doctoral thesis in breast cancer

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Electronic Theses and Dissertations

Computer aided diagnosis system for breast cancer using deep learning..

Asma Baccouche , University of Louisville Follow

Date on Master's Thesis/Doctoral Dissertation

Document type.

Doctoral Dissertation

Degree Name

Computer Engineering and Computer Science

Degree Program

Computer Science and Engineering, PhD

Committee Chair

Elmaghraby, Adel

Committee Co-Chair (if applicable)

Garcia-Zapirain, Maria Begona

Committee Member

Sierra-Sosa, Daniel

Gentili, Monica

Park, Juw Won

Author's Keywords

Medical imaging; breast cancer; deep learning; CAD; artificial intelligence; computer vision

The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous applications provided efficient solutions to assist radiologists and doctors for medical imaging analysis, which has remained the essence of the visual representation that is used to construct the final observation and diagnosis. Medical research in cancerology and oncology has been recently blended with the knowledge gained from computer engineering and data science experts. In this context, an automatic assistance or commonly known as Computer-aided Diagnosis (CAD) system has become a popular area of research and development in the last decades. As a result, the CAD systems have been developed using multidisciplinary knowledge and expertise and they have been used to analyze the patient information to assist clinicians and practitioners in their decision-making process. Treating and preventing cancer remains a crucial task that radiologists and oncologists face every day to detect and investigate abnormal tumors. Therefore, a CAD system could be developed to provide decision support for many applications in the cancer patient care processes, such as lesion detection, characterization, cancer staging, tumors assessment, recurrence, and prognosis prediction. Breast cancer has been considered one of the common types of cancers in females across the world. It was also considered the leading cause of mortality among women, and it has been increased drastically every year. Early detection and diagnosis of abnormalities in screened breasts has been acknowledged as the optimal solution to examine the risk of developing breast cancer and thus reduce the increasing mortality rate. Accordingly, this dissertation proposes a new state-of-the-art CAD system for breast cancer diagnosis that is based on deep learning technology and cutting-edge computer vision techniques. Mammography screening has been recognized as the most effective tool to early detect breast lesions for reducing the mortality rate. It helps reveal abnormalities in the breast such as Mass lesion, Architectural Distortion, Microcalcification. With the number of daily patients that were screened is continuously increasing, having a second reading tool or assistance system could leverage the process of breast cancer diagnosis. Mammograms could be obtained using different modalities such as X-ray scanner and Full-Field Digital mammography (FFDM) system. The quality of the mammograms, the characteristics of the breast (i.e., density, size) or/and the tumors (i.e., location, size, shape) could affect the final diagnosis. Therefore, radiologists could miss the lesions and consequently they could generate false detection and diagnosis. Therefore, this work was motivated to improve the reading of mammograms in order to increase the accuracy of the challenging tasks. The efforts presented in this work consists of new design and implementation of neural network models for a fully integrated CAD system dedicated to breast cancer diagnosis. The approach is designed to automatically detect and identify breast lesions from the entire mammograms at a first step using fusion models’ methodology. Then, the second step only focuses on the Mass lesions and thus the proposed system should segment the detected bounding boxes of the Mass lesions to mask their background. A new neural network architecture for mass segmentation was suggested that was integrated with a new data enhancement and augmentation technique. Finally, a third stage was conducted using a stacked ensemble of neural networks for classifying and diagnosing the pathology (i.e., malignant, or benign), the Breast Imaging Reporting and Data System (BI-RADS) assessment score (i.e., from 2 to 6), or/and the shape (i.e., round, oval, lobulated, irregular) of the segmented breast lesions. Another contribution was achieved by applying the first stage of the CAD system for a retrospective analysis and comparison of the model on Prior mammograms of a private dataset. The work was conducted by joining the learning of the detection and classification model with the image-to-image mapping between Prior and Current screening views. Each step presented in the CAD system was evaluated and tested on public and private datasets and consequently the results have been fairly compared with benchmark mammography datasets. The integrated framework for the CAD system was also tested for deployment and showcase. The performance of the CAD system for the detection and identification of breast masses reached an overall accuracy of 97%. The segmentation of breast masses was evaluated together with the previous stage and the approach achieved an overall performance of 92%. Finally, the classification and diagnosis step that defines the outcome of the CAD system reached an overall pathology classification accuracy of 96%, a BIRADS categorization accuracy of 93%, and a shape classification accuracy of 90%. Results given in this dissertation indicate that our suggested integrated framework might surpass the current deep learning approaches by using all the proposed automated steps. Limitations of the proposed work could occur on the long training time of the different methods which is due to the high computation of the developed neural networks that have a huge number of the trainable parameters. Future works can include new orientations of the methodologies by combining different mammography datasets and improving the long training of deep learning models. Moreover, motivations could upgrade the CAD system by using annotated datasets to integrate more breast cancer lesions such as Calcification and Architectural distortion. The proposed framework was first developed to help detect and identify suspicious breast lesions in X-ray mammograms. Next, the work focused only on Mass lesions and segment the detected ROIs to remove the tumor’s background and highlight the contours, the texture, and the shape of the lesions. Finally, the diagnostic decision was predicted to classify the pathology of the lesions and investigate other characteristics such as the tumors’ grading assessment and type of the shape. The dissertation presented a CAD system to assist doctors and experts to identify the risk of breast cancer presence. Overall, the proposed CAD method incorporates the advances of image processing, deep learning, and image-to-image translation for a biomedical application.

Recommended Citation

Baccouche, Asma, "Computer aided diagnosis system for breast cancer using deep learning." (2022). Electronic Theses and Dissertations. Paper 3931. https://doi.org/10.18297/etd/3931

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Daria Bulanova's doctoral thesis pinpoints GPRC5A as a novel breast cancer gene

The main aim of M.Sc. Daria Bulanova ’s thesis entitled "Novel Genetic Determinants of Breast Cancer Progression" , was to identify these new breast-cancer predisposing mutations and to elucidate the biological mechanisms underlying these genetic findings. She will defend her doctoral dissertation in the Faculty of Biological and Environmental Sciences on 5 December.

Daria graduated from the Kazan Federal University, Russia, in 2011 with microbiology and molecular biology as her major subjects. When looking for PhD positions abroad, she got interested in FIMM-EMBL Group Leader Sergey Kuznetsov ’s work, contacted him directly, and, after successful interviews, started at FIMM the same autumn.

Daria’s thesis consists of three publications. First, in collaboration with Prof. Imyanitov Lab at N.N.Petrov Institute of Oncology, St.Petersburg, she identified a promising mutation in an orphan G protein-coupled receptor-encoding gene GPRC5A using modern exome sequencing technologies. In the second publication, she explored the effect of GPRC5A deficiency on cancer progression. The third article describes drug sensitivity of BRCA1-decifient cell lines.

Interestingly, the identified mutation was found to be overrepresented among patients with BRCA1 mutation. Thus we focused our further efforts on studying the interaction of these two proteins and were able to show that GPRC5A modulates both the expression and function of BRCA1. - Daria Bulanova

The work was complicated by the fact that basically nothing was known about the function of the gene or the GPRC5A-protein in breast cancer at the time when the mutation was identified. The protein has seven transmembrane domains and structurally looks like a glutamate receptor.

We were surprised to found that the protein modulates cellular adhesion. Even more surprisingly, this effect seems to be tissue type specific: for example, we see it in breast and cervical cancer cell lines, but not in lung cancer cell lines. To me, this completely unexpected function of the protein as well as the tissue specificity we observed were the most exciting findings of my thesis. The more we learned about the protein, the more it stimulated our curiosity. Why precisely this protein but none of the very similar ones is overexpressed in cancer?

Hopefully some of these open questions will be answered by future collaborative studies that will focus on the role of GPRC5A in ovarian cancer.

We all know that to be a successful researcher, it is necessary to work hard. What I have found out during my thesis project is that when you do that while being surrounded by nice people, it is much less painful. The community is as important as the PhD project and the supervisors and I am grateful to the FIMM community, especially the people in my wing and FIMM’s Research Training Coordinator  Gretchen Repasky , for their support.

When asked about the personal characteristics that have helped her to complete the thesis project, Daria brings up her good time-management skills. The fact that she is also a mother of a 1.5 years old kid and started to work part-time in the lab when the baby was only three weeks of age truly speak for that!  

After careful consideration, Daria decided to continue her Post-Doctoral studies at FIMM, in the group of FIMM-EMBL Group Leader Krister Wennerberg . The aim of her project is to develop new ways to quantify drug sensitivity and resistance in ovarian cancer.

It might have been better for my CV to work at some other research institute, but both the project Krister had to offer and the great team he has, convinced me to continue here at FIMM. I believe many interesting discoveries are ahead.

The public examination of Daria Bulanova’s thesis will take place on 5 December 2016 at 12 o’clock noon in the lecture hall 1 of Haartman Institute, Haartmaninkatu 3. Professor  Thorarinn Gudjonsson  (University of Iceland) will serve as the opponent and Professor  Minna Nyström as the custos.

The dissertation is also available in electronic form and can be downloaded here .

Identifying miRNA as biomarker for breast cancer subtyping using association rule

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Home > Eppley Institute > Theses & Dissertations

Theses & Dissertations: Cancer Research

Theses/dissertations from 2024 2024.

Novel Spirocyclic Dimer (SpiD3) Displays Potent Preclinical Effects in Hematological Malignancies , Alexandria Eiken

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Development of Combination Therapy Strategies to Treat Cancer Using Dihydroorotate Dehydrogenase Inhibitors , Nicholas Mullen

Overcoming Resistance Mechanisms to CDK4/6 Inhibitor Treatment Using CDK6-Selective PROTAC , Sarah Truong

Theses/Dissertations from 2022 2022

Omics Analysis in Cancer and Development , Emalie J. Clement

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Post-Transcriptional Control of the Epithelial-to-Mesenchymal Transition (EMT) in Ras-Driven Colorectal Cancers , Chaitra Rao

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Characterization of Metastatic Cutaneous Squamous Cell Carcinoma in the Immunosuppressed Patient , Megan E. Wackel

Visceral adipose tissue remodeling in pancreatic ductal adenocarcinoma cachexia: the role of activin A signaling , Pauline Xu

Phos-Tag-Based Screens Identify Novel Therapeutic Targets in Ovarian Cancer and Pancreatic Cancer , Renya Zeng

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Functional Characterization of Cancer-Associated DNA Polymerase ε Variants , Stephanie R. Barbari

Pancreatic Cancer: Novel Therapy, Research Tools, and Educational Outreach , Ayrianne J. Crawford

Apixaban to Prevent Thrombosis in Adult Patients Treated With Asparaginase , Krishna Gundabolu

Molecular Investigation into the Biologic and Prognostic Elements of Peripheral T-cell Lymphoma with Regulators of Tumor Microenvironment Signaling Explored in Model Systems , Tyler Herek

Utilizing Proteolysis-Targeting Chimeras to Target the Transcriptional Cyclin-Dependent Kinases 9 and 12 , Hannah King

Insights into Cutaneous Squamous Cell Carcinoma Pathogenesis and Metastasis Using a Bedside-to-Bench Approach , Marissa Lobl

Development of a MUC16-Targeted Near-Infrared Antibody Probe for Fluorescence-Guided Surgery of Pancreatic Cancer , Madeline T. Olson

FGFR4 glycosylation and processing in cholangiocarcinoma promote cancer signaling , Andrew J. Phillips

Theses/Dissertations from 2020 2020

Cooperativity of CCNE1 and FOXM1 in High-Grade Serous Ovarian Cancer , Lucy Elge

Characterizing the critical role of metabolic and redox homeostasis in colorectal cancer , Danielle Frodyma

Genomic and Transcriptomic Alterations in Metabolic Regulators and Implications for Anti-tumoral Immune Response , Ryan J. King

Dimers of Isatin Derived Spirocyclic NF-κB Inhibitor Exhibit Potent Anticancer Activity by Inducing UPR Mediated Apoptosis , Smit Kour

From Development to Therapy: A Panoramic Approach to Further Our Understanding of Cancer , Brittany Poelaert

The Cellular Origin and Molecular Drivers of Claudin-Low Mammary Cancer , Patrick D. Raedler

Mitochondrial Metabolism as a Therapeutic Target for Pancreatic Cancer , Simon Shin

Development of Fluorescent Hyaluronic Acid Nanoparticles for Intraoperative Tumor Detection , Nicholas E. Wojtynek

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The role of E3 ubiquitin ligase FBXO9 in normal and malignant hematopoiesis , R. Willow Hynes-Smith

BRCA1 & CTDP1 BRCT Domainomics in the DNA Damage Response , Kimiko L. Krieger

Targeted Inhibition of Histone Deacetyltransferases for Pancreatic Cancer Therapy , Richard Laschanzky

Human Leukocyte Antigen (HLA) Class I Molecule Components and Amyloid Precursor-Like Protein 2 (APLP2): Roles in Pancreatic Cancer Cell Migration , Bailee Sliker

Theses/Dissertations from 2018 2018

FOXM1 Expression and Contribution to Genomic Instability and Chemoresistance in High-Grade Serous Ovarian Cancer , Carter J. Barger

Overcoming TCF4-Driven BCR Signaling in Diffuse Large B-Cell Lymphoma , Keenan Hartert

Functional Role of Protein Kinase C Alpha in Endometrial Carcinogenesis , Alice Hsu

Functional Signature Ontology-Based Identification and Validation of Novel Therapeutic Targets and Natural Products for the Treatment of Cancer , Beth Neilsen

Elucidating the Roles of Lunatic Fringe in Pancreatic Ductal Adenocarcinoma , Prathamesh Patil

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Metabolic Reprogramming of Pancreatic Ductal Adenocarcinoma Cells in Response to Chronic Low pH Stress , Jaime Abrego

Understanding the Relationship between TGF-Beta and IGF-1R Signaling in Colorectal Cancer , Katie L. Bailey

The Role of EHD2 in Triple-Negative Breast Cancer Tumorigenesis and Progression , Timothy A. Bielecki

Perturbing anti-apoptotic proteins to develop novel cancer therapies , Jacob Contreras

Role of Ezrin in Colorectal Cancer Cell Survival Regulation , Premila Leiphrakpam

Evaluation of Aminopyrazole Analogs as Cyclin-Dependent Kinase Inhibitors for Colorectal Cancer Therapy , Caroline Robb

Identifying the Role of Janus Kinase 1 in Mammary Gland Development and Breast Cancer , Barbara Swenson

DNMT3A Haploinsufficiency Provokes Hematologic Malignancy of B-Lymphoid, T-Lymphoid, and Myeloid Lineage in Mice , Garland Michael Upchurch

Theses/Dissertations from 2016 2016

EHD1 As a Positive Regulator of Macrophage Colony-Stimulating Factor-1 Receptor , Luke R. Cypher

Inflammation- and Cancer-Associated Neurolymphatic Remodeling and Cachexia in Pancreatic Ductal Adenocarcinoma , Darci M. Fink

Role of CBL-family Ubiquitin Ligases as Critical Negative Regulators of T Cell Activation and Functions , Benjamin Goetz

Exploration into the Functional Impact of MUC1 on the Formation and Regulation of Transcriptional Complexes Containing AP-1 and p53 , Ryan L. Hanson

DNA Polymerase Zeta-Dependent Mutagenesis: Molecular Specificity, Extent of Error-Prone Synthesis, and the Role of dNTP Pools , Olga V. Kochenova

Defining the Role of Phosphorylation and Dephosphorylation in the Regulation of Gap Junction Proteins , Hanjun Li

Molecular Mechanisms Regulating MYC and PGC1β Expression in Colon Cancer , Jamie L. McCall

Pancreatic Cancer Invasion of the Lymphatic Vasculature and Contributions of the Tumor Microenvironment: Roles for E-selectin and CXCR4 , Maria M. Steele

Altered Levels of SOX2, and Its Associated Protein Musashi2, Disrupt Critical Cell Functions in Cancer and Embryonic Stem Cells , Erin L. Wuebben

Theses/Dissertations from 2015 2015

Characterization and target identification of non-toxic IKKβ inhibitors for anticancer therapy , Elizabeth Blowers

Effectors of Ras and KSR1 dependent colon tumorigenesis , Binita Das

Characterization of cancer-associated DNA polymerase delta variants , Tony M. Mertz

A Role for EHD Family Endocytic Regulators in Endothelial Biology , Alexandra E. J. Moffitt

Biochemical pathways regulating mammary epithelial cell homeostasis and differentiation , Chandrani Mukhopadhyay

EPACs: epigenetic regulators that affect cell survival in cancer. , Catherine Murari

Role of the C-terminus of the Catalytic Subunit of Translesion Synthesis Polymerase ζ (Zeta) in UV-induced Mutagensis , Hollie M. Siebler

LGR5 Activates TGFbeta Signaling and Suppresses Metastasis in Colon Cancer , Xiaolin Zhou

LGR5 Activates TGFβ Signaling and Suppresses Metastasis in Colon Cancer , Xiaolin Zhou

Theses/Dissertations from 2014 2014

Genetic dissection of the role of CBL-family ubiquitin ligases and their associated adapters in epidermal growth factor receptor endocytosis , Gulzar Ahmad

Strategies for the identification of chemical probes to study signaling pathways , Jamie Leigh Arnst

Defining the mechanism of signaling through the C-terminus of MUC1 , Roger B. Brown

Targeting telomerase in human pancreatic cancer cells , Katrina Burchett

The identification of KSR1-like molecules in ras-addicted colorectal cancer cells , Drew Gehring

Mechanisms of regulation of AID APOBEC deaminases activity and protection of the genome from promiscuous deamination , Artem Georgievich Lada

Characterization of the DNA-biding properties of human telomeric proteins , Amanda Lakamp-Hawley

Studies on MUC1, p120-catenin, Kaiso: coordinate role of mucins, cell adhesion molecules and cell cycle players in pancreatic cancer , Xiang Liu

Epac interaction with the TGFbeta PKA pathway to regulate cell survival in colon cancer , Meghan Lynn Mendick

Theses/Dissertations from 2013 2013

Deconvolution of the phosphorylation patterns of replication protein A by the DNA damage response to breaks , Kerry D. Brader

Modeling malignant breast cancer occurrence and survival in black and white women , Michael Gleason

The role of dna methyltransferases in myc-induced lymphomagenesis , Ryan A. Hlady

Design and development of inhibitors of CBL (TKB)-protein interactions , Eric A. Kumar

Pancreatic cancer-associated miRNAs : expression, regulation and function , Ashley M. Mohr

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Optimization of therapeutics against lethal pancreatic cancer , Joshua J. Souchek

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Immune-based novel diagnostic mechanisms for pancreatic cancer , Michael J. Baine

Sox2 associated proteins are essential for cell fate , Jesse Lee Cox

KSR2 regulates cellular proliferation, transformation, and metabolism , Mario R. Fernandez

Discovery of a novel signaling cross-talk between TPX2 and the aurora kinases during mitosis , Jyoti Iyer

Regulation of metabolism by KSR proteins , Paula Jean Klutho

The role of ERK 1/2 signaling in the dna damage-induced G2 , Ryan Kolb

Regulation of the Bcl-2 family network during apoptosis induced by different stimuli , Hernando Lopez

Studies on the role of cullin3 in mitosis , Saili Moghe

Characteristics of amyloid precursor-like protein 2 (APLP2) in pancreatic cancer and Ewing's sarcoma , Haley Louise Capek Peters

Structural and biophysical analysis of a human inosine triphosphate pyrophosphatase polymorphism , Peter David Simone

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Walaa Ammar-Shehada about her PhD research: ‘’Silent Struggles: Breast Cancer Survival in the Shadows of Adversity: A Mixed Methods Study of the Experiences of Palestinian Women in the Gaza Strip, occupied Palestinian territory’’

( 19-09-2024 ) Walaa Ammar-Shehada successfully defended her dissertation on the 12th of September 2024.

Breast cancer is the most prevalent cancer among women in the occupied Palestinian territory, accounting for 33% of all cancer cases. In Gaza, where healthcare access and resources are scarce, the effects of the disease are particularly severe. This dissertation delves into the lived experiences of Palestinian women diagnosed with breast cancer, focusing on both the pre-diagnosis and post-diagnosis stages. It employs a framework of structural discrimination, using data from a cross-sectional self-administrative survey and 40 semi-structured interviews to explore how societal and political factors shape health-seeking behavior and decision-making processes before and during illness, as well as influencing coping mechanisms post-illness.

The research is structured into several empirical chapters. The first quantitative chapter analyzes socio-demographic factors that influence the stage at which the diagnosis occurs. A qualitative chapter then examines how structural factors either encourage or hinder women from seeking care, drawing on the Right to Health framework. Another chapter investigates the different types of support—both social and instrumental—that women receive, and how this influences their ability to cope with the disease post-diagnosis. The final chapter explores the significance of social support and how the absence of it can result in more negative outcomes.

The dissertation emphasizes the collective challenges faced by breast cancer survivors in Gaza, zooming in on their personal experiences. Through an intersectional lens, the research considers how the interplay of gender, social identity, and social, cultural, and political factors shape these experiences.  The findings show that the presence of social-emotional and instrumental support is crucial for women as they navigate the challenges of breast cancer, while its absence can exacerbate their hardships. The dissertation also sheds light on the broader context of structural discrimination and societal inequalities that prevent Palestinian women from accessing essential healthcare services. Racial segregation, national-origin-based discrimination, and geographically biased policies create compounded challenges, especially in Gaza. Blockade, restricted movement, inadequate access to services, and poor social determinants of health—such as extreme poverty and limited access to early detection —create an environment of uncertainty regarding treatment options.

Ammar-Shehada argues that these forms of structural discrimination obstruct the fulfillment of basic rights, including the Right to Health, particularly for underprivileged women. Her work calls for systemic changes and targeted interventions that address these health and social disparities from the patient’s perspective, advocating for equal access to healthcare and support systems for all women, regardless of race or social status.

Supervisor: Prof. dr. Piet Bracke Co-supervisor: dr. Melissa Ceuterick