Leonardo A. Ancora
Research Subjects
Leonardo A. Ancora has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Felix D. Beacher
Cool Clinical Consortium for AI and Clinical Science, 1092 Lausanne, Switzerland
1 shared publication - Lilianne Rivka Mujica-Parodi
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
1 shared publication - Shreyash Gupta
Cool Clinical Consortium for AI and Clinical Science, 1092 Lausanne, Switzerland
1 shared publication
Latest Publications
- Open Access
- 5 May 2021
- Published by MDPI AG in Algorithms
- Vol. 14 (5) , 147
Machine Learning Predicts Outcomes of Phase III Clinical Trials for Prostate Cancer
The ability to predict the individual outcomes of clinical trials could support the development of tools for precision medicine and improve the efficiency of clinical-stage drug development. However, there are no published attempts to predict individual outcomes of clinical trials for cancer. We used machine learning (ML) to predict individual responses to a two-year course of bicalutamide, a standard treatment for prostate cancer, based on data from three Phase III clinical trials (n = 3653). We developed models that used a merged dataset from all three studies. The best performing models using merged data from all three studies had an accuracy of 76%. The performance of these models was confirmed by further modeling using a merged dataset from two of the three studies, and a separate study for testing. Together, our results indicate the feasibility of ML-based tools for predicting cancer treatment outcomes, with implications for precision oncology and improving the efficiency of clinical-stage drug development.
- 5 May 2021
- Published by MDPI AG in Algorithms
- Vol. 14 (5) , 147
Open AccessMachine Learning Predicts Outcomes of Phase III Clinical Trials for Prostate Cancer
The ability to predict the individual outcomes of clinical trials could support the development of tools for precision medicine and improve the efficiency of clinical-stage drug development. However, there are no published attempts to predict individual outcomes of clinical trials for cancer. We used machine learning (ML) to predict individual responses to a two-year course of bicalutamide, a standard treatment for prostate cancer, based on data from three Phase III clinical trials (n = 3653). We developed models that used a merged dataset from all three studies. The best performing models using merged data from all three studies had an accuracy of 76%. The performance of these models was confirmed by further modeling using a merged dataset from two of the three studies, and a separate study for testing. Together, our results indicate the feasibility of ML-based tools for predicting cancer treatment outcomes, with implications for precision oncology and improving the efficiency of clinical-stage drug development.