Philip Wong Department of Medical Biophysics University of Western Ontario March 23, 2011.

Slides:

Advertisements

Similar presentations

Jessica Kishimoto The University of Western Ontario Medical Biophysics Undergrad April 7, 2010 The Effect of Dose on Image Quality When Using an Image.

Advertisements

Shibing Deng Pfizer, Inc. Efficient Outlier Identification in Lung Cancer Study.

K. PHILIP CHOONG and ZHAOHONG HAN Teachers College, Columbia University Task Complexity and Output Complexity:

Yao 1 Neuroendocrine Tumors James C. Yao, MD Associate Professor Deputy Chairman, Gastrointestinal Medical Oncology University of Texas M. D. Anderson.

Qianqian Fang, Stefan Carp, Mark Martino, Richard Moore, Daniel Kopans, David Boas Optics Division, Martinos Center for Biomedical Imaging, Massachusetts.

Sungtaek Huh April 3 rd, 2012 The University of Western Ontario Department of Medical Biophysics.

Breast Cancer - FACTS:  Breast carcinoma leading cause of cancer death in womean  Every 8-10 th woman affected during lifetime  About 4000 new cases/a.

Heterogeneity of Oxygen Saturation in Microvascular Networks Jeremy Flannery Supervisor: Dr. Dan Goldman Department of Medical Biophysics The University.

Progesterone receptor membrane component 1 (PGRMC1) and its role in the establishment and progression of female cancers Melissa L. McCallum 1, Cindy A.

Empirical/Asymptotic P-values for Monte Carlo-Based Hypothesis Testing: an Application to Cluster Detection Using the Scan Statistic Allyson Abrams, Martin.

Mutual Information Mathematical Biology Seminar

A Computer Aided Detection System For Digital Mammograms Based on Radial Basis Functions and Feature Extraction Techniques By Mohammed Jirari Shanghai,

ISP 121 Statistics That Deceive. Simpson’s Paradox It’s a well accepted rule of thumb that the larger the data set, the better Simpson’s Paradox demonstrates.

1 Masterseminar „A statistical framework for the diagnostic of meningioma cancer“ Chair for Bioinformatics, Saarland University Andreas Keller Supervised.

1 Diagnosing Breast Cancer with Ensemble Strategies for a Medical Diagnostic Decision Support System David West East Carolina University Paul Mangiameli.

Total Lesion Glycolysis by 18 F-FDG PET/CT a Reliable Predictor of Prognosis in Soft Tissue Sarcoma Ilkyu Han Musculoskeletal Tumor Center, Seoul National.

Using Random Peptide Phage Display Libraries for early Breast cancer detection Ekaterina Nenastyeva.

A statistical model in detecting small blood vessels with Power Doppler Imaging Department of Medical Biophysics 07/04/10.

Microwave Thermography

Using ArcGIS/SaTScan to detect higher than expected breast cancer incidence Jim Files, BS Appathurai Balamurugan, MD, MPH.

Comparison of MRI Perfusion and PET-CT in Differentiating Brain Tumor Progression from Radiation Injury after Cranial Irradiation T. Jonathan Yang, M.D.

Bayesian Network for Predicting Invasive and In-situ Breast Cancer using Mammographic Findings Jagpreet Chhatwal1 O. Alagoz1, E.S. Burnside1, H. Nassif1,

Tumor Cells and the Onset of Cancer

Comparison of Serum Human Epididymis Protein 4 with Cancer Antigen 125 as a Tumor Marker in Patients with Malignant and Nonmalignant Diseases J.M. Escudero,

Kunal Mitra Professor, Mechanical and Aerospace Engineering Department Director- Biomedical Engineering Program Florida Institute of Technology, Melbourne,

Analysis and Management of Microarray Data Dr G. P. S. Raghava.

◄ ▼ ► ▲ Figure 2: Percent change ([(P 1 – P 2 )/P 1 ]*100) in pre- to post-step test heart rates in oxygen saturation observed at different elevations.

Developing a Statistical Method of Quantifying Vascular Response after Radiotherapy Michal Stankiewicz Medical Biophysics 3970Z Western University March.

In Silico Simulation of a Translational Human Breast Cancer Model in Mice March 25 th, 2013 Mark Dawidek Department of Medical Biophysics.

Imaging examinations of breasts

Ultrasound Elastography: Prostate Cancer Detection Assessment Using A Phantom Brett Coelho Medical Biophysics 3970Z March 26, 2013.

CHILDHOOD CANCER SURVIVORS OF MALIGNANT BONE TUMORS AND SOFT TISSUE SARCOMAS ARE AT RISK OF HOSPITALIZATION Cristian Gonzalez, BS, BA Jennifer Wright,

How will you approach the 35-year old, with a 2x2x2cm, firm, mobile, well-circumscribed non-tender mass on her R breast?

Tumors Dr. N. Salmo. Adrenal cortical tumor. Describe it grossly. Benign or Malignant??

M. Alnafea1*, K. Wells1, N.M. Spyrou1 & M. Guy2

吳華席 Hua-Hsi Wu, MD OB/GYN, VGH-TPE Aug 12, 2008

INCREASED EXPRESSION OF PROTEIN KINASE CK2  SUBUNIT IN HUMAN GASTRIC CARCINOMA Kai-Yuan Lin 1 and Yih-Huei Uen 1,2,3 1 Department of Medical Research,

3D Mammography Ernesto Coto Sören Grimm Stefan Bruckner M. Eduard Gröller Institute of Computer Graphics and Algorithms Vienna University of Technology.

Cytologic and DNA- Cytometric Early Diagnosis of Oral Cancer Torsten W. Remmerbach, Horst Weidenbach, Natalja Pomjanski, Kristiane Knops, Stefanie Mathes,

1 Differential Diagnosis of Neoplastic Pancreatic Cysts: The Role of EUS with Guided FNA E.M.Santo,Y.Ron,O.Barkay,Y.Kopelman,M.Leshno,S.Marmor Dep. of.

Neural Network Classification versus Linear Programming Classification in breast cancer diagnosis Denny Wibisono December 10, 2001.

Prognostic Prediction of Breast Cancer Using C5 Sakina Begum May 1, 2001.

The Effect of Fluorescent Labelling on Deformability and Geometric Properties of Red Blood Cells Joanne Wong Supervisor: Dr. D. Jackson March 23,2011 Medical.

Visceral Adiposity at Diagnosis Correlates with Tumor Size and Metastatic Progression in Clear Cell Renal Carcinoma A Shuster, MD (1), M Patlas, MD (1),

Breast Tumors Diagnosed as Papillary Neoplasm by FNAB Dae-Kyum Kim, Sang-Uk Woo, Jeong-Han Kim, Seok-Jin Nam, Yoon-La Choi 1, Young-Lyun Oh 1 and Jung-Hyun.

Hemodynamically Constrained Dynamic Diffuse Optical Tomography Under Mammographic Compression Eleonora Vidolova 1, Dana Brooks 1, Eric Miller 2, Stefan.

GEOGRAPHIC DISTRIBUTION OF BREAST CANCER IN MISSOURI, Faustine Williams, MS., MPH, Stephen Jeanetta, Ph.D. Department of Rural Sociology, Division.

Characteristic Dynamic Enhancement Pattern of MR imaging for Malignant Thyroid Tumor XIX Symposium Neuroradiologicum Division of Head & Neck radiology.

12/10/02Sacks - Clinical Assessment1 Clinical Assessment – Part II William Sacks, PhD, MD Clinical Assessment – Part II William Sacks, PhD, MD COMPUTERIZED.

THIRD CLASSIFICATION OF MICROCALCIFICATION STAGES IN MAMMOGRAPHIC IMAGES THIRD REVIEW Supervisor: Mrs.P.Valarmathi HOD/CSE Project Members: M.HamsaPriya( )

By Prof. Stelmark. Digital Imaging In digital imaging, the latent image is stored as digital data and must be processed by the computer for viewing on.

Sonography and Ovarian Tumors Professor Galal Lotfi Obstetrics & Gynecology Suez Canal University. Egypt.

PD-L1 expression patterns in the metastatic tumors to the lung: a comparative study with the primary non-small cell lung cancer Zoran Gatalica1*, Jude.

Acoustic Structure Quantification (ASQ): A New Tool in the Sonographic Diagnosis of Liver Lesions in Hepatic Alveolar Echinococcosis Tanja Kaltenbach,

CORRELATION OF PHYSICAL EVALUATION AND MRI OF CERVICAL LYMPH NODE WITH HISTOPATHOLOGICAL FINDINGS IN ORAL SQUAMOUS CELL CARCINOMA: AN AMBIDIRECTIONAL STUDY.

Fine-needle aspiration of clinically suspicious palpable breast masses with histopathologic correlation Reshma Ariga, M.D., Kenneth Bloom, M.D., Vijaya.

Assist. Prof. Dr. Neslihan BAYRAMOĞLU TEPE

Wijendra Senarathne1, Peggy Gates1, Semir Vranic2, Zoran Gatalica1

In Search of the Optimal Set of Indicators when Classifying Histopathological Images Catalin Stoean University of Craiova, Romania

Mammogram Analysis – Tumor classification

Lecture 10 Technological Principles of Medical Instrumentation

Assessment of the Breast

سرطان الثدي Breast Cancer

By: Behrouz Rostami, Zeyun Yu Electrical Engineering Department

Cystic Neoplasm of the Pancreas Clinical Review of 60 Cases and Treatment Strategy D.K.Kim, S.I.Noh, J.S.Heo, J.H.Noh, T.S.Sohn, S.J.Kim, S.H.Choi, J.W.Joh,

Somi Jacob and Christian Bach

Botond K. Szabó * Peter Aspelin ** Maria Kristoffersen-Wiberg **

Submitted By : Pratish Singh Kuldeep Choudhary Chinmay Panchal

Signal Detection & Processing

Recent Incidences and Trends of the Top Cancers in Northeast Tennessee Appalachian Region Adekunle Oke1, Sylvester Orimaye2, Ndukwe Kalu1, Dr. Faustine.

Presentation transcript:

Philip Wong Department of Medical Biophysics University of Western Ontario March 23, 2011

Acknowledgments  Dr. Gelman  Fang Liu  Dr. MacDonald

Introduction  Breast cancer is a cancer that is found in the tissues of the breast  Most common cancer among women  Detected by the presence of a breast tumor Malignant Benign

Diagnosis  Accuracy of the diagnostic method used to differentiate between benign and malignant tumors is important  Dynamic contrast enhanced-MRI (DCE-MRI)  “Hot spots” refers to areas within the tumor where the contrast agent is rapidly taken up (3 minutes)

Motivation and Objective  Provide an objective means of diagnosis  Non-fibroadenoma (NF) (benign) and malignant tumors were not easily distinguished  Compare spatial distribution of hot spots in NF and malignant tumors

Approach  Developed a code in MATLAB to calculate hot spot volumes in the outer perimeter of the tumors  Statistical analysis (Mann-Whitney test)

Hypothesis  Malignant tumors will have a greater hot spot volume in the outer perimeter than NF tumors

Methods Tumor Mask Matrix

Methods Tumor Mask Matrix after one erosion

Methods Hot Spot Mask Matrix

Methods Hot Spot Mask Matrix from eroded tumor

Methods  Repeated this process for 16 NF tumors and 20 malignant tumors  Defined outer perimeter as 25% of tumor volume  Linear interpolation to find outer hot spot volume (OHS) (hot spot in outer perimeter)

Methods  Expressed OHS as a fraction of total tumor volume  Ran a Mann-Whitney test to compare the fractional OHS of NF and malignant tumors  Compared fractional OHS with fractional total hot spot volume (THS)

Results Fractional Outer Hot Spot Volume n =16 n =20 p = (p > 0.05) Tumor Type

Results  Fractional OHS and THS Non-fibroadenoma: p = Malignant: p =

Discussion  No significant difference between the OHS of NF and malignant tumors  Suggests that NF tumors behave similarly to malignant tumors in terms of their vasculature  Significant difference between OHS and THS in both NF and malignant tumors  Implies that most of hot spot volume is still located in the center of the tumor

Future Work  Observe effects of different thresholds and outer perimeters  Examine why NF are similar to malignant tumors  Run Mann-Whitney test between a non- fibroadenoma subgroup and malignant tumors

Conclusion  No significant difference in spatial distribution of hot spots between NF and malignant tumors  Significant difference between OHS and THS in both NF and malignant tumors

Questions?