Diagnostic accuracy and potential covariates for machine learning to identify IDH mutations in glioma patients: evidence from a meta-analysis

Zhao, Jing; Huang, Yingqian; Song, Yukun; Xie, Dingxiang; Hu, Manshi; Qiu, Haishan; Chu, Jianping

doi:10.1007/s00330-020-06717-9

Cited by 27 publications

(32 citation statements)

References 31 publications

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“…A total of 60 studies [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , …”

Section: Resultsunclassified

“…The included classification studies showed different references in classifying the presence or absence of the outcome of interest, such as standard-of-care diagnosis, (immuno)histopathology, and expert consensus. Twenty-two studies [ 17 , 18 , 19 , 23 , 25 , 28 , 32 , 33 , 37 , 39 , 43 , 45 , 49 , 51 , 56 , 61 , 63 , 66 , 69 , 70 , 71 , 74 ] focused on classifying the IDH mutation status in glioma. Mutations of the IDH genes serve as a diagnostic marker to diffuse WHO grade II and III gliomas as well as secondary glioblastomas.…”

Section: Resultsmentioning

confidence: 99%

“…Performance evaluation from the remaining 17 [ 19 , 25 , 28 , 32 , 37 , 43 , 45 , 49 , 51 , 56 , 61 , 63 , 66 , 69 , 70 , 71 , 74 ] studies in terms of the validated AUC ranged from 0.75 to 0.99. Additionally, 14 studies [ 18 , 19 , 27 , 32 , 33 , 37 , 43 , 45 , 51 , 56 , 61 , 63 , 69 , 71 ] presented the sensitivity and the specificity ranging from 54% to 98% and 67% to 99%, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…With regard to the use of MLAs in neuro-oncology imaging, various reports on the use of MLAs, using a broad range of extracted features on magnetic resonance imaging (MRI), showed promising results with regard to the prediction of molecular markers and genetic alterations (e.g., IDH genotype, 1p/19q codeletion status, P53 mutations, MGMT promoter mutation, TERT promoter mutation, BRAF status, EGFR receptor mutations) [ 3 , 12 , 16 , 17 , 18 , 19 ]. However, one of the limitations of this type of research is the relatively limited amount of data in each study, which could possibly be overcome by a systematic review and meta-analysis of the aggregated study results [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

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Accuracy of Machine Learning Algorithms for the Classification of Molecular Features of Gliomas on MRI: A Systematic Literature Review and Meta-Analysis

Kempen

Post

Mannil

et al. 2021

Cancers

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Treatment planning and prognosis in glioma treatment are based on the classification into low- and high-grade oligodendroglioma or astrocytoma, which is mainly based on molecular characteristics (IDH1/2- and 1p/19q codeletion status). It would be of great value if this classification could be made reliably before surgery, without biopsy. Machine learning algorithms (MLAs) could play a role in achieving this by enabling glioma characterization on magnetic resonance imaging (MRI) data without invasive tissue sampling. The aim of this study is to provide a performance evaluation and meta-analysis of various MLAs for glioma characterization. Systematic literature search and meta-analysis were performed on the aggregated data, after which subgroup analyses for several target conditions were conducted. This study is registered with PROSPERO, CRD42020191033. We identified 724 studies; 60 and 17 studies were eligible to be included in the systematic review and meta-analysis, respectively. Meta-analysis showed excellent accuracy for all subgroups, with the classification of 1p/19q codeletion status scoring significantly poorer than other subgroups (AUC: 0.748, p = 0.132). There was considerable heterogeneity among some of the included studies. Although promising results were found with regard to the ability of MLA-tools to be used for the non-invasive classification of gliomas, large-scale, prospective trials with external validation are warranted in the future.

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Section: Resultsunclassified

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Accuracy of Machine Learning Algorithms for the Classification of Molecular Features of Gliomas on MRI: A Systematic Literature Review and Meta-Analysis

Kempen

Post

Mannil

et al. 2021

Cancers

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“…52 Consequently, multiple studies investigated the prediction of IDH mutation status from MRI. Recently, Zhao et al 53 published a meta-analysis of 9 studies totaling 996 patients published before August 1, 2019. The largest data set used for training had 225 patients.…”

Section: Predicting Glioma Genomicsmentioning

confidence: 99%

Artificial Intelligence and Deep Learning in Neuroradiology: Exploring the New Frontier

2020

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There have been many recently published studies exploring machine learning (ML) and deep learning applications within neuroradiology. The improvement in performance of these techniques has resulted in an ever-increasing number of commercially available tools for the neuroradiologist. In this narrative review, recent publications exploring ML in neuroradiology are assessed with a focus on several key clinical domains. In particular, major advances are reviewed in the context of: (1) intracranial hemorrhage detection, (2) stroke imaging, (3) intracranial aneurysm screening, (4) multiple sclerosis imaging, (5) neuro-oncology, (6) head and tumor imaging, and (7) spine imaging.

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A subregion-based RadioFusionOmics model discriminates between grade 4 astrocytoma and glioblastoma on multisequence MRI

Wei,

Lu,

Lai

et al. 2024

J Cancer Res Clin Oncol

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Purpose To explore a subregion-based RadioFusionOmics (RFO) model for discrimination between adult-type grade 4 astrocytoma and glioblastoma according to the 2021 WHO CNS5 classification. Methods 329 patients (40 grade 4 astrocytomas and 289 glioblastomas) with histologic diagnosis was retrospectively collected from our local institution and The Cancer Imaging Archive (TCIA). The volumes of interests (VOIs) were obtained from four multiparametric MRI sequences (T1WI, T1WI + C, T2WI, T2-FLAIR) using (1) manual segmentation of the non-enhanced tumor (nET), enhanced tumor (ET), and peritumoral edema (pTE), and (2) K-means clustering of four habitats (H1: high T1WI + C, high T2-FLAIR; (2) H2: high T1WI + C, low T2-FLAIR; (3) H3: low T1WI + C, high T2-FLAIR; and (4) H4: low T1WI + C, low T2-FLAIR). The optimal VOI and best MRI sequence combination were determined. The performance of the RFO model was evaluated using the area under the precision-recall curve (AUPRC) and the best signatures were identified. Results The two best VOIs were manual VOI3 (putative peritumoral edema) and clustering H34 (low T1WI + C, high T2-FLAIR (H3) combined with low T1WI + C and low T2-FLAIR (H4)). Features fused from four MRI sequences ($${F}_{seq}^{\mathrm{1,2},\mathrm{3,4}}$$ F seq 1 , 2 , 3 , 4 ) outperformed those from either a single sequence or other sequence combinations. The RFO model that was trained using fused features $${F}_{seq}^{\mathrm{1,2},\mathrm{3,4}}$$ F seq 1 , 2 , 3 , 4 achieved the AUPRC of 0.972 (VOI3) and 0.976 (H34) in the primary cohort (p = 0.905), and 0.971 (VOI3) and 0.974 (H34) in the testing cohort (p = 0.402). Conclusion The performance of subregions defined by clustering was comparable to that of subregions that were manually defined. Fusion of features from the edematous subregions of multiple MRI sequences by the RFO model resulted in differentiation between grade 4 astrocytoma and glioblastoma.

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Diagnostic accuracy and potential covariates for machine learning to identify IDH mutations in glioma patients: evidence from a meta-analysis

Cited by 27 publications

References 31 publications

Accuracy of Machine Learning Algorithms for the Classification of Molecular Features of Gliomas on MRI: A Systematic Literature Review and Meta-Analysis

Accuracy of Machine Learning Algorithms for the Classification of Molecular Features of Gliomas on MRI: A Systematic Literature Review and Meta-Analysis

Artificial Intelligence and Deep Learning in Neuroradiology: Exploring the New Frontier

A subregion-based RadioFusionOmics model discriminates between grade 4 astrocytoma and glioblastoma on multisequence MRI

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