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Erratum

Erratum his is a correction to Yogananda CG, Shah BR, Nalawade SS, et al. MRI-based deep-learning method for determining glioma TMGMT 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-Net ), 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. Cross-validation results MGMT-Net Fold Description % 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) 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. 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 CrossRef Medline http://dx.doi.org/10.3174/ajnr.A7715 AJNR Am J Neuroradiol 44:E1 2023 www.ajnr.org E1 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png American Journal of Neuroradiology American Journal of Neuroradiology

Erratum

American Journal of Neuroradiology , Volume 44 (1): 1 – Jan 1, 2023
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Publisher
American Journal of Neuroradiology
Copyright
© 2023 by American Journal of Neuroradiology
ISSN
0195-6108
eISSN
1936-959X
DOI
10.3174/ajnr.a7715
Publisher site
See Article on Publisher Site

Abstract

his is a correction to Yogananda CG, Shah BR, Nalawade SS, et al. MRI-based deep-learning method for determining glioma TMGMT 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-Net ), 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. Cross-validation results MGMT-Net Fold Description % 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) 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. 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 CrossRef Medline http://dx.doi.org/10.3174/ajnr.A7715 AJNR Am J Neuroradiol 44:E1 2023 www.ajnr.org E1

Journal

American Journal of Neuroradiology – American Journal of Neuroradiology

Published: Jan 1, 2023

References

  • nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
    F. Isensee; PF. Jaeger; SAA. Kohl