Grant Number:	5R03CA191559-02 Interpret this number
Primary Investigator:	Kesler, Shelli
Organization:	University Of Tx Md Anderson Can Ctr
Project Title:	Machine Learning Comparison of Brain Anatomy in Cancer and Alzheimer's Disease
Fiscal Year:	2016

¿ DESCRIPTION (provided by applicant): Difficulties with cognitive skills such as memory, thinking and attention are very common after breast cancer chemotherapy. These cognitive difficulties have been associated with chemotherapy-related injury to several brain regions, particularly those regions that also tend to be affected by brain aging. There is concern that chemotherapy may accelerate brain aging and increase patients' risk for Alzheimer's disease. Certain previous studies support this concern while others do not and therefore the effect of breast cancer chemotherapy on risk for Alzheimer's disease remains unclear. The proposed research will compare brain anatomy of breast cancer survivors to that of women who later developed Alzheimer's disease. Using machine learning, a form of artificial intelligence, we will determine a pattern of brain anatomy that indicates the probability of developing Alzheimer's disease. This pattern, or classifier, will be determined using magnetic resonance imaging (MRI) scans that were obtained for approximately 100 women with Alzheimer's disease compared to MRI scans from a group of approximately 100 healthy, unaffected women. We will then apply the classifier to MRI scans from 108 breast cancer survivors, 67 who received chemotherapy treatment and 41 who did not. Using the machine learning classifier, we will calculate a score for each breast cancer survivor that indicates her probability of developing Alzheimer's disease based on her brain anatomy. We believe that probability scores will be significantly higher in the chemotherapy group compared to the no-chemotherapy group. We will also explore possible predictors of probability score such as demographic, disease, genetic and treatment factors. This project has the potential to improve our ability to identify patients at risk for persistent and/or progressive chemotherapy-related brain injury using a simple, non-invasive five minute MRI scan. This information could potentially inform treatment decision-making and prioritize patients for early intervention.




Functional and structural connectome properties in the 5XFAD transgenic mouse model of Alzheimer's disease.
Authors: Kesler S.R. , Acton P. , Rao V. , Ray W.J. .
Source: Network Neuroscience (cambridge, Mass.), 2018; 2(2), p. 241-258.
EPub date: 2018-06-01 00:00:00.0.
PMID: 30215035
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Probability of Alzheimer's disease in breast cancer survivors based on gray-matter structural network efficiency.
Authors: Kesler S.R. , Rao V. , Ray W.J. , Rao A. , Alzheimer's Disease Neuroimaging Initiative .
Source: Alzheimer's & Dementia (amsterdam, Netherlands), 2017; 9, p. 67-75.
EPub date: 2017-11-01 00:00:00.0.
PMID: 29201992
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The Effect Of Idh1 Mutation On The Structural Connectome In Malignant Astrocytoma
Authors: Kesler S.R. , Noll K. , Cahill D.P. , Rao G. , Wefel J.S. .
Source: Journal Of Neuro-oncology, 2016-11-15 00:00:00.0; , .
PMID: 27848136
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Atypical Structural Connectome Organization and Cognitive Impairment in Young Survivors of Acute Lymphoblastic Leukemia.
Authors: Kesler S.R. , Gugel M. , Huston-Warren E. , Watson C. .
Source: Brain Connectivity, 2016 05; 6(4), p. 273-82.
EPub date: 2016-03-29 00:00:00.0.
PMID: 26850738
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