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

Liu, Yanhao; Wang, Peng; Wang, Shaoyu; Zhang, Huapeng; Song, Yang; Yan, Xu; Gao, Yang

doi:10.3389/fonc.2023.1202170

Cited by 5 publications

(1 citation statement)

References 34 publications

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“…Some high-quality papers with topics related to but not quite fitting could not be included in this review. Several research groups are studying imaging correlates of cellular and genetic heterogeneity in gliomas either in different regions of interest or in imagingbased tumor habitats [58][59][60][61][62][63]. A technical consideration has been raised in [64] regarding the effect of motion on classification results, suggesting a further need for research in such technical aspects.…”

Section: Discussionmentioning

confidence: 99%

Novel Imaging Approaches for Glioma Classification in the Era of the World Health Organization 2021 Update: A Scoping Review

Richter,

Ernemann,

Bender

2024

Cancers

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The 2021 WHO classification of CNS tumors is a challenge for neuroradiologists due to the central role of the molecular profile of tumors. The potential of novel data analysis tools in neuroimaging must be harnessed to maintain its role in predicting tumor subgroups. We performed a scoping review to determine current evidence and research gaps. A comprehensive literature search was conducted regarding glioma subgroups according to the 2021 WHO classification and the use of MRI, radiomics, machine learning, and deep learning algorithms. Sixty-two original articles were included and analyzed by extracting data on the study design and results. Only 8% of the studies included pediatric patients. Low-grade gliomas and diffuse midline gliomas were represented in one-third of the research papers. Public datasets were utilized in 22% of the studies. Conventional imaging sequences prevailed; data on functional MRI (DWI, PWI, CEST, etc.) are underrepresented. Multiparametric MRI yielded the best prediction results. IDH mutation and 1p/19q codeletion status prediction remain in focus with limited data on other molecular subgroups. Reported AUC values range from 0.6 to 0.98. Studies designed to assess generalizability are scarce. Performance is worse for smaller subgroups (e.g., 1p/19q codeleted or IDH1/2 mutated gliomas). More high-quality study designs with diversity in the analyzed population and techniques are needed.

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Section: Discussionmentioning

confidence: 99%

Novel Imaging Approaches for Glioma Classification in the Era of the World Health Organization 2021 Update: A Scoping Review

Richter,

Ernemann,

Bender

2024

Cancers

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Diffusion kurtosis imaging‐based habitat analysis identifies high‐risk molecular subtypes and heterogeneity matching in diffuse gliomas

Yang,

Niu,

et al. 2024

Ann Clin Transl Neurol

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ObjectiveHigh‐risk types of diffuse gliomas in adults include isocitrate dehydrogenase (IDH) wild‐type glioblastomas and grade 4 astrocytomas. Achieving noninvasive prediction of high‐risk molecular subtypes of gliomas is important for personalized and precise diagnosis and treatment.MethodsWe retrospectively collected data from 116 patients diagnosed with adult diffuse gliomas. Multiple high‐risk molecular markers were tested, and various habitat models and whole‐tumor models were constructed based on preoperative routine and diffusion kurtosis imaging (DKI) sequences to predict high‐risk molecular subtypes of gliomas. Feature selection and model construction utilized Least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM). Finally, the Wilcoxon rank‐sum test was employed to explore the correlation between habitat quantitative features (intra‐tumor heterogeneity score，ITH score) and heterogeneity, as well as high‐risk molecular subtypes.ResultsThe results showed that the habitat analysis model based on DKI performed remarkably well (with AUC values reaching 0.977 and 0.902 in the training and test sets, respectively). The model's performance was further enhanced when combined with clinical variables. (The AUC values were 0.994 and 0.920, respectively.) Additionally, we found a close correlation between ITH score and heterogeneity, with statistically significant differences observed between high‐risk and non‐high‐risk molecular subtypes.InterpretationThe habitat model based on DKI is an ideal means for preoperatively predicting high‐risk molecular subtypes of gliomas, holding significant value for noninvasively alerting malignant gliomas and those with malignant transformation potential.

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Intratumoral and peritumoral habitat imaging based on multiparametric MRI to predict cervical stromal invasion in early-stage endometrial carcinoma

Wang,

Deng,

Kong

et al. 2024

Academic Radiology

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

Cited by 5 publications

References 34 publications

Novel Imaging Approaches for Glioma Classification in the Era of the World Health Organization 2021 Update: A Scoping Review

Novel Imaging Approaches for Glioma Classification in the Era of the World Health Organization 2021 Update: A Scoping Review

Diffusion kurtosis imaging‐based habitat analysis identifies high‐risk molecular subtypes and heterogeneity matching in diffuse gliomas

Intratumoral and peritumoral habitat imaging based on multiparametric MRI to predict cervical stromal invasion in early-stage endometrial carcinoma

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