Comparison of independent double readings and computer-aided diagnosis (CAD) for the diagnosis of breast calcifications

RATIONALE AND OBJECTIVES: The aim of the study is to compare independent double readings by radiologists and computer-aided diagnosis (CAD) in diagnostic interpretation of mammographic calcifications. MATERIALS AND METHODS: Ten radiologists independently interpreted 104 mammograms containing clustered microcalcifications. Forty-six of these were malignant and 58 were benign at biopsy. Radiologists read the images with and without a computer aid by using a counterbalanced study design. Sensitivity and specificity were calculated from observer biopsy recommendations, and receiver operating characteristic (ROC) curves were computed from their diagnostic confidence ratings. Unaided double-reading sensitivity and specificity values were derived post hoc by using three different objective rules and an additional rule of simulated-optimal double reading that assumed that consultations for resolving two radiologists' different independent diagnoses always produce the correct clinical recommendation. ROC curves of unaided double readings were obtained according to the literature. RESULTS: Single reading without computer aid yielded 74% sensitivity and 32% specificity, whereas CAD reading yielded 87% sensitivity and 42% specificity and appeared on a higher ROC curve (P < .0001). Three methods of formulating independent double readings generated sensitivities between 59% and 89%, specificities between 50% and 13%, and operating points that moved essentially along the average unaided single-reading ROC curve. ROC curves of unaided independent double readings showed small, statistically insignificant improvement over those of unaided single readings. Results of the simulated-optimal double reading were similar to CAD: 89% sensitivity and 50% specificity. CONCLUSION: Independent double readings of mammographic calcifications may not improve diagnostic performance. CAD reading improves diagnostic performance to an extent approaching the maximum possible performance.     

Detection and classification performance levels of mammographic masses under different computer-aided detection cueing environments

RATIONALE AND OBJECTIVES: The authors evaluated the impact of different computer-aided detection (CAD) cueing conditions on radiologists' performance levels in detecting and classifying masses depicted on mammograms. MATERIALS AND METHODS: In an observer performance study, eight radiologists interpreted 110 subtle cases six times under different display conditions to detect depicted masses and classify them as benign or malignant. Forty-five cases depicted biopsy-proven masses and 65 were negative. One mass-based cueing sensitivity of 80% and two false-positive cueing rates of 1.2 and 0.5 per image were used in this study. In one mode, radiologists first interpreted images without CAD results, followed by the display of cues and reinterpretation. In another mode, radiologists viewed CAD cues as images were presented and then interpreted images. Free-response receiver operating characteristic method was used to analyze and compare detection performance. The receiver operating characteristic method was used to evaluate classification performance. RESULTS: At these performance levels, providing cues after initial interpretation had little effect on the overall performance in detecting masses. However, in the mode with the highest false-positive cueing rate, viewing CAD cues immediately upon display of images significantly reduced average performance for both detection and classification tasks (P < .05). Viewing CAD cues during the initial display consistently resulted in fewer abnormalities being identified in noncued regions. CONCLUSION: CAD systems with low sensitivity (< or = 80% on mass-based detection) and high false-positive rate (> or = 0.5 per image) in a dataset with subtle abnormalities had little effect on radiologists' performance in the detection and classification of mammographic masses.     

Computer-aided detection systems for breast masses: comparison of performances on full-field digital mammograms and digitized screen-film mammograms

RATIONALE AND OBJECTIVES: To compare the performance of computer aided detection (CAD) systems on pairs of full-field digital mammogram (FFDM) and screen-film mammogram (SFM) obtained from the same patients. MATERIALS AND METHODS: Our CAD systems on both modalities have similar architectures that consist of five steps. For FFDMs, the input raw image is first log-transformed and enhanced by a multiresolution preprocessing scheme. For digitized SFMs, the input image is smoothed and subsampled to a pixel size of 100 microm x 100 microm. For both CAD systems, the mammogram after preprocessing undergoes a gradient field analysis followed by clustering-based region growing to identify suspicious breast structures. Each of these structures is refined in a local segmentation process. Morphologic and texture features are then extracted from each detected structure, and trained rule-based and linear discriminant analysis classifiers are used to differentiate masses from normal tissues. Two datasets, one with masses and the other without masses, were collected. The mass dataset contained 131 cases with 131 biopsy proven masses, of which 27 were malignant and 104 benign. The true locations of the masses were identified by an experienced Mammography Quality Standards Act (MQSA) radiologist. The no-mass data set contained 98 cases. The time interval between the FFDM and the corresponding SFM was 0 to 118 days. RESULTS: Our CAD system achieved case-based sensitivities of 70%, 80%, and 90% at 0.9, 1.5, and 2.6 false positive (FP) marks/image, respectively, on FFDMs, and the same sensitivities at 1.0, 1.4, and 2.6 FP marks/image, respectively, on SFMs. CONCLUSIONS: The difference in the performances of our FFDM and SFM CAD systems did not achieve statistical significance.     

Computer-aided diagnosis to distinguish benign from malignant solitary pulmonary nodules on radiographs: ROC analysis of radiologists' performance--initial experience

PURPOSE: To evaluate radiologists' performance for determining a distinction between benign and malignant pulmonary nodules on chest radiographs without and with use of a computer-aided diagnosis scheme. MATERIALS AND METHODS: Fifty-three chest radiographs that depicted 31 primary lung cancers and 22 benign nodules were used. The likelihood measure of malignancy for each nodule was determined by using an automated computerized scheme. Sixteen radiologists (nine attending radiologists and seven radiology residents) participated in an observer study in which cases were interpreted first without and then with use of the scheme. The radiologists' performance was evaluated with receiver operating characteristic analysis. RESULTS: The mean area under the best-fit binormal receiver operating characteristic curve plotted in the unit square (Az) values of radiologists who interpreted images without and with the scheme were 0.743 and 0.817, respectively. The performance of radiologists was improved significantly when the scheme was used (P =.002). However, the performance (Az = 0.889) of the computer alone exceeded these results by a substantial margin. The average change in radiologists' confidence level for interpretation without and with the scheme was highly correlated (r = 0.845) with the likelihood measure of malignancy, which was presented as computer output. CONCLUSION: This scheme for computer-aided diagnosis has the potential to improve the accuracy of radiologists' performance in the classification of benign and malignant solitary pulmonary nodules.     

Comparison of full-field digital mammography to screen-film mammography with respect to diagnostic accuracy of lesion characterization in breast tissue biopsy specimens

RATIONALE AND OBJECTIVES: This study was performed to investigate whether full-field digital mammography (FFDM) is at least as accurate as screen-film mammography with respect to breast lesion characterization. MATERIALS AND METHODS: Seventy-nine breast surgical specimens were obtained by means of preoperative needle localization with surgical excision from 79 patients. The specimens were imaged with both screen-film mammography and FFDM. Six radiologists specialized in breast imaging analyzed both sets of images and characterized the visualized lesions on a five-point scale: 1, definitely not malignant; 2, probably not malignant; 3, possibly malignant; 4, probably malignant; and 5, definitely malignant. Receiver operating characteristic curve analysis of the data was then performed to assess for differences between modalities in the radiologists' ability to predict breast malignancy. RESULTS: The areas under the receiver operating characteristic curves for the prediction of breast malignancy in surgical biopsy specimens were not statistically significantly different for FFDM and screen-film mammography. CONCLUSION: The results demonstrate that with breast surgical specimens, FFDM is similar in diagnostic accuracy to screen-film mammography.     

Observer variability in screen-film mammography versus full-field digital mammography with soft-copy reading

Full-field digital mammography (FFDM) with soft-copy reading is more complex than screen-film mammography (SFM) with hard-copy reading. The aim of this study was to compare inter- and intraobserver variability in SFM versus FFDM of paired mammograms from a breast cancer screening program. Six radiologists interpreted mammograms of 232 cases obtained with both techniques, including 46 cancers, 88 benign lesions, and 98 normals. Image interpretation included BI-RADS categories. A case consisted of standard two-view mammograms of one breast. Images were scored in two sessions separated by 5 weeks. Observer variability was substantial for SFM as well as for FFDM, but overall there was no significant difference between the observer variability at SFM and FFDM. Mean kappa values were lower, indicating less agreement, for microcalcifications compared with masses. The lower observer agreement for microcalcifications, and especially the low intraobserver concordance between the two imaging techniques for three readers, was noticeable. The level of observer agreement might be an indicator of radiologist performance and could confound studies designed to separate diagnostic differences between the two imaging techniques. The results of our study confirm the need for proper training for radiologists starting FFDM with soft-copy reading in breast cancer screening. Presented at ECR, Wien 2006.     

Potential of computer-aided diagnosis to reduce variability in radiologists' interpretations of mammograms depicting microcalcifications

PURPOSE: To evaluate whether computer-aided diagnosis can reduce interobserver variability in the interpretation of mammograms. MATERIALS AND METHODS: Ten radiologists interpreted mammograms showing clustered microcalcifications in 104 patients. Decisions for biopsy or follow-up were made with and without a computer aid, and these decisions were compared. The computer was used to estimate the likelihood that a microcalcification cluster was due to a malignancy. Variability in the radiologists' recommendations for biopsy versus follow-up was then analyzed. RESULTS: Variation in the radiologists' accuracy, as measured with the SD of the area under the receiver operating characteristic curve, was reduced by 46% with computer aid. Access to the computer aid increased the agreement among all observers from 13% to 32% of the total cases (P <.001), while the kappa value increased from 0.19 to 0.41 (P <.05). Use of computer aid eliminated two-thirds of the substantial disagreements in which two radiologists recommended biopsy and routine screening in the same patient (P <.05). CONCLUSION: In addition to its demonstrated potential to improve diagnostic accuracy, computer-aided diagnosis has the potential to reduce the variability among radiologists in the interpretation of mammograms.     

Breast cancer: effectiveness of computer-aided diagnosis observer study with independent database of mammograms

PURPOSE: To evaluate the effectiveness of a computerized classification method as an aid to radiologists reviewing clinical mammograms for which the diagnoses were unknown to both the radiologists and the computer. MATERIALS AND METHODS: Six mammographers and six community radiologists participated in an observer study. These 12 radiologists interpreted, with and without the computer aid, 110 cases that were unknown to both the 12 radiologist observers and the trained computer classification scheme. The radiologists' performances in differentiating between benign and malignant masses without and with the computer aid were evaluated with receiver operating characteristic (ROC) analysis. Two-tailed P values were calculated for the Student t test to indicate the statistical significance of the differences in performances with and without the computer aid. RESULTS: When the computer aid was used, the average performance of the 12 radiologists improved, as indicated by an increase in the area under the ROC curve (A(z)) from 0.93 to 0.96 (P <.001), by an increase in partial area under the ROC curve ((0.90)A(')(z)) from 0.56 to 0.72 (P <.001), and by an increase in sensitivity from 94% to 98% (P =.022). No statistically significant difference in specificity was found between readings with and those without computer aid (Delta = -0.014; P =.46; 95% CI: -0.054, 0.026), where Delta is difference in specificity. When we analyzed results from the mammographers and community radiologists as separate groups, a larger improvement was demonstrated for the community radiologists. CONCLUSION: Computer-aided diagnosis can potentially help radiologists improve their diagnostic accuracy in the task of differentiating between benign and malignant masses seen on mammograms.     

Improvement of radiologists' characterization of mammographic masses by using computer-aided diagnosis: an ROC study.

PURPOSE: To evaluate the effects of computer-aided diagnosis (CAD) on radiologists' classification of malignant and benign masses seen on mammograms. MATERIALS AND METHODS: The authors previously developed an automated computer program for estimation of the relative malignancy rating of masses. In the present study, the authors conducted observer performance experiments with receiver operating characteristic (ROC) methodology to evaluate the effects of computer estimates on radiologists' confidence ratings. Six radiologists assessed biopsy-proved masses with and without CAD. Two experiments, one with a single view and the other with two views, were conducted. The classification accuracy was quantified by using the area under the ROC curve, Az. RESULTS: For the reading of 238 images, the Az value for the computer classifier was 0.92. The radiologists' Az values ranged from 0.79 to 0.92 without CAD and improved to 0.87-0.96 with CAD. For the reading of a subset of 76 paired views, the radiologists' Az values ranged from 0.88 to 0.95 without CAD and improved to 0.93-0.97 with CAD. Improvements in the reading of the two sets of images were statistically significant (P = .022 and .007, respectively). An improved positive predictive value as a function of the false-negative fraction was predicted from the improved ROC curves. CONCLUSION: CAD may be useful for assisting radiologists in classification of masses and thereby potentially help reduce unnecessary biopsies.     

DIMA enlargement mammography in microcalcifications: a prospective study with ROC analysis

The purpose of this study was to investigate whether the four-fold magnification mammography (direct magnification, DIMA) technique would perform better than conventional 1.5-fold magnification mammography in the differentiation of breast microcalcifications into benign and malignant. Fifty patients with non-palpable microcalcifications detected by mammography were examined immediately prior to surgical biopsy using both a conventional (1.5-fold) and the DIMA (fourfold) magnification mammography techniques. The microcalcifications were classified by five experienced radiologists using morphological criteria. A receiver operating characteristics curve (ROC) analysis of the sensitivity and specificity of both techniques in assessing malignancy was then carried out. The DIMA mammography technique was slightly but non-significantly superior to the conventional method in detecting malignancy (p > 0.05). Coarse granular and pleomorphic calcifications were detected more frequently with the DIMA technique. Coarse calcifications were significantly more frequently associated with histologically benign findings, whereas fine granular calcifications were significantly more likely to be malignant lesions. Assessment of malignancy associated with microcalcifications using morphological criteria is not significantly improved by mammography techniques with higher magnification.     

Intracranial aneurysms at MR angiography: effect of computer-aided diagnosis on radiologists' detection performance

PURPOSE: To retrospectively evaluate the effect of computer-aided detection (CAD) on radiologists' performance in detection of intracranial aneurysms with magnetic resonance (MR) angiography. MATERIALS AND METHODS: The institutional review board approved this study and did not require patient informed consent. Fifty maximum intensity projection MR angiograms in 50 patients were used for observer performance study. The group included 22 patients (age range, 43-86 years; mean, 60.2 years; 6 men and 16 women) with intracranial aneurysms and 28 patients (age range, 32-80 years; mean, 58.8 years; 10 men and 18 women) without aneurysms. The MR angiograms were obtained with three-dimensional time-of-flight 1.5-T MR imaging. Fifteen radiologists, including eight neuroradiologists and seven general radiologists, participated in the observer performance test. They interpreted the angiograms first without and then with the aid of the computer output by using an automated computerized scheme. The observers' performance without and with the computer output was evaluated with receiver operating characteristic analysis. RESULTS: For all 15 observers, average area under the receiver operating characteristic curve (A(z)) value for detection of aneurysms was increased significantly from 0.931 to 0.983 (P = .001) when they used the computer output. A(z) values for general radiologists and neuroradiologists increased from 0.894 to 0.983 (P = .022) and from 0.963 to 0.984 (P = .014), respectively. Improvement in the performance of general radiologists in terms of the A(z) value was much greater than that of neuroradiologists. Performance of general radiologists with CAD (A(z) = 0.983) slightly exceeded that of neuroradiologists without CAD (A(z) = 0.963) (P = .048). CONCLUSION: CAD improved neuroradiologists' and general radiologists' performance for detection of intracranial aneurysms with MR angiography; improvement was greater for general radiologists than it was for neuroradiologists.     

Factors affecting radiologist inconsistency in screening mammography

RATIONALE AND OBJECTIVES: Although research has successfully documented variability in radiologists' interpretation of mammograms, it has failed to determine the relative contributions of case-specific features and reader inconsistency. Training interventions to improve consistency will be ineffectual if they do not target the principal determinants of disagreement among radiologists. The current study assessed the relative contributions of the case and the interpreter to the problem of inconsistent interpretation. MATERIALS AND METHODS: One hundred ten radiologists independently interpreted mammograms from the same 148 screening cases (43% with biopsy-proved cancers) and reported the presence or absence of calcifications, mass, architectural distortion, and asymmetric density in each of 296 breasts. The radiologists were blinded to disease status (established at biopsy or follow-up). RESULTS: Case-related differences accounted for a greater proportion of interpretation disagreement than did differences between interpreters. The presence of cancer was associated with increased disagreement, perhaps because of the multiplicity of findings. Patient age was also associated with increased disagreement in the reporting of calcifications. CONCLUSION: For screening mammography, increased consistency between radiologists in their recognition and reporting of clinically important findings will best be achieved by reducing disagreement in difficult cases. Current training in the United States addresses difficult cases only as they have been defined intuitively or experientially. The authors' population-based method provides an objective metric to measure case difficulty and basis from which to identify difficult cases for targeted training.     

Use of prior mammograms in the classification of benign and malignant masses

The purpose of this study was to determine the importance of using prior mammograms for classification of benign and malignant masses. Five radiologists and one resident classified mass lesions in 198 mammograms obtained from a population-based screening program. Cases were interpreted twice, once without and once with comparison of previous mammograms, in a sequential reading order using soft copy image display. The radiologists' performances in classifying benign and malignant masses without and with previous mammograms were evaluated with receiver operating characteristic (ROC) analysis. The statistical significance of the difference in performances was calculated using analysis of variance. The use of prior mammograms improved the classification performance of all participants in the study. The mean area under the ROC curve of the readers increased from 0.763 to 0.796. This difference in performance was statistically significant (P = 0.008).     

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.     

Improvement in radiologists' characterization of malignant and benign breast masses on serial mammograms with computer-aided diagnosis: an ROC study

PURPOSE: To evaluate the effects of computer-aided diagnosis (CAD) on radiologists' characterization of masses on serial mammograms. MATERIALS AND METHODS: Two hundred fifty-three temporal image pairs (138 malignant and 115 benign) obtained from 96 patients who had masses on serial mammograms were evaluated. The temporal pairs were formed by matching masses of the same view from two different examinations. Eight radiologists and two breast imaging fellows assessed the temporal pairs with and without computer aid. The classification of accuracy was quantified by using the area under receiver operating characteristic curve (A(z)). The statistical significance of the difference in A(z) between the different reading conditions was estimated with the Dorfman-Berbaum-Metz method for analysis of multireader multicase data and with the Student paired t test for analysis of observer-specific paired data. RESULTS: The average A(z) for radiologists' estimates of the likelihood of malignancy was 0.79 without CAD and improved to 0.84 with CAD. The improvement was statistically significant (P =.005). The corresponding average partial area index was 0.25 without CAD and improved to 0.37 with CAD. The improvement was also statistically significant (P =.005). On the basis of Breast Imaging Reporting and Data System assessments, it was estimated that with CAD, each radiologist, on average, reduced 0.7% (0.8 of 115) of unnecessary biopsies and correctly recommended 5.7% (7.8 of 138) of additional biopsies. CONCLUSION: CAD based on analysis of interval changes can significantly increase radiologists' accuracy in classification of masses and thereby may be useful in improving correct biopsy recommendations.     

Breast masses: computer-aided diagnosis with serial mammograms

PURPOSE: To retrospectively evaluate effects of computer-aided diagnosis (CAD) involving an interval change classifier (which uses interval change information extracted from prior and current mammograms and estimates a malignancy rating) on radiologists' accuracy in characterizing masses on two-view serial mammograms as malignant or benign. MATERIALS AND METHODS: The data collection protocol had institutional review board approval. Patient informed consent was waived for this HIPAA-compliant retrospective study. Ninety temporal pairs of two-view serial mammograms (depicting 47 malignant and 43 benign biopsy-proved masses) were obtained from 68 patient files and were digitized. Biopsy was the reference standard. Eight Mammography Quality Standards Act of 1992-accredited radiologists and two breast imaging fellows assessed digitized two-view temporal pairs (in preselected regions of interest only) by estimating likelihood of malignancy and Breast Imaging Reporting and Data System (BI-RADS) category without and with CAD. Observers' rating data were analyzed with Dorfman-Berbaum-Metz (DBM) multireader multicase method. Statistical significance of differences was estimated with the DBM method and Student two-tailed paired t test. RESULTS: Average area under the receiver operating characteristic curve for likelihood of malignancy across the 10 observers was 0.83 (range, 0.74-0.88) without CAD and improved to 0.87 (range, 0.80-0.92) with CAD (P < .05). The average partial area index above a sensitivity of 0.90 for likelihood of malignancy was 0.35 (range, 0.13-0.54) without CAD and 0.49 (range, 0.18-0.73) with CAD--a nonsignificant improvement (P = .11). For BI-RADS assessment, it was estimated that with CAD, six radiologists would correctly recommend additional biopsies for malignant masses (range, 4.3%-10.6%) and five would correctly recommend reduction of biopsy (ie, fewer biopsies) for benign masses (range, 2.3%-9.3%). However, five radiologists would incorrectly recommend additional biopsy for benign masses (range, 2.3%-14.0%), and one would incorrectly recommend reduction of biopsy (4.3%). CONCLUSION: CAD involving interval change analysis of preselected regions of interest can significantly improve radiologists' accuracy in classifying masses on digitized screen-film mammograms as malignant or benign.     

"Memory effect" in observer performance studies of mammograms

RATIONALE AND OBJECTIVE: To evaluate breast radiologists' recognition of mammograms showing cancers that they correctly detected or "missed" during clinical interpretations. MATERIALS AND METHODS: Two similar experiments were conducted. In the first, 33 bilateral screening mammograms were reviewed by four breast imagers. These included five cancers that each radiologist had detected, two cancers that each radiologist had "missed," and five mammograms recalled by other radiologists that were not cancer. Radiologists were asked if they had interpreted the mammogram in clinic and if the mammogram was suspicious for cancer. In the second experiment, four different breast imagers reviewed 48 mammograms that included five cancers that each radiologist had detected, two cancers that each radiologist had "missed," and five mammograms that were recalled by each radiologist but were not cancer. Using chi-square analysis, the performance of the radiologists on screening mammograms they had read in clinic was compared with their performance on mammograms read in clinic by other radiologists. RESULTS: Seven of eight radiologists did not remember interpreting any of the mammograms in clinic. One radiologist correctly remembered interpreting one mammogram in clinic, but interpreted it incorrectly. Average performance showed no significant difference (P = .60) between mammograms they had interpreted in clinic and those interpreted by others. CONCLUSION: Radiologists do not remember most mammograms showing cancer that they have interpreted, either correctly or incorrectly, after they are mixed with mammograms showing cancer that were interpreted by other radiologists. Screening mammograms can be used in observer performance studies in which the interpreting radiologist participates as an observer.     

Classification of breast lesions with multimodality computer-aided diagnosis: observer study results on an independent clinical data set

PURPOSE: To evaluate a computer-aided diagnosis multimodality intelligent workstation as an aid to radiologists in the interpretation of mammograms and breast sonograms. MATERIALS AND METHODS: An institutional review board approved the protocol for an observer study with signed consent, as well as the retrospective use of the mammograms, sonograms, and clinical data with waiver of consent. The HIPAA-compliant observer study was conducted with five breast radiologists and five breast imaging fellows, all of whom gave confidence ratings and patient management decisions, both without and with the computer aid, for 97 lesions that were unknown to both the observers and the computer. The performance of each observer without and with the computer aid was quantified by using four performance measures: area under the receiver operating characteristic curve (A(z)) value, partial A(z) value, sensitivity, and specificity. The statistical significance of the differences in the performance measures without and with the computer aid was determined by using a two-tailed t test for paired data. RESULTS: Use of the computer aid resulted in an improvement of the average performance of the 10 observers, as measured by means of a statistically significant increase in A(z) value (0.87-0.92; P < .001), partial A(z) value (0.47-0.68; P < .001), and sensitivity (0.88-0.93; P = .005). A statistically significant difference was not found in the specificity without and with the computer aid (0.66-0.69; P = .20). CONCLUSION: Use of multimodality intelligent workstations can improve the performance of radiologists in the task of differentiating malignant and benign lesions at mammography and sonography.     

Computer-aided preoperative diagnosis of microcalcifications on mammograms

PURPOSE: To evaluate of a computer-aided method for differentiating malignant from benign clustered microcalcifications. MATERIAL AND METHODS: Our material was 350 suspicious microcalcifications on mammograms from 330 female patients who underwent breast biopsy (after hook wire localization and under mammographic guidance). The histologic findings were malignant in 140 cases (40%) and benign in 210 cases (60%). Those clusters were manually detected, computer-aided analyzed and quantitatively estimated. Besides computer analysis, 3 physicians-observers (2 radiologists and 1 breast surgeon) evaluated the malignant or benign nature of the clustered microcalcifications. The performance of the artificial network, each observer and the three observers as a group was evaluated by receiver operating characteristics (ROC) curves. RESULTS: Comparison of the ROC curves revealed the following AUC values (area under the curve): computer - 0.950, physician 1 - 0.815, physician 2 - 0.830, physician 3 - 0.830, and physicians as a group - 0.825. The results, compared by the student t-test for paired data, showed a statistically significant difference between computer analysis and physicians' performance, independently and as a group. CONCLUSION: Our study showed that computer analysis achieved statistically significantly better performance than that of physicians in the classification of malignant and benign calcifications.