the converted MCI (cMCI), and the normal control (NC) groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI) were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM). An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI–cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI–NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI–NC comparison. The best performances obtained by the SVM classifier using the essential features were 5–40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and predict the risk of its conversion to Alzheimer's disease

Contribution of normal aging to brain atrophy in MS.

To identify the top brain regions affected by MS-specific atrophy (i.e., atrophy in excess of normal aging) and to test whether normal aging and MS-specific atrophy increase or decrease in these regions with age. Six hundred fifty subjects (2,790 MRI time points) were analyzed: 520 subjects with relapse-onset MS from a 5-year prospective cohort with annual standardized 1-mm 3D T1-weighted images (3DT1s; 2,483 MRIs) and 130 healthy controls with longitudinal 3DT1s (307 MRIs). Rates of change in all FreeSurfer regions (v5.3) and Structural Image Evaluation Using Normalization of Atrophy (SIENA) were estimated with mixed-effects models. All FreeSurfer regions were ranked by the MS-specific atrophy slope/standard error ratio (βMS × time/SEβMS × time). In the top regions, age was added as an effect modifier to test whether MS-specific atrophy varied by age. The top-ranked regions were all gray matter structures. For SIENA, normal aging increased from 0.01%/y at age 30 years to -0.31%/y at age 60 years (-0.11% ± 0.032%/decade, p < 0.01), whereas MS-specific atrophy decreased from -0.38%/y at age 30 years to -0.12%/y at age 60 years (0.09% ± 0.035%/decade, p = 0.01). Similarly, in the thalamus, normal aging increased from -0.15%/y at age 30 years to -0.62%/y at age 60 years (-0.16% ± 0.079%/decade, p < 0.05), and MS-specific atrophy decreased from -0.59%/y at age 30 years to -0.05%/y at age 60 years (0.18% ± 0.08%/decade, p < 0.05). In the putamen and caudate, normal aging and MS-specific atrophy did not vary by age. For SIENA and thalamic atrophy, the contribution of normal aging increases with age, but does not change in the putamen and caudate. This may have substantial implications to understand the biology of brain atrophy in MS

Spatial assessments in texture analysis: what the radiologist needs to know

To date, studies investigating radiomics-based predictive models have tended to err on the side of data-driven or exploratory analysis of many thousands of extracted features. In particular, spatial assessments of texture have proven to be especially adept at assessing for features of intratumoral heterogeneity in oncologic imaging, which likewise may correspond with tumor biology and behavior. These spatial assessments can be generally classified as spatial filters, which detect areas of rapid change within the grayscale in order to enhance edges and/or textures within an image, or neighborhood-based methods, which quantify gray-level differences of neighboring pixels/voxels within a set distance. Given the high dimensionality of radiomics datasets, data dimensionality reduction methods have been proposed in an attempt to optimize model performance in machine learning studies; however, it should be noted that these approaches should only be applied to training data in order to avoid information leakage and model overfitting. While area under the curve of the receiver operating characteristic is perhaps the most commonly reported assessment of model performance, it is prone to overestimation when output classifications are unbalanced. In such cases, confusion matrices may be additionally reported, whereby diagnostic cut points for model predicted probability may hold more clinical significance to clinical colleagues with respect to related forms of diagnostic testing

Adrenocorticotropic hormone methylprednisolone added to interferon β in patients with multiple sclerosis experiencing breakthrough disease: a randomized, rater-blinded trial

Background: The objective of this study was to evaluate monthly intramuscular adrenocorticotropic hormone (ACTH) gel versus intravenous methylprednisolone (IVMP) add-on therapy to interferon β for breakthrough disease in patients with relapsing forms of multiple sclerosis. Methods: This was a prospective, open-label, examiner-blinded, 15-month pilot study evaluating patients with Expanded Disability Status Scale (EDSS) score 3.0–6.5 and at least one clinical relapse or new T2 or gadolinium-enhanced lesion in the previous year. Twenty-three patients were randomized to ACTH ( n = 12) or IVMP ( n = 11) and completed the study. The primary outcome measure was the cumulative number of relapses. Secondary outcomes included EDSS, Mental Health Inventory (MHI), plasma cytokines, MS Functional Composite (MSFC), Quality-of-Life (MS-QOL) score, bone mineral density (BMD), and new or worsened psychiatric symptoms per month. Brain magnetic resonance imaging was analyzed post hoc . This was a preliminary and small-scale study. Results: Relapse rates differed significantly [ACTH 0.08, 95% confidence interval (CI) 0.01–0.54 versus IVMP 0.80, 95% CI 0.36–1.75; rate ratio, IVMP versus ACTH: 9.56, 95% CI 1.23–74.6; p = 0.03]. ACTH improved ( p = 0.03) MHI (slope 0.95 ± 0.38 points/month; p = 0.02 versus slope −0.38 ± 0.43 points/month; p = 0.39). On-study decreases (all p < 0.05) in eight cytokine levels occurred only in the ACTH group. However, on-study EDSS, MSFC, MS-QOL, BMD, and MRI lesion changes were not significant between groups. Psychiatric symptoms per patient were greater with IVMP than ACTH (0.55, 95% CI 0.12–2.6 versus 0; p < 0.0001). Other common adverse events were insomnia and urinary tract infections (IVMP, seven events each) and fatigue or flu symptoms (ACTH, five events each). Conclusions: This study provided class II evidence that ACTH produced better examiner-assessed cumulative rates of relapses per patient than IVMP in the adjunctive treatment of breakthrough disease in multiple sclerosis

Smoking Prevention for Ethnically Diverse Adolescents: 2-year Outcomes of a Multicultural, School-based Smoking Prevention Curriculum in Southern California

Background Effective school-based curricula are needed to prevent smoking among ethnically diverse adolescents. This study evaluated a multicultural smoking prevention curriculum in ethnically diverse Southern California middle schools. Methods Students in 24 middle schools (N = 3157 sixth graders) received the multicultural curriculum, a similar curriculum without references to cultural issues, or a control condition. Odds ratios for experimentation with smoking over a 2-year period were calculated. Results The multicultural program was associated with a lower risk of smoking between sixth and eighth grade, relative to the control group. Program effects varied according to the ethnic composition of the schools. In schools with predominantly Hispanic populations, the multicultural curriculum was more effective than the control, but the standard curriculum was not. In schools with predominantly Asian or multicultural populations, the standard curriculum was more effective than the control, but the multicultural curriculum was not. Analyses stratified by ethnicity within the schools revealed that the multicultural curriculum was effective among Hispanic students within predominantly Hispanic schools, but not among Hispanic students within predominantly Asian/multicultural schools. Conclusions Smoking prevention for adolescents in culturally diverse school contexts is a challenge. In this study, a multicultural curriculum was most effective among Hispanic students in predominantly Hispanic schools. Further research is needed to determine the best ways to prevent smoking in predominantly Asian and multicultural schools

Meaningful Gait Speed Improvement During the First 60 Days Poststroke: Minimal Clinically Important Difference

UnrestrictedIntroduction: The purpose of this study was to determine the minimal clinically important difference (MCID) for comfortable gait speed (CGS) for persons between 20 to 60 days post-stroke.; Methods: 283 persons with stroke were prospectively enrolled. CGS and the mRS were measured at 20 and 60-days post-stroke. Improvement ≥1 on the mRS was used to detect meaningful change.; Results: Mean CGS was 0.18 m/s at 20-days and 0.39 m/s at 60-days post-stroke (p<0.001). Among all participants, 47.3% experienced a ≥1 improvement in mRS. MCID was estimated as an improvement of 0.16 m/s in CGS anchored to the mRS.; Conclusions: Patients with sub-acute stroke, who increase gait speed ≥0.16 m/s, are more likely to experience a meaningful improvement in disability level than those who do not. Clinicians can use this reference value to develop goals and interpret progress in patients with sub-acute stroke