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P Bamberger

Publications and source records attributed to P Bamberger.

8 recordsLinked to original sources
PubMed2004

Computerized evaluation of mammographic lesions: what diagnostic role does the shape of the individual microcalcifications play compared with the geometry of the cluster?

OBJECTIVE: The objective of this study was to compare the diagnostic role of features reflecting the geometry of clusters with features reflecting the shape of the individual microcalcification in a mammographic computer-aided diagnosis system. MATERIALS AND METHODS: Three hundred twenty-four cases of clustered microcalcifications with biopsy-proven results were digitized at 42-microm resolution and analyzed on a computerized system. The shape factor and number of neighbors were computed for each microcalcification, and the eccentricity of the cluster was computed as well. The shape factor is related to the individual microcalcification; the average number of neighbors and the cluster eccentricity reflect the cluster geometry. Stepwise discriminant analysis was used to evaluate the contribution of the extracted features in predicting malignancy. The performance of a classifier based on the features selected by stepwise discriminant analysis was evaluated by receiver operating characteristic (ROC) analysis. RESULTS: To obtain the best discrimination model, we used stepwise discriminant analysis to select the average number of neighbors and the shape of the individual microcalcification, but excluded cluster eccentricity. A classification scheme assigned the average number of neighbors a weighting factor, which was 1.49 times greater than that assigned to the shape factor of the individual microcalcification. A scheme based only on these two features yielded an ROC curve with an area under the curve (A(z)) of 0.87, indicating a positive predictive value of 61% for 98% sensitivity. CONCLUSION: Computerized analysis permitted calculations reflecting the shape of individual microcalcification and the geometry of clusters of microcalcifications. For the computerized classification scheme studied, the cluster geometry was more effective in differentiating benign from malignant clusters than was the shape of individual microcalcification.

Adult↗
PubMed2000

Improved mammographic interpretation of masses using computer-aided diagnosis.

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.

Breast Neoplasms↗
PubMed2000

Optimizing parameters for computer-aided diagnosis of microcalcifications at mammography.

RATIONALE AND OBJECTIVES: The purpose of this study was to optimize selection of the mammographic features most useful in discriminating benign from malignant clustered microcalcifications. MATERIALS AND METHODS: The computer-aided diagnosis (CAD) system automatically extracted from digitized mammograms 13 quantitative features characterizing microcalcification clusters. Archival cases (n = 134; patient age range, 31-77 years; mean age, 56.8 years) with known histopathologic results (79 malignant, 55 benign) were selected. Three radiologists at three facilities independently analyzed the microcalcifications by using the CAD system. Stepwise discriminant analysis selected the features best discriminating benign from malignant microcalcifications. A classification scheme was constructed on the basis of these optimized features, and its performance was evaluated by using receiver operating characteristic (ROC) analysis. RESULTS: Six of the 13 variables extracted by the CAD system were selected by stepwise determinant analysis for generating the classification scheme, which yielded an ROC curve with an area (Az) of 0.98, specificity of 83.64%, positive predictive value of 89.53%, and accuracy of 91.79% for 98% sensitivity. When patient age was an additional variable, the scheme's performance improved, but this was not statistically significant (Az = 0.98). The ROC curve of the classifier (without age as an additional variable) yielded a high Az of 0.96 for patients younger than 50 years and an even higher (P < .02) Az of 0.99 for those 50 years or older. CONCLUSION: Stepwise discriminant analysis optimized performance of a classification scheme for microcalcifications by selecting six optimized features. Scheme performance was significantly (P < .02) higher for women 50 years or older, but the addition of patient age as a variable did not produce a statistically significant increase in performance.

Adult↗
PubMed2000

Quantitative characterization of mass lesions on digitized mammograms for computer-assisted diagnosis.

RATIONALE AND OBJECTIVES: To investigate features for discriminating benign from malignant mammographic findings by using computer-aided diagnosis (CAD) and to test the accuracy of CAD interpretations of mass lesions. METHODS: Fifty-five sequential, mammographically detected mass lesions, referred for biopsy, were digitized for computerized reevaluation with a CAD system. Quantitative features that characterize spiculation were automatically extracted by the CAD system. Data generated by 271 known retrospective cases were used to set reference values indicating the range for malignant and benign lesions. After conventional interpretation of the 55 prospective cases, they were evaluated a second time by the radiologist using the extracted features and the reference ranges. In addition, a pattern-recognition scheme based on the extracted features was used to classify the prospective cases. Accuracy of interpretation with and without the CAD system was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS: Sensitivity of the CAD diagnosis for the prospective cases improved from 92% to 100%. Specificity improved significantly from 26.7% to 66.7%. This was accompanied by a significant increase in the accuracy of diagnosis from 56.4% to 81.8% and in the positive predictive value from 51.1% to 71.4%. The Az for the CAD ROC curve significantly increased from 0.73 to 0.90. The performance of the classification scheme was slightly lower than that of the radiologists' interpretation with the CAD system. CONCLUSIONS: Use of the CAD system significantly improved the accuracy of diagnosis. The findings suggest that the classification scheme may improve the radiologist's ability to differentiate benign from malignant mass lesions in the interpretation of mammograms.

Adult↗
PubMed1999

The use of an interactive software program for quantitative characterization of microcalcifications on digitized film-screen mammograms.

RATIONALE AND OBJECTIVES: Mammography is relatively nonspecific for the early detection of breast cancer. This study evaluates the accuracy of mammographic interpretation using quantitative features characterizing microcalcifications, which are extracted by a computerized system. METHODS: A computer-aided diagnosis (CAD) system enabling digitization of film-screen mammograms and automatic feature extraction was developed. A classification scheme (discriminant analysis) based on these features was constructed and trained on 217 cases with known pathology. The diagnostic performance of the classification scheme was tested against the radiologist's conventional interpretation on 45 additional cases of microcalcifications, each analyzed independently by four radiologists. RESULTS: The sensitivity of the CAD system analysis (95.7%) was significantly better than that of conventional interpretation (84.8%). The positive predictive value of interpretation increased significantly, as did the area under the receiver operating characteristic curve. CONCLUSIONS: This classification scheme for microcalcifications, based on quantitative features characterizing the lesion, significantly improved the accuracy of mammographic interpretation.

Breast Diseases↗