Integrated multiparametric deep spatial phenotyping of mouse models of lung adenocarcinoma
Name
1144988339-MIT.pdf
Size
16.12 MB
Format
Adobe PDF
Checksum (MD5)
d86e837ef26ba69ccc0db75b8882d141
Author(s)
Dai, Yang,M. Eng.Massachusetts Institute of Technology.
Advisor(s)
Sandro Santagata and Tyler Jacks.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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