Integrated multiparametric deep spatial phenotyping of mouse models of lung adenocarcinoma
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2020
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| Online Access: | https://hdl.handle.net/1721.1/124238 |
| _version_ | 1826214707410763776 |
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| author | Dai, Yang,M. Eng.Massachusetts Institute of Technology. |
| author2 | Sandro Santagata and Tyler Jacks. |
| author_facet | Sandro Santagata and Tyler Jacks. Dai, Yang,M. Eng.Massachusetts Institute of Technology. |
| author_sort | Dai, Yang,M. Eng.Massachusetts Institute of Technology. |
| collection | MIT |
| description | This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. |
| first_indexed | 2024-09-23T16:09:46Z |
| format | Thesis |
| id | mit-1721.1/124238 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T16:09:46Z |
| publishDate | 2020 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1242382020-03-25T03:02:54Z Integrated multiparametric deep spatial phenotyping of mouse models of lung adenocarcinoma Dai, Yang,M. Eng.Massachusetts Institute of Technology. Sandro Santagata and Tyler Jacks. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Electrical Engineering and Computer Science. 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). 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. by Yang Dai. M. Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 2020-03-24T15:35:47Z 2020-03-24T15:35:47Z 2019 2019 Thesis https://hdl.handle.net/1721.1/124238 1144988339 eng 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 54 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Dai, Yang,M. Eng.Massachusetts Institute of Technology. Integrated multiparametric deep spatial phenotyping of mouse models of lung adenocarcinoma |
| title | Integrated multiparametric deep spatial phenotyping of mouse models of lung adenocarcinoma |
| title_full | Integrated multiparametric deep spatial phenotyping of mouse models of lung adenocarcinoma |
| title_fullStr | Integrated multiparametric deep spatial phenotyping of mouse models of lung adenocarcinoma |
| title_full_unstemmed | Integrated multiparametric deep spatial phenotyping of mouse models of lung adenocarcinoma |
| title_short | Integrated multiparametric deep spatial phenotyping of mouse models of lung adenocarcinoma |
| title_sort | integrated multiparametric deep spatial phenotyping of mouse models of lung adenocarcinoma |
| topic | Electrical Engineering and Computer Science. |
| url | https://hdl.handle.net/1721.1/124238 |
| work_keys_str_mv | AT daiyangmengmassachusettsinstituteoftechnology integratedmultiparametricdeepspatialphenotypingofmousemodelsoflungadenocarcinoma |