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Correction

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American Journal of Neuroradiology January 2023, 44 (1) E1; DOI: https://doi.org/10.3174/ajnr.A7715
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This is a correction to Yogananda CG, Shah BR, Nalawade SS, et al. MRI-based deep-learning method for determining glioma MGMT promoter methylation status. AJNR Am J Neuroradiol 2021;42:845–52 [10.3174/ajnr.A7029] [33664111]

There was an error in the Python code for the 3-fold cross-validation procedure. This resulted in the use of the training cases instead of the set-aside test cases for the testing procedure for molecular marker accuracy. This caused our reported accuracies from the TCIA/TCGA data set to be artificially inflated. The corrected accuracies for the Table (computed using nnU-Net1), along with the updated receiver operating characteristic (ROC) curve for Fig 3 are provided here. The updated accuracies do not outperform other reported methods for MGMT molecular marker prediction using MR imaging.

Fold DescriptionMGMT-Net
% AccuracyAUCDice Score
Fold no.
 Fold 159.750.49660.7906
 Fold 273.490.65880.7725
 Fold 364.630.58540.7874
Average65.95 (SD, 0.06)0.5802 (SD, 0.081)0.7835 (SD, 0.009)

Cross-validation results

FIG 3.
FIG 3.

ROC analysis for MGMT-net. Separate curves are plotted for each cross-validation fold along with corresponding area under the curve (AUC) values.

Reference

  1. 1.↵
    1. Isensee F,
    2. Jaeger PF,
    3. Kohl SAA, et al
    . nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 2021;18:203–11 doi:10.1038/s41592-020-01008-z pmid:33288961
    CrossRefPubMed
  • © 2023 by American Journal of Neuroradiology
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American Journal of Neuroradiology: 44 (1)
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