Functional diffusion map of malignant brain tumors: A surrogate imaging biomarker for early prediction of therapeutic response and patient survival

Dessouky, Basma Abdel Moneim; El, Osama Lotfy; Gowily, Ahmad Gaber El; Khawalka, Yasser Mohamed El

doi:10.1016/j.ejrnm.2010.08.005

Cited by 5 publications

(4 citation statements)

References 35 publications

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“…Several studies investigating ADC have been suffering from limitations like heterogeneous patient groups with different tumor grades [21,30–32]. In this context it is important to note that glioblastoma with IDH 1 or IDH 2 mutation and those without are now widely regarded as different tumors [33].…”

Section: Discussionmentioning

confidence: 99%

“…Moreover different approaches for determining the ADC-threshold were performed among the different groups. One group used an ADC-threshold determined by empiric data of 15 patients [30–32], while others used ADC-thresholds that equaled the 95% confidence interval of a mixture of grey and white matter scans of 69 patients with different tumor grades [21]. …”

Section: Discussionmentioning

confidence: 99%

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Differentiation of pseudoprogression and real progression in glioblastoma using ADC parametric response maps

et al. 2017

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PurposeThe purpose of this study was to investigate whether a voxel-wise analysis of apparent diffusion coefficient (ADC) values may differentiate between progressive disease (PD) and pseudoprogression (PsP) in patients with high-grade glioma using the parametric response map, a newly introduced postprocessing tool.MethodsTwenty-eight patients with proven PD and seven patients with PsP were identified in this retrospective feasibility study. For all patients ADC baseline and follow-up maps on four subsequent MRIs were available. ADC maps were coregistered on contrast enhanced T1-weighted follow-up images. Subsequently, enhancement in the follow-up contrast enhanced T1-weighted image was manually delineated and a reference region of interest (ROI) was drawn in the contralateral white matter. Both ROIs were transferred to the ADC images. Relative ADC (rADC) (baseline)/reference ROI values and rADC (follow up)/reference ROI values were calculated for each voxel within the ROI. The corresponding voxels of rADC (follow up) and rADC (baseline) were subtracted and the percentage of all voxels within the ROI that exceeded the threshold of 0.25 was quantified.ResultsrADC voxels showed a decrease of 59.2% (1st quartile (Q1) 36.7; 3rd quartile (Q3) 78.6) above 0.25 in patients with PD and 18.6% (Q1 3.04; Q3 26.5) in patients with PsP (p = 0.005). Receiver operating characteristic curve analysis showed the optimal decreasing rADC cut-off value for identifying PD of > 27.05% (area under the curve 0.844±0.065, sensitivity 0.86, specificity 0.86, p = 0.014).ConclusionThis feasibility study shows that the assessment of rADC using parametric response maps might be a promising approach to contribute to the differentiation between PD and PsP. Further research in larger patient cohorts is necessary to finally determine its clinical utility.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Differentiation of pseudoprogression and real progression in glioblastoma using ADC parametric response maps

et al. 2017

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“…Preliminary fDM studies have shown promise as an early predictive biomarker for a variety of tumor grades and using single ROIs. 10,12,28 The current study represents the largest, most comprehensive fDM study to date, examining a total of 143 patients with de novo glioblastoma treated with radiochemotherapy using a total of 4 different ROIs.…”

Section: Discussionmentioning

confidence: 99%

“…Previous fDM in radiochemotherapy studies had substantial limitations, 10,12,28 including an examination of a relatively heterogeneous patient population (all malignant gliomas), relatively small sample size (40 -66 patients), a single region of interest/mask (contrastenhancing regions exclusively), and a relatively arbitrary ADC threshold for fDM classification (0.55 mm 2 /ms, based on empirical data in 15 patients). In addition, previous fDM studies used ADC maps collected before and during radiotherapy (3 weeks after initiation of treatment), which does not represent the standard of care for clinical monitoring of malignant gliomas.…”

Section: Discussionmentioning

confidence: 99%

Functional diffusion maps (fDMs) evaluated before and after radiochemotherapy predict progression-free and overall survival in newly diagnosed glioblastoma

et al. 2012

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Functional diffusion mapping (fDM) has shown promise as a sensitive imaging biomarker for predicting survival in initial studies consisting of a small number of patients, mixed tumor grades, and before routine use of anti-angiogenic therapy. The current study tested whether fDM performed before and after radiochemotherapy could predict progression-free and overall survival in 143 patients with newly diagnosed glioblastoma from 2007 through 2010, many treated with anti-angiogenic therapy after recurrence. Diffusion and conventional MRI scans were obtained before and 4 weeks after completion of radiotherapy and concurrent temozolomide treatment. FDM was created by coregistering pre- and posttreatment apparent diffusion coefficient (ADC) maps and then performing voxel-wise subtraction. FDMs were categorized according to the degree of change in ADC in pre- and posttreatment fluid-attenuated inversion recovery (FLAIR) and contrast-enhancing regions. The volume fraction of fDM-classified increasing ADC(+), decreasing ADC(-), and change in ADC(+/-) were tested to determine whether they were predictive of survival. Both Bonferroni-corrected univariate log-rank analysis and Cox proportional hazards modeling demonstrated that patients with decreasing ADC in a large volume fraction of pretreatment FLAIR or contrast-enhancing regions were statistically more likely to progress earlier and expire sooner than in patients with a lower volume fraction. The current study supports the hypothesis that fDM is a sensitive imaging biomarker for predicting survival in glioblastoma.

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A brain tumor prediction system for detecting the tumor disease using mini batch K-Means clustering and CNN

Ganapathy,

Thoidingjam,

Sen

2024

Multimed Tools Appl

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Functional diffusion map of malignant brain tumors: A surrogate imaging biomarker for early prediction of therapeutic response and patient survival

Cited by 5 publications

References 35 publications

Differentiation of pseudoprogression and real progression in glioblastoma using ADC parametric response maps

Differentiation of pseudoprogression and real progression in glioblastoma using ADC parametric response maps

Functional diffusion maps (fDMs) evaluated before and after radiochemotherapy predict progression-free and overall survival in newly diagnosed glioblastoma

A brain tumor prediction system for detecting the tumor disease using mini batch K-Means clustering and CNN

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