Biomedical subjects

William R Poller

Publications and source records attributed to William R Poller.

7 recordsLinked to original sources

PubMed2006

Multicenter evaluation of the breast lesion excision system, a percutaneous, vacuum-assisted, intact-specimen breast biopsy device.

BACKGROUND: Percutaneous, vacuum-assisted, large-gauge core needle biopsy (VACNB) provides an alternative to open surgical biopsy as an initial diagnostic tool for breast lesions, yet rates of underestimating malignant diagnoses remain sufficiently high to warrant surgical biopsy in some cases. The current study was performed to determine if the Breast Lesion Excision System (BLES) provides a feasible alternative to VACNB. METHODS: A retrospective review was conducted of 742 consecutive mammographic lesions with microcalcifications classified as Breast Imaging Reporting and Data System (BIRADS) IV or V that had stereotactic percutaneous biopsy using BLES. Initial diagnoses obtained from the histopathologic examination of tissues retrieved at biopsy were compared with the histopathologic examination of tissues received from surgical excision or lumpectomy. Underestimation rates for atypical ductal hyperplasia (ADH) and ductal carcinoma in situ (DCIS) were recorded if open surgical biopsy revealed DCIS or invasive cancer, and invasive cancer, respectively. RESULTS: Of the 742 breast lesions, 34 displayed ADH upon biopsy with the BLES device. Two patients did not receive open surgical biopsy. Of the 32 patients who had open surgical excision, 3 (9.4%) had DCIS or invasive cancer. There were 119 diagnoses of DCIS upon biopsy with the BLES device. Four patients did not receive open surgical biopsy. Of the 115 patients who had open surgical excision, 6 (5.2%) had invasive cancer. CONCLUSIONS: Breast biopsy can be performed accurately using the BLES device. Compared with VACNB, it does not alter the need for surgical excision in women diagnosed with ADH or DCIS at core biopsy.

Biopsy, Needle↗

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

Changes in breast cancer detection and mammography recall rates after the introduction of a computer-aided detection system.

BACKGROUND: Computer-aided mammography is rapidly gaining clinical acceptance, but few data demonstrate its actual benefit in the clinical environment. We assessed changes in mammography recall and cancer detection rates after the introduction of a computer-aided detection system into a clinical radiology practice in an academic setting. METHODS: We used verified practice- and outcome-related databases to compute recall rates and cancer detection rates for 24 Mammography Quality Standards Act-certified academic radiologists in our practice who interpreted 115,571 screening mammograms with (n = 59,139) or without (n = 56,432) the use of a computer-aided detection system. All statistical tests were two-sided. RESULTS: For the entire group of 24 radiologists, recall rates were similar for mammograms interpreted without and with computer-aided detection (11.39% versus 11.40%; percent difference = 0.09, 95% confidence interval [CI] = -11 to 11; P =.96) as were the breast cancer detection rates for mammograms interpreted without and with computer-aided detection (3.49% versus 3.55% per 1000 screening examinations; percent difference = 1.7, 95% CI = -11 to 19; P =.68). For the seven high-volume radiologists (i.e., those who interpreted more than 8000 screening mammograms each over a 3-year period), the recall rates were similar for mammograms interpreted without and with computer-aided detection (11.62% versus 11.05%; percent difference = -4.9, 95% CI = -21 to 4; P =.16), as were the breast cancer detection rates for mammograms interpreted without and with computer-aided detection (3.61% versus 3.49% per 1000 screening examinations; percent difference = -3.2, 95% CI = -15 to 9; P =.54). CONCLUSION: The introduction of computer-aided detection into this practice was not associated with statistically significant changes in recall and breast cancer detection rates, both for the entire group of radiologists and for the subset of radiologists who interpreted high volumes of mammograms.

Breast Neoplasms↗

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

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↗