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Lara A Hardesty

Publications and source records attributed to Lara A Hardesty.

13 recordsLinked to original sources
PubMed2006

A method to improve visual similarity of breast masses for an interactive computer-aided diagnosis environment.

The purpose of this study was to develop and test a method for selecting "visually similar" regions of interest depicting breast masses from a reference library to be used in an interactive computer-aided diagnosis (CAD) environment. A reference library including 1000 malignant mass regions and 2000 benign and CAD-generated false-positive regions was established. When a suspicious mass region is identified, the scheme segments the region and searches for similar regions from the reference library using a multifeature based k-nearest neighbor (KNN) algorithm. To improve selection of reference images, we added an interactive step. All actual masses in the reference library were subjectively rated on a scale from 1 to 9 as to their "visual margins speculations". When an observer identifies a suspected mass region during a case interpretation he/she first rates the margins and the computerized search is then limited only to regions rated as having similar levels of spiculation (within +/-1 scale difference). In an observer preference study including 85 test regions, two sets of the six "similar" reference regions selected by the KNN with and without the interactive step were displayed side by side with each test region. Four radiologists and five nonclinician observers selected the more appropriate ("similar") reference set in a two alternative forced choice preference experiment. All four radiologists and five nonclinician observers preferred the sets of regions selected by the interactive method with an average frequency of 76.8% and 74.6%, respectively. The overall preference for the interactive method was highly significant (p < 0.001). The study demonstrated that a simple interactive approach that includes subjectively perceived ratings of one feature alone namely, a rating of margin "spiculation," could substantially improve the selection of "visually similar" reference images.

Algorithms↗
PubMed2005

Trends in recall, biopsy, and positive biopsy rates for screening mammography in an academic practice.

PURPOSE: To retrospectively evaluate whether recall, biopsy, and positive biopsy rates for a group of radiologists who met requirements of Mammography Quality Standards Act of 1992 (MQSA) demonstrated any change over time during a 27-month period (nine consecutive calendar quarters). MATERIALS AND METHODS: Institutional review board approved study protocol, and informed consent was waived. All screening mammograms that had been interpreted by MQSA-qualified radiologists between January 1, 2001, and March 31, 2003, were reviewed. Group recall rates, biopsy rates, and detected cancer rates for nine calendar quarters were computed and attributed to performance date of original screening mammogram. Type of biopsy performed was classified as follows: stereotactic vacuum-assisted biopsy, ultrasonography (US)-guided core biopsy, US-guided fine-needle aspiration biopsy, surgical excision, and multiple biopsies. chi(2) Test for trend (two sided) and linear regression were used to assess trends over time for recall and biopsy rates, biopsy rates according to type of biopsy performed, and percentage of biopsy results positive for cancer. RESULTS: Group recall rate did not show a statistically significant trend during period studied (P = .59). Biopsy rates increased significantly from 13.02 to 20.12 per 1000 screening examinations (P < .001). A corresponding substantial decrease was seen in percentage of biopsies in which malignancy was found, although this trend was not statistically significant (P = .24). A significant increase (from 4.72 to 9.88 per 1000 screening examinations) was found in rate of stereotactic vacuum-assisted 11-gauge core biopsies performed (P < .001). CONCLUSION: Observed increase in biopsy rates reinforces the need to carefully select patients for biopsy to achieve efficient, efficacious, and cost-effective programs for early detection of breast cancers.

Biopsy↗
PubMed2005

"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.

Breast Neoplasms↗
PubMed2005

Is maximum positive predictive value a good indicator of an optimal screening mammography practice?

OBJECTIVE: Positive predictive value (PPV1) has been used as one important indicator of the quality of screening mammography programs. We show how the relationship between sensitivity and recall rate may affect the operating point at which optimal (maximum) PPV1 occurs. CONCLUSION: Optimal (maximum) PPV1 can occur at any sensitivity level and should not be used as the sole indicator for practice optimization because it does not take into account the number of cancers that would be missed at that sensitivity.

Breast Neoplasms↗
PubMed2004

Computer-aided detection performance in mammographic examination of masses: assessment.

PURPOSE: To compare performance of two computer-aided detection (CAD) systems and an in-house scheme applied to five groups of sequentially acquired screening mammograms. MATERIALS AND METHODS: Two hundred nineteen film-based mammographic examinations, classified into five groups, were included in this study. Group 1 included 58 examinations in which verified malignant masses were detected during screening; group 2, 39 in which all available latest examinations were performed prior to diagnosis of these malignant masses (subset of 39 women from group 1); group 3, 22 in which findings were interpreted as negative but were verified as cancer within 1 year from the negative interpretation (missed cancers); group 4, 50 in which findings were negative and patients were not recalled for additional procedures; and group 5, 50 in which patients were recalled for additional procedures and findings were negative for cancer. In all examinations, images were processed with two Food and Drug Administration-approved commercially available CAD systems and an in-house scheme. Performance levels in terms of true-positive detection rates and number of false-positive identifications per image and per examination were compared. RESULTS: Mass detection rates in positive examinations (group 1) were 67%-72%. Detection rates among three systems were not significantly different (P > .05). In 50 negative screening examinations (group 4), false-positive rates ranged from 1.08 to 1.68 per four-view examination. Performance level differences among systems were significant for false-positive rates (P = .008). Performance of all systems was at levels lower than publicly suggested in some retrospective studies. False-positive CAD cueing rates were significantly higher for negative examinations in which patients were recalled (group 5) than they were for those in which patients were not recalled (group 4) (P < or = .002). CONCLUSION: Performance of CAD systems for mass detection at mammography varies significantly, depending on examination and system used. Actual performance of all systems in clinical environment can be improved.

Adult↗
PubMed2004

Recall and detection rates in screening mammography.

BACKGROUND: The authors investigated the correlation between recall and detection rates in a group of 10 radiologists who had read a high volume of screening mammograms in an academic institution. METHODS: Practice-related and outcome-related databases of verified cases were used to compute recall rates and tumor detection rates for a group of 10 Mammography Quality Standard Act (MQSA)-certified radiologists who interpreted a total of 98,668 screening mammograms during the years 2000, 2001, and 2002. The relation between recall and detection rates for these individuals was investigated using parametric Pearson (r) and nonparametric Spearman (rho) correlation coefficients. The effect of the volume of mammograms interpreted by individual radiologists was assessed using partial correlations controlling for total reading volumes. RESULTS: A wide variability of recall rates (range, 7.7-17.2%) and detection rates (range, 2.6-5.4 per 1000 mammograms) was observed in the current study. A statistically significant correlation (P < 0.05) between recall and detection rates was observed in this group of 10 experienced radiologists. The results remained significant (P < 0.05) after accounting for the volume of mammograms interpreted by each radiologist. CONCLUSIONS: Optimal performance in screening mammography should be evaluated quantitatively. The general pressure to reduce recall rates through "practice guidelines" to below a fixed level for all radiologists should be assessed carefully.

Breast Neoplasms↗
PubMed2004

A method to test the reproducibility and to improve performance of computer-aided detection schemes for digitized mammograms.

The purpose of this study is to develop a new method for assessment of the reproducibility of computer-aided detection (CAD) schemes for digitized mammograms and to evaluate the possibility of using the implemented approach for improving CAD performance. Two thousand digitized mammograms (representing 500 cases) with 300 depicted verified masses were selected in the study. Series of images were generated for each digitized image by resampling after a series of slight image rotations. A CAD scheme developed in our laboratory was applied to all images to detect suspicious mass regions. We evaluated the reproducibility of the scheme using the detection sensitivity and false-positive rates for the original and resampled images. We also explored the possibility of improving CAD performance using three methods of combining results from the original and resampled images, including simple grouping, averaging output scores, and averaging output scores after grouping. The CAD scheme generated a detection score (from 0 to 1) for each identified suspicious region. A region with a detection score >0.5 was considered as positive. The CAD scheme detected 238 masses (79.3% case-based sensitivity) and identified 1093 false-positive regions (average 0.55 per image) in the original image dataset. In eleven repeated tests using original and ten sets of rotated and resampled images, the scheme detected a maximum of 271 masses and identified as many as 2359 false-positive regions. Two hundred and eighteen masses (80.4%) and 618 false-positive regions (26.2%) were detected in all 11 sets of images. Combining detection results improved reproducibility and the overall CAD performance. In the range of an average false-positive detection rate between 0.5 and 1 per image, the sensitivity of the scheme could be increased approximately 5% after averaging the scores of the regions detected in at least four images. At low false-positive rate (e.g., < or =average 0.3 per image), the grouping method alone could increase CAD sensitivity by 7%. The study demonstrated that reproducibility of a CAD scheme can be tested using a set of slightly rotated and resampled images. Because the reproducibility of true-positive detections is generally higher than that of false-positive detections, combining detection results generated from subsets of rotated and resampled images could improve both reproducibility and overall performance of CAD schemes.

Algorithms↗
PubMed2003

Mammography with computer-aided detection: reproducibility assessment initial experience.

PURPOSE: To examine the performance and reproducibility of a commercially available computer-aided detection (CAD) system with a set of mammograms obtained in 100 patients who had undergone biopsy after positive findings at mammography. MATERIALS AND METHODS: One hundred positive mammographic examinations (four views each), depicting 96 masses and 50 microcalcification clusters, were scanned and analyzed three times by the CAD system. Reproducibility of detection sensitivity and the individual CAD-generated cues in the three images were examined. Both abnormality- and region-based detection sensitivities were compared. RESULTS: Forty-eight (96.0%) of 50 microcalcification clusters were marked on all three images in the abnormality-based analysis. Of the remaining two clusters, one was marked in two images and one was marked in only one. The abnormality-based sensitivity for mass detection ranged from 66.7% (64 of 96) to 70.8% (68 of 96). The system generated identical patterns (including images with and those without cues) for all three images in 53.3% (213 of 400) of images. For true-positive cluster regions, 88.9% (80 of 90) were marked at the same location in all images. For true-positive mass regions, 69.5% (82 of 118) were marked at the same locations in all images. In false-positive detections, only 44.0% (81 of 184) of false-positive mass regions and 31.9% (38 of 119) of false-positive cluster regions were marked at the same locations on all three images. CONCLUSION: Reproducibility of marked regions generated by the CAD system is improved from that reported previously, largely as a result of the substantial reduction in the false-positive detection rates. Reproducibility of true-positive identification of masses remains an important issue that may have methodologic and clinical practice implications.

Biopsy↗
PubMed2003

Integrated density of a lesion: a quantitative, mammographically derived, invariable measure.

A method for quantitatively estimating lesion "size" from mammographic images was developed and evaluated. The main idea behind the measure, termed "integrated density" (ID), is that the total x-ray attenuation attributable to an object is theoretically invariant with respect to the projected view and object deformation. Because it is possible to estimate x-ray attenuation of a lesion from relative film densities, after appropriate corrections for background, the invariant property of the measure is expected to result in an objective method for evaluating the "sizes" of breast lesions. ID was calculated as the integral of the estimated image density attributable to a lesion, relative to surrounding background, over the area of the lesion and after corrections for the nonlinearity of the film characteristic curve. This effectively provides a measure proportional to lesion volume. We computed ID and more traditional measures of size (such as "mass diameter" and "effective size") for 100 pairs of ipsilateral mammographic views, each containing a lesion that was relatively visible in both views. The correlation between values calculated for each measure from corresponding pairs of ipsilateral views were computed and compared. All three size-related measures (mass diameter, effective size, and ID) exhibited reasonable linear relationship between paired views (r2>0.7, P<0.001). Specifically, the ID measures for the 100 masses were found to be highly correlated (r2=0.9, P<0.001) between ipsilateral views of the same mass. The correlation increased substantially (r2=0.95), when a measure with linear dimensions of length was defined as the cube root of ID. There is a high degree of correlation between ID-based measures obtained from different views of the same mass. ID-based measures showed a higher degree of invariance than mass diameter or effective size.

Absorptiometry, Photon↗
PubMed2003

Automated assessment of the composition of breast tissue revealed on tissue-thickness-corrected mammography.

OBJECTIVE: Variations in the thickness of a compressed breast and the resulting variations in mammographic densities confound current automated procedures for estimating tissue composition of breasts from digitized mammograms. We sought to determine whether adjusting mammographic data for tissue thickness before estimating tissue composition could improve the accuracy of the tissue estimates. MATERIALS AND METHODS: We developed methods for locally estimating breast thickness from mammograms and then adjusting pixel values so that the values correlated with the tissue composition over the breast area. In our technique, the pixel values are corrected for the nonlinearity of the combined characteristic curve from the film and film digitizer; the approximate relative thickness as a function of distance from the skin line is measured; and the pixel values are adjusted to reflect their distance from the skin line. To estimate tissue composition, we created a backpropagation neural network classifier from features extracted from the histogram of pixel values, after the data had been adjusted for characteristic curve and tissue thickness. We used a 10-fold cross-validation method to evaluate the neural network. The averaged scores of three radiologists were our gold standard. RESULTS: The performance of the neural network was calculated as the percentage of correct classifications of images that were or were not corrected to reflect tissue thickness. With its parameters derived from the pixel-value histogram, the neural network based on corrected images performed better (71% accuracy) than that based on uncorrected images (67% accuracy) (p < 0.05). CONCLUSION: Our results show that adjusting tissue thickness before estimating tissue composition improved the performance of our estimation procedure in reproducing the tissue composition values determined by radiologists.

Aged↗
PubMed2003

Optimal reference mammography: a comparison of mammograms obtained 1 and 2 years before the present examination.

OBJECTIVE: We assessed and compared the benefit of using images acquired 1 year or 2 years previously during mammography interpretations. MATERIALS AND METHODS: Eleven radiologists and one resident reviewed 128 cases three times: once without prior mammograms for comparison, once with mammograms from the most recent (1 year) examination, and once with mammograms acquired 2 years previously. They were asked to determine whether the patient should be recalled for additional procedures. Performances under the three conditions were compared. RESULTS: Radiologists were significantly more accurate (p < 0.001) when comparison mammograms (obtained 1 or 2 years previously) were available. Although sensitivity was not significantly affected between the availability of mammograms from 1 or 2 years earlier (p > 0.10), the specificity was. Specificity using mammograms from the latest examination (obtained 1 year previously) as a reference was significantly better (p = 0.03) than specificity using mammograms obtained 2 years previously. CONCLUSION: Comparison mammograms are important for accurate diagnosis-in particular, for increasing specificity. The latest prior examination seems to be the optimal one for this purpose.

Breast Neoplasms↗
PubMed2002

Computerized assessment of tissue composition on digitized mammograms.

RATIONALE AND OBJECTIVES: The authors developed a computerized method for the quantitative assessment of breast tissue composition on digitized mammograms. MATERIALS AND METHODS: Three radiologists were asked to review 200 digitized mammograms and independently provide a Breast Imaging Reporting and Data System-like rating for breast tissue composition on a scale of 0 to 4. These values were incorporated into a "consensus" rating that was used as a reference point in the development and evaluation of a computerized method. After tissue segmentation that excluded nontissue areas, a set of quantitative features was computed. A computerized summary index that attempts to reproduce the radiologists' ratings was developed. Correlation coefficients (Pearson r) were used to compare the computerized index with the consensus ratings. RESULTS: Some individual features computed for the relatively dense breast areas showed good correlation (r > 0.8) with the radiologists' subjective ratings. The summary index of tissue composition demonstrated a significant correlation (r = 0.87), as well. CONCLUSION: Computerized methods that show good correlation with radiologists' ratings of breast tissue composition can be developed.

Breast↗