Cost-Effectiveness Analysis of 68Ga-DOTATATE PET/MRI in Radiotherapy Planning in Patients with Intermediate-Risk Meningioma

BACKGROUND AND PURPOSE

While contrast-enhanced MR imaging is the criterion standard in meningioma diagnosis and treatment response assessment, gallium 68Ga-DOTATATE PET/MR imaging has increasingly demonstrated utility in meningioma diagnosis and management. Integrating 68Ga-DOTATATE PET/MR imaging in postsurgical radiation planning reduces the planning target volume and organ-at-risk dose. However, 68Ga-DOTATATE PET/MR imaging is not widely implemented in clinical practice due to higher perceived costs. Our study analyzes the cost-effectiveness of 68Ga-DOTATATE PET/MR imaging for postresection radiation therapy planning in patients with intermediate-risk meningioma.

MATERIALS AND METHODS

We developed a decision-analytical model based on both recommended guidelines on meningioma management and our institutional experience. Markov models were implemented to estimate quality-adjusted life-years (QALY). Cost-effectiveness analyses with willingness-to-pay thresholds of $50,000/QALY and $100,000/QALY were performed from a societal perspective. Sensitivity analyses were conducted to validate the results. Model input values were based on published literature.

RESULTS

The cost-effectiveness results demonstrated that 68Ga-DOTATATE PET/MR imaging yields higher QALY (5.47 versus 5.05) at a higher cost ($404,260 versus $395,535) compared with MR imaging alone. The incremental cost-effectiveness ratio analysis determined that 68Ga-DOTATATE PET/MR imaging is cost-effective at a willingness to pay of $50,000/QALY and $100,000/QALY. Furthermore, sensitivity analyses showed that 68Ga-DOTATATE PET/MR imaging is cost-effective at $50,000/QALY ($100,000/QALY) for specificity and sensitivity values above 76% (58%) and 53% (44%), respectively.

CONCLUSIONS

68Ga-DOTATATE PET/MR imaging as an adjunct imaging technique is cost-effective in postoperative treatment planning in patients with meningiomas. Most important, the model results show that the sensitivity and specificity cost-effective thresholds of 68Ga-DOTATATE PET/MR imaging could be attained in clinical practice.

NCCT Markers of Intracerebral Hemorrhage Expansion Using Revised Criteria: An External Validation of Their Predictive Accuracy

BACKGROUND AND PURPOSE

Several NCCT expansion markers have been proposed to improve the prediction of hematoma expansion. We retrospectively evaluated the predictive accuracy of 9 expansion markers.

MATERIALS AND METHODS

Patients admitted for intracerebral hemorrhage within 24 hours of last seen well were retrospectively included from April 2016 to April 2020. The primary outcome was revised hematoma expansion, defined as any of a ≥6-mL or ≥33% increase in intracerebral hemorrhage volume, a ≥ 1-mL increase in intraventricular hemorrhage volume, or de novo intraventricular hemorrhage. We assessed the predictive accuracy of expansion markers and determined their association with revised hematoma expansion.

RESULTS

We included 124 patients, of whom 51 (41%) developed revised hematoma expansion. The sensitivity of each marker for the prediction of revised hematoma expansion ranged from 4% to 78%; the specificity, 37%-97%; the positive likelihood ratio, 0.41-7.16; and the negative likelihood ratio, 0.49-1.06. By means of univariable logistic regressions, 5 markers were significantly associated with revised hematoma expansion: black hole (OR = 8.66; 95% CI, 2.15-58.14; P = .007), hypodensity (OR = 3.18; 95% CI, 1.49-6.93; P = .003), blend (OR = 2.90; 95% CI, 1.08-8.38; P = .04), satellite (OR = 2.84; 95% CI, 1.29-6.61; P = .01), and Barras shape (OR = 2.41, 95% CI; 1.17-5.10; P = .02). In multivariable models, only the black hole marker remained independently associated with revised hematoma expansion (adjusted OR = 5.62; 95% CI, 1.23-40.23; P = .03).

CONCLUSIONS

No single NCCT expansion marker had both high sensitivity and specificity for the prediction of revised hematoma expansion. Improved image-based analysis is needed to tackle limitations associated with current NCCT-based expansion markers.

Intensive Blood Pressure Management Preserves Functional Connectivity in Patients with Hypertension from the Systolic Blood Pressure Intervention Randomized Trial

BACKGROUND AND PURPOSE

The Systolic Blood Pressure Intervention (SPRINT) randomized trial demonstrated that intensive blood pressure management resulted in slower progression of cerebral white matter hyperintensities, compared with standard therapy. We assessed longitudinal changes in brain functional connectivity to determine whether intensive treatment results in less decline in functional connectivity and how changes in brain functional connectivity relate to changes in brain structure.

MATERIALS AND METHODS

Five hundred forty-eight participants completed longitudinal brain MR imaging, including resting-state fMRI, during a median follow-up of 3.84 years. Functional brain networks were identified using independent component analysis, and a mean connectivity score was calculated for each network. Longitudinal changes in mean connectivity score were compared between treatment groups using a 2-sample t test, followed by a voxelwise t test. In the full cohort, adjusted linear regression analysis was performed between changes in the mean connectivity score and changes in structural MR imaging metrics.

RESULTS

Four hundred six participants had longitudinal imaging that passed quality control. The auditory-salience-language network demonstrated a significantly larger decline in the mean connectivity score in the standard treatment group relative to the intensive treatment group (P = .014), with regions of significant difference between treatment groups in the cingulate and right temporal/insular regions. There was no treatment group difference in other networks. Longitudinal changes in mean connectivity score of the default mode network but not the auditory-salience-language network demonstrated a significant correlation with longitudinal changes in white matter hyperintensities (P = .013).

CONCLUSIONS

Intensive treatment was associated with preservation of functional connectivity of the auditory-salience-language network, while mean network connectivity in other networks was not significantly different between intensive and standard therapy. A longitudinal increase in the white matter hyperintensity burden is associated with a decline in mean connectivity of the default mode network.

Population level multimodal neuroimaging correlates of attention-deficit hyperactivity disorder among children

Objectives

Leveraging a large population-level morphologic, microstructural, and functional neuroimaging dataset, we aimed to elucidate the underlying neurobiology of attention-deficit hyperactivity disorder (ADHD) in children. In addition, we evaluated the applicability of machine learning classifiers to predict ADHD diagnosis based on imaging and clinical information.

Methods

From the Adolescents Behavior Cognitive Development (ABCD) database, we included 1,798 children with ADHD diagnosis and 6,007 without ADHD. In multivariate logistic regression adjusted for age and sex, we examined the association of ADHD with different neuroimaging metrics. The neuroimaging metrics included fractional anisotropy (FA), neurite density (ND), mean-(MD), radial-(RD), and axial diffusivity (AD) of white matter (WM) tracts, cortical region thickness and surface areas from T1-MPRAGE series, and functional network connectivity correlations from resting-state fMRI.

Results

Children with ADHD showed markers of pervasive reduced microstructural integrity in white matter (WM) with diminished neural density and fiber-tracks volumes - most notable in the frontal and parietal lobes. In addition, ADHD diagnosis was associated with reduced cortical volume and surface area, especially in the temporal and frontal regions. In functional MRI studies, ADHD children had reduced connectivity among default-mode network and the central and dorsal attention networks, which are implicated in concentration and attention function. The best performing combination of feature selection and machine learning classifier could achieve a receiver operating characteristics area under curve of 0.613 (95% confidence interval = 0.580-0.645) to predict ADHD diagnosis in independent validation, using a combination of multimodal imaging metrics and clinical variables.

Conclusion

Our study highlights the neurobiological implication of frontal lobe cortex and associate WM tracts in pathogenesis of childhood ADHD. We also demonstrated possible potentials and limitations of machine learning models to assist with ADHD diagnosis in a general population cohort based on multimodal neuroimaging metrics.