André Diamant

Name: André Diamant, MSc

Level at MPU: PhD

Email: andre.diamantboustead [at] mail.mcgill.ca

Supervisor(s): Dr. Jan Seuntjens

Research interests: Machine Learning - Outcome Modeling

 

Research summary

Radiation therapy is often used to treat nearly all forms of cancer. Although loco-regional control of most cancers can be reasonably good (80%+), long-term survival can be quite poor (5-year survival rates as low as 50%), in large part due to the development of distant metastasis or second primary cancers. Thus, the development of a model capable of identifying potential high-risk patients prior to treatment is critical. With such a model, a better-informed decision could be made regarding patient risk stratification. A high-risk patient could be assigned a more aggressive treatment regimen, potentially improving their outcome. Similarily, a low-risk patient could receive a more conservative treatment, delivering less radiation in order to reduce the chance of harmful side effects. The primary focus of my work it to build models using deep learning that are cpable of discerning high-risk cancer patients prior to their treatment using solely information available at diagnosis.

 

Key publications

  1. A Diamant et al - "Deep learning in head & neck cancer outcome prediction" - Scientific Reports (2019)
  2. A Diamant et al - "Can dose outside the PTV influence the risk of distant metastases in stage I lung cancer patients treated with stereotactic body radiotherapy (SBRT)?" - Radiotherapy and Oncology (2018)

 

Awards

Bourse de doctorat en recherche - FRQNT (2017-2020)

Research Award - RI MUHC (2017)

Young Investigator's Symposium - COMP 2016 - 2nd place

Young Investigator's Symposium - COMP 2018 - Finalist

 

 

 

 

 

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