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Meningiomas are the most common primary intracranial tumors and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on brain MRI for diagnosis, treatment planning, and longitudinal treatment monitoring. However, automated, objective, and quantitative tools for non-invasive assessment of meningiomas on multi-sequence MR images are not available. Here we present the BraTS Pre-operative Meningioma Dataset, as the largest multi-institutional expert annotated multilabel meningioma multi-sequence MR image dataset to date. This dataset includes 1,141 multi-sequence MR images from six sites, each with four structural MRI sequences (T2-, T2/FLAIR-, pre-contrast T1-, and post-contrast T1-weighted) accompanied by expert manually refined segmentations of three distinct meningioma sub-compartments: enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Basic demographic data are provided including age at time of initial imaging, sex, and CNS WHO grade. The goal of releasing this dataset is to facilitate the development of automated computational methods for meningioma segmentation and expedite their incorporation into clinical practice, ultimately targeting improvement in the care of meningioma patients.

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A tree model for diagnostic educational testing is described along with Monte Carlo simulations designed to evaluate measurement accuracy based on the model. The model is implemented in an assessment of inferential reading comprehension, the Multiple-Choice Online Causal Comprehension Assessment (MOCCA), through a sequential, multidimensional, computerized adaptive testing (CAT) strategy. Assessment of the first dimension, reading comprehension (RC), is based on the three-parameter logistic model. For diagnostic and intervention purposes, the second dimension, called process propensity (PP), is used to classify struggling students based on their pattern of incorrect responses. In the simulation studies, CAT item selection rules and stopping rules were varied to evaluate their effect on measurement accuracy along dimension RC and classification accuracy along dimension PP. For dimension RC, methods that improved accuracy tended to increase test length. For dimension PP, however, item selection and stopping rules increased classification accuracy without materially increasing test length. A small live-testing pilot study confirmed some of the findings of the simulation studies. Development of the assessment has been guided by psychometric theory, Monte Carlo simulation results, and a theory of instruction and diagnosis.

Neural networks were trained for segmentation and longitudinal assessment of posttreatment diffuse glioma. A retrospective cohort (from January 2018 to December 2019) of 298 patients with diffuse glioma (mean age, 52 years ± 14 [SD]; 177 men; 152 patients with glioblastoma, 72 patients with astrocytoma, and 74 patients with oligodendroglioma) who underwent two consecutive multimodal MRI examinations were randomly selected into training (n = 198) and testing (n = 100) samples. A posttreatment tumor segmentation three-dimensional nnU-Net convolutional neural network with multichannel inputs (T1, T2, and T1 postcontrast and fluid-attenuated inversion recovery [FLAIR]) was trained to segment three multiclass tissue types (peritumoral edematous, infiltrated, or treatment-changed tissue [ED]; active tumor or enhancing tissue [AT]; and necrotic core). Separate longitudinal change nnU-Nets were trained on registered and subtracted FLAIR and T1 postlongitudinal images to localize and better quantify and classify changes in ED and AT. Segmentation Dice scores, volume similarities, and 95th percentile Hausdorff distances ranged from 0.72 to 0.89, 0.90 to 0.96, and 2.5 to 3.6 mm, respectively. Accuracy rates of the posttreatment tumor segmentation and longitudinal change networks being able to classify longitudinal changes in ED and AT as increased, decreased, or unchanged were 76%-79% and 90%-91%, respectively. The accuracy levels of the longitudinal change networks were not significantly different from those of three neuroradiologists (accuracy, 90%-92%; κ, 0.58-0.63; P > .05). The results of this study support the potential clinical value of artificial intelligence-based automated longitudinal assessment of posttreatment diffuse glioma. Keywords: MR Imaging, Neuro-Oncology, Neural Networks, CNS, Brain/Brain Stem, Segmentation, Quantification, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2022.

PurposeTo assess how well a brain MRI lesion segmentation algorithm trained at one institution performed at another institution, and to assess the effect of multi-institutional training datasets for mitigating performance loss.Materials and methodsIn this retrospective study, a three-dimensional U-Net for brain MRI abnormality segmentation was trained on data from 293 patients from one institution (IN1) (median age, 54 years; 165 women; patients treated between 2008 and 2018) and tested on data from 51 patients from a second institution (IN2) (median age, 46 years; 27 women; patients treated between 2003 and 2019). The model was then trained on additional data from various sources: (a) 285 multi-institution brain tumor segmentations, (b) 198 IN2 brain tumor segmentations, and (c) 34 IN2 lesion segmentations from various brain pathologic conditions. All trained models were tested on IN1 and external IN2 test datasets, assessing segmentation performance using Dice coefficients.ResultsThe U-Net accurately segmented brain MRI lesions across various pathologic conditions. Performance was lower when tested at an external institution (median Dice score, 0.70 [IN2] vs 0.76 [IN1]). Addition of 483 training cases of a single pathologic condition, including from IN2, did not raise performance (median Dice score, 0.72; P = .10). Addition of IN2 training data with heterogeneous pathologic features, representing only 10% (34 of 329) of total training data, increased performance to baseline (Dice score, 0.77; P < .001). This final model produced total lesion volumes with a high correlation to the reference standard (Spearman r = 0.98).ConclusionFor brain MRI lesion segmentation, adding a modest amount of relevant training data from an external institution to a previously trained model supported successful application of the model to this external institution.Keywords: Neural Networks, Brain/Brain Stem, Segmentation Supplemental material is available for this article. © RSNA, 2021.

Adaptive measurement of change (AMC) is a psychometric method for measuring intra-individual change on one or more latent traits across testing occasions. Three hypothesis tests--a Z test, likelihood ratio test, and score ratio index--have demonstrated desirable statistical properties in this context, including low false positive rates and high true positive rates. However, the extant AMC research has assumed that the item parameter values in the simulated item banks were devoid of estimation error. This assumption is unrealistic for applied testing settings, where item parameters are estimated from a calibration sample before test administration. Using Monte Carlo simulation, this study evaluated the robustness of the common AMC hypothesis tests to the presence of item parameter estimation error when measuring omnibus change across four testing occasions. Results indicated that item parameter estimation error had at most a small effect on false positive rates and latent trait change recovery, and these effects were largely explained by the computerized adaptive testing item bank information functions. Differences in AMC performance as a function of item parameter estimation error and choice of hypothesis test were generally limited to simulees with particularly low or high latent trait values, where the item bank provided relatively lower information. These simulations highlight how AMC can accurately measure intra-individual change in the presence of item parameter estimation error when paired with an informative item bank. Limitations and future directions for AMC research are discussed.

Economic inequality has been consistently rising in recent decades in many Western countries including Germany. This is a pressing issue as greater economic inequality within a society has detrimental consequences for well-being, social stability, productivity, and even life expectancy. However, little is known about how individuals of different ages experience and respond to social inequality across adulthood. Because status differences are perceived as more malleable in young adulthood (i.e., young adults can expect to move up the social ladder) and only manifest across adulthood, we predicted that negative emotional reactions to the perceived standing in the social hierarchy should become increasingly pronounced with age. Consistently, a first study based on a national representative sample in Germany (N = 2,542; 18-91 years) confirmed that subjective social status had a much stronger effect on the acceptance of social inequality among middle-aged and older, as compared with younger, adults. In a second experimental study (N = 387; 18-89 years), participants of any age responded with negative emotional reactions when rising inequality was made salient. However, subjective social status moderated this effect only in middle-aged and older, but not younger, adults. Finally, a third experimental study (N = 605; 18-82 years) showed that, compared with middle-aged and older adults, younger adults maintained stronger upward mobility beliefs that accounted for the age-differential effects of subjective social status on negative emotional reactivity to rising inequality. We discuss the central role of upward mobility beliefs for individuals' responses to social inequality across the adult life span.