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Integrated multiparametric deep spatial phenotyping of mouse models of lung adenocarcinoma

Author(s)
Dai, Yang,M. Eng.Massachusetts Institute of Technology.
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Download1144988339-MIT.pdf (16.12Mb)
Other Contributors
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Sandro Santagata and Tyler Jacks.
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MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582
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Abstract
In this thesis, I developed computational pipelines and algorithms that use high dimensional biomarker imaging data to predict features of tumor tissues taken from a genetically engineered mouse model (GEMM) of lung adenocarcinoma. I extracted biomarker expression levels and morphological, textural, and spatial motifs of single cells from the imaging data and used these features to train algorithms to predict tumor histologic grade, a measure correlated with the malignant potential of a tumor. The algorithm predictions were evaluated through comparison to a validated deep learning model. The random forest algorithm achieved a 72% accuracy classifying cells as belonging to a non-tumor, grade 1, grade 2, or grade 3 region and achieved a 87% accuracy classifying cells as belonging to a tumor or non-tumor region. A combination of biomarker, morphological, textural, and spatial features generated models that performed better than any single group of markers by itself; spatial features in particular significantly improved model performance.
Description
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
 
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
 
Cataloged from student-submitted PDF version of thesis.
 
Includes bibliographical references (pages 53-54).
 
Date issued
2019
URI
https://hdl.handle.net/1721.1/124238
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.

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  • Electrical Engineering and Computer Sciences - Master's degree
  • Electrical Engineering and Computer Sciences - Master's degree

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