Development and validation of a new MRI simulation technique that can reliably estimate optimal in vivo scanning parameters in a glioblastoma murine model

BACKGROUND: Magnetic Resonance Imaging (MRI) relies on optimal scanning parameters to achieve maximal signal-to-noise ratio (SNR) and high contrast-to-noise ratio (CNR) between tissues resulting in high quality images. The optimization of such parameters is often laborious, time consuming, and user-dependent, making harmonization of imaging parameters a difficult task. In this report, we aim to develop and validate a computer simulation technique that can reliably provide optimal in vivo scanning parameters ready to be used for in vivo evaluation of disease models. METHODS: A glioblastoma murine model was investigated using several MRI imaging methods. Such MRI methods underwent a simulated and an in vivo scanning parameter optimization in pre- and post-contrast conditions that involved the investigation of tumor, brain parenchyma and cerebrospinal fluid (CSF) CNR values in addition to the time relaxation values of the related tissues. The CNR tissues information were analyzed and the derived scanning parameters compared in order to validate the simulated methodology as a reliable technique for optimal in vivo scanning parameters estimation. RESULTS: The CNRs and the related scanning parameters were better correlated when spin-echo-based sequences were used rather than the gradient-echo-based sequences due to augmented inhomogeneity artifacts affecting the latter methods. Optimal in vivo scanning parameters were generated successfully by the simulations after initial scanning parameter adjustments that conformed to some of the parameters derived from the in vivo experiment. CONCLUSION: Scanning parameter optimization using the computer simulation was shown to be a valid surrogate to the in vivo approach in a glioblastoma murine model yielding in a better delineation and differentiation of the tumor from the contralateral hemisphere. In addition to drastically reducing the time invested in choosing optimal scanning parameters when compared to an in vivo approach, this simulation program could also be used to harmonize MRI acquisition parameters across scanners from different vendors

Association between myopia progression and quantity of laser treatment for retinopathy of prematurity.

BackgroundPrevious studies found that infants with retinopathy of prematurity (ROP) who were treated for more posterior disease with a greater number of laser spots developed higher myopia. These studies included multiple physicians with variations in laser density. In treatments by a single physician, laser spot count is a better surrogate for area of avascular retina and anterior-posterior location of disease, so that the relationship with myopia can be better assessed.MethodsOur retrospective study included infants treated with laser for ROP by a single surgeon at a single center. Exclusion criteria were irregularities during laser and additional treatment for ROP. We assessed correlation between laser spot count and change in refractive error over time using a linear mixed effects model.ResultsWe studied 153 eyes from 78 subjects treated with laser for ROP. The average gestational age at birth was 25.3±1.8 weeks, birth weight 737±248 grams, laser spot count 1793±728, and post-treatment follow up 37±29 months. Between corrected ages 0-1 years, the mean spherical equivalent was +0.4±2.3 diopters; between ages 1-2, it was -1.3±3.2D; and ages 2-3 was -0.8±3.1D. Eyes that received more laser spots had significantly greater change in refractive error over time (0.30D more myopia per year per 1000 spots). None of the eyes with hyperopia before 18 months developed myopia during the follow-up period.ConclusionsGreater myopia developed over time in infants with ROP treated by laser to a larger area of avascular retina

Use of targeted next generation sequencing to characterize tumor mutational burden and efficacy of immune checkpoint inhibition in small cell lung cancer

Abstract Background Clinically-available biomarkers to identify the fraction of patients with small cell lung cancer (SCLC) who respond to immune-checkpoint inhibitors (ICIs) are lacking. High nonsynonymous tumor mutational burden (TMB), as assessed by whole exome sequencing, correlates with improved clinical outcomes for patients with SCLC treated with ICIs. Whether TMB as assessed by targeted next generation sequencing (NGS) is associated with improved efficacy of ICIs in patients with SCLC is currently unknown. Here we determined whether TMB by targeted NGS is associated with efficacy of ICIs in patients with SCLC. Methods We collected clinicopathologic data from patients with relapsed or refractory SCLC which underwent targeted NGS with TMB assessment by the Dana-Farber Cancer Institute OncoPanel platform. The relationship between TMB and clinical outcomes after treatment with ICIs was investigated. Results Among the 52 patients treated with ICIs, we found no significant difference in the objective response rate (ORR) between patients with a TMB above the 50th percentile (“TMB high”) and those with a TMB at or below the 50th percentile (“TMB low”). The median progression-free survival (mPFS) and median overall survival (mOS) were significantly longer in patients with a high TMB compared to those with a low TMB (mPFS: 3.3 versus 1.2 months, HR: 0.37 [95% CI: 0.20–0.69], P < 0.01; mOS: 10.4 versus 2.5 months, HR: 0.38 [95% CI: 0.19–0.77], P < 0.01). The one-year PFS and OS rates improved with increasing mutational load when TMB was divided into tertiles. Conclusions These findings show that targeted NGS, a readily available clinical diagnostic test, can be used to identify patients with SCLC who are most likely to benefit from treatment with immune checkpoint inhibitors

Association between cognitive function and large optic nerve cupping, accounting for cup-disc-ratio genetic risk score.

PurposeTo investigate if accounting for a cup-to-disc ratio (CDR) genetic risk score (GRS) modified the association between large CDR and cognitive function among women.DesignThis was a retrospective study using data from the Women's Health Initiative.MethodsPatients with glaucoma or ocular hypertension were excluded. Large CDR was defined as ≥ 0.6 in either eye. Cognitive function was measured by the Modified Mini-Mental State Examination (3MSE). We used the combined effects from 13 single nucleotide polymorphisms (SNPs) to formulate the GRS for CDR. We used logistic regression to investigate associations between weighted GRS and large CDR, then a linear regression to assess the association between weighted GRS and 3MSE scores, and between weighted GRS, CDR, and 3MSE scores, adjusted for demographic and clinical characteristics.ResultsFinal analyses included 1,196 White women with mean age of 69.60 ± 3.62 years and 7.27% with large CDR. Mean GRS in women with and without large CDR was 1.51 ± 0.31 vs. 1.41 ± 0.36, respectively (p = 0.004). The odds of large CDR for a one unit increase in GRS was 2.30 (95% CI: (1.22, 4.36), p = 0.011). Adding the CDR GRS in the model with CDR and 3MSE, women with large CDR still had statistically significantly lower 3MSE scores than those without large CDR, yielding a predicted mean difference in 3MSE scores of 0.84 (p = 0.007).ConclusionsIndependent of the CDR GRS, women with large CDR had a lower cognitive function

T1, T2 and T2* values (mean ± SD in the 8 tumor-bearing mice) for brain parenchyma, CSF and tumor tissues of a glioblastoma murine model under pre-contrast and post-contrast conditions.

<p>T1, T2 and T2* values (mean ± SD in the 8 tumor-bearing mice) for brain parenchyma, CSF and tumor tissues of a glioblastoma murine model under pre-contrast and post-contrast conditions.</p

Gradient-echo based sequences diagrams.

<p>Gradient-echo (GRE) brain parenchyma-tumor mean CNR graphical representation during scanning parameters optimization. Figs A_1,3 and A_2,4 report respectively in vivo and simulated CNR GRE data when changing FA and TR in addition to NSA (5-minute scan limit) in pre-contrast conditions. Same approach is shown in Figs B₁,₃ and B₂,₄ in post-contrast conditions. Black arrows point at selected parameters that typically coincide with the highest CNR providing the <i>“optimal in vivo scanning parameters”</i> in the in vivo approach and the “<i>optimal computed scanning parameters</i>” in the simulated approach. The latter parameters are compared with the “Scanning parameters comparison analysis” and the outcome was reported as successful (YES) or failing (NO). Note that although the graphs are related to a single mouse dataset, there is a good representation of the entire animal cohort.</p

Fast-Spin-Echo CNR data and scanning parameters comparison analysis.

<p>Fast-spin-echo T1-weighted (FSET1) brain parenchyma-tumor mean CNR graphical representation. In pre-contrast conditions: graphs A_1,3,5) and A_2,4,₆) report respectively in vivo and simulated CNR FSET1 data when changing ESP, ETL, TR and NSA (the scan time was limited to 5 minutes). In post-contrast conditions: graphs B₁,₃) and B₂,₄) reported respectively in vivo and simulated CNR FSET1 data when changing ESP, TR and NSA (the scan time was limited to 5 minutes). Black arrows point at selected parameters that typically coincide with the highest CNR providing the <i>“optimal in vivo scanning parameters”</i> for the in vivo method, and the “<i>optimal computed scanning parameters</i>” with the simulated approach. The latter parameters are compared by the “Scanning parameters comparison analysis” and the outcome reported as successful (YES) or failing (NO). Note that although the graphs are related to a single mouse dataset, they are a good representation of the entire animal cohort.</p

Pre- and post-contrast images “optimal in vivo scanning parameters” of a glioblastoma murine model brain section.

<p>Figs A₁, A₂, A₃ and A₄ represent FSET2 FSET1, GRE and FLAIR pre-contrast images respectively. Tumor areas are well differentiated from the valuable brain parenchyma in FSET2. On FLAIR images, excellent CSF signal saturation is achieved. Figs B₁, B₂, B₃ and B₄ represent FSET2 FSET1, GRE and MP-RAGE (the image contrast was inverted for visualization purposes) post-contrast images respectively. Tumor areas are well differentiated from the healthy brain parenchyma in all scans. Contrast enhancement effects are prominent in all T1 examinations.</p