Gradient nonlinearity correction to improve apparent diffusion coefficient accuracy and standardization in the american college of radiology imaging network 6698 breast cancer trial

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/113725/1/jmri24883.pd

Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis.

Radiomics is an emerging technology for imaging biomarker discovery and disease-specific personalized treatment management. This paper aims to determine the benefit of using multi-modality radiomics data from PET and MR images in the characterization breast cancer phenotype and prognosis. Eighty-four features were extracted from PET and MR images of 113 breast cancer patients. Unsupervised clustering based on PET and MRI radiomic features created three subgroups. These derived subgroups were statistically significantly associated with tumor grade (p = 2.0 × 10-6), tumor overall stage (p = 0.037), breast cancer subtypes (p = 0.0085), and disease recurrence status (p = 0.0053). The PET-derived first-order statistics and gray level co-occurrence matrix (GLCM) textural features were discriminative of breast cancer tumor grade, which was confirmed by the results of L2-regularization logistic regression (with repeated nested cross-validation) with an estimated area under the receiver operating characteristic curve (AUC) of 0.76 (95% confidence interval (CI) = [0.62, 0.83]). The results of ElasticNet logistic regression indicated that PET and MR radiomics distinguished recurrence-free survival, with a mean AUC of 0.75 (95% CI = [0.62, 0.88]) and 0.68 (95% CI = [0.58, 0.81]) for 1 and 2 years, respectively. The MRI-derived GLCM inverse difference moment normalized (IDMN) and the PET-derived GLCM cluster prominence were among the key features in the predictive models for recurrence-free survival. In conclusion, radiomic features from PET and MR images could be helpful in deciphering breast cancer phenotypes and may have potential as imaging biomarkers for prediction of breast cancer recurrence-free survival

Cardiac fibrosis in aging mice

Dystrophic cardiac calcinosis (DCC), also called epicardial and myocardial fibrosis and mineralization, has been detected in mice of a number of laboratory inbred strains, most commonly C3H/HeJ and DBA/2J. In previous mouse breeding studies between these DCC susceptible and the DCC-resistant strain C57BL/6J, 4 genetic loci harboring genes involved in DCC inheritance were identified and subsequently termed Dyscalc loci 1 through 4. Here, we report susceptibility to cardiac fibrosis, a sub-phenotype of DCC, at 12 and 20 months of age and close to natural death in a survey of 28 inbred mouse strains. Eight strains showed cardiac fibrosis with highest frequency and severity in the moribund mice. Using genotype and phenotype information of the 28 investigated strains, we performed genome-wide association studies (GWAS) and identified the most significant associations on chromosome (Chr) 15 at 72 million base pairs (Mb) (P < 10(-13)) and Chr 4 at 122 Mb (P < 10(-11)) and 134 Mb (P < 10(-7)). At the Chr 15 locus, Col22a1 and Kcnk9 were identified. Both have been reported to be morphologically and functionally important in the heart muscle. The strongest Chr 4 associations were located approximately 6 Mb away from the Dyscalc 2 quantitative trait locus peak within the boundaries of the Extl1 gene and in close proximity to the Trim63 and Cap1 genes. In addition, a single-nucleotide polymorphism association was found on chromosome 11. This study provides evidence for more than the previously reported 4 genetic loci determining cardiac fibrosis and DCC. The study also highlights the power of GWAS in the mouse for dissecting complex genetic traits.The authors thank Jesse Hammer and Josiah Raddar for technical assistance. Research reported in this publication was supported by the Ellison Medical Foundation, Parker B. Francis Foundation, and the National Institutes of Health (R01AR055225 and K01AR064766). Mouse colonies were supported by the National Institutes of Health under Award Number AG025707 for the Jackson Aging Center. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The Jackson Laboratory Shared Scientific Services were supported in part by a Basic Cancer Center Core Grant from the National Cancer Institute (CA34196).This is the author accepted manuscript. The final version is available from Springer via http://dx.doi.org/10.1007/s00335-016-9634-

Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL.

Dynamic contrast-enhanced (DCE) MRI provides both morphological and functional information regarding breast tumor response to neoadjuvant chemotherapy (NAC). The purpose of this retrospective study is to test if prediction models combining multiple MRI features outperform models with single features. Four features were quantitatively calculated in each MRI exam: functional tumor volume, longest diameter, sphericity, and contralateral background parenchymal enhancement. Logistic regression analysis was used to study the relationship between MRI variables and pathologic complete response (pCR). Predictive performance was estimated using the area under the receiver operating characteristic curve (AUC). The full cohort was stratified by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status (positive or negative). A total of 384 patients (median age: 49 y/o) were included. Results showed analysis with combined features achieved higher AUCs than analysis with any feature alone. AUCs estimated for the combined versus highest AUCs among single features were 0.81 (95% confidence interval [CI]: 0.76, 0.86) versus 0.79 (95% CI: 0.73, 0.85) in the full cohort, 0.83 (95% CI: 0.77, 0.92) versus 0.73 (95% CI: 0.61, 0.84) in HR-positive/HER2-negative, 0.88 (95% CI: 0.79, 0.97) versus 0.78 (95% CI: 0.63, 0.89) in HR-positive/HER2-positive, 0.83 (95% CI not available) versus 0.75 (95% CI: 0.46, 0.81) in HR-negative/HER2-positive, and 0.82 (95% CI: 0.74, 0.91) versus 0.75 (95% CI: 0.64, 0.83) in triple negatives. Multi-feature MRI analysis improved pCR prediction over analysis of any individual feature that we examined. Additionally, the improvements in prediction were more notable when analysis was conducted according to cancer subtype

Brain volumetric deficits in MAPT mutation carriers: a multisite study

Objective: MAPT mutations typically cause behavioral variant frontotemporal dementia with or without parkinsonism. Previous studies have shown that symptomatic MAPT mutation carriers have frontotemporal atrophy, yet studies have shown mixed results as to whether presymptomatic carriers have low gray matter volumes. To elucidate whether presymptomatic carriers have lower structural brain volumes within regions atrophied during the symptomatic phase, we studied a large cohort of MAPT mutation carriers using a voxelwise approach. Methods: We studied 22 symptomatic carriers (age 54.7 ± 9.1, 13 female) and 43 presymptomatic carriers (age 39.2 ± 10.4, 21 female). Symptomatic carriers’ clinical syndromes included: behavioral variant frontotemporal dementia (18), an amnestic dementia syndrome (2), Parkinson’s disease (1), and mild cognitive impairment (1). We performed voxel-based morphometry on T1 images and assessed brain volumetrics by clinical subgroup, age, and mutation subtype. Results: Symptomatic carriers showed gray matter atrophy in bilateral frontotemporal cortex, insula, and striatum, and white matter atrophy in bilateral corpus callosum and uncinate fasciculus. Approximately 20% of presymptomatic carriers had low gray matter volumes in bilateral hippocampus, amygdala, and lateral temporal cortex. Within these regions, low gray matter volume

Plasma Neurofilament Light for Prediction of Disease Progression in Familial Frontotemporal Lobar Degeneration

Objective: We tested the hypothesis that plasma neurofilament light chain (NfL) identifies asymptomatic carriers of familial frontotemporal lobar degeneration (FTLD)-causing mutations at risk of disease progression. Methods: Baseline plasma NfL concentrations were measured with single-molecule array in original (n = 277) and validation (n = 297) cohorts. C9orf72, GRN, and MAPT mutation carriers and noncarriers from the same families were classified by disease severity (asymptomatic, prodromal, and full phenotype) using the CDR Dementia Staging Instrument plus behavior and language domains from the National Alzheimer's Disease Coordinating Center FTLD module (CDR+NACC-FTLD). Linear mixed-effect models related NfL to clinical variables. Results: In both cohorts, baseline NfL was higher in asymptomatic mutation carriers who showed phenoconversion or disease progression compared to nonprogressors (original: 11.4 ± 7 pg/mL vs 6.7 ± 5 pg/mL, p = 0.002; validation: 14.1 ± 12 pg/mL vs 8.7 ± 6 pg/mL, p = 0.035). Plasma NfL discriminated symptomatic from asymptomatic mutation carriers or those with prodromal disease (original cutoff: 13.6 pg/mL, 87.5% sensitivity, 82.7% specificity; validation cutoff: 19.8 pg/mL, 87.4% sensitivity, 84.3% specificity). Higher baseline NfL correlated with worse longitudinal CDR+NACC-FTLD sum of boxes scores, neuropsychological function, and atrophy, regardless of genotype or disease severity, including asymptomatic mutation carriers. Conclusions: Plasma NfL identifies asymptomatic carriers of FTLD-causing mutations at short-term risk of disease progression and is a potential tool to select participants for prevention clinical trials. Trial registration information: ClinicalTrials.gov Identifier: NCT02372773 and NCT02365922. Classification of evidence: This study provides Class I evidence that in carriers of FTLD-causing mutations, elevation of plasma NfL predicts short-term risk of clinical progression

Bayesian k -Space-Time Reconstruction of MR Spectroscopic Imaging for Enhanced Resolution

A k -space-time Bayesian statistical reconstruction method (K-Bayes) is proposed for the reconstruction of metabolite images of the brain from proton ( 1 H) magnetic resonance (MR) spectroscopic imaging (MRSI) data. K-Bayes performs full spectral fitting of the data while incorporating structural (anatomical) spatial information through the prior distribution. K-Bayes provides increased spatial resolution over conventional discrete Fourier transform (DFT) based methods by incorporating structural information from higher resolution coregistered and segmented structural MR images. The structural information is incorporated via a Markov random field (MRF) model that allows for differential levels of expected smoothness in metabolite levels within homogeneous tissue regions and across tissue boundaries. By further combining the structural prior model with a k -space-time MRSI signal and noise model (for a specific set of metabolites and based on knowledge from prior spectral simulations of metabolite signals), the impact of artifacts generated by low-resolution sampling is also reduced. The posterior-mode estimates are used to define the metabolite map reconstructions, obtained via a generalized expectation-maximization algorithm. K-Bayes was tested using simulated and real MRSI datasets consisting of sets of k-space-time-series (the recorded free induction decays). The results demonstrated that K-Bayes provided qualitative and quantitative improvement over DFT methods

Correction to "Bayesian k-Space-Time Reconstruction of MR Spectroscopic Imaging for Enhanced Resolution" [Jul 10 1333-1350]

In the above titled paper (ibid., vol. 29, no. 7, pp. 1333-1350), there were errors in equations (7) and (16). The corrected equations are presented here