Rare solid and cystic presentation of hemangiopericytoma/ solitary fibrous tumor: A case report

Hemangiopericytoma/Solitary Fibrous Tumor (HPC/SFT) is a rare fibroblastic sarcoma characterized by hyper-vasculature and STAT6 trans-nuclear localization. Cystic HPC/SFT is extremely rare. Due to the scarcity of cystic HPC/SFT cases, diagnostic and treatment guidelines are not well established. To our knowledge, we present the first case of cystic HPC/SFT observed in the liver. In addition, the patient had over 6 years of recurrent hypervascular solid HPC/SFT in the brain, bone, leptomeninges, liver and lung prior to developing a cystic HPC/SFT. Briefly, a 37-year-old Caucasian female with a history of HPC/SFT presented with several enlarging cystic hepatic lesions on surveillance MRI. The cystic/nonenhancing nature of these liver metastases were confirmed by contrast-enhanced ultrasound. Due to diagnostic uncertainty, two of these hepatic cysts were removed laparoscopically and pathology confirmed cystic HPC/SFT with a high MIB-1 index. Previously, in 2014, the patient was diagnosed with solid intracranial grade III pseudopapillary mesenchymal HPC/SFT in the posterior fossa and underwent subtotal resection followed by external beam radiation. In 2017, she had recurrent intracranial, vertebral, and intraspinal intradural extramedullary HPC/SFTs followed by surgery, proton therapy, and SRS radiotherapy. In 2019, after an uneventful pregnancy and birth, routine surveillance revealed metastases in the liver requiring an extended right hepatectomy. In 2020-2021 two solid hypervascular hepatic HPC/SFT were found and treated with microwave ablation. Shortly afterwards, several rapidly growing hepatic cystic HPC/SFT lesions developed. Of note, she has not taken any systemic therapy, indicating the cystic tumors are from metastases rather than cystic degradation as a sequela of therapy. Overall, this case highlights that cystic metastasis are a potential clinical manifestation of solid HPC/SFT. Moreover, cystic HPC/SFT can co-exist with the more typical primary solid hypervascular HPC/SFTs in the same patient. Lastly, in this case cystic HPC/SFT had a higher growth rate and propensity to metastasize as compared to the solid equivalent.Peer reviewe

Radiation Induced Optic Neuropathy With Exceptional Responses to Bevacizumab, A Case Series

Radiation induced optic neuropathy (RION) is a rare cause of vision loss due to necrosis of the anterior visual pathway. The literature reflects a poor prognosis with limited treatment strategies. (1-4) We seek to describe 3 cases of RION with exceptional responses to intravenous bevacizumab (Avastin)

Considerations of target surface area and the risk of radiosurgical toxicity.

OBJECTIVE:The goal of this study was to explore conceptual benefits of characterizing delineated target volumes based on surface area and to utilize the concept for assessing risk of therapeutic toxicity in radiosurgery. METHODS AND MATERIALS:Four computer-generated targets, a sphere, a cylinder, an ellipsoid and a box, were designed for two distinct scenarios. In the first scenario, all targets had identical volumes, and in the second one, all targets had identical surface areas. High quality stereotactic radiosurgery plans with at least 95% target coverage and selectivity were created for each target in both scenarios. Normal brain volumes V12Gy, V14Gy and V16Gy corresponding to received dose of 12 Gy, 14 Gy and 16 Gy, respectively, were computed and analyzed. Additionally, V12Gy and V14Gy volumes and values for seven prospective toxicity variables were recorded for 100 meningioma patients after Gamma Knife radiosurgery. Multivariable stepwise linear regression and best subset linear regression analyses were performed in two statistical software packages, SAS/STAT and R, respectively. RESULTS:In a phantom study, for the constant volume targets, the volumes of 12 Gy, 14 Gy and 16 Gy isodose clouds were the lowest for the spherical target as an expected corollary of the isoperimetric inequality. For the constant surface area targets, a conventional wisdom is confirmed, as the target volume increases the corresponding volumes V12Gy, V14Gy and V16Gy also increase. In the 100-meningioma patient cohort, the best univariate model featured tumor surface area as the most significantly associated variable with both V12Gy and V14Gy volumes, corresponding to the adjusted R2 values of 0.82 and 0.77, respectively. Two statistical methods converged to matching multivariable models. CONCLUSIONS:In a univariate model, target surface area is a better predictor of spilled dose to normal tissue than target largest dimension or target volume itself. In complex multivariate models, target surface area is an independent variable for modeling radiosurgical normal tissue toxicity risk

A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery.

Accurate and automatic brain metastases target delineation is a key step for efficient and effective stereotactic radiosurgery (SRS) treatment planning. In this work, we developed a deep learning convolutional neural network (CNN) algorithm for segmenting brain metastases on contrast-enhanced T1-weighted magnetic resonance imaging (MRI) datasets. We integrated the CNN-based algorithm into an automatic brain metastases segmentation workflow and validated on both Multimodal Brain Tumor Image Segmentation challenge (BRATS) data and clinical patients' data. Validation on BRATS data yielded average DICE coefficients (DCs) of 0.75±0.07 in the tumor core and 0.81±0.04 in the enhancing tumor, which outperformed most techniques in the 2015 BRATS challenge. Segmentation results of patient cases showed an average of DCs 0.67±0.03 and achieved an area under the receiver operating characteristic curve of 0.98±0.01. The developed automatic segmentation strategy surpasses current benchmark levels and offers a promising tool for SRS treatment planning for multiple brain metastases

Illustration of image patch extraction.

<p>Illustration of image patch extraction.</p

Performance of DCs (±SD) in BRATS challenge 2015.

<p>Performance of DCs (±SD) in BRATS challenge 2015.</p

ROC curve of the En-DeepMedic with different local patch size.

<p>ROC curve of the En-DeepMedic with different local patch size.</p