Retracted: A novel fully automated MRI-based deep-learning method for classification of IDH mutation status in brain gliomas

Yogananda, Chandan Ganesh Bangalore; Shah, Bhavya; Vejdani‐Jahromi, Maryam; Nalawade, Sahil S.; Murugesan, Gowtham Krishnan; Yu, Frank; Pinho, Marco C.; Wagner, Benjamin; Mickey, Bruce; Patel, Toral; Fei, Baowei; Madhuranthakam, Ananth J.; Maldjian, Joseph A.

doi:10.1093/neuonc/noz199

“…Next, we will conduct more extensive and in-depth research with other research centers to verify the reliability of the model. In addition, some new imaging techniques have been used to predict IDH genotypes in gliomas, such as quantitative imaging of D-2-hydroxyglutarate [ 21 ] and MRI-based deep learning method [ 22 ]; these noninvasive highly accurate methods for the determination of IDH status can predict IDH status thereby facilitating clinical translation. However, our research is only based on the radiomic model for noninvasive prediction of glioma IDH genotypes, lacking of multiparameter deep learning method.…”

Section: Discussionmentioning

confidence: 99%

The Value of Enhanced MR Radiomics in Estimating the IDH1 Genotype in High-Grade Gliomas

Niu

¹

,

Feng

²

,

Duan

³

et al. 2020

BioMed Research International

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Background. The prognosis of IDH1-mutant glioma is significantly better than that of wild-type glioma, and the preoperative identification of IDH mutations in glioma is essential for the formulation of surgical procedures and prognostic assessment. Purpose. To explore the value of a radiomic model based on preoperative-enhanced MR images in the assessment of the IDH1 genotype in high-grade glioma. Materials and Methods. A retrospective analysis was performed on 182 patients with high-grade glioma confirmed by surgical pathology between December 2012 and January 2019 in our hospital with complete preoperative brain-enhanced MR images, including 79 patients with an IDH1 mutation (45 patients with WHO grade III and 34 patients with WHO grade IV) and 103 patients with wild-type IDH1 (33 patients with WHO grade III and 70 patients with WHO grade IV). Patients were divided into a primary dataset and a validation dataset at a ratio of 7 : 3 using a stratified random sampling; radiomic features were extracted using A.K. (Analysis Kit, GE Healthcare) software and were initially reduced using the Kruskal-Wallis and Spearman analyses. Lasso was finally conducted to obtain the optimized subset of the feature to build the radiomic model, and the model was then tested with cross-validation. ROC (receiver operating characteristic curve) analysis was performed to evaluate the performance of the model. Results. The radiomic model showed good discrimination in both the primary dataset ( AUC = 0.87 , 95% CI: 0.754 to 0.855, ACC = 0.798 , sensitivity = 85.5 % , specificity = 75.4 % , positive predictive value = 0.734 , and negative predictive value = 0.867 ) and the validation dataset ( AUC = 0.86 , 95% CI: 0.690 to 0.913, ACC = 0.789 , sensitivity = 91.3 % , specificity = 69.0 % , positive predictive value = 0.700 , and negative predictive value = 0.909 ). Conclusion. The radiomic model, based on the preoperative-enhanced MR, can effectively predict the IDH1 genotype in high-grade glioma.

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“…Radiomics analysis from multimodality MRI or FDG PET images have been reported to be sufficient for IDH prediction ( 13 ). A recent study ( 14 ) by Maldjian et al evaluated the usefulness of a non-invasive, only T2 weighted MRI based deep-learning method for the determination of IDH status. The results are inspiring since T2-weighted MR imaging is widely available and routinely performed in the assessment of gliomas.…”

Section: Introductionmentioning

confidence: 99%

A Nomogram Modeling 11C-MET PET/CT and Clinical Features in Glioma Helps Predict IDH Mutation

Zhou

¹

,

Zhou

²

,

Wen

³

et al. 2020

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Purpose: We developed a 11 C-Methionine positron emission tomography/computed tomography ( 11 C-MET PET/CT)-based nomogram model that uses easy-accessible imaging and clinical features to achieve reliable non-invasive isocitrate dehydrogenase (IDH)-mutant prediction with strong clinical translational capability. Methods: One hundred and ten patients with pathologically proven glioma who underwent pretreatment 11 C-MET PET/CT were retrospectively reviewed. IDH genotype was determined by IDH1 R132H immunohistochemistry staining. Maximum, mean and peak tumor-to-normal brain tissue (TNRmax, TNRmean, TNRpeak), metabolic tumor volume (MTV), total lesion methionine uptake (TLMU), and standard deviation of SUV (SUV SD ) of the lesions on MET PET images were obtained via a dedicated workstation (Siemens. syngo.via). Univariate and multivariate logistic regression models were used to identify the predictive factors for IDH mutation. Nomogram and calibration plots were further performed. Results: In the entire population, TNRmean, TNRmax, TNRpeak, and SUV SD of IDH-mutant glioma patients were significantly lower than these values of IDH wildtype. Receiver operating characteristic (ROC) analysis suggested SUV SD had the best performance for IDH-mutant discrimination (AUC = 0.731, cut-off ≤ 0.29, p < 0.001). All pairs of the 11 C-MET PET metrics showed linear associations by Pearson correlation coefficients between 0.228 and 0.986. Multivariate analyses demonstrated that SUV SD (>0.29 vs. ≤ 0.29 OR: 0.053, p = 0.010), dichotomized brain midline structure involvement (no vs. yes OR: 26.52, p = 0.000) and age (≤ 45 vs. >45 years OR: 3.23, p = 0.023), were associated with a higher incidence of IDH mutation. The nomogram modeling showed good discrimination, with a C-statistics of 0.866 (95% CI: 0.796–0.937) and was well-calibrated. Conclusions: 11 C-Methionine PET/CT imaging features (SUV SD and the involvement of brain midline structure) can be conveniently used to facilitate the pre-operative prediction of IDH genotype. The nomogram model based on 11 C-Methionine PET/CT and clinical age features might be clinically useful in non-invasive IDH mutation status prediction for untreated glioma patients.

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“…However, it is still difficult to timely know genetic examination results during the surgery, therefore the analysis of preoperative images has become the most possible way to predict these indicators. Several studies(Bangalore Yogananda et al, 2020; K. S. Choi, Choi, & Jeong, 2019; Y.…”

Section: Discussionmentioning

confidence: 99%

Probing individual-level structural atrophy in frontal glioma patients

Zhang

¹

,

Huang

²

,

Zhang

³

et al. 2021

Preprint

0

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Molecular indicators are vital to glioma diagnosis and prognosis. The conventional examination of molecular characteristics requires surgical removal of tumor tissues, therefore could not be fully used in the surgery planning. 52 glioma patients in frontal lobe (mean age 43.2±9.3 years, 34 male) and 117 healthy controls (mean age 32.6±9.8 years, 83 male) participated in the study. All patients underwent neurocognitive test and molecular examinations, including 1p/19q co-deletion, isocitrate dehydrogenase (IDH) mutation, telomerase reverse transcriptase (TERT) promoter mutation and O6-methylguanine-DNA methyltransferase (MGMT) promotor methylation. A series of individual structural abnormality indexes based on preoperative structural MRI were proposed and explored the associations with these clinical indicators. Individual structural abnormality maps displayed that bilateral hippocampus, parahippocampus, insula, putamen and thalamus were constantly affected by glioma regardless of the histological grade, tumor hemisphere and molecular status. Higher grade glioma patients suffered more structural abnormalities, especially in the contralateral hemisphere and non-tumor regions. The molecular indicators, including IDH1 mutation, 1p/19q co-deletion, TERT promoter mutations and MGMT promoter methylation, as well as neurocognitive performance were all significantly correlated to individual structural abnormality indexes. Our proposed individual structural abnormality indexes show great potentials to access the glioma pathological, neurocognitive and molecular indicators, which is very helpful for neurosurgeons to determine the personalized treatment strategies.

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Retracted: A novel fully automated MRI-based deep-learning method for classification of IDH mutation status in brain gliomas

Cited by 116 publications

References 39 publications

The Value of Enhanced MR Radiomics in Estimating the IDH1 Genotype in High-Grade Gliomas

The Value of Enhanced MR Radiomics in Estimating the IDH1 Genotype in High-Grade Gliomas

A Nomogram Modeling 11C-MET PET/CT and Clinical Features in Glioma Helps Predict IDH Mutation

Probing individual-level structural atrophy in frontal glioma patients

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