Behavioral and biologic characteristics of cancer-related cognitive impairment biotypes.

Psychiatric diagnosis is moving away from symptom-based classification and towards multi-dimensional, biologically-based characterization, or biotyping. We previously identified three biotypes of chemotherapy-related cognitive impairment based on functional brain connectivity. In this follow-up study of 80 chemotherapy-treated breast cancer survivors and 80 non-cancer controls, we evaluated additional factors to help explain biotype expression: neurofunctional stability, brain age, apolipoprotein (APOE) genotype, and psychoneurologic symptoms. We also compared the discriminative ability of a traditional, symptom-based cognitive impairment definition with that of biotypes. We found significant differences in cortical brain age (F = 10.50, p < 0.001), neurofunctional stability (F = 2.83, p = 0.041), APOE e4 genotype (X2 = 7.68, p = 0.050), and psychoneurological symptoms (Pillai = 0.378, p < 0.001) across the three biotypes. The more resilient Biotype 2 demonstrated significantly higher neurofunctional stability compared to the other biotypes. Symptom-based classification of cognitive impairment did not differentiate biologic or other behavioral variables, suggesting that traditional categorization of cancer-related cognitive effects may miss important characteristics which could inform targeted treatment strategies. Additionally, biotyping, but not symptom-typing, was able to distinguish survivors with cognitive versus psychological effects. Our results suggest that Biotype 1 survivors might benefit from first addressing symptoms of anxiety and fatigue, Biotype 3 might benefit from a treatment plan which includes sleep hygiene, and Biotype 2 might benefit most from cognitive skills training or rehabilitation. Future research should include additional demographic and clinical information to further investigate biotype expression related to risk and resilience and examine integration of more clinically feasible imaging approaches.

Quantitative Analysis of the Cerebral Vasculature on Magnetic Resonance Angiography.

The arterial connections in the Circle of Willis are a central source of collateral blood flow and play an important role in pathologies such as stroke and mental illness. Analysis of the Circle of Willis and its variants can shed light on optimal methods of diagnosis, treatment planning, surgery, and quantification of outcomes. We developed an automated, standardized, objective, and high-throughput approach for categorizing and quantifying the Circle of Willis vascular anatomy using magnetic resonance angiography images. This automated algorithm for processing of MRA images isolates and automatically identifies key features of the cerebral vasculature such as branching of the internal intracranial internal carotid artery and the basilar artery. Subsequently, physical features of the segments of the anterior cerebral artery were acquired on a sample and intra-patient comparisons were made. We demonstrate the feasibility of using our approach to automatically classify important structures of the Circle of Willis and extract biomarkers from cerebrovasculature. Automated image analysis can provide clinically-relevant vascular features such as aplastic arteries, stenosis, aneurysms, and vessel caliper for endovascular procedures. The developed algorithm could facilitate clinical studies by supporting high-throughput automated analysis of the cerebral vasculature.

MR Imaging Biomarkers in Oncology Clinical Trials.

The authors discuss eight areas of quantitative MR imaging that are currently used (RECIST, DCE-MR imaging, DSC-MR imaging, diffusion MR imaging) in clinical trials or emerging (CEST, elastography, hyperpolarized MR imaging, multiparameter MR imaging) as promising techniques in diagnosing cancer and assessing or predicting response of cancer to therapy. Illustrative applications of the techniques in the clinical setting are summarized before describing the current limitations of the methods.

Amide proton transfer imaging of the human breast at 7T: development and reproducibility.

Chemical exchange saturation transfer (CEST) can offer information about protons associated with mobile proteins through the amide proton transfer (APT) effect, which has been shown to discriminate tumor from healthy tissue and, more recently, has been suggested as a prognosticator of response to therapy. Despite this promise, APT effects are small (only a few percent of the total signal), and APT imaging is often prone to artifacts resulting from system instability. Here we present a procedure that enables the detection of APT effects in the human breast at 7T while mitigating these issues. Adequate signal-to-noise ratio (SNR) was achieved via an optimized quadrature RF breast coil and 3D acquisitions. To reduce the influence of fat, effective fat suppression schemes were developed that did not degrade SNR. To reduce the levels of ghosting artifacts, dummy scans have been integrated into the scanning protocol. Compared with results obtained at 3T, the standard deviation of the measured APT effect was reduced by a factor of four at 7T, allowing for the detection of APT effects with a standard deviation of 1% in the human breast at 7T. Together, these results demonstrate that the APT effect can be reliably detected in the healthy human breast with a high level of precision at 7T.

Amide proton transfer imaging of the breast at 3 T: establishing reproducibility and possible feasibility assessing chemotherapy response.

Chemical exchange saturation transfer imaging can generate contrast that is sensitive to amide protons associated with proteins and peptides (termed amide proton transfer, APT). In breast cancer, APT contrast may report on underlying changes in microstructural tissue composition. However, to date, there have been no developments or applications of APT chemical exchange saturation transfer to breast cancer. As a result, the aims of this study were to (i) experimentally explore optimal scan parameters for breast chemical exchange saturation transfer near the amide resonance at 3 T, (ii) establish the reliability of APT imaging of healthy fibroglandular tissue, and (iii) demonstrate preliminary results on APT changes in locally advanced breast cancer observed during the course of neoadjuvant chemotherapy. Chemical exchange saturation transfer measurements were experimentally optimized on cross-linked bovine serum albumin phantoms, and the reliability of APT imaging was assessed in 10 women with no history of breast disease. The mean difference between test-retest APT values was not significantly different from zero, and the individual difference values were not dependent on the average APT value. The 95% confidence interval limits were ±0.70% (α = 0.05), and the repeatability was 1.91. APT measurements were also performed in three women before and after one cycle of chemotherapy. Following therapy, APT increased in the one patient with progressive disease and decreased in the two patients with a partial or complete response. Together, these results suggest that APT imaging may report on treatment response in these patients.

Current and emerging quantitative magnetic resonance imaging methods for assessing and predicting the response of breast cancer to neoadjuvant therapy.

Reliable early assessment of breast cancer response to neoadjuvant therapy (NAT) would provide considerable benefit to patient care and ongoing research efforts, and demand for accurate and noninvasive early-response biomarkers is likely to increase. Response assessment techniques derived from quantitative magnetic resonance imaging (MRI) hold great potential for integration into treatment algorithms and clinical trials. Quantitative MRI techniques already available for assessing breast cancer response to neoadjuvant therapy include lesion size measurement, dynamic contrast-enhanced MRI, diffusion-weighted MRI, and proton magnetic resonance spectroscopy. Emerging yet promising techniques include magnetization transfer MRI, chemical exchange saturation transfer MRI, magnetic resonance elastography, and hyperpolarized MR. Translating and incorporating these techniques into the clinical setting will require close attention to statistical validation methods, standardization and reproducibility of technique, and scanning protocol design.

Optimal echo spacing for multi-echo imaging measurements of Bi-exponential T2 relaxation.

Calculations, analytical solutions, and simulations were used to investigate the trade-off of echo spacing and receiver bandwidth for the characterization of bi-exponential transverse relaxation using a multi-echo imaging pulse sequence. The Cramer-Rao lower bound of the standard deviation of the four parameters of a two-pool model was computed for a wide range of component T(2) values and echo spacing. The results demonstrate that optimal echo spacing (TE(opt)) is not generally the minimal available given other pulse sequence constraints. The TE(opt) increases with increasing value of the short T(2) time constant and decreases as the ratio of the long and short time constant decreases. A simple model of TE(opt) as a function of the two T(2) time constants and four empirically derived scalars is presented.