Dataset Information

Deep Learning Can Differentiate IDH-Mutant from IDH-Wild GBM.

ABSTRACT: Isocitrate dehydrogenase (IDH) mutant and wildtype glioblastoma multiforme (GBM) often show overlapping features on magnetic resonance imaging (MRI), representing a diagnostic challenge. Deep learning showed promising results for IDH identification in mixed low/high grade glioma populations; however, a GBM-specific model is still lacking in the literature. Our aim was to develop a GBM-tailored deep-learning model for IDH prediction by applying convoluted neural networks (CNN) on multiparametric MRI. We selected 100 adult patients with pathologically demonstrated WHO grade IV gliomas and IDH testing. MRI sequences included: MPRAGE, T1, T2, FLAIR, rCBV and ADC. The model consisted of a 4-block 2D CNN, applied to each MRI sequence. Probability of IDH mutation was obtained from the last dense layer of a softmax activation function. Model performance was evaluated in the test cohort considering categorical cross-entropy loss (CCEL) and accuracy. Calculated performance was: rCBV (accuracy 83%, CCEL 0.64), T1 (accuracy 77%, CCEL 1.4), FLAIR (accuracy 77%, CCEL 1.98), T2 (accuracy 67%, CCEL 2.41), MPRAGE (accuracy 66%, CCEL 2.55). Lower performance was achieved on ADC maps. We present a GBM-specific deep-learning model for IDH mutation prediction, with a maximal accuracy of 83% on rCBV maps. Highest predictivity achieved on perfusion images possibly reflects the known link between IDH and neoangiogenesis through the hypoxia inducible factor.

SUBMITTER: Pasquini L

PROVIDER: S-EPMC8069494 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Deep Learning Can Differentiate IDH-Mutant from IDH-Wild GBM.

Pasquini Luca L Napolitano Antonio A Tagliente Emanuela E Dellepiane Francesco F Lucignani Martina M Vidiri Antonello A Ranazzi Giulio G Stoppacciaro Antonella A Moltoni Giulia G Nicolai Matteo M Romano Andrea A Di Napoli Alberto A Bozzao Alessandro A

Journal of personalized medicine 20210409 4

Isocitrate dehydrogenase (IDH) mutant and wildtype glioblastoma multiforme (GBM) often show overlapping features on magnetic resonance imaging (MRI), representing a diagnostic challenge. Deep learning showed promising results for IDH identification in mixed low/high grade glioma populations; however, a GBM-specific model is still lacking in the literature. Our aim was to develop a GBM-tailored deep-learning model for IDH prediction by applying convoluted neural networks (CNN) on multiparametric ...[more]

PMID: 33918828

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Project description:Identification of isocitrate dehydrogenase (IDH)-mutant glioma patients at high risk of early progression is critical for radiotherapy treatment planning. Currently tools to stratify risk of early progression are lacking. We sought to identify a combination of molecular markers that could be used to identify patients who may have a greater need for adjuvant radiation therapy machine learning technology. 507 WHO Grade 2 and 3 glioma cases from The Cancer Genome Atlas, and 1309 cases from AACR GENIE v13.0 datasets were studied for genetic disparities between IDH1-wildtype and IDH1-mutant cohorts, and between different age groups. Genetic features such as mutations and copy number variations (CNVs) correlated with IDH1 mutation status were selected as potential inputs to train artificial neural networks (ANNs) to predict IDH1 mutation status. Grade 2 and 3 glioma cases from the Memorial Sloan Kettering dataset (n = 404) and Grade 3 glioma cases with subtotal resection (STR) from Northwestern University (NU) (n = 21) were used to further evaluate the best performing ANN model as independent datasets. IDH1 mutation is associated with decreased CNVs of EGFR (21% vs. 3%), CDKN2A (20% vs. 6%), PTEN (14% vs. 1.7%), and increased percentage of mutations for TP53 (15% vs. 63%), and ATRX (10% vs. 54%), which were all statistically significant (p < 0.001). Age > 40 was unable to identify high-risk IDH1-mutant with early progression. A glioma early progression risk prediction (GlioPredictor) score generated from the best performing ANN model (6/6/6/6/2/1) with 6 inputs, including CNVs of EGFR, PTEN and CDKN2A, mutation status of TP53 and ATRX, patient's age can predict IDH1 mutation status with over 90% accuracy. The GlioPredictor score identified a subgroup of high-risk IDH1-mutant in TCGA and NU datasets with early disease progression (p = 0.0019, 0.0238, respectively). The GlioPredictor that integrates age at diagnosis, CNVs of EGFR, CDKN2A, PTEN and mutation status of TP53, and ATRX can identify a small cohort of IDH-mutant with high risk of early progression. The current version of GlioPredictor mainly incorporated clinically often tested genetic biomarkers. Considering complexity of clinical and genetic features that correlate with glioma progression, future derivatives of GlioPredictor incorporating more inputs can be a potential supplement for adjuvant radiotherapy patient selection of IDH-mutant glioma patients.

Project description:BackgroundConflicting data exist regarding the prognostic impact of the isocitrate dehydrogenase (IDH) mutation in intrahepatic cholangiocarcinoma (ICC), and limited data exist in patients with advanced-stage disease. Similarly, the clinical phenotype of patients with advanced IDH mutant (IDHm) ICC has not been characterized. In this study, we report the correlation of IDH mutation status with prognosis and clinicopathologic features in patients with advanced ICC.MethodsPatients with histologically confirmed advanced ICC who underwent tumor mutational profiling as a routine part of their care between 2009 and 2014 were evaluated. Clinical and pathological data were collected by retrospective chart review for patients with IDHm versus IDH wild-type (IDHwt) ICC. Pretreatment tumor volume was calculated on computed tomography or magnetic resonance imaging.ResultsOf the 104 patients with ICC who were evaluated, 30 (28.8%) had an IDH mutation (25.0% IDH1, 3.8% IDH2). The median overall survival did not differ significantly between IDHm and IDHwt patients (15.0 vs. 20.1 months, respectively; p = .17). The pretreatment serum carbohydrate antigen 19-9 (CA19-9) level in IDHm and IDHwt patients was 34.5 and 118.0 U/mL, respectively (p = .04). Age at diagnosis, sex, histologic grade, and pattern of metastasis did not differ significantly by IDH mutation status.ConclusionThe IDH mutation was not associated with prognosis in patients with advanced ICC. The clinical phenotypes of advanced IDHm and IDHwt ICC were similar, but patients with IDHm ICC had a lower median serum CA19-9 level at presentation.Implications for practicePrevious studies assessing the prognostic impact of the isocitrate dehydrogenase (IDH) gene mutation in intrahepatic cholangiocarcinoma (ICC) mainly focused on patients with early-stage disease who have undergone resection. These studies offer conflicting results. The target population for clinical trials of IDH inhibitors is patients with unresectable or metastatic disease, and the current study is the first to focus on the prognosis and clinical phenotype of this population and reports on the largest cohort of patients with advanced IDH mutant ICC to date. The finding that the IDH mutation lacks prognostic significance in advanced ICC is preliminary and needs to be confirmed prospectively in a larger study.

Dataset Information

Deep Learning Can Differentiate IDH-Mutant from IDH-Wild GBM.

Publications

Deep Learning Can Differentiate IDH-Mutant from IDH-Wild GBM.

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