Discrimination of benign from malignant breast lesions in dense breasts with model-based analysis of regions-of-interest using directional diffusion-weighted images.

BACKGROUND: There is an increasing interest in non-contrast-enhanced magnetic resonance imaging (MRI) for detecting and evaluating breast lesions. We present a methodology utilizing lesion core and periphery region of interest (ROI) features derived from directional diffusion-weighted imaging (DWI) data to evaluate performance in discriminating benign from malignant lesions in dense breasts. METHODS: We accrued 55 dense-breast cases with 69 lesions (31 benign; 38 cancer) at a single institution in a prospective study; cases with ROIs exceeding 7.50 cm2 were excluded, resulting in analysis of 50 cases with 63 lesions (29 benign, 34 cancers). Spin-echo echo-planar imaging DWI was acquired at 1.5 T and 3 T. Data from three diffusion encoding gradient directions were exported and processed independently. Lesion ROIs were hand-drawn on DWI images by two radiologists. A region growing algorithm generated 3D lesion models on augmented apparent-diffusion coefficient (ADC) maps and defined lesion core and lesion periphery sub-ROIs. A lesion-core and a lesion-periphery feature were defined and combined into an overall classifier whose performance was compared to that of mean ADC using receiver operating characteristic (ROC) analysis. Inter-observer variability in ROI definition was measured using Dice Similarity Coefficient (DSC). RESULTS: The region-growing algorithm for 3D lesion model generation improved inter-observer variability over hand drawn ROIs (DSC: 0.66 vs 0.56 (p < 0.001) with substantial agreement (DSC > 0.8) in 46% vs 13% of cases, respectively (p < 0.001)). The overall classifier improved discrimination over mean ADC, (ROC- area under the curve (AUC): 0.85 vs 0.75 and 0.83 vs 0.74 respectively for the two readers). CONCLUSIONS: A classifier generated from directional DWI information using lesion core and lesion periphery information separately can improve lesion discrimination in dense breasts over mean ADC and should be considered for inclusion in computer-aided diagnosis algorithms. Our model-based ROIs could facilitate standardization of breast MRI computer-aided diagnostics (CADx).

Mammographic Density Change With Estrogen and Progestin Therapy and Breast Cancer Risk.

Background: Estrogen plus progestin therapy increases both mammographic density and breast cancer incidence. Whether mammographic density change associated with estrogen plus progestin initiation predicts breast cancer risk is unknown. Methods: We conducted an ancillary nested case-control study within the Women's Health Initiative trial that randomly assigned postmenopausal women to daily conjugated equine estrogen 0.625 mg plus medroxyprogesterone acetate 2.5 mg or placebo. Mammographic density was assessed from mammograms taken prior to and one year after random assignment for 174 women who later developed breast cancer (cases) and 733 healthy women (controls). Logistic regression analyses included adjustment for confounders and baseline mammographic density when appropriate. Results: Among women in the estrogen plus progestin arm (97 cases/378 controls), each 1% positive change in percent mammographic density increased breast cancer risk 3% (odds ratio [OR] = 1.03, 95% confidence interval [CI] = 1.01 to 1.06). For women in the highest quintile of mammographic density change (>19.3% increase), breast cancer risk increased 3.6-fold (95% CI = 1.52 to 8.56). The effect of estrogen plus progestin use on breast cancer risk (OR = 1.28, 95% CI = 0.90 to 1.82) was eliminated in this study, after adjusting for change in mammographic density (OR = 1.00, 95% CI = 0.66 to 1.51). Conclusions: We found the one-year change in mammographic density after estrogen plus progestin initiation predicted subsequent increase in breast cancer risk. All of the increased risk from estrogen plus progestin use was mediated through mammographic density change. Doctors should evaluate changes in mammographic density with women who initiate estrogen plus progestin therapy and discuss the breast cancer risk implications.

Dedicated Three-dimensional Breast Computed Tomography: Lesion Characteristic Perception by Radiologists.

OBJECTIVES: To assess radiologist confidence in the characterization of suspicious breast lesions with a dedicated three-dimensional breast computed tomography (DBCT) system in comparison to diagnostic two-dimensional digital mammography (dxDM). MATERIALS AND METHODS: Twenty women were recruited who were to undergo a breast biopsy for a Breast Imaging-Reporting and Data System (BI-RADS) 4 or 5 lesion evaluated with dxDM in this Institutional Review Board-approved study. The enrolled subjects underwent imaging of the breast(s) of concern using DBCT. Seven radiologists reviewed the cases. Each reader compared DBCT to the dxDM and was asked to specify the lesion type and BI-RADS score for each lesion and modality. They also compared lesion characteristics: Shape for masses or morphology for calcifications; and margins for masses or distribution for calcifications between the modalities using confidence scores (0-100). RESULTS: Twenty-four biopsied lesions were included in this study: 17 (70.8%) masses and 7 (29.2%) calcifications. Eight (33.3%) lesions were malignant, and 16 (66.7%) were benign. Across all lesions, there was no significant difference in the margin/distribution (Δ = -0.99, P = 0.84) and shape/morphology (Δ = -0.10, P = 0.98) visualization confidence scores of DBCT in relation to dxDM. However, analysis by lesion type showed a statistically significant increase in reader shape (Δ =11.34, P = 0.013) and margin (Δ =9.93, P = 0.023) visualization confidence with DBCT versus dxDM for masses and significant decrease in reader morphology (Δ = -29.95, P = 0.001) and distribution (Δ = -28.62, P = 0.002) visualization confidence for calcifications. CONCLUSION: Reader confidence in the characterization of suspicious masses is significantly improved with DBCT, but reduced for calcifications. Further study is needed to determine whether this technology can be used for breast cancer screening.

Tomosynthesis for breast cancer screening.

Breast tomosynthesis, a three-dimensional x-ray based breast imaging technology, has been available for clinical use in the United States since 2011. In this paper we review the literature on breast cancer screening with this new technology including where gaps in knowledge remain.

Impact of computer-aided detection systems on radiologist accuracy with digital mammography.

OBJECTIVE: The purpose of this study was to assess the impact of computer-aided detection (CAD) systems on the performance of radiologists with digital mammograms acquired during the Digital Mammographic Imaging Screening Trial (DMIST). MATERIALS AND METHODS: Only those DMIST cases with proven cancer status by biopsy or 1-year follow-up that had available digital images were included in this multireader, multicase ROC study. Two commercially available CAD systems for digital mammography were used: iCAD SecondLook, version 1.4; and R2 ImageChecker Cenova, version 1.0. Fourteen radiologists interpreted, without and with CAD, a set of 300 cases (150 cancer, 150 benign or normal) on the iCAD SecondLook system, and 15 radiologists interpreted a different set of 300 cases (150 cancer, 150 benign or normal) on the R2 ImageChecker Cenova system. RESULTS: The average AUC was 0.71 (95% CI, 0.66-0.76) without and 0.72 (95% CI, 0.67-0.77) with the iCAD system (p = 0.07). Similarly, the average AUC was 0.71 (95% CI, 0.66-0.76) without and 0.72 (95% CI 0.67-0.77) with the R2 system (p = 0.08). Sensitivity and specificity differences without and with CAD for both systems also were not significant. CONCLUSION: Radiologists in our studies rarely changed their diagnostic decisions after the addition of CAD. The application of CAD had no statistically significant effect on radiologist AUC, sensitivity, or specificity performance with digital mammograms from DMIST.

Automated delineation of calcified vessels in mammography by tracking with uncertainty and graphical linking techniques.

As a potential biomarker for women's cardiovascular and chronic kidney diseases, breast arterial calcification (BAC) in mammography has become an emerging research topic in recent years. To provide more objective measurement for vascular structures with calcium depositions in mammography, a new computerized method is introduced in this paper to delineate the calcified vessels. Specifically, we leverage two underlying cues, namely calcification and vesselness, into a multiple seeded tracking with uncertainty scheme. This new vessel-tracking scheme generates plenty of sampling paths to describe the complicated topology of the vascular structures with calcium depositions. A compiling and linking process is further carried out to organize the sampling paths together to be the vessel segments that likely belong to the same vessel tract. The proposed method has been evaluated on 63 mammograms, by comparison with manual delineations from two experts using various assessment metrics. The experiment results confirm the efficacy and stability of the proposed method, and also indicate that the proposed method can be potentially used as a convenient BAC measurement tool in replacement of the trivial and tedious manual delineation tasks.

Assessing the stand-alone sensitivity of computer-aided detection with cancer cases from the Digital Mammographic Imaging Screening Trial.

OBJECTIVE: The purpose of this study was to assess the sensitivities and false-detection rates of two computer-aided detection (CADe) systems when applied to digital or film-screen mammograms in detecting the known breast cancer cases from the Digital Mammographic Imaging Screening Trial (DMIST) breast cancer screening population. MATERIALS AND METHODS: Available film-screen and digital mammograms of 161 breast cancer cases from DMIST were analyzed by two CADe systems, iCAD Second-Look and R2 ImageChecker. Three experienced breast-imaging radiologists reviewed the CADe marks generated for each available cancer case, recording the number and locations of CADe marks and whether each CADe mark location corresponded with the known location of the cancer. RESULTS: For the 161 cancer cases included in this study, the sensitivities of the DMIST reader without CAD were 0.43 (69/161, 95% CI 0.35-0.51) for digital and 0.41 (66/161, 0.33-0.49) for film-screen mammography. The sensitivities of iCAD were 0.74 (119/161, 0.66-0.81) for digital and 0.69 (111/161, 0.61-0.76) for film-screen mammography, both significantly higher than the DMIST study sensitivities (p < 0.0001 for both). The average number of false CADe marks per case of iCAD was 2.57 (SD, 1.92) for digital and 3.06(1.72) for film-screen mammography. The sensitivity of R2 was 0.74 (119/161, 0.66-0.81) for digital, and 0.60 (97/161, 0.52-0.68) for film-screen mammography, both significantly higher than the DMIST study sensitivities (p < 0.0001 for both). The average number of false CADe marks per case of R2 was 2.07 (1.57) for digital and 1.52 (1.45) for film-screen mammography. CONCLUSION: Our results suggest the use of CADe in interpretation of digital and film-screen mammograms could lead to improvements in cancer detection.

Comparison of radiologist performance with photon-counting full-field digital mammography to conventional full-field digital mammography.

RATIONALE AND OBJECTIVES: The purpose of this study was to assess the performance of a MicroDose photon-counting full-field digital mammography (PCM) system in comparison to full-field digital mammography (FFDM) for area under the receiver-operating characteristic (ROC) curve (AUC), sensitivity, specificity, and feature analysis of standard-view mammography for women presenting for screening mammography, diagnostic mammography, or breast biopsy. MATERIALS AND METHODS: A total of 133 women were enrolled in this study at two European medical centers, with 67 women who had a pre-existing 10-36 months FFDM enrolled prospectively into the study and 66 women who underwent breast biopsy and had screening PCM and diagnostic FFDM, including standard craniocaudal and mediolateral oblique views of the breast with the lesion, enrolled retrospectively. The case mix consisted of 49 cancers, 17 biopsy-benign cases, and 67 normal cases. Sixteen radiologists participated in the reader study and interpreted all 133 cases in both conditions, separated by washout period of ≥4 weeks. ROC curve and free-response ROC curve analyses were performed for noninferiority of PCM compared to FFDM using a noninferiority margin Δ value of 0.10. Feature analysis of the 66 cases with lesions was conducted with all 16 readers at the conclusion of the blinded reads. Mean glandular dose was recorded for all cases. RESULTS: The AUC for PCM was 0.947 (95% confidence interval [CI], 0.920-0.974) and for FFDM was 0.931 (95% CI, 0.898-0.964). Sensitivity per case for PCM was 0.936 (95% CI, 0.897-0.976) and for FFDM was 0.908 (95% CI, 0.856-0.960). Specificity per case for PCM was 0.764 (95% CI, 0.688-0.841) and for FFDM was 0.749 (95% CI, 0.668-0.830). Free-response ROC curve figures of merit were 0.920 (95% CI, 0.881-0.959) and 0.903 (95% CI, 0.858-0.948) for PCM and FFDM, respectively. Sensitivity per lesion was 0.903 (95% CI, 0.846-0.960) and 0.883 (95% CI, 0.823-0.944) for PCM and FFDM, respectively. The average false-positive marks per image of noncancer cases were 0.265 (95% CI, 0.171-0.359) and 0.281 (95% CI, 0.188-0.374) for PCM and FFDM, respectively. Noninferiority P values for AUC, sensitivity (per case and per lesion), specificity, and average false-positive marks per image were all statistically significant (P < .001). The noninferiority P value for free-response ROC was <.025, from the 95% CI for the difference. Feature analysis resulted in PCM being preferred to FFDM by the readers for ≥70% of the cases. The average mean glandular dose for PCM was 0.74 mGy (95% CI, 0.722-0.759 mGy) and for FFDM was 1.23 mGy (95% CI, 1.199-1.262 mGy). CONCLUSIONS: In this study, radiologist performance with PCM was not inferior to that with conventional FFDM at an average 40% lower mean glandular dose.

Diffraction enhanced imaging of a rat model of gastric acid aspiration pneumonitis.

RATIONALE AND OBJECTIVES: Diffraction-enhanced imaging (DEI) is a type of phase contrast x-ray imaging that has improved image contrast at a lower dose than conventional radiography for many imaging applications, but no studies have been done to determine if DEI might be useful for diagnosing lung injury. The goals of this study were to determine if DEI could differentiate between healthy and injured lungs for a rat model of gastric aspiration and to compare diffraction-enhanced images with chest radiographs. MATERIALS AND METHODS: Radiographs and diffraction-enhanced chest images of adult Sprague Dawley rats were obtained before and 4 hours after the aspiration of 0.4 mL/kg of 0.1 mol/L hydrochloric acid. Lung damage was confirmed with histopathology. RESULTS: The radiographs and diffraction-enhanced peak images revealed regions of atelectasis in the injured rat lung. The diffraction-enhanced peak images revealed the full extent of the lung with improved clarity relative to the chest radiographs, especially in the portion of the lower lobe that extended behind the diaphragm on the anteroposterior projection. CONCLUSIONS: For a rat model of gastric acid aspiration, DEI is capable of distinguishing between a healthy and an injured lung and more clearly than radiography reveals the full extent of the lung and the lung damage.

Detection of arterial calcification in mammograms by random walks.

A fully automatic algorithm is developed for breast arterial calcification extraction in mammograms. This algorithm is implemented in two major steps: a random-walk based tracking step and a compiling and linking step. With given seeds from detected calcification points, the tracking algorithm traverses the vesselness map by exploring the uncertainties of three tracking factors, i.e., traversing direction, jumping distance, and vesselness value, to generate all possible sampling paths. The compiling and linking algorithm further organizes and groups all sampling paths into calcified vessel tracts. The experimental results show that the performance of the proposed automatic calcification extraction algorithm is statistically close to that obtained by manual delineations.

Cancer cases from ACRIN digital mammographic imaging screening trial: radiologist analysis with use of a logistic regression model.

PURPOSE: To determine which factors contributed to the Digital Mammographic Imaging Screening Trial (DMIST) cancer detection results. MATERIALS AND METHODS: This project was HIPAA compliant and institutional review board approved. Seven radiologist readers reviewed the film hard-copy (screen-film) and digital mammograms in DMIST cancer cases and assessed the factors that contributed to lesion visibility on both types of images. Two multinomial logistic regression models were used to analyze the combined and condensed visibility ratings assigned by the readers to the paired digital and screen-film images. RESULTS: Readers most frequently attributed differences in DMIST cancer visibility to variations in image contrast--not differences in positioning or compression--between digital and screen-film mammography. The odds of a cancer being more visible on a digital mammogram--rather than being equally visible on digital and screen-film mammograms--were significantly greater for women with dense breasts than for women with nondense breasts, even with the data adjusted for patient age, lesion type, and mammography system (odds ratio, 2.28; P < .0001). The odds of a cancer being more visible at digital mammography--rather than being equally visible at digital and screen-film mammography--were significantly greater for lesions imaged with the General Electric digital mammography system than for lesions imaged with the Fischer (P = .0070) and Fuji (P = .0070) devices. CONCLUSION: The significantly better diagnostic accuracy of digital mammography, as compared with screen-film mammography, in women with dense breasts demonstrated in the DMIST was most likely attributable to differences in image contrast, which were most likely due to the inherent system performance improvements that are available with digital mammography. The authors conclude that the DMIST results were attributable primarily to differences in the display and acquisition characteristics of the mammography devices rather than to reader variability.

Comparison of soft-copy and hard-copy reading for full-field digital mammography.

PURPOSE: To compare radiologists' performance in detecting breast cancer when reading full-field digital mammographic (FFDM) images either displayed on monitors or printed on film. MATERIALS AND METHODS: This study received investigational review board approval and was HIPAA compliant, with waiver of informed consent. A reader study was conducted in which 26 radiologists read screening FFDM images displayed on high-resolution monitors (soft-copy digital) and printed on film (hard-copy digital). Three hundred thirty-three cases were selected from the Digital Mammography Image Screening Trial screening study (n = 49,528). Of these, 117 were from patients who received a diagnosis of breast cancer within 15 months of undergoing screening mammography. The digital mammograms were displayed on mammographic workstations and printed on film according to the manufacturer's specifications. Readers read both hard-copy and soft-copy images 6 weeks apart. Each radiologist read a subset of the total images. Twenty-two readers were assigned to evaluate images from one of three FFDM systems, and four readers were assigned to evaluate images from two mammographic systems. Each radiologist assigned a malignancy score on the basis of overall impression by using a seven-point scale, where 1 = definitely not malignant and 7 = definitely malignant. RESULTS: There were no significant differences in the areas under the receiver operating characteristic curves (AUCs) for the primary comparison. The AUCs for soft-copy and hard-copy were 0.75 and 0.76, respectively (95% confidence interval: -0.04, 0.01; P = .36). Secondary analyses showed no significant differences in AUCs on the basis of manufacturer type, lesion type, or breast density. CONCLUSION: Soft-copy reading does not provide an advantage in the interpretation of digital mammograms. However, the display formats were not optimized and display software remains an evolving process, particularly for soft-copy reading.

Accuracy of soft-copy digital mammography versus that of screen-film mammography according to digital manufacturer: ACRIN DMIST retrospective multireader study.

PURPOSE: To retrospectively compare the accuracy for cancer diagnosis of digital mammography with soft-copy interpretation with that of screen-film mammography for each digital equipment manufacturer, by using results of biopsy and follow-up as the reference standard. MATERIALS AND METHODS: The primary HIPAA-compliant Digital Mammographic Imaging Screening Trial (DMIST) was approved by the institutional review board of each study site, and informed consent was obtained. The approvals and consent included use of data for future HIPAA-compliant retrospective research. The American College of Radiology Imaging Network DMIST collected screening mammography studies performed by using both digital and screen-film mammography in 49 528 women (mean age, 54.6 years; range, 19-92 years). Digital mammography systems from four manufacturers (Fischer, Fuji, GE, and Hologic) were used. For each digital manufacturer, a cancer-enriched reader set of women screened with both digital and screen-film mammography in DMIST was constructed. Each reader set contained all cancer-containing studies known for each digital manufacturer at the time of reader set selection, together with a subset of negative and benign studies. For each reader set, six or 12 experienced radiologists attended two randomly ordered reading sessions 6 weeks apart. Each radiologist identified suspicious findings and rated suspicion of breast cancer in identified lesions by using a seven-point scale. Results were analyzed according to digital manufacturer by using areas under the receiver operating characteristic curve (AUCs), sensitivity, and specificity for soft-copy digital and screen-film mammography. Results for Hologic digital are not presented owing to the fact that few cancer cases were available. The implemented design provided 80% power to detect average AUC differences of 0.09, 0.08, and 0.06 for Fischer, Fuji, and GE, respectively. RESULTS: No significant difference in AUC, sensitivity, or specificity was found between Fischer, Fuji, and GE soft-copy digital and screen-film mammography. Large reader variations occurred with each modality. CONCLUSION: No statistically significant differences were found between soft-copy digital and screen-film mammography for Fischer, Fuji, and GE digital mammography equipment.

Correlation of HER-2/neu overexpression with mammography and age distribution in primary breast carcinomas.

RATIONALE AND OBJECTIVES: HER-2/neu is a valuable prognostic and therapeutic marker in primary breast carcinoma. The objective of this study was to determine the mammographic and patient characteristics (age) that correlate with HER-2/neu overexpression in primary breast carcinoma. MATERIALS AND METHODS: HER-2/neu characteristics and preoperative mammograms were available in 498 patients with 543 primary breast carcinomas (526 invasive carcinomas and 17 ductal carcinoma in situ). HER-2/neu status was determined by immunohistochemistry and fluorescence in situ hybridization. For evaluation of patient age distribution, age was divided into 5 groups. For mammography, breast composition and abnormal findings were categorized. Abnormal findings were divided into mass, calcification, architectural distortion, asymmetric density, or none. RESULTS: For age distribution, women under than 50 years had more frequent HER-2/neu overexpression than women aged 60-69 years (P < .05). On mammography, there was no significant correlation between breast composition and HER-2/neu status (P > .05). Calcifications were more significantly frequent in carcinomas with HER-2/neu overexpression (56%) than in those without HER-2/neu overexpression (40%) (P = .001). Of the 242 carcinomas with calcifications on mammography, fine linear morphology was more significantly frequent in carcinomas with HER-2/neu overexpression (20%) when compared with those without HER-2/neu overexpression (10%) (P = .023). Diffuse distribution of calcifications was more common in carcinomas with HER-2/neu overexpression (11%) compared with carcinomas without HER-2/neu overexpression (5%) (P = .051). CONCLUSION: HER-2/neu overexpression in primary breast carcinoma is correlated with patients' age (under age 50) and calcifications at mammography.

The positive predictive value for diagnosis of breast cancer full-field digital mammography versus film-screen mammography in the diagnostic mammographic population.

RATIONALE AND OBJECTIVES: Diagnostic mammography is performed on women with clinical symptoms that suggest breast cancer or women for whom further mammographic evaluation has been requested because of an abnormal screening mammography. We assessed whether the use of full-field digital mammography would improve the positive predictive value (PPV) for the diagnosis of breast cancer in a diagnostic population compared with film-screen mammography. MATERIALS AND METHODS: From January 2002 to December 2003, 11,621 patients underwent diagnostic mammography at the University of North Carolina Hospital, Chapel Hill. Among these 11,621 patients, 1400 lesions in 1121 patients underwent biopsy. We included the biopsy-performed lesions, so PPV3 was used for comparison of PPVs between film-screen mammography and full-field digital mammography. Six breast radiologists interpreted the images using the Breast Imaging Reporting and Data System of the American College of Radiology. PPV3s were compared between film-screen and full-field digital mammography in the entire study cohort and in specified subgroups according to different radiologists, breast density, and lesion type on mammography. The chi(2) and Fisher's exact tests were used for comparison of PPV3s between two modalities of mammography with the Bonferroni procedure for subgroup analysis. RESULTS: In the entire study cohort, PPV3s of full-field digital mammography and film-screen mammography were similar (difference in PPV3,-0.007; 95% confidence interval, -0.081 to 0.068; P = .8602). In predefined subgroups, there was no difference in PPV3 by the radiologist, breast density, or lesion type between two modalities of mammography (P > .005). CONCLUSION: There is no improvement in PPV for the diagnosis of breast cancer with full-field digital mammography compared with film-screen mammography in a large diagnostic population.

Comparison of calcification specificity in digital mammography using soft-copy display versus screen-film mammography.

OBJECTIVE: The purpose of this study was to compare specificity in the interpretation of calcifications in soft-copy reviewing of digital mammograms versus hard-copy reviewing of screen-film mammograms. MATERIALS AND METHODS: A total of 130 consecutive cases with calcifications (44 malignant and 86 benign) that had been evaluated with needle or surgical biopsy were collected. Both screen-film mammography and soft-copy digital mammography were obtained in the same patients under existing research protocols using Fischer Imaging's SenoScan (n = 71), Lorad's digital mammography system (n = 35), and GE Healthcare's Senographe 2000D (n = 24). Eight trained radiologists scored all lesions--cropped or masked to display just the region of interest--both on screen-film and soft-copy digital mammography with a month between reviews to reduce the effects of learning and memory. A 5-point malignancy scale was used, with 1 as definitely not, 2 as probably not, 3 as possibly, 4 as probably, and 5 as definitely. Reviewers were randomly assigned condition order, and images within each condition were randomly ordered. Repeated measures analysis of variance was used to test for differences between conditions in specificity computed via nonparametric receiver operating characteristic (ROC) study separately for each reviewer and condition. RESULTS: Across all reviewers, the mean specificity for 1 or 2 versus 3, 4, or 5 was 0.803 for screen-film mammography (range, 0.413-0.938; SD +/- 0.166) and 0.833 for soft-copy image (range, 0.375-0.951; SD +/- 0.187). Although not statistically significant (Student's t test p values from 0.19 to 0.99 across all cut points), numeric values of specificity were consistently higher for soft-copy versus screen-film mammography. No statistical significance in specificity was seen using all possible cut points in the 5-point scale, although the primary analysis used the cutpoint for differentiation between benign and malignant cases as 1 or 2 versus 3, 4, or 5. CONCLUSION: No statistically significant difference was shown in specificity achievable using soft-copy digital versus screen-film mammography in this study.

Comparison of full-field digital mammography to screen-film mammography with respect to contrast and spatial resolution in tissue equivalent breast phantoms.

To determine if the improved contrast resolution of full-field digital mammography (FFDM) with reduced spatial resolution allows for superior or equal phantom object detection compared with screen-film mammography (SFM). Tissue equivalent breast phantoms simulating an adipose to glandular ratio of 50/50,30/70, and 20/80 were imaged according to each manufacturers' recommendation with four full-field digital mammography units (Fuji, Sectra, Fischer, and General Electric) and a screen-film mammography unit (MammoMatII 2000, Siemens, Munich, Germany). A total of 20 images were obtained in both hard- and soft-copy formats. For the purpose of soft-copy display, the screen-film hard-copy images were digitized with a 50 microm micron scanner. Six radiologists, experts in breast imaging, and three physicists, experts in scoring mammography phantoms, participated in a reader study where each reader scored each phantom for visibility of line-pairs and for 24 objects (fibers, clusters of specks, and masses). The data were recorded, entered into a database, and analyzed by a mixed-effect model. The limiting spatial resolution in line-pairs per millimeter visible with the digital units was less, regardless of display modality used, than that provided by the screen-film unit. The difference was statistically significant for the General Electric (p < 0.01) and Fuji digital mammography units (p = 0.03). With respect to the number of visible objects, a statistically significant higher number could be detected with the screen-film unit as compared to the Fischer (p < 0.01) and Sectra (p < 0.01) digital mammography units, but there was no significant difference between the other digital units and screen film. Overall, there was significantly better performance on the 50/50 phantom than with the 30/70 and 20/80 phantoms (p = 0.01, p < 0.01) for object visibility. For the digital mammography units, soft-copy display performed better than hard-copy display for the Fischer and Sectra images, but worse for Fuji and General Electric. In addition, soft-copy display of digitized screen-film images was significantly better than hard-copy display (p =0.02) of the original screen films for object visibility, but worse for spatial resolution. The higher contrast resolution of the FFDM units tested did not result in improved detection of line-pair resolution or objects in the phantoms tested versus screen-film mammography. The phantom performance of a digital mammography unit seems to be influenced by the type of detection task (line-pair resolution versus object visibility), the display modality (soft-copy versus hard-copy) chosen to score the phantoms, and the parenchymal pattern composition of the phantom.

The effects of gray scale image processing on digital mammography interpretation performance.

RATIONALE AND OBJECTIVES: To determine the effects of three image-processing algorithms on diagnostic accuracy of digital mammography in comparison with conventional screen-film mammography. MATERIALS AND METHODS: A total of 201 cases consisting of nonprocessed soft copy versions of the digital mammograms acquired from GE, Fischer, and Trex digital mammography systems (1997-1999) and conventional screen-film mammograms of the same patients were interpreted by nine radiologists. The raw digital data were processed with each of three different image-processing algorithms creating three presentations-manufacturer's default (applied and laser printed to film by each of the manufacturers), MUSICA, and PLAHE-were presented in soft copy display. There were three radiologists per presentation. RESULTS: Area under the receiver operating characteristic curve for GE digital mass cases was worse than screen-film for all digital presentations. The area under the receiver operating characteristic for Trex digital mass cases was better, but only with images processed with the manufacturer's default algorithm. Sensitivity for GE digital mass cases was worse than screen film for all digital presentations. Specificity for Fischer digital calcifications cases was worse than screen film for images processed in default and PLAHE algorithms. Specificity for Trex digital calcifications cases was worse than screen film for images processed with MUSICA. CONCLUSION: Specific image-processing algorithms may be necessary for optimal presentation for interpretation based on machine and lesion type.

Diagnostic accuracy of digital mammography in patients with dense breasts who underwent problem-solving mammography: effects of image processing and lesion type.

PURPOSE: To determine effects of lesion type (calcification vs mass) and image processing on radiologist's performance for area under the receiver operating characteristic curve (AUC), sensitivity, and specificity for detection of masses and calcifications with digital mammography in women with mammographically dense breasts. MATERIALS AND METHODS: This study included 201 women who underwent digital mammography at seven U.S. and Canadian medical centers. Three image-processing algorithms were applied to the digital images, which were acquired with Fischer, General Electric, and Lorad digital mammography units. Eighteen readers participated in the reader study (six readers per algorithm). Baseline values for reader performance with screen-film mammograms were obtained through the additional interpretation of 179 screen-film mammograms. A repeated-measures analysis of covariance allowing unequal slopes was used in each of the nine analyses (AUC, sensitivity, and specificity for each of three machines). Bonferroni correction was used. RESULTS: Although lesion type did not affect the AUC or sensitivity for Fischer digital images, it did affect specificity (P =.0004). For the General Electric digital images, AUC, sensitivity, and specificity were not affected by lesion type. For Lorad digital images, the results strongly suggested that lesion type affected AUC and sensitivity (P <.0001). None of the three image-processing methods tested affected the AUC, sensitivity, or specificity for the Fischer, General Electric, or Lorad digital images. CONCLUSION: Findings in this study indicate that radiologist's interpretation accuracy in interpreting digital mammograms depends on lesion type. Interpretation accuracy was not influenced by the image-processing method.

Interpretation of digital mammograms: comparison of speed and accuracy of soft-copy versus printed-film display.

PURPOSE: To compare the speed and accuracy of the interpretations of digital mammograms by radiologists by using printed-film versus soft-copy display. MATERIALS AND METHODS: After being trained in interpretation of digital mammograms, eight radiologists interpreted 63 digital mammograms, all with old studies for comparison. All studies were interpreted by all readers in soft-copy and printed-film display, with interpretations of images in the same cases at least 1 month apart. Mammograms were interpreted in cases that included six biopsy-proved cancers and 20 biopsy-proved benign lesions, 20 cases of probably benign findings in patients who underwent 6-month follow-up, and 17 cases without apparent findings. Area under the receiver operating characteristic curve (A(z)), sensitivity, and specificity were calculated for soft-copy and printed-film display. RESULTS: There was no significant difference in the speed of interpretation, but interpretations with soft-copy display were slightly faster. The differences in A(z), sensitivity, and specificity were not significantly different; A(z) and sensitivity were slightly better for interpretations with printed film, and specificity was slightly better for interpretations with soft copy. CONCLUSION: Interpretation with soft-copy display is likely to be useful with digital mammography and is unlikely to significantly change accuracy or speed.