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REVIEW 5 major objections 5 minor 30 references

Comparative Analysis of 2D and 3D ResNet Architectures for IDH and MGMT Mutation Detection in Glioma Patients

T0 review · 5 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A three-view 2D ResNet ensemble can classify IDH mutation status from glioma MRI as accurately as 3D volume models, with the best 2D model at AUROC 0.9096.

desk verdict Useful 2D-vs-3D ResNet benchmark for IDH prediction, but the 'no notable difference' claim needs error bars and the MGMT results are honestly reported but not informative. read the letter →

arxiv 2412.21091 v1 pith:DXIQICAN submitted 2024-12-30 eess.IV

Danial Elyassirad , Benyamin Gheiji , Mahsa Vatanparast , Amir Mahmoud Ahmadzadeh , Neda Kamandi , Amirmohammad Soleimanian , Sara Salehi , Shahriar Faghani This is my paper · ORCID
classification eess.IV
keywords radiogenomicsgliomaIDHmutationMGMTpromotermethylationResNet2Dversus3DCNNMRIensemblemodel
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper compares two ways of reading MRI to predict molecular markers in glioma: 2D ResNet models that look at the largest tumor slice from each of three anatomical views, ensembled by logistic regression, and 3D ResNet models that consume the entire segmented tumor volume. For IDH mutation status, the paper's central claim is that the 2D ensemble matches 3D performance: the best 2D model reached a test AUROC of 0.9096, the best 3D model 0.8999, and the mean 2D AUROC (0.8782) was slightly higher than the 3D mean (0.8586). The paper reads this as evidence that 3D volume processing is not necessary for accurate IDH classification from MRI and costs more resources. For MGMT promoter methylation, the same comparison failed: the best 2D model scored 0.6168 and most 3D models fell below chance, leading the paper to question whether MGMT status is reliably encoded in MRI at all. The contribution is the systematic head-to-head comparison of 2D and 3D ResNet variants on the same dataset and evaluation protocol.

What carries the argument

Residual networks (ResNets), convolutional networks with skip connections that allow deeper training. The 2D arm uses one ResNet per anatomical view, with each network fed the single axial, coronal, or sagittal slice that contains the largest tumor area, and logistic regression fuses the three view-level predictions. The 3D arm replaces the single slice with the full segmented tumor volume as input. The load-bearing design choice is the largest-tumor-area slice per view: it converts a volume problem into three 2D problems while retaining some three-dimensional context, and it is what makes the claimed resource saving possible.

What would settle it

Train the same 2D ResNet ensemble on all tumor slices within each view, or on randomly chosen slices, and compare test AUROC with the 3D baseline on the same patient split; if the 2D advantage disappears or reverses, the paper's equivalence claim depends on the largest-slice selection rather than on 2D processing itself.

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Extended reading notes

Core claim

The central claim, stated in the discussion, is that '2D ensemble models, employing three different views, can predict IDH mutation status as effectively as 3D models, without notable differences in performance but with reduced resource utilization.' Concretely, a 2D ResNet50 ensemble on T1-weighted MRI reached a test AUROC of 0.9096, while the best 3D model, ResNet34 on contrast-enhanced T1, reached 0.8999. The paper also reports a secondary pattern: within both 2D and 3D families, deeper ResNet variants tended to produce higher IDH AUROCs, most clearly among 3D models. For MGMT, the paper's finding is negative: the best model barely exceeded chance and most 3D models scored below 0.5 AUROC, which it attributes to a possible absence of imaging signal or to a suboptimal positive/negative labeling cutoff.

Load-bearing premise

The comparison is fair only if the single largest tumor slice in each of the three views carries the imaging features that separate IDH-mutant from IDH-wildtype tumors; if that signal is spread across other slices or lives at the tumor margin, the 2D models are handicapped before the comparison starts.

Editorial extensions

If this is right

  • IDH mutation status can be predicted non-invasively from preoperative MRI with a test AUROC near 0.91 using 2D ResNet ensembles.
  • 3D volume processing does not add a meaningful accuracy advantage over 2D multi-view ensembles for IDH classification in this dataset.
  • T1-weighted and contrast-enhanced T1 sequences carry more IDH-discriminative signal than FLAIR in both 2D and 3D models.
  • Deeper ResNet variants tend to improve IDH prediction, most consistently in the 3D models.
  • MGMT promoter methylation status is not reliably predicted from MRI here; most models performed at or below chance, so the paper treats MGMT prediction as an unresolved problem.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • An untested implication is that the largest-slice protocol may be leaving useful volumetric context unused: feeding several adjacent slices per view, a 2.5D input, could test whether the 2D arm can be pushed even higher.
  • The MGMT labeling cutoff, with index 0 as negative and 1-17 as positive, is a plausible confound; modeling the methylation index as a continuous target or using a standardized assay would be a direct test before concluding MRI carries no MGMT signal.
  • If the 2D/3D equivalence survives external multi-institutional validation, IDH classification could run on standard 2D computational infrastructure, lowering the barrier for clinical deployment.
  • The observed complexity trend in 3D models raises the untested possibility that deeper 3D architectures than ResNet34, not included here, could eventually separate from the 2D ensemble.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. The manuscript compares 2D and 3D ResNet models for predicting IDH and MGMT mutation status from preoperative multiparametric MRI (T1, T1c, FLAIR) in the UCSF glioma dataset. The 2D models use the single largest tumor slice in each of the axial, coronal, and sagittal views, with the three view-specific models combined by logistic regression; the 3D models use the full tumor volume as input. The authors report a best IDH test AUROC of 0.9096 for a 2D ResNet50 (T1) and 0.8999 for a 3D ResNet34 (T1c), while MGMT prediction was near chance, with a best 2D AUROC of 0.6168. The central claim, stated in the Discussion, is that 2D ensemble models can predict IDH status as effectively as 3D models with reduced resource utilization.

Significance. If the IDH result is robust, the manuscript would provide a useful empirical data point: 2D ensembles operating on largest tumor slices may match the accuracy of 3D volume processing, with potential savings in compute and memory. The study uses a reasonably sized public dataset, performs a patient-level split, trains multiple ResNet depths in both 2D and 3D, uses weighted loss for class imbalance, and honestly discusses the MGMT negative result in the context of conflicting literature. However, the significance is conditional: the central equivalence claim rests on point estimates from a single split, with no confidence intervals, repeated validation, or resource measurements. As presented, the evidence supports a descriptive comparison of specific models on one internal test set, not a general claim of 2D/3D equivalence.

major comments (5)
  1. [Discussion, 'There are no notable differences' paragraph] The central claim that 2D ensemble models perform as effectively as 3D models is not statistically supported. The paper reports only point estimates from one 60/20/20 split: best 2D AUROC 0.9096 versus best 3D AUROC 0.8999, and mean 0.8782 versus 0.8586. No confidence intervals, bootstrap resampling, repeated splits, DeLong tests, or equivalence tests are provided. With a test set of roughly 99 patients and about 21% IDH-mutant prevalence, these differences are well within plausible sampling noise. The phrase 'no notable differences' therefore cannot be concluded from the presented analysis.
  2. [Methods, '2D approach' and Results, Tables 2-3] The comparison confounds input dimensionality with model depth and ensembling. The 2D arm ensembles three view-specific models and includes ResNet50, ResNet101, and ResNet152, while the 3D arm uses single models only up to ResNet34. The paper itself reports a positive depth-performance trend for 3D models (Figures 1-2), so the apparent 2D/3D parity could be explained by higher model capacity or by ensembling rather than by dimensionality. To support the central claim, the design should include depth-matched single-model comparisons or ensemble 3D models as well.
  3. [Results, MGMT prediction; Abstract] The MGMT result is internally inconsistent. The Results state that the best MGMT model is the 2D ResNet152 using T1 (AUROC 0.6168), while the Discussion states that this model used T1c. In addition, no MGMT performance tables are provided, so the reader cannot verify the claim that '14 out of 16 models' fell below 0.5, nor can per-view, per-sequence, or per-depth results be inspected. The sequence should be corrected and the MGMT tables added.
  4. [Methods, '2D approach' slice selection] The 2D equivalence claim depends on the assumption that the single largest masked slice in each orthogonal view preserves the imaging features needed for IDH classification. No slice-level ablation or comparison with multi-slice or full-volume 2D inputs is provided to show that features outside the largest slice, or at the tumor margin, are not needed. This is a load-bearing assumption for the fairness of the 2D/3D comparison and should be explicitly tested or clearly stated as a limitation.
  5. [Results, resource utilization claim] The claim of 'reduced resource utilization' for the 2D approach is asserted without any supporting measurements. No FLOPs, parameter counts, memory footprints, training time, or inference time are reported for either the 2D or 3D models. Please provide quantitative resource comparisons or weaken the claim to reflect that resource use was not directly measured.
minor comments (5)
  1. [Abstract and Methods] 'USCF glioma dataset' should be 'UCSF glioma dataset' to match the cited dataset name.
  2. [Table 2] The layout of Table 2 interleaves model names, views, modalities, validation AUROC, and test AUROC in a way that makes row-by-row mapping very difficult; consider a conventional wide table with one row per architecture-sequence combination.
  3. [Results, MGMT prediction] The statement '14 out of 16 models' is ambiguous; clarify whether this counts the 2D arm only and specify which 16 configurations are included.
  4. [Methods, training details] No seed information or number of training runs per configuration is reported; adding this would help the reader assess the variability of the point estimates.
  5. [Discussion, MGMT cutoff] The discussion of the MGMT index cutoff is useful, but the statement that 'an MGMT index of 0 was defined as negative' should be reconciled with the wide range of MGMT index values in Table 1 and the possibility of a continuous-score analysis.

Circularity Check

0 steps flagged · score 2.0 of 10

No circular derivation; the 2D-vs-3D IDH comparison rests on held-out test AUROCs, with only non-load-bearing self-citations for standard methodological choices.

full rationale

The paper is an empirical evaluation rather than a derivation chain, so no mathematical circularity is present. The central claim that 2D ensembled ResNets predict IDH status comparably to 3D ResNets is supported by test AUROCs computed on a separate patient-level test split (60/20/20), with model selection based on tuning loss. The reported AUROCs in Tables 2 and 3 are out-of-sample evaluations, not fitted values renamed as predictions; the logistic-regression ensemble is trained on the three view-level predictions and evaluated on held-out data. The main self-citations (Refs. 17, 19, 20, 24, 28) cover data splitting, weighted loss, performance metrics, uncertainty quantification, and an external MGMT comparison; none of these is load-bearing for the IDH result, and the cited works are independent methodological or comparative studies rather than definitions that force the outcome. The statement that there are 'no notable differences' between 2D and 3D models is not supported by confidence intervals or equivalence testing, and the comparison is confounded by model depth and ensembling, but that is a statistical-validity concern, not circularity. Similarly, the selection of the largest masked slice per view is an input assumption about feature preservation, not a restatement of the prediction target. Overall, no circular step is identifiable; the score reflects only the presence of several minor self-citations that do not carry the central argument.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central IDH comparison rests on the UCSF segmentation masks, the largest-slice assumption, and unspecified training hyperparameters. No new physical or biological entities are introduced.

free parameters (4)
  • Training hyperparameters (learning rate, batch size, epochs, optimizer, augmentation magnitudes) = not reported
    These are tuned via the tuning set but not listed; they directly affect the reported AUROCs and make exact replication impossible.
  • Class imbalance loss weights for IDH and MGMT = not reported
    Weighted binary cross-entropy is used, but the weights are omitted; they affect the decision boundary and therefore the AUROC.
  • MGMT positivity cutoff (MGMT index > 0) = 1 (index above 0)
    The dataset labels index 0 as negative and 1 through 17 as positive; the authors themselves call this cutoff suboptimal, and it defines the MGMT prediction task.
  • Normalization intensity ranges per subregion = not reported
    The pipeline assigns an intensity range to each segmentation subregion; the ranges are unspecified and affect input features.
assumptions (4)
  • domain assumption The UCSF segmentation masks are accurate enough to define tumor regions for feature extraction.
    Both 2D slice selection and 3D volume inputs rely on these masks; errors in masks propagate into inputs.
  • domain assumption The largest tumor slice in each orthogonal view contains the imaging features needed for IDH classification.
    The 2D approach discards the rest of the tumor volume, so the equivalence conclusion depends on this slice.
  • domain assumption MRI intensity patterns after normalization carry information about IDH and MGMT molecular status.
    This is the core radiogenomics premise; the paper does not independently validate it beyond the reported AUROCs.
  • domain assumption A single patient-level 60/20/20 split is a representative evaluation protocol.
    No cross-validation or repeated splits are used, so all AUROC estimates are conditional on this one partition.

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Cite this review

Pith. "Pith review of Comparative Analysis of 2D and 3D ResNet Architectures for IDH and MGMT Mutation Detection in Glioma Patients." pith.science (2026). https://pith.science/paper/DXIQICAN

@misc{pith2026241221091,
  author       = {Pith},
  title        = {Pith review of: Comparative Analysis of 2D and 3D ResNet Architectures for IDH and MGMT Mutation Detection in Glioma Patients},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DXIQICAN}},
  note         = {Machine review of arXiv:2412.21091}
}
read the original abstract

Gliomas are the most common cause of mortality among primary brain tumors. Molecular markers, including Isocitrate Dehydrogenase (IDH) and O[6]-methylguanine-DNA methyltransferase (MGMT) influence treatment responses and prognosis. Deep learning (DL) models may provide a non-invasive method for predicting the status of these molecular markers. To achieve non-invasive determination of gene mutations in glioma patients, we compare 2D and 3D ResNet models to predict IDH and MGMT status, using T1, post-contrast T1, and FLAIR MRI sequences. USCF glioma dataset was used, which contains 495 patients with known IDH and 410 patients with known MGMT status. The dataset was divided into training (60%), tuning (20%), and test (20%) subsets at the patient level. The 2D models take axial, coronal, and sagittal tumor slices as three separate models. To ensemble the 2D predictions the three different views were combined using logistic regression. Various ResNet architectures (ResNet10, 18, 34, 50, 101, 152) were trained. For the 3D approach, we incorporated the entire brain tumor volume in the ResNet10, 18, and 34 models. After optimizing each model, the models with the lowest tuning loss were selected for further evaluation on the separate test sets. The best-performing models in IDH prediction were the 2D ResNet50, achieving a test area under the receiver operating characteristic curve (AUROC) of 0.9096, and the 3D ResNet34, which reached a test AUROC of 0.8999. For MGMT status prediction, the 2D ResNet152 achieved a test AUROC of 0.6168; however, all 3D models yielded AUROCs less than 0.5. Overall, the study indicated that both 2D and 3D models showed high predictive value for IDH prediction, with slightly better performance in 2D models.

Figures

Figures reproduced from arXiv: 2412.21091 by the authors.

Figure 1
Figure 1. Test AUROC of 2D models in IDH prediction [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Test AUROC of 3D models in IDH prediction [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

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Reference graph

Works this paper leans on

30 extracted references · 29 canonical work pages

  1. [1]

    The 2021 WHO classification of tumors of the central nervous system: a summary

    Louis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D, Hawkins C, Ng HK, Pfister SM, Reifenberger G, Soffietti R. The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro-oncology. 2021 Aug 1;23(8):1231-51

    work page 2021

  2. [2]

    IDH1 and IDH2 mutations in gliomas

    Yan H, Parsons DW, Jin G, McLendon R, Rasheed BA, Yuan W, Kos I, Batinic-Haberle I, Jones S, Riggins GJ, Friedman H. IDH1 and IDH2 mutations in gliomas. New England journal of medicine. 2009 Feb 19;360(8):765-73

    work page 2009

  3. [3]

    Prognostic significance of IDH mutation in adult low-grade gliomas: a meta-analysis

    Sun H, Yin L, Li S, Han S, Song G, Liu N, Yan C. Prognostic significance of IDH mutation in adult low-grade gliomas: a meta-analysis. Journal of Neuro-oncology. 2013 Jun;113:277-84

    work page 2013

  4. [4]

    MGMT in TMZ-based glioma therapy: multifaceted insights and clinical trial perspectives

    Shaw R, Basu M, Karmakar S, Ghosh MK. MGMT in TMZ-based glioma therapy: multifaceted insights and clinical trial perspectives. Biochimica et Biophysica Acta (BBA)-Molecular Cell Research. 2024 Jan 18:119673

    work page 2024

  5. [5]

    Advances in the molecular genetics of gliomas—implications for classification and therapy

    Reifenberger G, Wirsching HG, Knobbe-Thomsen CB, Weller M. Advances in the molecular genetics of gliomas—implications for classification and therapy. Nature reviews Clinical oncology. 2017 Jul;14(7):434-52

    work page 2017

  6. [6]

    Radiomics in neuro-oncology: Basics, workflow, and applications

    Lohmann P, Galldiks N, Kocher M, Heinzel A, Filss CP , Stegmayr C, Mottaghy FM, Fink GR, Shah NJ, Langen KJ. Radiomics in neuro-oncology: Basics, workflow, and applications. Methods. 2021 Apr 1;188:112-21

    work page 2021

  7. [7]

    The biological meaning of radiomic features

    Tomaszewski MR, Gillies RJ. The biological meaning of radiomic features. Radiology. 2021 Mar;298(3):505-16

    work page 2021

  8. [8]

    Imaging signatures of glioblastoma molecular characteristics: a radiogenomics review

    Fathi Kazerooni A, Bakas S, Saligheh Rad H, Davatzikos C. Imaging signatures of glioblastoma molecular characteristics: a radiogenomics review. Journal of Magnetic Resonance Imaging. 2020 Jul;52(1):54-69

    work page 2020

Show all 30 references
  1. [9]

    Radiomics: images are more than pictures, they are data

    Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016 Feb;278(2):563-77

    2016

  2. [10]

    Radiomics improves cancer screening and early detection

    Gillies RJ, Schabath MB. Radiomics improves cancer screening and early detection. Cancer Epidemiology, Biomarkers & Prevention. 2020 Dec 1;29(12):2556-67

    2020

  3. [11]

    Radiogenomics: a key component of precision cancer medicine

    Liu Z, Duan T, Zhang Y , Weng S, Xu H, Ren Y , Zhang Z, Han X. Radiogenomics: a key component of precision cancer medicine. British Journal of Cancer. 2023 Sep 21;129(5):741-53

    2023

  4. [12]

    Clinical, conventional CT and radiomic feature-based machine learning models for predicting ALK rearrangement status in lung adenocarcinoma patients

    Song L, Zhu Z, Mao L, Li X, Han W, Du H, Wu H, Song W, Jin Z. Clinical, conventional CT and radiomic feature-based machine learning models for predicting ALK rearrangement status in lung adenocarcinoma patients. Frontiers in Oncology. 2020 Mar 20;10:369

    2020

  5. [13]

    Artificial Intelligence in Neuro-Oncology: predicting molecular markers and response to therapy

    Moassefi M, Faghani S, Erickson BJ. Artificial Intelligence in Neuro-Oncology: predicting molecular markers and response to therapy. Medical Research Archives. 2024 Jun 24;12(6)

    2024

  6. [14]

    The era of radiogenomics in precision medicine: an emerging approach to support diagnosis, treatment decisions, and prognostication in oncology

    Shui L, Ren H, Yang X, Li J, Chen Z, Yi C, Zhu H, Shui P. The era of radiogenomics in precision medicine: an emerging approach to support diagnosis, treatment decisions, and prognostication in oncology. Frontiers in Oncology. 2021 Jan 26;10:570465

    2021

  7. [15]

    Bridging Pixels to Genes

    Moassefi M, Erickson BJ. Bridging Pixels to Genes. Radiology: Artificial Intelligence. 2024 Jun 20;6(4):e240262

    2024

  8. [16]

    The University of California San Francisco preoperative diffuse glioma MRI dataset

    Calabrese E, Villanueva-Meyer JE, Rudie JD, Rauschecker AM, Baid U, Bakas S, Cha S, Mongan JT, Hess CP. The University of California San Francisco preoperative diffuse glioma MRI dataset. Radiology: Artificial Intelligence. 2022 Oct 5;4(6):e220058

    2022

  9. [17]

    Mitigating bias in radiology machine learning: 1

    Rouzrokh P, Khosravi B, Faghani S, Moassefi M, Vera Garcia DV , Singh Y , Zhang K, Conte GM, Erickson BJ. Mitigating bias in radiology machine learning: 1. Data handling. Radiology: Artificial Intelligence. 2022 Aug 24;4(5):e210290

    2022

  10. [18]

    Monai: An open-source framework for deep learning in healthcare

    Cardoso MJ, Li W, Brown R, Ma N, Kerfoot E, Wang Y , Murrey B, Myronenko A, Zhao C, Yang D, Nath V . Monai: An open-source framework for deep learning in healthcare. arXiv preprint arXiv:2211.02701. 2022 Nov 4

    2022 arXiv

  11. [19]

    Mitigating bias in radiology machine learning: 2

    Zhang K, Khosravi B, Vahdati S, Faghani S, Nugen F, Rassoulinejad-Mousavi SM, Moassefi M, Jagtap JM, Singh Y , Rouzrokh P, Erickson BJ. Mitigating bias in radiology machine learning: 2. Model development. Radiology: Artificial Intelligence. 2022 Aug 24;4(5):e220010

    2022

  12. [20]

    Mitigating bias in radiology machine learning: 3

    Faghani S, Khosravi B, Zhang K, Moassefi M, Jagtap JM, Nugen F, Vahdati S, Kuanar SP, Rassoulinejad-Mousavi SM, Singh Y , Vera Garcia DV . Mitigating bias in radiology machine learning: 3. Performance metrics. Radiology: Artificial Intelligence. 2022 Aug 24;4(5):e220061

    2022

  13. [21]

    Biologically interpretable multi-task deep learning pipeline predicts molecular alterations, grade, and prognosis in glioma patients

    Wu X, Zhang S, Zhang Z, He Z, Xu Z, Wang W, Jin Z, You J, Guo Y , Zhang L, Huang W. Biologically interpretable multi-task deep learning pipeline predicts molecular alterations, grade, and prognosis in glioma patients. NPJ Precision Oncology. 2024 Aug 16;8(1):181

    2024

  14. [22]

    IDH and 1p19q Diagnosis in Diffuse Glioma from Preoperative MRI Using Artificial Intelligence

    McHugh H, Safaei S, Maso Talou GD, Gock SL, Yeun Kim J, Wang A. IDH and 1p19q Diagnosis in Diffuse Glioma from Preoperative MRI Using Artificial Intelligence. medRxiv. 2023 Apr 29:2023-04

    2023

  15. [23]

    Predicting glioblastoma molecular subtypes and prognosis with a multimodal model integrating convolutional neural network, radiomics, and semantics

    Zhong S, Ren JX, Yu ZP, Peng YD, Yu CW, Deng D, Xie Y , He ZQ, Duan H, Wu B, Li H. Predicting glioblastoma molecular subtypes and prognosis with a multimodal model integrating convolutional neural network, radiomics, and semantics. Journal of Neurosurgery. 2022 Dec 2;139(2):305-14

    2022

  16. [24]

    Quantifying uncertainty in deep learning of radiologic images

    Faghani S, Moassefi M, Rouzrokh P, Khosravi B, Baffour FI, Ringler MD, Erickson BJ. Quantifying uncertainty in deep learning of radiologic images. Radiology. 2023 Aug 1;308(2):e222217

    2023

  17. [25]

    Deep-learning convolutional neural networks accurately classify genetic mutations in gliomas

    Chang P, Grinband J, Weinberg BD, Bardis M, Khy M, Cadena G, Su MY , Cha S, Filippi CG, Bota D, Baldi P. Deep-learning convolutional neural networks accurately classify genetic mutations in gliomas. American Journal of Neuroradiology. 2018 Jul 1;39(7):1201-7

    2018

  18. [26]

    XGBoost improves classification of MGMT promoter methylation status in IDH1 wildtype glioblastoma

    Le NQ, Do DT, Chiu FY , Yapp EK, Yeh HY , Chen CY . XGBoost improves classification of MGMT promoter methylation status in IDH1 wildtype glioblastoma. Journal of personalized medicine. 2020 Sep 15;10(3):128

    2020

  19. [27]

    Improving MGMT methylation status prediction of glioblastoma through optimizing radiomics features using genetic algorithm-based machine learning approach

    Do DT, Yang MR, Lam LH, Le NQ, Wu YW. Improving MGMT methylation status prediction of glioblastoma through optimizing radiomics features using genetic algorithm-based machine learning approach. Scientific Reports. 2022 Aug 4;12(1):13412

    2022

  20. [28]

    A comparison of three different deep learning-based models to predict the MGMT promoter methylation status in glioblastoma using brain MRI

    Faghani S, Khosravi B, Moassefi M, Conte GM, Erickson BJ. A comparison of three different deep learning-based models to predict the MGMT promoter methylation status in glioblastoma using brain MRI. Journal of Digital Imaging. 2023 Jun;36(3):837-46

    2023

  21. [29]

    MGMT promoter methylation status prediction using MRI scans? An extensive experimental evaluation of deep learning models

    Saeed N, Ridzuan M, Alasmawi H, Sobirov I, Yaqub M. MGMT promoter methylation status prediction using MRI scans? An extensive experimental evaluation of deep learning models. Medical Image Analysis. 2023 Dec 1;90:102989

    2023

  22. [30]

    MRI-based deep learning tools for mgmt promoter methylation detection: a thorough evaluation

    Robinet L, Siegfried A, Roques M, Berjaoui A, Cohen-Jonathan Moyal E. MRI-based deep learning tools for mgmt promoter methylation detection: a thorough evaluation. Cancers. 2023 Apr 12;15(8):2253

    2023

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Reviewed August 10, 2026 · model on record in the stance chip above.