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PURPOSE - Brain shift during tumor resection can progressively invalidate the accuracy of neuronavigation systems and affect neurosurgeons' ability to achieve optimal resections. This paper compares two methods that have been presented in the literature to compensate for brain shift: a thin-plate spline deformation model and a finite element method (FEM). For this comparison, both methods are driven by identical sparse data. Specifically, both methods are driven by displacements between automatically detected and matched feature points from intraoperative 3D ultrasound (iUS). Both methods have been shown to be fast enough for intraoperative brain shift correction (Machado et al. in Int J Comput Assist Radiol Surg 13(10):1525-1538, 2018; Luo et al. in J Med Imaging (Bellingham) 4(3):035003, 2017). However, the spline method requires no preprocessing and ignores physical properties of the brain while the FEM method requires significant preprocessing and incorporates patient-specific physical and geometric constraints. The goal of this work was to explore the relative merits of these methods on recent clinical data.
METHODS - Data acquired during 19 sequential tumor resections in Brigham and Women's Hospital's Advanced Multi-modal Image-Guided Operating Suite between December 2017 and October 2018 were considered for this retrospective study. Of these, 15 cases and a total of 24 iUS to iUS image pairs met inclusion requirements. Automatic feature detection (Machado et al. in Int J Comput Assist Radiol Surg 13(10):1525-1538, 2018) was used to detect and match features in each pair of iUS images. Displacements between matched features were then used to drive both the spline model and the FEM method to compensate for brain shift between image acquisitions. The accuracies of the resultant deformation models were measured by comparing the displacements of manually identified landmarks before and after deformation.
RESULTS - The mean initial subcortical registration error between preoperative MRI and the first iUS image averaged 5.3 ± 0.75 mm. The mean subcortical brain shift, measured using displacements between manually identified landmarks in pairs of iUS images, was 2.5 ± 1.3 mm. Our results showed that FEM was able to reduce subcortical registration error by a small but statistically significant amount (from 2.46 to 2.02 mm). A large variability in the results of the spline method prevented us from demonstrating either a statistically significant reduction in subcortical registration error after applying the spline method or a statistically significant difference between the results of the two methods.
CONCLUSIONS - In this study, we observed less subcortical brain shift than has previously been reported in the literature (Frisken et al., in: Miller (ed) Biomechanics of the brain, Springer, Cham, 2019). This may be due to the fact that we separated out the initial misregistration between preoperative MRI and the first iUS image from our brain shift measurements or it may be due to modern neurosurgical practices designed to reduce brain shift, including reduced craniotomy sizes and better control of intracranial pressure with the use of mannitol and other medications. It appears that the FEM method and its use of geometric and biomechanical constraints provided more consistent brain shift correction and better correction farther from the driving feature displacements than the simple spline model. The spline-based method was simpler and tended to give better results for small deformations. However, large variability in the spline results and relatively small brain shift prevented this study from demonstrating a statistically significant difference between the results of the two methods.
The complexity of modern in vivo magnetic resonance imaging (MRI) methods in oncology has dramatically changed in the last 10 years. The field has long since moved passed its (unparalleled) ability to form images with exquisite soft-tissue contrast and morphology, allowing for the enhanced identification of primary tumors and metastatic disease. Currently, it is not uncommon to acquire images related to blood flow, cellularity, and macromolecular content in the clinical setting. The acquisition of images related to metabolism, hypoxia, pH, and tissue stiffness are also becoming common. All of these techniques have had some component of their invention, development, refinement, validation, and initial applications in the preclinical setting using in vivo animal models of cancer. In this review, we discuss the genesis of quantitative MRI methods that have been successfully translated from preclinical research and developed into clinical applications. These include methods that interrogate perfusion, diffusion, pH, hypoxia, macromolecular content, and tissue mechanical properties for improving detection, staging, and response monitoring of cancer. For each of these techniques, we summarize the 1) underlying biological mechanism(s); 2) preclinical applications; 3) available repeatability and reproducibility data; 4) clinical applications; and 5) limitations of the technique. We conclude with a discussion of lessons learned from translating MRI methods from the preclinical to clinical setting, and a presentation of four fundamental problems in cancer imaging that, if solved, would result in a profound improvement in the lives of oncology patients. Level of Evidence: 5 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1377-1392.
© 2019 International Society for Magnetic Resonance in Medicine.
PURPOSE - Stereotactic radiosurgery (SRS) is used for local control treatment of patients with intracranial metastases. As a result of SRS, some patients develop radiation-induced necrosis. Radiographically, radiation-induced necrosis can appear similar to tumor recurrence in magnetic resonance (MR) T -weighted contrast-enhanced imaging, T -weighted MR imaging, and Fluid-Attenuated Inversion Recovery (FLAIR) MR imaging. Radiographic ambiguities often necessitate invasive brain biopsies to determine lesion etiology or cause delayed subsequent therapy initiation. We use a biomechanically coupled tumor growth model to estimate patient-specific model parameters and model-derived measures to noninvasively classify etiology of enhancing lesions in this patient population.
METHODS - In this initial, preliminary retrospective study, we evaluated five patients with tumor recurrence and five with radiation-induced necrosis. Longitudinal patient-specific MR imaging data were used in conjunction with the model to parameterize tumor cell proliferation rate and tumor cell diffusion coefficient, and Dice correlation coefficients were used to quantify degree of correlation between model-estimated mechanical stress fields and edema visualized from MR imaging.
RESULTS - Results found four statistically relevant parameters which can differentiate tumor recurrence and radiation-induced necrosis.
CONCLUSIONS - This preliminary investigation suggests potential of this framework to noninvasively determine the etiology of enhancing lesions in patients who previously underwent SRS for intracranial metastases.
© 2019 American Association of Physicists in Medicine.
Whether patients with glioblastoma that contacts the ventricular-subventricular zone stem cell niche (VSVZ + GBM) have a distinct survival profile from VSVZ - GBM patients independent of other known predictors or molecular profiles is unclear. Using multivariate Cox analysis to adjust survival for widely-accepted predictors, hazard ratios (HRs) for overall (OS) and progression free (PFS) survival between VSVZ + GBM and VSVZ - GBM patients were calculated in 170 single-institution patients and 254 patients included in both The Cancer Genome (TCGA) and Imaging (TCIA) atlases. An adjusted, multivariable analysis revealed that VSVZ contact was independently associated with decreased survival in both datasets. TCGA molecular data analyses revealed that VSVZ contact by GBM was independent of mutational, DNA methylation, gene expression, and protein expression signatures in the bulk tumor. Therefore, while survival of GBM patients is independently stratified by VSVZ contact, with VSVZ + GBM patients displaying a poor prognosis, the VSVZ + GBMs do not possess a distinct molecular signature at the bulk sample level. Focused examination of the interplay between the VSVZ microenvironment and subsets of GBM cells proximal to this region is warranted.
Conventional radiation therapy of brain tumors often produces cognitive deficits, particularly in children. We investigated the potential efficacy of merging Orthovoltage X-ray Minibeams (OXM). It segments the beam into an array of parallel, thin (~0.3 mm), planar beams, called minibeams, which are known from synchrotron x-ray experiments to spare tissues. Furthermore, the slight divergence of the OXM array make the individual minibeams gradually broaden, thus merging with their neighbors at a given tissue depth to produce a solid beam. In this way the proximal tissues, including the cerebral cortex, can be spared. Here we present experimental results with radiochromic films to characterize the method's dosimetry. Furthermore, we present our Monte Carlo simulation results for physical absorbed dose, and a first-order biologic model to predict tissue tolerance. In particular, a 220-kVp orthovoltage beam provides a 5-fold sharper lateral penumbra than a 6-MV x-ray beam. The method can be implemented in arc-scan, which may include volumetric-modulated arc therapy (VMAT). Finally, OXM's low beam energy makes it ideal for tumor-dose enhancement with contrast agents such as iodine or gold nanoparticles, and its low cost, portability, and small room-shielding requirements make it ideal for use in the low-and-middle-income countries.
BACKGROUND - Malignant peripheral nerve sheath tumors (MPNSTs) are rare, aggressive soft tissue sarcomas. MPNST intracranial metastasis is exceedingly rare with only 22 documented cases in the literature and, to our knowledge, only 1 case with intraparenchymal brain metastasis. Most have been managed surgically; however, 2 documented cases were treated with Gamma Knife radiosurgery. Excluding this case report, there are no other documented cases of linear accelerator-based stereotactic radiosurgery (SRS) to treat MPNST brain metastasis.
CASE DESCRIPTION - A 41-year-old man with MPNST of the lung initially underwent tumor resection. He developed multiple systemic metastases that were managed with directed radiation therapy. A parietal brain metastasis was treated with linear accelerator-based SRS. Following SRS therapy, the patient was treated with a tropomyosin receptor kinase inhibitor. Complete resolution of brain metastasis was seen on brain magnetic resonance imaging 5 months after treatment with SRS. At 11 months after SRS, there was no evidence of recurrence or progression of the intraparenchymal disease. The patient continued to have stable extracranial disease on his ninth cycle of systemic treatment.
CONCLUSIONS - This report provides important insights into efficacy of linear accelerator-based SRS to treat MPNST brain metastases.
Copyright © 2018 Elsevier Inc. All rights reserved.
PURPOSE - To test the ability of a novel pulse sequence applied in vivo at 3 Tesla to separate the contributions to the water signal from amide proton transfer (APT) and relayed nuclear Overhauser enhancement (rNOE) from background direct water saturation and semisolid magnetization transfer (MT). The lack of such signal source isolation has confounded conventional chemical exchange saturation transfer (CEST) imaging.
METHODS - We quantified APT and rNOE signals using a chemical exchange rotation transfer (CERT) metric, MTR . A range of duty cycles and average irradiation powers were applied, and results were compared with conventional CEST analyses using asymmetry (MTR ) and extrapolated magnetization transfer (EMR).
RESULTS - Our results indicate that MTR is more specific than MTR and, because it requires as few as 3 data points, is more rapid than methods requiring a complete Z-spectrum, such as EMR. In white matter, APT (1.5 ± 0.5%) and rNOE (2.1 ± 0.7%) were quantified by using MTR with a 30% duty cycle and a 0.5-µT average power. In addition, our results suggest that MTR is insensitive to B inhomogeneity, further magnifying its speed advantage over CEST metrics that require a separate B measurement. However, MTR still has nontrivial sensitivity to B inhomogeneities.
CONCLUSION - We demonstrated that MTR is an alternative metric to evaluate APT and rNOE, which is fast, robust to B inhomogeneity, and easy to process.
© 2018 International Society for Magnetic Resonance in Medicine.
PURPOSE - To develop and investigate a set of biophysical models based on a mechanically coupled reaction-diffusion model of the spatiotemporal evolution of tumor growth after radiation therapy.
METHODS AND MATERIALS - Post-radiation therapy response is modeled using a cell death model (M), a reduced proliferation rate model (M), and cell death and reduced proliferation model (M). To evaluate each model, rats (n = 12) with C6 gliomas were imaged with diffusion-weighted magnetic resonance imaging (MRI) and contrast-enhanced MRI at 7 time points over 2 weeks. Rats received either 20 or 40 Gy between the third and fourth imaging time point. Diffusion-weighted MRI was used to estimate tumor cell number within enhancing regions in contrast-enhanced MRI data. Each model was fit to the spatiotemporal evolution of tumor cell number from time point 1 to time point 5 to estimate model parameters. The estimated model parameters were then used to predict tumor growth at the final 2 imaging time points. The model prediction was evaluated by calculating the error in tumor volume estimates, average surface distance, and voxel-based cell number.
RESULTS - For both the rats treated with either 20 or 40 Gy, significantly lower error in tumor volume, average surface distance, and voxel-based cell number was observed for the M and M models compared with the M model. The M model fit, however, had significantly lower sum squared error compared with the M and M models.
CONCLUSIONS - The results of this study indicate that for both doses, the M and M models result in accurate predictions of tumor growth, whereas the M model poorly describes response to radiation therapy.
Copyright © 2017 Elsevier Inc. All rights reserved.
Biophysical models designed to predict the growth and response of tumors to treatment have the potential to become a valuable tool for clinicians in care of cancer patients. Specifically, individualized tumor forecasts could be used to predict response or resistance early in the course of treatment, thereby providing an opportunity for treatment selection or adaption. This chapter discusses an experimental and modeling framework in which noninvasive imaging data is used to initialize and parameterize a subject-specific model of tumor growth. This modeling approach is applied to an analysis of murine models of glioma growth.