The long term goal of the project is to develop an effective computer-aided diagnosis (CAD) system to assist radiologists in making diagnostic decisions in breast imaging. In this proposed project, we will concentrate on the characterization of masses using mammograms and ultrasound images. We propose a new approach to CAD based on a classifier that can simultaneously estimate the likelihood of malignancy for the mass and retrieve similar cases from a large library of cases with known diagnosis for the radiologist's references. The new CAD system thus combines the advantages of a rating-based and an image-retrieval- based CAD system. It will aid radiologists not only by the malignancy estimate but also by enhancing their similarity-based decision making process. We will also design a relevance feedback image retrieval system that allows the radiologist to interactively and efficiently retrieve similar cases from a large data set as a tool to help develop the automated CAD system. We hypothesize that the reference images will increase the characterization accuracy of less experienced readers for masses, and that the computerized classification and image retrieval system to be developed in this study will significantly improve radiologists' accuracy. To test these hypotheses, we will perform the following specific tasks: (1) collect a database of sonograms and mammograms containing masses; (2) extract features for mass characterization; (3) develop decision tree and k-nearest neighbor classifiers, compare decision tree training with and without boosting, and investigate methods for the retrieval of similar cases based on the developed classifiers; (4) develop a relevance feedback image retrieval method; (5) compare the performances of less experienced radiologists without and with aid by reference images retrieved by experienced radiologists; and (6) compare radiologists' performances without and with the fully-automated classification and image-retrieval CAD system by a receiver operating characteristic (ROC) study. If successfully developed, the CAD system may not only reduce benign biopsies, but also reduce the variation in interpretation between experienced and less experienced radiologists. The relevance of this project to public health is that 70-85% of breast biopsies are performed for benign lesions. Any reduction in this number without a decrease in breast cancer detection sensitivity will decrease health care costs, as well as contribute to the well-being of the patient by reducing anxiety and morbidity. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21CA118305-01A1
Application #
7147093
Study Section
Special Emphasis Panel (ZRG1-SBIB-J (01))
Program Officer
Croft, Barbara
Project Start
2006-09-25
Project End
2008-07-31
Budget Start
2006-09-25
Budget End
2007-07-31
Support Year
1
Fiscal Year
2006
Total Cost
$162,252
Indirect Cost
Name
University of Michigan Ann Arbor
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
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Cho, Hyun-Chong; Hadjiiski, Lubomir; Sahiner, Berkman et al. (2011) Similarity evaluation in a content-based image retrieval (CBIR) CADx system for characterization of breast masses on ultrasound images. Med Phys 38:1820-31
Cui, Jing; Sahiner, Berkman; Chan, Heang-Ping et al. (2009) A new automated method for the segmentation and characterization of breast masses on ultrasound images. Med Phys 36:1553-65
Sahiner, Berkman; Chan, Heang-Ping; Hadjiiski, Lubomir M et al. (2009) Multi-modality CADx: ROC study of the effect on radiologists' accuracy in characterizing breast masses on mammograms and 3D ultrasound images. Acad Radiol 16:810-8
Shi, Jiazheng; Sahiner, Berkman; Chan, Heang-Ping et al. (2009) Treatment response assessment of breast masses on dynamic contrast-enhanced magnetic resonance scans using fuzzy c-means clustering and level set segmentation. Med Phys 36:5052-63
Sahiner, Berkman; Chan, Heang-Ping; Hadjiiski, Lubomir (2008) Classifier performance estimation under the constraint of a finite sample size: resampling schemes applied to neural network classifiers. Neural Netw 21:476-83