Improved mammographic interpretation of masses using computer-aided diagnosis

Abstract

The aim of this study was to evaluate the effectiveness of computerized image enhancement, to investigate criteria for discriminating benign from malignant mammographic findings by computer-aided diagnosis (CAD), and to test the role of quantitative analysis in improving the accuracy of interpretation of mass lesions. Forty sequential mammographically detected mass lesions referred for biopsy were digitized at high resolution for computerized evaluation. A prototype CAD system which included image enhancement algorithms was used for a better visualization of the lesions. Quantitative features which characterize the spiculation were automatically extracted by the CAD system for a user-defined region of interest (ROI). Reference ranges for malignant and benign cases were acquired from data generated by 214 known retrospective cases. The extracted parameters together with the reference ranges were presented to the radiologist for the analysis of 40 prospective cases. A pattern recognition scheme based on discriminant analysis was trained on the 214 retrospective cases, and applied to the prospective cases. Accuracy of interpretation with and without the CAD system, as well as the performance of the pattern recognition scheme, were analyzed using receiver operating characteristics (ROC) curves. A significant difference (p < 0.005) was found between features extracted by the CAD system for benign and malignant cases. Specificity of the CAD-assisted diagnosis improved significantly (p < 0.02) from 14 % for the conventional assessment to 50 %, and the positive predictive value increased from 0.47 to 0.62 (p < 0.04). The area under the ROC curve (A(z)) increased significantly (p < 0. 001) from 0.66 for the conventional assessment to 0.81 for the CAD-assisted analysis. The A(z) for the results of the pattern recognition scheme was higher (0.95). The results indicate that there is an improved accuracy of diagnosis with the use of the mammographic CAD system above that of the unassisted radiologist. Our findings suggest that objective quantitative features extracted from digitized mammographic findings may help in differentiating between benign and malignant masses, and can assist the radiologist in the interpretation of mass lesions.

Tumour detection rate of a new commercially available computer-aided detection system

“…The current study did not compare to most other studies which used different protocols [9,14,20,21,24,27,28], examining lesions of a limited size or structure (due to limitations of the CAD systems used, e.g. [9]) or using pre-selected or pre-scanned cases [27].…”

“…The quality of such systems depends critically on the tumour detection rate and the number of falsepositive (FP) marks per image. Both sensitivity and FP marks per image have already been well investigated for the ªImageCheckerº system (R2, Los Altos, Calif.) [12,13,14]. The sensitivity data varies between 59 and 87 %, depending on the case selection criteria and the definition of true positive (TP) used [12,13,15,16,17]; however, to date, no studies, have assessed the sensitivity and FP marks per image for the newly available ªSec-ond Lookº CAD system (CADx Medical Systems, Quebec, Canada).…”

Probabilistic Computer Model Developed from Clinical Data in National Mammography Database Format to Classify Mammographic Findings

“…There are also substantial differences between our Bayesian network and other computer-assisted diagnosis systems that use suspicious cases selected from biopsy databases to train their systems (24,25,27,30,31,34,46,47). Our Bayesian network, in contrast, was trained on consecutively collected mammographic findings, perhaps allowing it to more accurately estimate posttest probabilities and better balance improvements in sensitivity and specificity with more realistic estimates of breast cancer prevalence.…”

“…In fact, it has been suggested that the suboptimal performance indicates that computer-assisted detection may have unanticipated negative effects on radiologist decision making, perhaps by deferring recall when marks are not present (22,23). Several groups have improved classification of mammographic abnormalities (computerassisted diagnosis) with computer-extracted imaging features (24)(25)(26) or with radiologist-observed features (27)(28)(29)(30)(31) in selected biopsy cases. We aim to extend this research by developing a Bayesian network that uses radiologistobserved features found in the American College of Radiology National Mammography Database (NMD) format (32,33).…”

Recent development on computer aided tissue engineering — a review

“…Today's computer-aided technologies, medical imaging, modern design and manufacturing have further assisted in those advances and created new possibilities in the development of tissue engineering. Such possibilities include, for example, using noninvasive computed tomography (CT) or magnetic resonance imaging (MRI) techniques to generate tissue structural views for 3-D anatomical model, for tissue classification and trauma/tumor identification [24][25][26][27][28][29][30][31][32][33], using computer-aided design/computer-aided manufacturing (CAD/CAM) and rapid prototyping (RP) technology to fabricate the physical models of hard tissues, tissue scaffolds, and the custom-made tissue implant prostheses [34][35][36][37][38][39][40][41][42], and applying the anatomical and physical modeling for reconstructive surgeons and tissue implementation [43][44][45][46][47].…”