Dual-Layer Spectral CT Virtual-Non-Contrast Images Aid in Parathyroid Adenoma Analysis and Radiation Dose Reduction: Confirmation of Findings From Dual-Energy CT

4D-parathyroid CT scans have become a mainstay in the evaluation and pre-surgical planning for parathyroid adenomas. Most protocols typically rely on non-contrast images, prior to the arterial and delayed phases. Previous reports with dual-energy CT imaging have highlighted the utility of virtual non-contrast images to help reduce radiation dose while maintaining diagnostic accuracy. Herein, we report two cases of surgically proven parathyroid adenomas diagnosed with 4D-parathyroid CT scans performed on dual-layer spectral scanners, and in retrospect highlight the utility of virtual non-contrast images. To our knowledge, this report provides the first description of virtual non-contrast images from dual-layer spectral CT scanners that could aid in the diagnosis of parathyroid adenomas, confirming similar findings described with dual-energy CT scanners

Machine learning for semi-automated classification of glioblastoma, brain metastasis and central nervous system lymphoma using magnetic resonance advanced imaging.

BACKGROUND: Differentiating glioblastoma, brain metastasis, and central nervous system lymphoma (CNSL) on conventional magnetic resonance imaging (MRI) can present a diagnostic dilemma due to the potential for overlapping imaging features. We investigate whether machine learning evaluation of multimodal MRI can reliably differentiate these entities. METHODS: Preoperative brain MRI including diffusion weighted imaging (DWI), dynamic contrast enhanced (DCE), and dynamic susceptibility contrast (DSC) perfusion in patients with glioblastoma, lymphoma, or metastasis were retrospectively reviewed. Perfusion maps (rCBV, rCBF), permeability maps (K-trans, Kep, Vp, Ve), ADC, T1C+ and T2/FLAIR images were coregistered and two separate volumes of interest (VOIs) were obtained from the enhancing tumor and non-enhancing T2 hyperintense (NET2) regions. The tumor volumes obtained from these VOIs were utilized for supervised training of support vector classifier (SVC) and multilayer perceptron (MLP) models. Validation of the trained models was performed on unlabeled cases using the leave-one-subject-out method. Head-to-head and multiclass models were created. Accuracies of the multiclass models were compared against two human interpreters reviewing conventional and diffusion-weighted MR images. RESULTS: Twenty-six patients enrolled with histopathologically-proven glioblastoma (n=9), metastasis (n=9), and CNS lymphoma (n=8) were included. The trained multiclass ML models discriminated the three pathologic classes with a maximum accuracy of 69.2% accuracy (18 out of 26; kappa 0.540, P=0.01) using an MLP trained with the VpNET2 tumor volumes. Human readers achieved 65.4% (17 out of 26) and 80.8% (21 out of 26) accuracies, respectively. Using the MLP VpNET2 model as a computer-aided diagnosis (CADx) for cases in which the human reviewers disagreed with each other on the diagnosis resulted in correct diagnoses in 5 (19.2%) additional cases. CONCLUSIONS: Our trained multiclass MLP using VpNET2 can differentiate glioblastoma, brain metastasis, and CNS lymphoma with modest diagnostic accuracy and provides approximately 19% increase in diagnostic yield when added to routine human interpretation

The Evolution of Pituitary Cysts in Growth Hormone-Treated Children

OBJECTIVES: We have previously shown that pituitary cysts may affect growth hormone secretion. This study sought to determine cyst evolution during growth hormone treatment in children. METHODS: Forty-nine patients with short stature, a pituitary cyst, and at least two brain MRI scans were included. The percent of the pituitary gland occupied by the cyst (POGO) was calculated, and a cyst with a POGO of ≤15% was considered small, while a POGO \u3e15% was considered large. RESULTS: Thirty-five cysts were small, and 14 were large. Five of the 35 small cysts grew into large cysts, while 6 of the 14 large cysts shrunk into small cysts. Of 4 cysts that fluctuated between large and small, 3 presented as large and 1 as small. Small cysts experienced greater change in cyst volume (CV) (mean=61.5%) than large cysts (mean=-0.4%). However, large cysts had a greater net change in CV (mean=44.2 mm) than small cysts (mean=21.0 mm). Older patients had significantly larger mean pituitary volume than younger patients (435.4 mm vs. 317.9 mm) and significantly larger mean CV than younger patients (77.4 mm vs. 45.2 mm), but there was no significant difference in POGO between groups. CONCLUSIONS: Pituitary cyst size can vary greatly over time. Determination of POGO over time is a useful marker for determining the possibility of a pathologic effect on pituitary function since it factors both cyst and gland volume. Large cysts should be monitored closely, given their extreme, erratic behavior