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A PROGNOSTIC AND PREDICTIVE COMPUTATIONAL PATHOLOGY BASED COMPANION DIAGNOSTIC APPROACH: PRECISION MEDICINE FOR LUNG CANCER

Wang, Xiangxue
http://orcid.org/0000-0003-3341-9871
http://rave.ohiolink.edu/etdc/view?acc_num=case1574125440501667

Abstract Details

2019, Doctor of Philosophy, Case Western Reserve University, Biomedical Engineering.
About 2 million patients worldwide are diagnosed with lung cancer annually and 80% of them are non-small cell lung cancer (NSCLC). While early-stage (I and II) patients might be treated with adjuvant chemotherapy (ACT) after surgery, the majority will not receive additional benefit and still experience cancer recurrence. Recently, immunotherapy (IO) via the immune-checkpoint inhibitor (ICI) has been approved for the treatment of advanced stage NSCLC and shows promising outcomes for a fraction of patients (~20%). Unfortunately, there is no predictive companion diagnostic tool to identify candidate for receiving ICI. Hence, there is a clear unmet clinical need for rigorously validated prognostic and predictive biomarkers to stratify early-stage NSCLC patients into low and high risk of recurrence and identify which early-stage NSCLC patients will not receive additional benefit from ACT and which advanced stage NSCLC patients are the recipients of ICI to maximize the survival. Tissue specimen from resected tumor stained with hematoxylin and eosin (H&E) has been the gold standard of pathological examination over a hundred years. These slides provide an insight into the tumor morphology, which is critical for tumor diagnosis and prognosis. Recently, increased tumor-infiltrating lymphocytes (TIL) has been found to be associated with better treatment response and survival. Computational analysis of tumor morphology from digitized tissue slide has been shown to be prognostic in NSCLC. This method combines quantitative measurements of cells with machine learning tools to predict clinical outcome. However, there is no comprehensive study of using computational pathology for better lung cancer management by predicting response to therapy. We hence present a comprehensive computational pathology based image analysis approach and its application in precision medicine for lung cancer. We first develop a set of quantitative measurements (e.g. shape, architecture) of cancer nuclei, lymphocytes, and their spatial interaction from different compartments (i.e. epithelium and stroma) within tumor. Then, we construct three different models to stratify recurrence risk, predict the added benefits of ACT for early-stage NSCLC patients and response to IO for advanced stage NSCLC patients, respectively.
Anant Madabhushi (Advisor)
139 p.
Bioinformatics; Biomedical Engineering; Biostatistics; Computer Engineering; Computer Science; Statistics
computational pathology, precision medicine, lung cancer, machine learning, artificial intelligence, prognostic and predictive

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Citations

  • Wang, X. (2019). A PROGNOSTIC AND PREDICTIVE COMPUTATIONAL PATHOLOGY BASED COMPANION DIAGNOSTIC APPROACH: PRECISION MEDICINE FOR LUNG CANCER [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1574125440501667

    APA Style (7th edition)

  • Wang, Xiangxue. A PROGNOSTIC AND PREDICTIVE COMPUTATIONAL PATHOLOGY BASED COMPANION DIAGNOSTIC APPROACH: PRECISION MEDICINE FOR LUNG CANCER. 2019. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1574125440501667.

    MLA Style (8th edition)

  • Wang, Xiangxue. "A PROGNOSTIC AND PREDICTIVE COMPUTATIONAL PATHOLOGY BASED COMPANION DIAGNOSTIC APPROACH: PRECISION MEDICINE FOR LUNG CANCER." Doctoral dissertation, Case Western Reserve University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1574125440501667

    Chicago Manual of Style (17th edition)

case1574125440501667
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© 2019, some rights reserved.Creative Commons License A PROGNOSTIC AND PREDICTIVE COMPUTATIONAL PATHOLOGY BASED COMPANION DIAGNOSTIC APPROACH: PRECISION MEDICINE FOR LUNG CANCER by Xiangxue Wang is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.