Effects of Multi-Shell Free Water Correction on Glioma Characterization

Starck, Lea; Zaccagna, Fulvio; Pasternak, Ofer; Gallagher, Ferdia A.; Grüner, Renate; Riemer, Frank

doi:10.3390/diagnostics11122385

Cited by 6 publications

(2 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Future studies should focus on developing ML/DL models by combining different data sources (i.e., clinical, radiomic, semantic and PWI/DWI), which are correlated and may provide complementary information [ 95 ] for improving the clinical decision-making tasks [ 96 ]. Moreover, given the proven importance of quantitative techniques such as PWI and DWI, future ML/DL models should leverage the wealth of data provided by novel and more refined diffusion and perfusion techniques [ 97 , 98 , 99 , 100 , 101 ], potentially also including information upon cerebral metabolism [ 99 , 102 ]. By integrating all these data into a single multimodal model, further improvements in performance could be achieved.…”

Section: Discussionmentioning

confidence: 99%

Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI

et al. 2022

Self Cite

View full text Add to dashboard Cite

Given growing clinical needs, in recent years Artificial Intelligence (AI) techniques have increasingly been used to define the best approaches for survival assessment and prediction in patients with brain tumors. Advances in computational resources, and the collection of (mainly) public databases, have promoted this rapid development. This narrative review of the current state-of-the-art aimed to survey current applications of AI in predicting survival in patients with brain tumors, with a focus on Magnetic Resonance Imaging (MRI). An extensive search was performed on PubMed and Google Scholar using a Boolean research query based on MeSH terms and restricting the search to the period between 2012 and 2022. Fifty studies were selected, mainly based on Machine Learning (ML), Deep Learning (DL), radiomics-based methods, and methods that exploit traditional imaging techniques for survival assessment. In addition, we focused on two distinct tasks related to survival assessment: the first on the classification of subjects into survival classes (short and long-term or eventually short, mid and long-term) to stratify patients in distinct groups. The second focused on quantification, in days or months, of the individual survival interval. Our survey showed excellent state-of-the-art methods for the first, with accuracy up to ∼98%. The latter task appears to be the most challenging, but state-of-the-art techniques showed promising results, albeit with limitations, with C-Index up to ∼0.91. In conclusion, according to the specific task, the available computational methods perform differently, and the choice of the best one to use is non-univocal and dependent on many aspects. Unequivocally, the use of features derived from quantitative imaging has been shown to be advantageous for AI applications, including survival prediction. This evidence from the literature motivates further research in the field of AI-powered methods for survival prediction in patients with brain tumors, in particular, using the wealth of information provided by quantitative MRI techniques.

show abstract

Section: Discussionmentioning

confidence: 99%

Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…This would be especially relevant when analysing diseased tissues, such as brain tumors. The necrotic and oedematous tumor compartments typically contain large fractions of free water, which could bias the recovered metrics [20]. Therefore, methods accounting for possible artifacts arising from free water partial voluming should be considered for better in vivo assessment of tissue heterogeneity.…”

Section: Discussionmentioning

confidence: 99%

Diffusivity-limited q-space trajectory imaging

Boito¹,

Herberthson²,

Özarslan³

ISMRM Annual Meeting

View full text Add to dashboard Cite

Q-space trajectory imaging (QTI) characterizes microstructures through the statistical moments of the diffusion tensor distribution. A constrained estimation framework named QTI+ was recently proposed to achieve mathematically and physically acceptable estimates of these moments. Here we consider expanding QTI+ with a new set of conditions based on the theoretical maximum value of water diffusivity. We show where these conditions are violated for different bulk diffusivity values, and how the new constraints affect the QTI scalar maps. The results show violations occurring almost exclusively in voxels containing gray matter and CSF. Imposing the constraints produces metrics closer to the expected values.

show abstract

Assessment the Impact of IDH Mutation Status on MRI Assessments of White Matter Integrity in Glioma Patients: Insights From Peak Width of Skeletonized Mean Diffusivity and Free Water Metrics

Li,

Zhu,

Liu

et al. 2024

Magnetic Resonance Imaging

View full text Add to dashboard Cite

BackgroundGliomas are highly invasive brain tumors that evade accurate geographic assessment by conventional MRI due to microscopic invasion along white matter (WM) tracts. Advanced diffusion MRI techniques are needed to assess occult WM involvement.PurposeTo evaluate peak width of skeletonized mean diffusivity (PSMD) and peak width of skeletonized free water (PSFW), and axonal water fraction (AWF) for assessing glioma‐induced alterations in normal‐appearing WM and their relationship with isocitrate dehydrogenase 1 (IDH1) mutation.Study TypeRetrospective.PopulationOne hundred five glioma patients (46 ± 13 years), 53 healthy controls (HCs) (46 ± 9 years).Field Strength/Sequence3.0 T, T1WI, T1‐CE, T2WI, T2FLAIR, and DKI.AssessmentPSMD and PSFW were compared between lesion and contralateral sides in glioma patients and between patients and HCs. The associations between these metrics and clinical variables, including IDH1 mutation, was assessed. Corpus callosum (CC) injury, quantified by the AWF, was evaluated for its mediated effect of IDH1 mutation on contralesional PSMD and PSFW.Statistical TestsPaired‐t tests, ANCOVA, univariate and multivariate linear regression, and mediation analysis with significance set at P < 0.05.ResultsContralateral PSMD and PSFW were significantly higher in left‐sided gliomas (PSMD: 0.206 ± 0.027 vs. 0.193 ± 0.023; PSFW: 0.119 ± 0.019 vs. 0.106 ± 0.020) than in HCs, with similar increases in right‐sided gliomas (PSMD: 0.219 ± 0.036 vs. 0.195 ± 0.023; PSFW: 0.129 ± 0.031 vs. 0.109 ± 0.020). IDH1 wild‐type gliomas were associated with higher contralateral PSMD and PSFW (β = −0.302 and −0.412). AWF of CC mediated the impact of IDH1 mutations on contralesional PSMD and PSFW (mediated proportion: 42.7% and 53.7%).Data ConclusionPSMD and PSFW are effective biomarkers for assessing WM integrity in gliomas, significantly associated with IDH1 mutation status. AWF of CC mediates the relationship between IDH1 mutation and contralesional PSMD and PSFW.Evidence Level4Technical EfficacyStage 2

show abstract

Effects of Multi-Shell Free Water Correction on Glioma Characterization

Cited by 6 publications

References 47 publications

Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI

Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI

Diffusivity-limited q-space trajectory imaging

Assessment the Impact of IDH Mutation Status on MRI Assessments of White Matter Integrity in Glioma Patients: Insights From Peak Width of Skeletonized Mean Diffusivity and Free Water Metrics

Contact Info

Product

Resources

About