Erratum: Brain tumor IDH, 1p/19q, and MGMT molecular classification using MRI-based deep learning: an initial study on the effect of motion and motion correction (Journal of Medical Imaging DOI: 10.1117/1.JMI.9.1.016001)

The original article was published in Volume 9 Issue 1 of Journal of Medical Imaging (JMI) on 27 January 2022 with an error in the Python code for the 3-fold cross validation procedure. This error resulted in the use of the training cases instead of the set-aside test cases for the molecular marker accuracy testing procedure. This caused the reported accuracies from the TCIA/TCGA data set to be artificially inflated for the 3 markers. The original and corrected accuracies for Figures 5 and 6 are provided here below. In addition, the following errors in the text were identified and corrected: 1. The corrected 3^rdsentence in the Results section of the abstract states, "Motion correction of uncorrupted images exceeded the original performance of the network," rather than the IDH network achieving 99% classification accuracy originally reported. 2. The corrected 1^stsentence in the 3^rdparagraph of the Introduction states, "Our group has developed molecular marker classification networks for IDH, 1p/19q, and MGMT in primary brain tumors utilizing T2w MR images alone," rather than achieving 97%, 93%, and 95% classification accuracies for IDH, 1p/19q and MGMT, respectively using T2w MR images alone originally reported. 3. Corrected sentences in Sec. 2, Materials and Methods: "The trained IDH network demonstrated a 67% mean cross-validation accuracy for IDH-prediction on the TCIA data," rather than a mean cross-validation accuracy of 97% originally reported. "A mean cross-validation accuracy of 80% was obtained for 1p/19q network on the TCIA data," rather than achieving a mean cross-validation accuracy of 93% originally reported. "A mean cross-validation accuracy of 75% was obtained for MGMT network on the TCIA data," rather than achieving a mean cross-validation accuracy of 95% originally reported. 4. The corrected 2^ndsentence in Sec. 3.2 states, "IDH classification began to fail on the motion corrupted images at a CR of 80%," rather than the CR of 40% originally reported. 5. The corrected 3^rdsentence in Sec. 3.2 states, "Model-1 achieved the best results out to 100% CR," rather than Model-1 achieving and maintaining a 97% IDH classification accuracy through a CR of 92% originally reported. 6. The corrected 2^ndsentence in the 2^ndparagraph of Sec. 3.2 states, "The classification accuracy on the corrupted images declined at 80% CR for both IDH and MGMT, while 1p/19q performance declined at 63% CR," rather than the classification accuracy declining at 42% CR for both IDH and 1p/19q, and MGMT at 63% CR originally reported. 7. The corrected 3^rdsentence in the 2^ndparagraph of Sec. 3.2 states, "IDH classification was maintained at 68% accuracy out to 65% CR and recovered to 63% accuracy even at 100% CR," rather than 97% accuracy out to 92% CR originally reported. 8. The corrected 4th sentence in the 2^ndparagraph of Sec. 3.2 states, "IDH classification accuracy exceeded the performance of the uncorrupted images achieving up to 69% accuracy," rather than achieving up to 99% accuracy originally reported. 9. The corrected 5th sentence in the 2^ndparagraph of Sec. 3.2 states, "For 1p/19q and MGMT, 82% and 76% accuracy was recovered out to 100% CR respectively," rather than achieving 82% accuracy for 1p/19q & MGMTout to 100% CR originally reported. 10. The corrected 4th sentence in Sec. 4 (Discussion) states, "In the case of IDH classification, 68% accuracy was achieved following motion correction, exceeding the performance on the ground truth images," rather than the 99% originally reported. 11. The corrected 4th sentence in the 2^ndparagraph of Sec. 4 (Discussion) states, "performance declined at image corruption levels beyond CR = 80%," rather than CR=42% originally reported. 12. The corrected 5th sentence in the 2^ndparagraph of Sec. 4 (Discussion) states that the motion correction network boosted the IDH classification accuracy "by 2% for the native images without any added simulated motion," rather than achieving 99% accuracy originally reported. 13. The corrected 4th sentence in the final paragraph of the Discussion section states that the IDH classification accuracy was fully recovered "extending out to a corruption level of 65%," rather than 92% originally reported. 14. The corrected 1^stsentence of Sec. 6 states that the classification accuracies for IDH, 1p/ 19q and MGMT "improved" upon application of a motion correction network, rather than achieving 99% IDH classification accuracy originally reported. The article was corrected and republished under the same doi (https://doi.org/10.1117/1.JMI .9.1.016001) on 18 January 2023.