Biology subjects

Starosolski, Z. A.

Publications and source records attributed to Starosolski, Z. A..

3 recordsLinked to original sources

bioRxiv2022

Multi-modal Imaging of Disease Progression in TH-MYCN Mouse Models of Neuroblastoma

MYCN is a major driver for neuroblastoma (NB) and the tyrosine hydroxylase (TH)-MYCN transgenic mouse model is extensively used for preclinical NB studies. However, spatio-temporal NB progression in the TH-MYCN model has not been studied, and questions remain about the value of implanted models as a surrogate for transgenic mice. In this work, we used magnetic resonance imaging (MRI) to study tumor progression and nanoparticle contrast-enhanced computed tomography (n-CECT) to assess tumor vascular architecture in TH-MYCN transgenic mice (2-7 weeks of age) and TH-MYCN+/+-derived orthotopic allograft and syngeneic mice (2-5 weeks post-tumor implantation). Tumors in TH-MYCN transgenic mice became evident in the abdominal paraspinal region at week 5. A delayed thoracic paraspinal mass became evident at week 6 and most mice succumbed by week 7. In allograft and syngeneic mice, single mass tumor growth was restricted to the peritoneal cavity. N-CECT revealed a predominantly microvascular network in TH-MYCN tumors while implanted tumors exhibited heterogeneous and tortuous vessels. N-CECT quantitative analysis demonstrated high vascularity (tumor fractional blood volume ~ 0.12) in all models. Multi-modal imaging of TH-MYCN transgenic and implanted models revealed differences in growth patterns and vascular architecture that should be considered in designing preclinical studies.

cancer biology↗

bioRxiv2021

A novel method for improving the accuracy of MR-derived patient-specific vascular models using X-ray angiography

MR imaging is a noninvasive imaging modality that is commonly used during clinical follow up and has been widely utilized to reconstruct realistic 3D vascular models for patient-specific analysis. In a recent work, we utilized patient-specific hemodynamic analysis of the circle of Willis to noninvasively assess stroke risk in pediatric Moyamoya disease (MMD)--a progressive steno-occlusive cerebrovascular disease that leads to recurrent stroke. The objective was to identify vascular regions with critically high wall shear rate (WSR), signifying elevated stroke risk. However, sources of error including insufficient resolution of MR images can negatively impact vascular model accuracy, especially in areas of severe pathological narrowing, and thus diminish clinical relevance of simulation results, as local hemodynamics are sensitive to vessel geometry. We have developed a novel method to improve the accuracy of MR-derived 3D vascular models utilizing 2D X-ray angiography (XA), which is considered the gold standard for clinically assessing vessel caliber. In this workflow, "virtual angiographies" (VA) of 3D MR-derived vascular models are conducted, producing 2D projections that are compared to corresponding XA images guiding the local adjustment of modeled vessels. This VA-comparison-adjustment loop is iterated until the two agree, as confirmed by an expert neuroradiologist. Using this method, we generated models of the circle of Willis of two patients with a history of unilateral stroke. Blood flow simulations were performed using a Navier-Stokes solver within an isogeoemtric analysis framework and WSR distributions were quantified. Results for one patient show as much as 45% underestimation of local WSR in the stenotic left anterior cerebral artery (LACA) and up to a 60% underestimation in the right anterior cerebral artery when using the initial MR-derived model compared to the XA-adjusted model, emphasizing the need for verifying improved accuracy of the adjusted model. To that end, vessel cross-sectional areas of the pre- and post-adjustment models were compared to those seen in 3D CTA images of the same patient. CTA has superior resolution and signal to noise ratio compared to MR imaging but is not commonly used in clinic due to radiation exposure concerns, especially in pediatric patients. While the vessels in the initial model had normalized root mean squared deviations (NRMSDs) ranging from 26% to 182% and 31% to 69% in two patients with respect to CTA, the adjusted vessel NRMSDs were comparatively smaller (32% to 53% and 11% to 42%). In the mildly stenotic LACA of patient 1, the NRMSDs for the pre- and post-adjusted models were 49% and 32%, respectively. These findings suggest that our XA-based adjustment method can considerably improve the accuracy of vascular models, and thus, stroke-risk prediction. An accurate individualized assessment of stroke risk would be of substantial clinical benefit because it would help guide the timing of preventative surgical interventions in pediatric MMD patients.

bioengineering↗

bioRxiv2018

CNN-based radiographic acute tibial fracture detection in the setting of open growth plates

Pediatric tibial fractures are commonly diagnosed by radiographs and constitute one of the common tasks performed by pediatric radiologists. Here, we assess the performance of a convolutional neural network for the detection of acute tibial fractures trained with a limited number of cases in skeletally immature patients. This retrospective study was performed on radiology reports manually classified as normal or tibial fracture. Classified images of orthopaedic implants, casting, and images including other pathology were excluded. The remaining cases constituted 516 studies containing 2118 radiographs. These radiographs were truncated to include a limited investigated field of view which included the distal third of the leg, inclusive of the distal physis. After exclusions, the culled dataset was randomly divided into a training set containing 784 radiographs, a validation set containing 98 radiographs, and a test set 98 radiographs. We used a modified transfer learning approach based on the Xception architecture with additional fully convoluted reasoning and drop-out layers. Of 49 fractures, two were misdiagnosed as normal. Of 49 normal exams, none were misdiagnosed. This led to model accuracy of 97.9%, sensitivity 95.9%, and specificity 100%, comparable to or better than human radiologists. In no instances were normal physes or normal developmental epiphyseal fragmentation of the tibial tuberosity or medial malleolus misclassified as a fracture. We report an efficient method to use a pre-trained network and adapt it to a medical classification task using only a small number of radiographs dedicated to precise anatomical location.

bioinformatics↗