Erratum
=======


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](http://www.ajnr.org/lookup/volpage/42/845) [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 Description | *MGMT*-Net       |
|:----------------:| ---------------- | ------------------ |
|    % Accuracy    | AUC              | Dice Score         |
|     Fold no.     |                  |                    |                    |
|      Fold 1      | 59.75            | 0.4966             | 0.7906             |
|      Fold 2      | 73.49            | 0.6588             | 0.7725             |
|      Fold 3      | 64.63            | 0.5854             | 0.7874             |
|     Average      | 65.95 (SD, 0.06) | 0.5802 (SD, 0.081) | 0.7835 (SD, 0.009) |

Cross-validation results

![FIG 3.](http://www.ajnr.org/http://ajnr-stage2.highwire.org/content/ajnr/44/1/E1/F1.medium.gif)

[FIG 3.](http://www.ajnr.org/content/44/1/E1/F1)

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.Isensee F, Jaeger PF, 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
    
    [CrossRef](http://www.ajnr.org/lookup/external-ref?access_num=10.1038/s41592-020-01008-z&link_type=DOI) 
    
    [PubMed](http://www.ajnr.org/lookup/external-ref?access_num=33288961&link_type=MED&atom=%2Fajnr%2F44%2F1%2FE1.atom) 

*   © 2023 by American Journal of Neuroradiology