Imaging of GBM in the Age of Molecular Markers and MRI Guided Adaptive Radiation Therapy.
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ABSTRACT:
SUBMITTER: Dajani S
PROVIDER: S-EPMC9572863 | biostudies-literature | 2022 Oct
REPOSITORIES: biostudies-literature
Publications
Imaging of GBM in the Age of Molecular Markers and MRI Guided Adaptive Radiation Therapy.
Dajani Salah S Hill Virginia B VB Kalapurakal John A JA Horbinski Craig M CM Nesbit Eric G EG Sachdev Sean S Yalamanchili Amulya A Thomas Tarita O TO
Journal of clinical medicine 20221010 19
Glioblastoma (GBM) continues to be one of the most lethal malignancies and is almost always fatal. In this review article, the role of radiation therapy, systemic therapy, as well as the molecular basis of classifying GBM is described. Technological advances in the treatment of GBM are outlined as well as the diagnostic imaging characteristics of this tumor. In addition, factors that affect prognosis such as differentiating progression from treatment effect is discussed. The role of MRI guided r ...[more]
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Project description:Hepatocellular carcinoma (HCC) is the most common liver tumor, with a continually rising incidence. The curative treatment for HCC is surgical resection or liver transplantation; however, only a small portion of patients are eligible due to local tumor burden or underlying liver dysfunction. Most HCC patients receive nonsurgical liver-directed therapies (LDTs), including thermal ablation, transarterial chemoembolization (TACE), transarterial radioembolization (TARE), and external beam radiation therapy (EBRT). Stereotactic ablative body radiation (SABR) is a specific type of EBRT that can precisely deliver a high dose of radiation to ablate tumor cells using a small number of treatments (or fractions, typically 5 or less). With onboard MRI imaging, MRI-guided SABR can improve therapeutic dose while minimizing normal tissue exposure. In the current review, we discuss different LDTs and compare them with EBRT, specifically SABR. The emerging MRI-guided adaptive radiation therapy has been reviewed, highlighting its advantages and potential role in HCC management.
| S-EPMC10218854 | biostudies-literature
Project description:BackgroundMagnetic resonance imaging (MRI) offers superb non-invasive, soft tissue imaging of the human body. However, extensive data sampling requirements severely restrict the spatiotemporal resolution achievable with MRI. This limits the modality's utility in real-time guidance applications, particularly for the rapidly growing MRI-guided radiation therapy approach to cancer treatment. Recent advances in artificial intelligence (AI) could reduce the trade-off between the spatial and the temporal resolution of MRI, thus increasing the clinical utility of the imaging modality.MethodsWe trained deep learning-based super-resolution neural networks to increase the spatial resolution of real-time MRI. We developed a framework to integrate neural networks directly onto a 1.0 T MRI-linac enabling real-time super-resolution imaging. We integrated this framework with the targeting system of the MRI-linac to demonstrate real-time beam adaptation with super-resolution-based imaging. We tested the integrated system using large publicly available datasets, healthy volunteer imaging, phantom imaging, and beam tracking experiments using bicubic interpolation as a baseline comparison.ResultsDeep learning-based super-resolution increases the spatial resolution of real-time MRI across a variety of experiments, offering measured performance benefits compared to bicubic interpolation. The temporal resolution is not compromised as measured by a real-time adaptation latency experiment. These two effects, an increase in the spatial resolution with a negligible decrease in the temporal resolution, leads to a net increase in the spatiotemporal resolution.ConclusionsDeployed super-resolution neural networks can increase the spatiotemporal resolution of real-time MRI. This has applications to domains such as MRI-guided radiation therapy and interventional procedures.
| S-EPMC10994938 | biostudies-literature
Project description:Accurate radiation therapy (RT) targeting is crucial for glioblastoma treatment but may be challenging using clinical imaging alone due to the infiltrative nature of glioblastomas. Precise targeting by whole-brain spectroscopic MRI, which maps tumor metabolites including choline (Cho) and N-acetylaspartate (NAA), can quantify early treatment-induced molecular changes that other traditional modalities cannot measure. We developed a pipeline to determine how spectroscopic MRI changes during early RT are associated with patient outcomes to provide insight into the utility of adaptive RT planning. Data were obtained from a study (NCT03137888) where glioblastoma patients received high-dose RT guided by the pre-RT Cho/NAA twice normal (Cho/NAA ≥ 2x) volume, and received spectroscopic MRI scans pre- and mid-RT. Overlap statistics between pre- and mid-RT scans were used to quantify metabolic activity changes after two weeks of RT. Log-rank tests were used to quantify the relationship between imaging metrics and patient overall and progression-free survival (OS/PFS). Patients with lower Jaccard/Dice coefficients had longer PFS (p = 0.045 for both), and patients with lower Jaccard/Dice coefficients had higher OS trending towards significance (p = 0.060 for both). Cho/NAA ≥ 2x volumes changed significantly during early RT, putting healthy tissue at risk of irradiation, and warranting further study into using adaptive RT planning.
| S-EPMC10204497 | biostudies-literature
Project description:The full complement of molecular pathways contributing to Parkinson’s disease (PD) pathogenesis remains unknown. Here, to address this issue, we began by using a high-resolution variant of functional magnetic resonance imaging (fMRI) to pinpoint brainstem regions differentially affected by, and resistant to, the disease. Then, relying on the imaging information as a guide, we profiled gene expression levels of postmortem brain samples and used a factorial statistical model to identify a disease related decrease in the expression of the polyamine enzyme spermidine/spermine N1-acetyltransferase 1 (SAT1). Next, a series of studies were performed to confirm the pathogenic relevance of this finding. First, to test for a causal link between polyamines and α-synuclein toxicity, we investigated a yeast model expressing α-synuclein. Polyamines were found to enhance the toxicity of α-synuclein, and an unbiased genome-wide screen for modifiers of α-synuclein toxicity identified Tpo4, a member of a family of proteins responsible for polyamine transport. Second, to test for a causal link between SAT1 activity and PD histopathology we investigated a mouse model expressing α-synuclein. DENSPM (N1, N11-diethylnorspermine), a polyamine analog that increases SAT1 activity, was found to reduce PD histopathology, while Berenil (diminazene aceturate), a pharmacological agent that reduces SAT1 activity, worsened the histopathology. Third, we genotyped PD patients and controls and isolated a rare but novel variant in the SAT1 gene, although the functional significance of this genetic variant was not identified. Taken together, the results suggest that the polyamine pathway contributes to PD pathogenesis. Imaging-guided microarray In principle, gene expression profiling techniques like microarray are well suited to identify molecular pathways contributing to the pathogenesis of complex diseases. In practice, however, microarray applied to diseases of the brain present a number of analytic challenges. By identifying regions within the same brain structure that are differentially targeted by and resistant to a disease, imaging-guided microarray is an approach designed to address these limitations. Specifically, guided by the spatial information generated from high resolution functional imaging, a 2x2 factorial analysis-of-variance can be designed, including both within and between group factors, and this “double subtraction” model is effective in improving signal-to-noise in a microarray experiment. Relying on imaging findings, we harvested the DMNV from 6 postmortem brains with evidence of PD and from 5 control brains. The postmortem PD cases were evaluated for pathological changes (Lewy body-containing neurons and Lewy neurites evidenced with antibodies directed against α-synuclein aggregates) that matched the pattern proposed by Braak. We relied on the imaging results to identify a neighboring medullary region relatively unaffected by the disease to be used as a within-brain control. We decided on the inferior olivary nucleus (ION), because it is histologically identifiable, and harvested the ION from each of the 6 PD cases and 5 controls. Microarray techniques were used to generate gene expression profiles for each of the 22 tissue samples. A repeated-measures 2x2 factorial ANOVA model constructed for the imaging study was applied to the expression dataset, in which expression levels from two regions of the medulla (DMNV vs. ION) were included as the first within group factor, diagnosis (PD vs. controls) was the between group factor, and age and sex were included as covariates. Based on current literature, one of the top hits (SAT1) was investigated further to determine if it played a role in PD pathogenesis.
2010-08-19 | E-GEOD-19587 | biostudies-arrayexpress
Project description:PurposeOnline adaptive replanning (OLAR) is generally labor-intensive and time-consuming during MRI-guided adaptive radiation therapy (MRgART). This work aims to develop a method to determine OLAR necessity during MRgART.MethodsA machine learning classifier was developed to predict OLAR necessity based on wavelet multiscale texture features extracted from daily MRIs and was trained and tested with data from 119 daily MRI datasets acquired during MRgART for 24 pancreatic cancer patients treated on a 1.5 T MR-Linac. Spearman correlations, interclass correlation (ICC), coefficient of variance (COV), t-test (p < 0.05), self-organized map (SOM) and maximum stable extremal region (MSER) algorithm were used to determine candidate features, which were used to build the prediction models using Bayesian classifiers. The model performance was judged using the AUC of the ROC curve.ResultsSpearman correlation identified 123 features that were not redundant (r < 0.9). Of them 82 showed high ICC for repositioning > 0.6, 67 had a COV greater than 9% for OLAR. Among the 38 features passed the t-test, 25 passed the SOM and 12 passed the MSER. These final 12 features were used to build the classifier model. The combination of 2-3 features at a time was used to build the classifier models. The best performing model was a 3-feature combination, which can predict OLAR necessity with a CV-AUC of 0.98.ConclusionsA machine learning classifier model based on the wavelet features extracted from daily MRI for pancreatic cancer was developed to automatically and objectively determine if OLAR is necessary for a treatment fraction avoiding unnecessary effort during MRgART.
| S-EPMC9838213 | biostudies-literature
Project description:Objective.Auto-delineation of air regions on daily MRI for MR-guided online adaptive radiotherapy (MRgOART) of abdominal tumors is challenging since the air packets occur randomly and their MR intensities can be similar to some other tissue types. This work reports a new method to auto-delineate air regions on MRI.Approach.The proposed method (named DIFF method) consists of (1) generating a combined volumeVcomb, which is a union of the air-containing organs on a reference MR image offline, (2) transferringVcombfrom the reference MR to a daily MR via DIR, (3) combining the transferredVcombwith a region of high DIR inaccuracy, and (4) applying a threshold to the obtained final combined volume to generate the air volumes. The high DIR inaccuracy region was calculated from the absolute difference between the deformed daily and the reference images. This method was tested on 36 abdominal daily MRI sets acquired from 7 patients on a 1.5 T MR-Linac. The performance of DIFF was compared with alternative auto-air generation methods that (1) does not account for DIR inaccuracies, and (2) uses rigid registration instead of DIR.Main results.The results show that the proposed DIFF method can be fully automated and can be executed within 25 s. The Dice similarity coefficient of manual and DIFF auto-generated air contours was >92% for all cases, while it was 90% for the alternative auto-delineation methods. Dosimetrically, the auto-generated air regions using DIFF resulted in practically identical DVHs as those generated by using manual air contours.Significance.The DIFF method is robust and accurate and can be implemented to automatically consider the inter- and intra- fractional air volume variations during MRgOART for abdominal tumors. The use of DIFF method improves dosimetric accuracy as compared to other methods, especially beneficial for the patients with large daily abdominal air volume variations.
| S-EPMC9639194 | biostudies-literature
Project description:The full complement of molecular pathways contributing to Parkinson’s disease (PD) pathogenesis remains unknown. Here, to address this issue, we began by using a high-resolution variant of functional magnetic resonance imaging (fMRI) to pinpoint brainstem regions differentially affected by, and resistant to, the disease. Then, relying on the imaging information as a guide, we profiled gene expression levels of postmortem brain samples and used a factorial statistical model to identify a disease related decrease in the expression of the polyamine enzyme spermidine/spermine N1-acetyltransferase 1 (SAT1). Next, a series of studies were performed to confirm the pathogenic relevance of this finding. First, to test for a causal link between polyamines and α-synuclein toxicity, we investigated a yeast model expressing α-synuclein. Polyamines were found to enhance the toxicity of α-synuclein, and an unbiased genome-wide screen for modifiers of α-synuclein toxicity identified Tpo4, a member of a family of proteins responsible for polyamine transport. Second, to test for a causal link between SAT1 activity and PD histopathology we investigated a mouse model expressing α-synuclein. DENSPM (N1, N11-diethylnorspermine), a polyamine analog that increases SAT1 activity, was found to reduce PD histopathology, while Berenil (diminazene aceturate), a pharmacological agent that reduces SAT1 activity, worsened the histopathology. Third, we genotyped PD patients and controls and isolated a rare but novel variant in the SAT1 gene, although the functional significance of this genetic variant was not identified. Taken together, the results suggest that the polyamine pathway contributes to PD pathogenesis.
2010-08-19 | GSE19587 | GEO
Project description:PurposeFast and accurate auto-segmentation on daily images is essential for magnetic resonance imaging (MRI)-guided adaptive radiation therapy (ART). However, the state-of-the-art auto-segmentation based on deep learning still has limited success, particularly for complex structures in the abdomen. This study aimed to develop an automatic contour refinement (ACR) process to quickly correct for unacceptable auto-segmented contours.Methods and materialsAn improved level set-based active contour model (ACM) was implemented for the ACR process and was tested on the deep learning-based auto-segmentation of 80 abdominal MRI sets along with their ground truth contours. The performance of the ACR process was evaluated using 4 contour accuracy metrics: the Dice similarity coefficient (DSC), mean distance to agreement (MDA), surface DSC, and added path length (APL) on the auto-segmented contours of the small bowel, large bowel, combined bowels, pancreas, duodenum, and stomach.ResultsA portion (3%-39%) of the corrected contours became practically acceptable per the American Association of Physicists in Medicine Task Group 132 (TG-132) recommendation (DSC >0.8 and MDA <3 mm). The best correction performance was seen in the combined bowels, where for the contours with major errors (initial DSC <0.5 or MDA >8 mm), the mean DSC increased from 0.34 to 0.59, the mean MDA decreased from 7.02 mm to 5.23 mm, and the APL reduced by almost 20 mm, whereas for the contours with minor errors, the mean DSC increased from 0.72 to 0.79, the mean MDA decreased from 3.35 mm to 3.29 mm, and more than one-third (39%) of the ACR contours became clinically acceptable. The execution time for the ACR process on one subregion was less than 2 seconds using an NVIDIA GTX 1060 GPU.ConclusionsThe ACR process implemented based on the ACM was able to quickly correct for some inaccurate contours produced from MRI-based deep learning auto-segmentation of complex abdominal anatomy. The ACR method may be integrated into the auto-segmentation step to accelerate the process of MRI-guided ART.
| S-EPMC9280040 | biostudies-literature
Project description:PurposeCBCT-guided online-adaptive radiotherapy (oART) systems have been made possible by using artificial intelligence and automation to substantially reduce treatment planning time during on-couch adaptive sessions. Evaluating plans generated during an adaptive session presents significant challenges to the clinical team as the planning process gets compressed into a shorter window than offline planning. We identified MU variations up to 30% difference between the adaptive plan and the reference plan in several oART sessions that caused the clinical team to question the accuracy of the oART dose calculation. We investigated the cause of MU variation and the overall accuracy of the dose delivered when MU variations appear unnecessarily large.MethodsDosimetric and adaptive plan data from 604 adaptive sessions of 19 patients undergoing CBCT-guided oART were collected. The analysis included total MU per fraction, planning target volume (PTV) and organs at risk (OAR) volumes, changes in PTV-OAR overlap, and DVH curves. Sessions with MU greater than two standard deviations from the mean were reoptimized offline, verified by an independent calculation system, and measured using a detector array.ResultsMU variations relative to the reference plan were normally distributed with a mean of -1.0% and a standard deviation of 11.0%. No significant correlation was found between MU variation and anatomic changes. Offline reoptimization did not reliably reproduce either reference or on-couch total MUs, suggesting that stochastic effects within the oART optimizer are likely causing the variations. Independent dose calculation and detector array measurements resulted in acceptable agreement with the planned dose.ConclusionsMU variations observed between oART plans were not caused by any errors within the oART workflow. Providers should refrain from using MU variability as a way to express their confidence in the treatment planning accuracy. Clinical decisions during on-couch adaptive sessions should rely on validated secondary dose calculations to ensure optimal plan selection.
| S-EPMC11492364 | biostudies-literature