Multi-omics investigation on the prognostic and predictive factors in metastatic breast cancer using data from Phase III ribociclib clinical trials: A statistical and machine learning analysis plan
Coroller, T. P.; Sahiner, B.; Amatya, A.; Gossman, A.; Karagiannis, K.; Samala, R. K.; Santana-Quintero, L.; Solovieff, N.; Wang, C.; Amiri-Kordestani, L.; Cao, Q.; Cha, K. H.; Charlab, R.; Cross, F. H.; Hu, T.; Huang, R.; Kraft, J.; Krusche, P.; Li, Y.; Li, Z.; Mazo, I.; Moloney, C.; Paul, R.; Plawinski, J.; Schnakenberg, S.; Serra, P.; Smith, S.; Song, C.; Su, F.; Subramaniam, S.; Tiwari, M.; Vechery, C.; Xiong, X.; Zarate, J. P.; Ziegler, J.; Zhu, H.; Chakravartty, A.; Liu, Q.; Ohlssen, D.; Petrick, N.; Schneider, J. A.; Walderhaug, M.; Zuber, E.
Show abstract
AO_SCPLOWBSTRACTC_SCPLOWIn 2020, Novartis Pharmaceuticals Corporation and the U.S. Food and Drug Administration (FDA) started a 4-year scientific collaboration to find novel radiogenomics-based prognostic and predictive factors for HR+/HER2-metastatic breast cancer under a Research Collaboration Agreement. This manuscript aims to detail the guiding principles and methodology for this study. We include a discussion of internal and external clinical, genomics, imaging datasets, data processing workflows, and machine learning model development strategies. We also prospectively define our success criteria to ensure robust scientific outputs. DisclosureThis publication reflects the views of the authors and should not be construed to represent FDAs views or policies.
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