Multimodal Imaging Can Personalize and Predict Therapeutic Needs

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https://medicalresearch.com/radiology/multimodal-imaging-can-personalize-and-predict-therapeutic-needs/43124/

MedicalResearch.com Interview with:

Yasser Iturria-Medina, PhD Primary Investigator, Ludmer Centre for Neuroinformatics & Mental Health Assistant Professor, Department of Neurology and Neurosurgery Faculty of Medicine McGill University

Dr. turria-Medina

Yasser Iturria-Medina, PhD
Primary Investigator, Ludmer Centre for Neuroinformatics & Mental Health
Assistant Professor, Department of Neurology and Neurosurgery
Faculty of Medicine
McGill University

MedicalResearch.com: What is the background for this study? What are the main findings?

Response: There are millions of patients following therapeutic interventions that will not benefit them. In this study, we aimed to illustrate that it is possible to identify the most beneficial intervention for each patient, in correspondence with the principles of the personalized medicine (PM). Our results show that using multimodal imaging and computational models it is possible to predict individualized therapeutic needs. The predictions are in correspondence with the individual molecular properties, which validate our findings and the used computational techniques.

The results highly also the imprecision of the traditional clinical evaluations and categories for understanding the individual therapeutic needs, evidencing the positive impact that would have to use multimodal data and data-driven techniques in the clinic, in addition to the medical doctor’s criterion/evaluations.  

MedicalResearch.com: What are the take-home messages of your study for practicing clinicians and broader health systems?

Response: In the near future, it will be possible to identify the optimum treatment for each patient in the clinic. Clinicians will be able to use sophisticated mathematical/computational techniques, implemented in user-friendly interfaces, to apply patient-specific interventions, instead of the traditional generalized interventions.

MedicalResearch.com: What should readers take away from your report?

Response: Although still needing further validation, the proposed model and its associated results illustrate the importance of data-driven computational models for understanding neurological disorders and accelerating the identification of effective personalized interventions.

MedicalResearch.com: What recommendations do you have for future research as a result of this work?

Response: Clinical trials have been traditionally considering heterogeneous populations. I recommend using data-driven computational models to enroll participants with a predicted capacity to respond to a treatment under evaluation, which would considerably accelerate the creation-evaluation cycle of new therapeutic agents, decrease undesired secondary effects and cause a substantial reduction of pharmaceutical/clinical costs.   

Citation:

Neuroimage. 2018 Jun 14;179:40-50. doi: 10.1016/j.neuroimage.2018.06.028. [Epub ahead of print]
Multimodal imaging-based therapeutic fingerprints for optimizing personalized interventions: Application to neurodegeneration.
Iturria-Medina Y1, Carbonell FM2, Evans AC3; Alzheimer’s Disease Neuroimaging Initiative.

Jul 13, 2018 @ 12:19 am
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