Advanced Physiologic Imaging: Perfusion – Theory and Applications

Kickingereder, Philipp; Park, Ji Eun; Boxerman, Jerrold L.

doi:10.1007/978-3-030-27359-0_5

Cited by 3 publications

(2 citation statements)

References 190 publications

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“…Noninvasive characterization of intratumoral angiogenesis on DSC MRI has gained substantial interest for studying the effects of antiangiogenic agents like bevacizumab (25). Specifically, previous uncontrolled retrospective studies demonstrated that angiogenesis-related parameters obtained from DSC MRI may correlate with treatment outcome and help identify subsets in which bevacizumab is most beneficial in terms of OS (9,10,17,18,26,27).…”

Section: Discussionmentioning

confidence: 99%

Noninvasive Characterization of Tumor Angiogenesis and Oxygenation in Bevacizumab-treated Recurrent Glioblastoma by Using Dynamic Susceptibility MRI: Secondary Analysis of the European Organization for Research and Treatment of Cancer 26101 Trial

Kickingereder¹,

Brugnara²,

Hansen³

et al. 2020

Radiology

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Section: Discussionmentioning

confidence: 99%

Noninvasive Characterization of Tumor Angiogenesis and Oxygenation in Bevacizumab-treated Recurrent Glioblastoma by Using Dynamic Susceptibility MRI: Secondary Analysis of the European Organization for Research and Treatment of Cancer 26101 Trial

Kickingereder¹,

Brugnara²,

Hansen³

et al. 2020

Radiology

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“…[17][18][19][20] Third, the differentiation of T2/FLAIR hyperintensities as well as contrast-enhancing lesions during the follow-up into treatment or tumor-related changes, may still be a challenge in the field of neuro-oncology, particularly with treatment concepts that incorporate immunotherapies or anti-angiogenic drugs. 21,22 Consequently, the future incorporation of advanced MRI modalities such as diffusion or perfusion-weighted imaging 23,24 or metabolic imaging with radiolabeled molecules from positron emission tomography (PET) 25 will be important to overcome limitations of structural MRI and may allow to further optimize the clinical value of the AI-based decision support applied in the present study.…”

Section: Vollmuth Et Al Ai Decision Support For Response Assessment N...mentioning

confidence: 99%

Artificial intelligence (AI)-based decision support improves reproducibility of tumor response assessment in neuro-oncology: An international multi-reader study

et al. 2022

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Background To assess whether AI-based decision support allows more reproducible and standardized assessment of treatment response on MRI in neuro-oncology as compared to manual 2-dimensional measurements of tumor burden using the RANO criteria. Methods A series of 30 patients (15 lower-grade gliomas, 15 glioblastoma) with availability of consecutive MRI scans was selected. The time to progression (TTP) on MRI was separately evaluated for each patient by 15 investigators over two rounds. In the 1 st round the TTP was evaluated based on the RANO-criteria, whereas in the 2 nd round the TTP was evaluated by incorporating additional information from AI-enhanced MRI-sequences depicting the longitudinal changes in tumor volumes. The agreement of the TTP-measurements between investigators was evaluated using concordance correlation coefficients (CCC) with confidence intervals (CI) and p-values obtained using bootstrap resampling. Results The CCC of TTP-measurements between investigators was 0.77 (95%CI=0.69,0.88) with RANO alone and increased to 0.91 (95%CI=0.82,0.95) with AI-based decision support (p=0.005). This effect was significantly greater (p=0.008) for patients with lower-grade gliomas (CCC=0.70 [95%CI=0.56,0.85] without vs. 0.90 [95%CI=0.76,0.95] with AI-based decision support) as compared to glioblastoma (CCC=0.83 [95%CI=0.75,0.92] without vs. 0.86 [95%CI=0.78,0.93] with AI-based decision support). Investigators with less years of experience judged the AI-based decision as more helpful (p=0.02). Conclusions AI-based decision support has the potential to yield more reproducible and standardized assessment of treatment response in neuro-oncology as compared to manual 2-dimensional measurements of tumor burden, particularly in patients with lower-grade gliomas. A fully-functional version of this AI-based processing pipeline is provided as open-source (https://github.com/NeuroAI-HD/HD-GLIO-XNAT).

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T2/FLAIR-mismatch sign for noninvasive detection of IDH-mutant 1p/19q non-codeleted gliomas: validity and pathophysiology

Foltyn

Taborda

Neuberger

et al. 2020

Neuro-Oncology Advances

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Background This study aimed to assess the validity and pathophysiology of the T2/FLAIR-mismatch sign for noninvasive identification of isocitrate dehydrogenase (IDH)-mutant 1p/19q non-codeleted glioma. Methods Magnetic resonance imaging scans from 408 consecutive patients with newly diagnosed glioma (113 lower-grade gliomas and 295 glioblastomas) were evaluated for the presence of T2/FLAIR-mismatch sign by 2 independent reviewers. Sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess the performance of the T2/FLAIR-mismatch sign for identifying IDH-mutant 1p/19q non-codeleted tumors. An exploratory analysis of differences in contrast-enhancing tumor volumes, apparent diffusion coefficient (ADC) values, and relative cerebral blood volume (rCBV) values in IDH-mutant gliomas with versus without the presence of a T2/FLAIR-mismatch sign (as well as analysis of spatial differences within tumors with the presence of a T2/FLAIR-mismatch sign) was performed. Results The T2/FLAIR-mismatch sign was present in 12 cases with lower-grade glioma (10.6%), all of them being IDH-mutant 1p/19q non-codeleted tumors (sensitivity = 10.9%, specificity = 100%, PPV = 100%, NPV = 3.0%, accuracy = 13.3%). There was a substantial interrater agreement to identify the T2/FLAIR-mismatch sign (Cohen’s kappa = 0.75 [95% CI, 0.57–0.93]). The T2/FLAIR-mismatch sign was not identified in any other molecular subgroup, including IDH-mutant glioblastoma cases (n = 5). IDH-mutant gliomas with a T2/FLAIR-mismatch sign showed significantly higher ADC (P < .0001) and lower rCBV values (P = .0123) as compared to IDH-mutant gliomas without a T2/FLAIR-mismatch sign. Moreover, in IDH-mutant gliomas with T2/FLAIR-mismatch sign the ADC values were significantly lower in the FLAIR-hyperintense rim as compared to the FLAIR-hypointense core of the tumor (P = .0005). Conclusions This study confirms the high specificity of the T2/FLAIR-mismatch sign for noninvasive identification of IDH-mutant 1p/19q non-codeleted gliomas; however, sensitivity is low and applicability is limited to lower-grade gliomas. Whether the higher ADC and lower rCBV values in IDH-mutant gliomas with a T2/FLAIR-mismatch sign (as compared to those without) translate into a measurable prognostic effect requires investigation in future studies. Moreover, spatial differences in ADC values between the core and rim of tumors with a T2/FLAIR-mismatch sign potentially reflect specific distinctions in tumor cellularity and microenvironment.

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Advanced Physiologic Imaging: Perfusion – Theory and Applications

Cited by 3 publications

References 190 publications

Noninvasive Characterization of Tumor Angiogenesis and Oxygenation in Bevacizumab-treated Recurrent Glioblastoma by Using Dynamic Susceptibility MRI: Secondary Analysis of the European Organization for Research and Treatment of Cancer 26101 Trial

Noninvasive Characterization of Tumor Angiogenesis and Oxygenation in Bevacizumab-treated Recurrent Glioblastoma by Using Dynamic Susceptibility MRI: Secondary Analysis of the European Organization for Research and Treatment of Cancer 26101 Trial

Artificial intelligence (AI)-based decision support improves reproducibility of tumor response assessment in neuro-oncology: An international multi-reader study

T2/FLAIR-mismatch sign for noninvasive detection of IDH-mutant 1p/19q non-codeleted gliomas: validity and pathophysiology

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