Applying MAP-MRI to Identify the WHO Grade and Main Genetic Features of Adult-type Diffuse Gliomas: A Comparison of Three Diffusion-weighted MRI Models

Wang, Peng; He, Jinlong; Ma, Xueying; Weng, Lixin; Wu, Qiong; Zhao, Pengfei; Ban, Chao; Hao, Xiangcheng; Hao, Zhiyue; Yuan, Pengxuan; Hao, Fene; Wang, Shaoyu; Zhang, Huapeng; Xie, Shaolei; Gao, Yang

doi:10.1016/j.acra.2022.10.009

Cited by 12 publications

(4 citation statements)

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“…Although these single factors reflect the complexity of the intratumoral and peritumoral spaces of glioma, the independent use of a single factor is not reliable. Notably, previous studies have also shown (9,13) that the diagnostic performance when using a single parameter in diffusion tensor imaging or DKI in predicting IDH status is not satisfactory (AUC 0.74-0.86). HANDINESS, as one comprehensive model, which was constructed by fusing several features, showed the highest diagnostic value (AUC 0.95), reflecting the superior performance of the integrated model.…”

Section: Discussionmentioning

confidence: 99%

“…Fifty-five of sixty-seven subjects with adult-type diffuse gliomas from a previous study ( 13 ) were all included in the present study. The purpose of the previous study was to compare the accuracy and stability of 3 diffusion models, based separately on mean apparent propagator-MRI, DKI and diffusion tensor imaging, in predicting WHO grade and major genetic features in adult-type diffuse gliomas.…”

Section: Methodsmentioning

confidence: 99%

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Heterogeneity matching and IDH prediction in adult-type diffuse gliomas: a DKI-based habitat analysis

Liu,

Wang,

Wang

et al. 2023

Front. Oncol.

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ObjectiveTo explain adult-type diffuse gliomas heterogeneity through diffusion kurtosis imaging-based habitat characteristics and develop and validate a comprehensive model for predicting isocitrate dehydrogenase (IDH) status.Materials and methodsIn this prospective secondary analysis, 103 participants (mean age, 52 years; range, 21-77; 54 [52%] male) pathologically diagnosed with adult-type diffuse gliomas were enrolled between June 2018 and February 2022. The Otsu method was used to generate habitat maps with mean diffusivity (MD) and mean kurtosis (MK) for a total of 4 subhabitats containing 16 habitat features. Habitat heatmaps were created based on the Pearson correlation coefficient. The Habitat imAging aNd clinicraD INtegrated prEdiction SyStem (HANDINESS) was created by combining clinical features, conventional MRI morphological features, and habitat image features. ROC, calibration curve, and decision curve analyses were used to select the optimal model after 32 pipelines for model training and validation.ResultsIn the restricted diffusion and high-density subhabitat, MK was highly correlated with MD (R2 = 0.999), volume (0.608) and percentage of volume (0.663), and this region had the highest MK value (P<.001). The unrestricted diffusion and low-density subhabitat had the highest MD value (P<.001). When MK was less than the Otsu threshold, there was still a difference between restricted diffusion and low-density and unrestricted diffusion and low-density subhabitats (P<.01). The HANDINESS enabled more accurate prediction of the IDH status in the training (AUC=0.951 [0.902-0.987]) and internal validation cohorts (0.938 [0.881-0.949]). AUC values for single-modality models and independent factors ranged from 0.593 to 0.916. Calibration and decision curve analyses showed that the HANDINESS demonstrated a high level of clinical applicability and predictive consistency.ConclusionDiffusion kurtosis imaging-based habitat analysis provides additional important information on microscopic tumor spatial heterogeneity. The HANDINESS has higher diagnostic performance and robustness than single-modality models.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Heterogeneity matching and IDH prediction in adult-type diffuse gliomas: a DKI-based habitat analysis

Liu,

Wang,

Wang

et al. 2023

Front. Oncol.

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“…MAP-MRI metrics have been shown to be sensitive to microstructural changes in brain tissues in various neurological conditions and neurodevelopment (Bouhrara et al, 2022;C.-L. Chen, Shih, et al, 2019;H.-J. Chen et al, 2021;Le et al, 2020;Ma et al, 2020;Moody et al, 2022;Mustafi et al, 2019;Pines et al, 2020;Qiu et al, 2022;Sun et al, 2022;Wang, He, et al, 2022;Wang, Deng, et al, 2022;Xie et al, 2022), with one study documenting an association between RTOP and clinical measures in mTBI patients assessed within 15 days of injury (Y.-C. Wu et al, 2018). To our knowledge, however, no prior work has applied the MAP-MRI technique to investigate the longterm effects of concussion.…”

Section: Introductionmentioning

confidence: 99%

“…Chen, Shih, et al, 2019 ; H.‐J. Chen et al, 2021 ; Le et al, 2020 ; Ma et al, 2020 ; Moody et al, 2022 ; Mustafi et al, 2019 ; Pines et al, 2020 ; Qiu et al, 2022 ; Sun et al, 2022 ; Wang, Gao, et al, 2022 ; Wang, He, et al, 2022 ; Wang, Deng, et al, 2022 ; Xie et al, 2022 ), with one study documenting an association between RTOP and clinical measures in mTBI patients assessed within 15 days of injury (Y.‐C. Wu et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Associations of prior concussion severity with brain microstructure using mean apparent propagator magnetic resonance imaging

Goeckner,

Brett,

Mayer

et al. 2023

Human Brain Mapping

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Magnetic resonance imaging (MRI) diffusion studies have shown chronic microstructural tissue abnormalities in athletes with history of concussion, but with inconsistent findings. Concussions with post‐traumatic amnesia (PTA) and/or loss of consciousness (LOC) have been connected to greater physiological injury. The novel mean apparent propagator (MAP) MRI is expected to be more sensitive to such tissue injury than the conventional diffusion tensor imaging. This study examined effects of prior concussion severity on microstructure with MAP‐MRI. Collegiate‐aged athletes (N = 111, 38 females; ≥6 months since most recent concussion, if present) completed semistructured interviews to determine the presence of prior concussion and associated injury characteristics, including PTA and LOC. MAP‐MRI metrics (mean non‐Gaussian diffusion [NG Mean], return‐to‐origin probability [RTOP], and mean square displacement [MSD]) were calculated from multi‐shell diffusion data, then evaluated for associations with concussion severity through group comparisons in a primary model (athletes with/without prior concussion) and two secondary models (athletes with/without prior concussion with PTA and/or LOC, and athletes with/without prior concussion with LOC only). Bayesian multilevel modeling estimated models in regions of interest (ROI) in white matter and subcortical gray matter, separately. In gray matter, the primary model showed decreased NG Mean and RTOP in the bilateral pallidum and decreased NG Mean in the left putamen with prior concussion. In white matter, lower NG Mean with prior concussion was present in all ROI across all models and was further decreased with LOC. However, only prior concussion with LOC was associated with decreased RTOP and increased MSD across ROI. Exploratory analyses conducted separately in male and female athletes indicate associations in the primary model may differ by sex. Results suggest microstructural measures in gray matter are associated with a general history of concussion, while a severity‐dependent association of prior concussion may exist in white matter.

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Tumor Multiregional Mean Apparent Propagator (MAP) Features in Evaluating Gliomas—A Comparative Study With Diffusion Kurtosis Imaging (DKI)

Zeng,

Ma,

Xie

et al. 2023

Magnetic Resonance Imaging

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BackgroundGlioma classification affects treatment and prognosis. Reliable imaging methods for preoperatively evaluating gliomas are essential.PurposeTo evaluate tumor multiregional mean apparent propagator (MAP) features in glioma diagnosis and to compare those with diffusion‐kurtosis imaging (DKI).Study TypeRetrospective study.Subjects70 untreated glioma patients (31 LGGs (low‐grade gliomas), 34 women; mean age, 47 ± 12 years, training (60%, n = 42) and testing cohorts (40%, n = 28)).Field Strength/Sequence3‐T, diffusion‐MRI using q‐space Cartesian grid sampling with 11 different b‐values.AssessmentTumor multiregional MAP (mean squared displacement (MSD); q‐space inverse variance (QIV); non‐Gaussianity (NG); axial/radial non‐Gaussianity (NGAx, NGRad); return‐to‐origin/axis/plane probability (RTOP, RTAP, and RTPP)); and DKI metrics (axial/mean/radial kurtosis (AK, MK, and RK)) on tumor parenchyma (TP) and peritumoral areas (PT) in histopathologically gliomas grading and genotyping were assessed.Statistical TestsMann–Whitney U; Kruskal–Wallis; Benjamini–Hochberg; Bonferroni‐correction; receiver operating curve (ROC) and area under curve (AUC); DeLong's test; Random Forest (RF). P value<0.05 was considered statistically significant after multiple comparisons correction.ResultsCompared with LGGs, MSD, and QIV were significantly lower in TP, whereas NG, NGAx, NGRad, RTOP, RTAP, RTPP, and DKI metrics were significantly higher in HGGs (high‐grade gliomas) (P ≤ 0.007), as well as in isocitrate‐dehydrogenase (IDH)‐mutated than IDH‐wildtype gliomas (P ≤ 0.039). These trends were reversed for PT (tumor grades, P ≤ 0.011; IDH‐mutation status, P ≤ 0.012). ROC analysis showed that, in TP, DKI metrics performed best in TP (AUC 0.83), whereas in PT, RTPP performed best (AUC 0.77) in glioma grading. AK performed best in TP (AUC 0.77), whereas MSD and RTPP performed best in PT (AUC 0.73) in IDH genotyping. Further RF analysis with DKI and MAP demonstrated good performance in grading (AUC 0.91, Accuracy 82%) and IDH genotyping (AUC 0.87, Accuracy 79%).Data ConclusionTumor multiregional MAP features could effectively evaluate gliomas. The performance of MAP may be similar to DKI in TP, while in PT, MAP may outperform DKI.Level of Evidence4Technical EfficacyStage 2

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Applying MAP-MRI to Identify the WHO Grade and Main Genetic Features of Adult-type Diffuse Gliomas: A Comparison of Three Diffusion-weighted MRI Models

Cited by 12 publications

References 35 publications

Heterogeneity matching and IDH prediction in adult-type diffuse gliomas: a DKI-based habitat analysis

Heterogeneity matching and IDH prediction in adult-type diffuse gliomas: a DKI-based habitat analysis

Associations of prior concussion severity with brain microstructure using mean apparent propagator magnetic resonance imaging

Tumor Multiregional Mean Apparent Propagator (MAP) Features in Evaluating Gliomas—A Comparative Study With Diffusion Kurtosis Imaging (DKI)

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