Parametric Response Mapping of FLAIR MRI Provides an Early Indication of Progression Risk in Glioblastoma

Hoff, Benjamin A.; Lemasson, Benjamin; Chenevert, Thomas L.; Luker, Gary D.; Tsien, Christina; Amouzandeh, Ghoncheh; Johnson, Timothy D.; Ross, Brian D.

doi:10.1016/j.acra.2020.08.015

Cited by 8 publications

(3 citation statements)

References 59 publications

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“…Quantitative mapping of TAA growth allows for investigation of unique parameters (eg, eccentricity, longitudinal extent, multifocality) that are otherwise unable to be easily captured. While VDM represents one of the first techniques for quantitative mapping of disease progression in TAA, similar image analysis techniques using deformable image registration have been used to phenotype and assess progression of diseases of the lungs (19,20), brain (21)(22)(23), and bones (24,25). The development of similar quantitative methods to assess TAA progression promises to improve risk stratification by more clearly separating intervals with slow versus no growth and may serve as a metric to better assess the effects of pharmacologic and surgical interventions.…”

Section: Discussionmentioning

confidence: 99%

Vascular Deformation Mapping for CT Surveillance of Thoracic Aortic Aneurysm Growth

Burris¹,

Bian²,

Dominic³

et al. 2022

Radiology

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VASCULAR AND INTERVENTIONAL RADIOLOGYT horacic aortic aneurysm (TAA) is common and is increasing in prevalence worldwide, with approximately 3% of patients older than 50 years having a dilated thoracic aorta (1-3) and recommended to undergo imaging surveillance (4). Most patients with TAA have an indolent disease course, with aortic growth occurring either slowly or not at all over a period of years or decades (5). However, life-threatening complications, such as aortic dissection and rupture, can occur in otherwise asymptomatic patients at presurgical aneurysm sizes (6,7), emphasizing the need for better techniques with which to assess disease progression, inform surgical candidacy, and predict complications. A fundamental limitation to improved management of TAA is the lack of image analysis techniques with which to accurately assess aortic growth.Current assessment techniques are based on measurements of maximal aortic diameter. However, the degree of variability associated with aortic diameter measurements (within 1-5 mm despite optimal measurement technique) frequently prevents confident assessment of disease progression at typical TAA growth rates (,1 mm per year) (8-11). Also, diameter measurements are inherently two dimensional and are performed in fixed anatomic locations; thus, they are unable to capture the three-dimensional (3D) nature of TAA growth.To overcome these limitations, prior research has described the feasibility of a medical image analysis technique, termed vascular deformation mapping (VDM), in 3D assessment of aortic growth using deformable image registration techniques (12,13). This approach uses high Background: Aortic diameter measurements in patients with a thoracic aortic aneurysm (TAA) show wide variation. There is no technique to quantify aortic growth in a three-dimensional (3D) manner.Purpose: To validate a CT-based technique for quantification of 3D growth based on deformable registration in patients with TAA. Materials and Methods:Patients with ascending and descending TAA with two or more CT angiography studies between 2006 and 2020 were retrospectively identified. The 3D aortic growth was quantified using vascular deformation mapping (VDM), a technique that uses deformable registration to warp a mesh constructed from baseline aortic anatomy. Growth assessments between VDM and clinical CT diameter measurements were compared. Aortic growth was quantified as the ratio of change in surface area at each mesh element (area ratio). Manual segmentations were performed by independent raters to assess interrater reproducibility. Registration error was assessed using manually placed landmarks. Agreement between VDM and clinical diameter measurements was assessed using Pearson correlation and Cohen k coefficients.Results: A total of 38 patients (68 surveillance intervals) were evaluated (mean age, 69 years 6 9 [standard deviation]; 21 women), with TAA involving the ascending aorta (n = 26), descending aorta (n = 10), or both (n = 2). VDM was technically successful in 35 of 38 (92%) pati...

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Section: Discussionmentioning

confidence: 99%

Vascular Deformation Mapping for CT Surveillance of Thoracic Aortic Aneurysm Growth

Burris¹,

Bian²,

Dominic³

et al. 2022

Radiology

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“…The bias inherent in these sets relates to image acquisition but also to the patient and clinical features that may be embedded in the selection of the data set itself. DL has been employed in segmentation tasks [ 46 , 52 ], organ and lesion detection [ 4 ], lesion, tissue and tumor classification tasks [ 4 , 53 , 96 ], diagnosis [ 4 ], prognosis, staging and outcome prediction [ 4 , 30 , 46 , 71 , 78 , 97 , 98 ], image registration and quality assurance [ 4 ]. Segmentation is arguably the task that has been explored the most [ 34 ].…”

Section: Segmentationmentioning

confidence: 99%

AI-Driven Image Analysis in Central Nervous System Tumors-Traditional Machine Learning, Deep Learning and Hybrid Models

AV¹,

Zhuge²,

Zhao³

et al. 2022

J Biotechnol Biomed

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The interpretation of imaging in medicine in general and in oncology specifically remains problematic due to several limitations which include the need to incorporate detailed clinical history, patient and disease-specific history, clinical exam features, previous and ongoing treatment, and account for the dependency on reproducible human interpretation of multiple factors with incomplete data linkage. To standardize reporting, minimize bias, expedite management, and improve outcomes, the use of Artificial Intelligence (AI) has gained significant prominence in imaging analysis. In oncology, AI methods have as a result been explored in most cancer types with ongoing progress in employing AI towards imaging for oncology treatment, assessing treatment response, and understanding and communicating prognosis. Challenges remain with limited available data sets, variability in imaging changes over time augmented by a growing heterogeneity in analysis approaches. We review the imaging analysis workflow and examine how hand-crafted features also referred to as traditional Machine Learning (ML), Deep Learning (DL) approaches, and hybrid analyses, are being employed in AI-driven imaging analysis in central nervous system tumors. ML, DL, and hybrid approaches coexist, and their combination may produce superior results although data in this space is as yet novel, and conclusions and pitfalls have yet to be fully explored. We note the growing technical complexities that may become increasingly separated from the clinic and enforce the acute need for clinician engagement to guide progress and ensure that conclusions derived from AI-driven imaging analysis reflect that same level of scrutiny lent to other avenues of clinical research.

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“…Recent evidence suggests that tumor volume may be superior for determining response assessment in LGGs due to more stable measures of tumor growth rates allowing for assessment of tumor growth over time with less variability, highest and possibly superior inter-reader agreement, and lowest reader discordance rates [44].The extent and alteration overtime of the T2 FLAIR signal remains a significant challenge in LGG since these tumors are non-enhancing upfront but is also pivotal in HGG since the T2 FLAIR signal is altered by radiation therapy as well other factors [18,22]. Recent evidence when analyzing longitudinal normalized FLAIR images using voxel-wise Parametric Response Mapping (PRM) to monitor volume fractions of increased, decreased, or unchanged altered FLAIR intensity, showed that PRMrFLAIR+ exceeding 10%, stratified patients for at risk of failure after 5.6 months (p<0.0001), while RANO criteria did not stratify these patients until 15.4 months (p <0.0001) [45]. Similarly, Gatson et al used the T2 FLAIR signal to show that 75% of patients in their cohort developed progression on average 3.4 months before RANO-assessed progression with 84% sensitivity.…”

Section: Is the Mri The Best Imaging Modality To Examine Progression ...mentioning

confidence: 99%

Using Artificial Intelligence and Magnetic Resonance Imaging to Address Limitations in Response Assessment in Glioma

Krauze

2022

Oncol Insights

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Gliomas are rapidly progressive, neurologically devastating, nearly uniformly fatal brain tumors. In WHO grade IV tumors like glioblastoma, the standard of care involves maximal surgical resection followed by concurrent radiation therapy (RT) and temozolomide (TMZ) chemotherapy followed by adjuvant TMZ. This results in overall survival (OS) of less than 30% at two years. Currently, tumor progression assessment is based on clinician assessment and MRI interpretation using Response Assessment in Neuro-Oncology (RANO) criteria. These criteria classify response as complete, partial, stable, or progression. This approach, however, suffers from significant limitations due to the difficulty in interpreting MRI findings on T1 gad and T2 FLAIR sequences, lack of concurrent correlation with radiation therapy fields, inconsistent follow-up imaging, concurrent administration of steroids, and systemic management, including immunotherapy. The neuro-oncology field struggles with classifying true progression vs. pseudoprogression vs. pseudoresponse with progression guidelines actively evolving. The lack of consensus on the definition of progression impairs the ability to initiate earlier management upon progression, judge the impact of therapies, and optimize and personalize management. Due to the pivotal role of imaging, radiology is at the center of the question of optimizing and advancing response criteria [1-5]. The hypothesis is that MRI images of patients with glioma, when subjected to change over time analysis (at diagnosis, prior to and post-radiation therapy), can identify features predictive of treatment failure helping guide patient management in the clinic. Likely a combination of imaging and biospecimen-driven biomarkers is needed. Given the large amount of data generated by both approaches, success in this space hinges on leveraging computational approaches and artificial intelligence algorithms validated using large-scale publicly available data sets to disentangle the complexity and heterogeneity inherent in glioma progression.

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Parametric Response Mapping of FLAIR MRI Provides an Early Indication of Progression Risk in Glioblastoma

Cited by 8 publications

References 59 publications

Vascular Deformation Mapping for CT Surveillance of Thoracic Aortic Aneurysm Growth

Vascular Deformation Mapping for CT Surveillance of Thoracic Aortic Aneurysm Growth

AI-Driven Image Analysis in Central Nervous System Tumors-Traditional Machine Learning, Deep Learning and Hybrid Models

Using Artificial Intelligence and Magnetic Resonance Imaging to Address Limitations in Response Assessment in Glioma

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