18F-FDG PET/CT for the Evaluation of Therapy Response in Hormone Receptor-Positive Bone-Dominant Metastatic Breast Cancer.

Fluorine 18 (18F) fluorodeoxyglucose (FDG) PET/CT has shown promise for use in assessing treatment response in patients with bone-only or bone-dominant (BD) metastatic breast cancer (mBC). In this single-institution, prospective single-arm study of 23 women (median age, 59 years [range, 38-81 years]) with biopsy-proven estrogen receptor-positive bone-only or BD mBC about to begin new endocrine therapy between October 3, 2013, and August 3, 2018, the value of early 4-week 18F-FDG PET/CT in predicting progression-free survival (PFS) was evaluated. 18F-FDG PET/CT was performed at baseline, 4 weeks, and 12 weeks. Maximum standardized uptake value (SUVmax) and peak SUV (SUVpeak) were measured for up to five index lesions. The primary end point was PFS. Secondary end points were overall survival (OS) and time to skeletal-related events (tSREs). All end points were compared between responders (reduction of 30% or more in the sum of SUVmax for target lesions) and nonresponders at 4 weeks and 12 weeks. Percentage change from baseline in SUVmax at 4- and 12-week 18F-FDG PET/CT were highly correlated (r = 0.81). At the 4-week time point PET responders had numerically longer PFS (14.2 months vs 6.3 months; P = .53), OS (44.0 months vs 29.7 months; P = .47), and tSRE (27.4 months vs 25.2 months; P = .66) compared with nonresponders, suggesting the clinical utility of 4-week 18F-FDG PET/CT as an early predictor of treatment failure. Keywords: Breast Cancer, Metastatic Breast Cancer, Bone-Dominant Metastatic Breast Cancer, FDG PET/CT, Estrogen-Receptor Positive Metastatic Breast Cancer Supplemental material is available for this article. Clinical trial registration no. NCT04316117 © RSNA, 2022.

Mobile footprinting: linking individual distinctiveness in mobility patterns to mood, sleep, and brain functional connectivity.

Mapping individual differences in behavior is fundamental to personalized neuroscience, but quantifying complex behavior in real world settings remains a challenge. While mobility patterns captured by smartphones have increasingly been linked to a range of psychiatric symptoms, existing research has not specifically examined whether individuals have person-specific mobility patterns. We collected over 3000 days of mobility data from a sample of 41 adolescents and young adults (age 17-30 years, 28 female) with affective instability. We extracted summary mobility metrics from GPS and accelerometer data and used their covariance structures to identify individuals and calculated the individual identification accuracy-i.e., their "footprint distinctiveness". We found that statistical patterns of smartphone-based mobility features represented unique "footprints" that allow individual identification (p < 0.001). Critically, mobility footprints exhibited varying levels of person-specific distinctiveness (4-99%), which was associated with age and sex. Furthermore, reduced individual footprint distinctiveness was associated with instability in affect (p < 0.05) and circadian patterns (p < 0.05) as measured by environmental momentary assessment. Finally, brain functional connectivity, especially those in the somatomotor network, was linked to individual differences in mobility patterns (p < 0.05). Together, these results suggest that real-world mobility patterns may provide individual-specific signatures relevant for studies of development, sleep, and psychopathology.

Recovery kinetics of creatine in mild plantar flexion exercise using 3D creatine CEST imaging at 7 Tesla.

PURPOSE: Two-dimensional creatine CEST (2D-CrCEST), with a slice thickness of 10-20 mm and temporal resolution (τRes ) of about 30 seconds, has previously been shown to capture the creatine-recovery kinetics in healthy controls and in patients with abnormal creatine-kinase kinetics following the mild plantar flexion exercise. Since the distribution of disease burden may vary across the muscle length for many musculoskeletal disorders, there is a need to increase coverage in the slice-encoding direction. Here, we demonstrate the feasibility of 3D-CrCEST with τRes of about 30 seconds, and propose an improved voxel-wise B1+ -calibration approach for CrCEST. METHODS: The current 7T study with enrollment of 5 volunteers involved collecting the baseline CrCEST imaging for the first 2 minutes, followed by 2 minutes of plantar flexion exercise and then 8 minutes of postexercise CrCEST imaging, to detect the temporal evolution of creatine concentration following exercise. RESULTS: Very good repeatability of 3D-CrCEST findings for activated muscle groups on an intraday and interday basis was established, with coefficient of variance of creatine recovery constants (τCr ) being 7%-15.7%, 7.5%, and 5.8% for lateral gastrocnemius, medial gastrocnemius, and peroneus longus, respectively. We also established a good intraday and interday scan repeatability for 3D-CrCEST and also showed good correspondence between τCr measurements using 2D-CrCEST and 3D-CrCEST acquisitions. CONCLUSION: In this study, we demonstrated for the first time the feasibility and the repeatability of the 3D-CrCEST method in calf muscle with improved B1+ correction to measure creatine-recovery kinetics within a large 3D volume of calf muscle.

TAPAS: A Thresholding Approach for Probability Map Automatic Segmentation in Multiple Sclerosis.

Total brain white matter lesion (WML) volume is the most widely established magnetic resonance imaging (MRI) outcome measure in studies of multiple sclerosis (MS). To estimate WML volume, there are a number of automatic segmentation methods available, yet manual delineation remains the gold standard approach. Automatic approaches often yield a probability map to which a threshold is applied to create lesion segmentation masks. Unfortunately, few approaches systematically determine the threshold employed; many methods use a manually selected threshold, thus introducing human error and bias into the automated procedure. In this study, we propose and validate an automatic thresholding algorithm, Thresholding Approach for Probability Map Automatic Segmentation in Multiple Sclerosis (TAPAS), to obtain subject-specific threshold estimates for probability map automatic segmentation of T2-weighted (T2) hyperintense WMLs. Using multimodal MRI, the proposed method applies an automatic segmentation algorithm to obtain probability maps. We obtain the true subject-specific threshold that maximizes the Sørensen-Dice similarity coefficient (DSC). Then the subject-specific thresholds are modeled on a naive estimate of volume using a generalized additive model. Applying this model, we predict a subject-specific threshold in data not used for training. We ran a Monte Carlo-resampled split-sample cross-validation (100 validation sets) using two data sets: the first obtained from the Johns Hopkins Hospital (JHH) on a Philips 3 Tesla (3T) scanner (n = 94) and a second collected at the Brigham and Women's Hospital (BWH) using a Siemens 3T scanner (n = 40). By means of the proposed automated technique, in the JHH data we found an average reduction in subject-level absolute error of 0.1 mL per one mL increase in manual volume. Using Bland-Altman analysis, we found that volumetric bias associated with group-level thresholding was mitigated when applying TAPAS. The BWH data showed similar absolute error estimates using group-level thresholding or TAPAS likely since Bland-Altman analyses indicated no systematic biases associated with group or TAPAS volume estimates. The current study presents the first validated fully automated method for subject-specific threshold prediction to segment brain lesions.

Distance-based analysis of variance for brain connectivity.

The field of neuroimaging dedicated to mapping connections in the brain is increasingly being recognized as key for understanding neurodevelopment and pathology. Networks of these connections are quantitatively represented using complex structures, including matrices, functions, and graphs, which require specialized statistical techniques for estimation and inference about developmental and disorder-related changes. Unfortunately, classical statistical testing procedures are not well suited to high-dimensional testing problems. In the context of global or regional tests for differences in neuroimaging data, traditional analysis of variance (ANOVA) is not directly applicable without first summarizing the data into univariate or low-dimensional features, a process that might mask the salient features of high-dimensional distributions. In this work, we consider a general framework for two-sample testing of complex structures by studying generalized within-group and between-group variances based on distances between complex and potentially high-dimensional observations. We derive an asymptotic approximation to the null distribution of the ANOVA test statistic, and conduct simulation studies with scalar and graph outcomes to study finite sample properties of the test. Finally, we apply our test to our motivating study of structural connectivity in autism spectrum disorder.

A dual modeling approach to automatic segmentation of cerebral T2 hyperintensities and T1 black holes in multiple sclerosis.

BACKGROUND AND PURPOSE: Magnetic resonance imaging (MRI) is crucial for in vivo detection and characterization of white matter lesions (WML) in multiple sclerosis (MS). The most widely established MRI outcome measure is the volume of hyperintense lesions on T2-weighted images (T2L). Unfortunately, T2L are non-specific for the level of tissue destruction and show a weak relationship to clinical status. Interest in lesions that appear hypointense on T1-weighted images (T1L) ("black holes") has grown because T1L provide more specificity for axonal loss and a closer link to neurologic disability. The technical difficulty of T1L segmentation has led investigators to rely on time-consuming manual assessments prone to inter- and intra-rater variability. This study aims to develop an automatic T1L segmentation approach, adapted from a T2L segmentation algorithm. MATERIALS AND METHODS: T1, T2, and fluid-attenuated inversion recovery (FLAIR) sequences were acquired from 40 MS subjects at 3 Tesla (3 T). T2L and T1L were manually segmented. A Method for Inter-Modal Segmentation Analysis (MIMoSA) was then employed. RESULTS: Using cross-validation, MIMoSA proved to be robust for segmenting both T2L and T1L. For T2L, a Sørensen-Dice coefficient (DSC) of 0.66 and partial AUC (pAUC) up to 1% false positive rate of 0.70 were achieved. For T1L, 0.53 DSC and 0.64 pAUC were achieved. Manual and MIMoSA segmented volumes were correlated and resulted in 0.88 for T1L and 0.95 for T2L. The correlation between Expanded Disability Status Scale (EDSS) scores and manual versus automatic volumes were similar for T1L (0.32 manual vs. 0.34 MIMoSA), T2L (0.33 vs. 0.32), and the T1L/T2L ratio (0.33 vs 0.33). CONCLUSIONS: Though originally designed to segment T2L, MIMoSA performs well for segmenting T1 black holes in patients with MS.

MIMoSA: An Automated Method for Intermodal Segmentation Analysis of Multiple Sclerosis Brain Lesions.

BACKGROUND AND PURPOSE: Magnetic resonance imaging (MRI) is crucial for in vivo detection and characterization of white matter lesions (WMLs) in multiple sclerosis. While WMLs have been studied for over two decades using MRI, automated segmentation remains challenging. Although the majority of statistical techniques for the automated segmentation of WMLs are based on single imaging modalities, recent advances have used multimodal techniques for identifying WMLs. Complementary modalities emphasize different tissue properties, which help identify interrelated features of lesions. METHODS: Method for Inter-Modal Segmentation Analysis (MIMoSA), a fully automatic lesion segmentation algorithm that utilizes novel covariance features from intermodal coupling regression in addition to mean structure to model the probability lesion is contained in each voxel, is proposed. MIMoSA was validated by comparison with both expert manual and other automated segmentation methods in two datasets. The first included 98 subjects imaged at Johns Hopkins Hospital in which bootstrap cross-validation was used to compare the performance of MIMoSA against OASIS and LesionTOADS, two popular automatic segmentation approaches. For a secondary validation, a publicly available data from a segmentation challenge were used for performance benchmarking. RESULTS: In the Johns Hopkins study, MIMoSA yielded average Sørensen-Dice coefficient (DSC) of .57 and partial AUC of .68 calculated with false positive rates up to 1%. This was superior to performance using OASIS and LesionTOADS. The proposed method also performed competitively in the segmentation challenge dataset. CONCLUSION: MIMoSA resulted in statistically significant improvements in lesion segmentation performance compared with LesionTOADS and OASIS, and performed competitively in an additional validation study.