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Volume 16
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10.3390/cancers16030493
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Retraction of Cancers 2023, 15(16), 4044.
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Retraction

RETRACTED: Liu et al. Prediction of Ovarian Cancer Response to Therapy Based on Deep Learning Analysis of Histopathology Images. Cancers 2023, 15, 4044

by
Yuexin Liu
1,*,
Barrett C. Lawson
2,
Xuelin Huang
3,
Bradley M. Broom
1 and
John N. Weinstein
1,4,*
1
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
2
Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
3
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
4
Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
*
Authors to whom correspondence should be addressed.
Cancers 2024, 16(3), 493; https://doi.org/10.3390/cancers16030493
Submission received: 3 January 2024 / Accepted: 12 January 2024 / Published: 24 January 2024
(This article belongs to the Section Cancer Informatics and Big Data)
Versions Notes
The journal and authors wish to retract the article entitled ‘Prediction of Ovarian Cancer Response to Therapy Based on Deep Learning Analysis of Histopathology Images’ cited above [1].
Soon after publication, the authors contacted the Editorial Office to request retraction of the article [1]. Their further extensive computations and deeper exploration of the learning process had revealed an error, and it was found that the results presented were, therefore, statistical outliers for reasons that apparently related to the random selection of held-out test data. As a result, the original findings of clear prediction by the deep learning model could not be validated.
Adhering to our complaint’s procedure, an investigation was conducted by the Editorial Office and Editorial Board that confirmed that the central finding cannot be considered to be reliable. Consequently, the Editorial Office, the Editorial Board, and the authors have decided to retract the article [1] as per MDPI’s retraction policy (https://www.mdpi.com/ethics#_bookmark30).
This retraction was approved by the Editor-in-Chief of the journal Cancers.
The authors agreed to this retraction.

Reference

  1. Liu, Y.; Lawson, B.C.; Huang, X.; Broom, B.M.; Weinstein, J.N. RETRACTED: Prediction of Ovarian Cancer Response to Therapy Based on Deep Learning Analysis of Histopathology Images. Cancers 2023, 15, 4044. [Google Scholar] [CrossRef] [PubMed]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

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MDPI and ACS Style

Liu, Y.; Lawson, B.C.; Huang, X.; Broom, B.M.; Weinstein, J.N. RETRACTED: Liu et al. Prediction of Ovarian Cancer Response to Therapy Based on Deep Learning Analysis of Histopathology Images. Cancers 2023, 15, 4044. Cancers 2024, 16, 493. https://doi.org/10.3390/cancers16030493

AMA Style

Liu Y, Lawson BC, Huang X, Broom BM, Weinstein JN. RETRACTED: Liu et al. Prediction of Ovarian Cancer Response to Therapy Based on Deep Learning Analysis of Histopathology Images. Cancers 2023, 15, 4044. Cancers. 2024; 16(3):493. https://doi.org/10.3390/cancers16030493

Chicago/Turabian Style

Liu, Yuexin, Barrett C. Lawson, Xuelin Huang, Bradley M. Broom, and John N. Weinstein. 2024. "RETRACTED: Liu et al. Prediction of Ovarian Cancer Response to Therapy Based on Deep Learning Analysis of Histopathology Images. Cancers 2023, 15, 4044" Cancers 16, no. 3: 493. https://doi.org/10.3390/cancers16030493

APA Style

Liu, Y., Lawson, B. C., Huang, X., Broom, B. M., & Weinstein, J. N. (2024). RETRACTED: Liu et al. Prediction of Ovarian Cancer Response to Therapy Based on Deep Learning Analysis of Histopathology Images. Cancers 2023, 15, 4044. Cancers, 16(3), 493. https://doi.org/10.3390/cancers16030493

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