Integrated multiparametric deep spatial phenotyping of mouse models of lung adenocarcinoma

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Dai, Yang,M. Eng.Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Sandro Santagata and Tyler Jacks.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/124238

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/124238