黑料不打烊

Odette Rios Ibacache

Odette Rios Ibacache
Contact Information
Email address: 
odette.riosibacache [at] mail.mcgill.ca
Address: 

Medical Physics,

Cedars Cancer Centre DS1.4556,

黑料不打烊 University Health Centre,

1001 boul. D茅carie,

Montreal, QC H4A 3J1

Group: 
Ph.D. Graduate
Degree(s): 

Licentiate, Physics (Pontificia U Catolica de Chile '21)

MSc, Medical Radiation Physics (黑料不打烊 '24)

PhD, Physics (Medical Physics) (黑料不打烊, in progress)

Location: 
RI-MUHC
Graduate supervision: 

Dr. John Kildea, 黑料不打烊 University

Dr. Amal Zouaq, Polytechnique Montreal (co-supervisor)

Current research: 

Currently, health data in the real world are disorganized and scattered which limits the benefits that artificial intelligence can provide to medical physics and radiation oncology. The objective of our research project is to address this lack of organization and standardization by integrating, consolidating, and leveraging radiotherapy medical records using a new oncology data standard called the Minimal Common Oncology Data Elements (mCODE). This approach will facilitate the exchange of health data by constructing an oncology knowledge-based repository that contains comprehensive information. Our project would provide significant value to the oncology community by serving as a reliable source for digital twins development.

Areas of interest: 

AI, Treatment outcomes prediction, Radiotherapy treatment planning. Radiation oncology informatics, Image processing and data science, Software Development, Radiomics

Selected publications: 

- Rios-Ibacache O., Manalad J., Andrade Hernandez AX., O鈥橲ullivan-Steben K., Poon E., Galarneau L., and Kildea J. (2024). 鈥淒evelopment and evaluation of novel parameters for describing anatomical changes and predicting radiotherapy replanning for head and neck cancer patients鈥. XXth International Conference on the use of Computers in Radiation Therapy (ICCR)
- Dominguez J鈥., Rios-Ibacache O鈥., Caprile P., Gonzalez J., San Francisco I.F. & Besa C. 鈥淢RI-Based Surrogate Imaging Markers of Aggressiveness in Prostate Cancer: Development of a Machine Learning Model Based on Radiomic features鈥. Diagnostics (2023), 13, 2779. h;ps://doi.org/10.3390/diagnos0cs13172779. (鈥 shared first authorship).
- Rios-Ibacache O., Caprile P., Dominguez J. & Besa C. PO-1767 ESTRO (2022). 鈥淒evelopment of a MRI radiomic-based ML model to predict the aggressiveness of prostate cancer鈥. (22)03731-8.

Awards, honours, and fellowships: 

- Third Place in Young Rising Stars Posters Competition at the XXth International Conference on the use of Computers in Radiation therapy (ICCR) (2024)
- AAPM/RSNA Doctoral Fellowship (2024)
- Second Place in Science Slam Competition at 5th DKFZ Summer School in Medical Physics 2023: Data Science and Machine Learning in Radiotherapy (2023)
- 黑料不打烊 - Graduate Excellence Award (2023)
- RI-MUHC Studentship (2023)
- 黑料不打烊 - Graduate Excellence Award (2022)
- Differential Fee Waiver for International Students Award (2022)
- Padre Hurtado Award (2017 - 2021)
- Bicentenario Scholarship (2017 - 2021)
- Presidente de la Republica Scholarship (2017)

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