Deep Learning Predicts Underlying Features on Pathology Images with Therapeutic Relevance for Breast and Gastric Cancer
Abstract
:Simple Summary
Abstract
1. Introduction
2. Results
2.1. Weakly Supervised Deep Learning for DNA Repair Deficiency (DRD) Assessment
2.2. Mutational Signature as a Quantitative Label for DNA Damage Repair Status
2.3. Image Classification Recapitulates Unique Molecular Drivers
3. Discussion
4. Materials and Methods
4.1. Slide Pre-Processing
4.2. Model Definition and Training
4.3. Datasets
4.4. Biomarker for HRD and MMRD Status
4.5. Data Availability
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
References
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Valieris, R.; Amaro, L.; Osório, C.A.B.d.T.; Bueno, A.P.; Rosales Mitrowsky, R.A.; Carraro, D.M.; Nunes, D.N.; Dias-Neto, E.; Silva, I.T.d. Deep Learning Predicts Underlying Features on Pathology Images with Therapeutic Relevance for Breast and Gastric Cancer. Cancers 2020, 12, 3687. https://doi.org/10.3390/cancers12123687
Valieris R, Amaro L, Osório CABdT, Bueno AP, Rosales Mitrowsky RA, Carraro DM, Nunes DN, Dias-Neto E, Silva ITd. Deep Learning Predicts Underlying Features on Pathology Images with Therapeutic Relevance for Breast and Gastric Cancer. Cancers. 2020; 12(12):3687. https://doi.org/10.3390/cancers12123687
Chicago/Turabian StyleValieris, Renan, Lucas Amaro, Cynthia Aparecida Bueno de Toledo Osório, Adriana Passos Bueno, Rafael Andres Rosales Mitrowsky, Dirce Maria Carraro, Diana Noronha Nunes, Emmanuel Dias-Neto, and Israel Tojal da Silva. 2020. "Deep Learning Predicts Underlying Features on Pathology Images with Therapeutic Relevance for Breast and Gastric Cancer" Cancers 12, no. 12: 3687. https://doi.org/10.3390/cancers12123687