Development of a Breast MRI Computer-Aided Diagnosis System
Creators: name: University Of California Office Of The President
Publisher: name: University of California Office of the President
publisherIdentifier: https://ror.org/00dmfq477
publisherIdentifierScheme: ROR
Year: 2024

description: Breast MRI has evolved into an established complimentary clinical tool for detection and diagnosis of breast lesions. In March 2007 the American Cancer Society has issued a guideline recommending annual breast MRI screening to women with greater than 20-25% lifetime risk for breast cancer. Unlike the conventional imaging using mammography or ultrasound, MRI acquires a large number of images, and that poses a great difficulty for the radiologists to evaluate all presented information. As more studies are being performed in small community hospitals, lack of sufficient training of breast imagers in reading MRI becomes an issue. The current commercially available Computer-Aided Diagnosis (CAD) systems developed commercially can extract most important information from a large number of images obtained in a single MR examination to help radiologists’ interpretation. But, this approach offers only a display platform, not a true CAD. In contrast, mammographic CAD is a current standard to provide “a second reader”, which has demonstrated a great success to improve diagnostic sensitivity. Despite the urgency and the great need for MR, there is very limited work to develop “a true CAD” which can provide “an intelligent thinking and final impression”. 
 
 
 
 
 
 
 
 Our objective is to develop a systematic breast MRI CAD system which can perform lesion detection and characterization to help radiologists’ interpretation, also to provide a “second reader” to improve their diagnostic accuracy. We believe that a CAD system, which integrates all morphology, texture, and enhancement kinetics information, may be able to achieve a high accuracy in detection and diagnosis of breast cancer. The proposed study will use automated computer algorithms to simulate the entire procedure of radiologists’ interpretation for breast MRI. This computer system will detect and segment suspicious region (possible lesion), then analyze their morphologic and kinetic features. A full panel of features to allow differentiating between malignant and benign lesions will be investigated. All together we have 250 malignant cases and 150 benign cases in our first database collected using a 1.5T MRI scanner, which can be used to find the best diagnostic feature set (i.e. malignant signature) for breast cancer. The performance of each step in breast segmentation and lesion segmentation, as well as the final system performance, will be compared to that of experienced radiologists. Finally, a color-coded quantitative labeling scale will be developed to label the suspicious lesion with a color and a BI-RADS (Breast Imaging Reporting and Data System, a standardized classification for mammographic studies) score between 1-5 from normal to highly suspicious of malignancy. Then the developed CAD will be tested in one additional database collected using a 3.0T MRI scanner.
 
 
 
 
 
 
 
 Computer aided diagnosis system has been proven to improve the diagnostic accuracy in mammography, and given the insufficient training of breast imagers in MRI, it is anticipated that a breast MRI CAD will make a crucial contribution. This research will mostly benefits patients who are recommended to undergo MRI, especially those with dense breasts, particularly in young or Asian women.