Production and clinical evaluation of breast lesion skin markers for automated three-dimensional ultrasonography of the breast: a pilot study.

OBJECTIVES: Automated ultrasound of the breast has the advantage to have the whole breast scanned by technicians. Consequently, feedback to the radiologist about concurrent focal abnormalities (e.g., palpable lesions) is lost. To enable marking of patient- or physician-reported focal abnormalities, we aimed to develop skin markers that can be used without disturbing the interpretability of the image. METHODS: Disk-shaped markers were casted out of silicone. In this IRB-approved prospective study, 16 patients were included with a mean age of 57 (39-85). In all patients, the same volume was imaged twice using an automated breast ultrasound system, once with and once without a marker in place. Nine radiologists from two medical centers filled scoring forms regarding image quality, image interpretation, and confidence in providing a diagnosis based on the images. RESULTS: Marker adhesion was sufficient for automated scanning. Observer scores showed a significant shift in scores from excellent to good regarding diagnostic yield/image quality (χ2, 15.99, p < 0.01), and image noise (χ2, 21.20, p < 0.01) due to marker presence. In 93% of cases, the median score of observers "agree" with the statement that marker-induced noise did not influence image interpretability. Marker presence did not interfere with confidence in diagnosis (χ2, 6.00, p = 0.20). CONCLUSION: Inexpensive, easy producible skin markers can be used for accurate lesion marking in automated ultrasound examinations of the breast while image interpretability is preserved. Any marker-induced noise and decreased image quality did not affect confidence in providing a diagnosis. KEY POINTS: • The use of a skin marker enables the reporting radiologist to identify a location which a patient is concerned about. • The developed skin marker can be used for accurate breast lesion marking in ultrasound examinations.

The added value of mammography in different age-groups of women with and without BRCA mutation screened with breast MRI.

BACKGROUND: Breast magnetic resonance imaging (MRI) is the most sensitive imaging method for breast cancer detection and is therefore offered as a screening technique to women at increased risk of developing breast cancer. However, mammography is currently added from the age of 30 without proven benefits. The purpose of this study is to investigate the added cancer detection of mammography when breast MRI is available, focusing on the value in women with and without BRCA mutation, and in the age groups above and below 50 years. METHODS: This retrospective single-center study evaluated 6553 screening rounds in 2026 women at increased risk of breast cancer (1 January 2003 to 1 January 2014). Risk category (BRCA mutation versus others at increased risk of breast cancer), age at examination, recall, biopsy, and histopathological diagnosis were recorded. Cancer yield, false positive recall rate (FPR), and false positive biopsy rate (FPB) were calculated using generalized estimating equations for separate age categories (< 40, 40-50, 50-60, ≥ 60 years). Numbers of screens needed to detect an additional breast cancer with mammography (NSN) were calculated for the subgroups. RESULTS: Of a total of 125 screen-detected breast cancers, 112 were detected by MRI and 66 by mammography: 13 cancers were solely detected by mammography, including 8 cases of ductal carcinoma in situ. In BRCA mutation carriers, 3 of 61 cancers were detected only on mammography, while in other women 10 of 64 cases were detected with mammography alone. While 77% of mammography-detected-only cancers were detected in women ≥ 50 years of age, mammography also added more to the FPR in these women. Below 50 years the number of mammographic examinations needed to find an MRI-occult cancer was 1427. CONCLUSIONS: Mammography is of limited added value in terms of cancer detection when breast MRI is available for women of all ages who are at increased risk. While the benefit appears slightly larger in women over 50 years of age without BRCA mutation, there is also a substantial increase in false positive findings in these women.

Multireader Study on the Diagnostic Accuracy of Ultrafast Breast Magnetic Resonance Imaging for Breast Cancer Screening.

OBJECTIVES: Breast cancer screening using magnetic resonance imaging (MRI) has limited accessibility due to high costs of breast MRI. Ultrafast dynamic contrast-enhanced breast MRI can be acquired within 2 minutes. We aimed to assess whether screening performance of breast radiologist using an ultrafast breast MRI-only screening protocol is as good as performance using a full multiparametric diagnostic MRI protocol (FDP). MATERIALS AND METHODS: The institutional review board approved this study, and waived the need for informed consent. Between January 2012 and June 2014, 1791 consecutive breast cancer screening examinations from 954 women with a lifetime risk of more than 20% were prospectively collected. All women were scanned using a 3 T protocol interleaving ultrafast breast MRI acquisitions in a full multiparametric diagnostic MRI protocol consisting of standard dynamic contrast-enhanced sequences, diffusion-weighted imaging, and T2-weighted imaging. Subsequently, a case set was created including all biopsied screen-detected lesions in this period (31 malignant and 54 benign) and 116 randomly selected normal cases with more than 2 years of follow-up. Prior examinations were included when available. Seven dedicated breast radiologists read all 201 examinations and 153 available priors once using the FDP and once using ultrafast breast MRI only in 2 counterbalanced and crossed-over reading sessions. RESULTS: For reading the FDP versus ultrafast breast MRI alone, sensitivity was 0.86 (95% confidence interval [CI], 0.81-0.90) versus 0.84 (95% CI, 0.78-0.88) (P = 0.50), specificity was 0.76 (95% CI, 0.74-0.79) versus 0.82 (95% CI, 0.79-0.84) (P = 0.002), positive predictive value was 0.40 (95% CI, 0.36-0.45) versus 0.45 (95% CI, 0.41-0.50) (P = 0.14), and area under the receiver operating characteristics curve was 0.89 (95% CI, 0.82-0.96) versus 0.89 (95% CI, 0.82-0.96) (P = 0.83). Ultrafast breast MRI reading was 22.8% faster than reading FDP (P < 0.001). Interreader agreement is significantly better for ultrafast breast MRI (κ = 0.730; 95% CI, 0.699-0.761) than for the FDP (κ = 0.665; 95% CI, 0.633-0.696). CONCLUSIONS: Breast MRI screening using only an ultrafast breast MRI protocol is noninferior to screening with an FDP and may result in significantly higher screening specificity and shorter reading time.

Automated Three-dimensional Breast US for Screening: Technique, Artifacts, and Lesion Characterization.

Automated breast (AB) ultrasonography (US) scanners have recently been brought to market for breast imaging. AB US devices use mechanically driven wide linear-array transducers that can image whole-breast US volumes in three dimensions. AB US is proposed for screening as a supplemental modality to mammography in women with dense breasts and overcomes important limitations of whole-breast US using handheld devices, such as operator dependence and limited reproducibility. A literature review of supplemental whole-breast US for screening was performed, which showed that both AB US and handheld US allow detection of mammographically negative early-stage invasive breast cancers but also increase the false-positive recall rate. Technicians with limited training can perform AB US; nevertheless, there is a learning curve for acquiring optimal images. Proper acquisition technique may allow avoidance of common artifacts that could impair interpretation of AB US results. Regardless, interpretation of AB US results can be challenging. This article reviews the US appearance of common benign and malignant lesions and presents examples of false-positive and false-negative AB US results. In situ breast cancers are rarely detected with supplemental whole-breast US. The most discriminating feature that separates AB US from handheld US is the retraction phenomenon on coronal reformatted images. The retraction phenomenon is rarely seen with benign findings but accompanies almost all breast cancers. In conclusion, women with dense breasts may benefit from supplemental AB US examinations. Understanding the pitfalls in acquisition technique and lesion interpretation, both of which can lead to false-positive recalls, might reduce the potential harm of performing supplemental AB US. Online supplemental material is available for this article. ©RSNA, 2018.

Dedicated computer-aided detection software for automated 3D breast ultrasound; an efficient tool for the radiologist in supplemental screening of women with dense breasts.

OBJECTIVES: To determine the effect of computer-aided-detection (CAD) software for automated breast ultrasound (ABUS) on reading time (RT) and performance in screening for breast cancer. MATERIAL AND METHODS: Unilateral ABUS examinations of 120 women with dense breasts were randomly selected from a multi-institutional archive of cases including 30 malignant (20/30 mammography-occult), 30 benign, and 60 normal cases with histopathological verification or ≥ 2 years of negative follow-up. Eight radiologists read once with (CAD-ABUS) and once without CAD (ABUS) with > 8 weeks between reading sessions. Readers provided a BI-RADS score and a level of suspiciousness (0-100). RT, sensitivity, specificity, PPV and area under the curve (AUC) were compared. RESULTS: Average RT was significantly shorter using CAD-ABUS (133.4 s/case, 95% CI 129.2-137.6) compared with ABUS (158.3 s/case, 95% CI 153.0-163.3) (p < 0.001). Sensitivity was 0.84 for CAD-ABUS (95% CI 0.79-0.89) and ABUS (95% CI 0.78-0.88) (p = 0.90). Three out of eight readers showed significantly higher specificity using CAD. Pooled specificity (0.71, 95% CI 0.68-0.75 vs. 0.67, 95% CI 0.64-0.70, p = 0.08) and PPV (0.50, 95% CI 0.45-0.55 vs. 0.44, 95% CI 0.39-0.49, p = 0.07) were higher in CAD-ABUS vs. ABUS, respectively, albeit not significantly. Pooled AUC for CAD-ABUS was comparable with ABUS (0.82 vs. 0.83, p = 0.53, respectively). CONCLUSION: CAD software for ABUS may decrease the time needed to screen for breast cancer without compromising the screening performance of radiologists. KEY POINTS: • ABUS with CAD software may speed up reading time without compromising radiologists' accuracy. • CAD software for ABUS might prevent non-detection of malignant breast lesions by radiologists. • Radiologists reading ABUS with CAD software might improve their specificity without losing sensitivity.

Surveillance of Women with the BRCA1 or BRCA2 Mutation by Using Biannual Automated Breast US, MR Imaging, and Mammography.

Purpose To evaluate a multimodal surveillance regimen including yearly full-field digital (FFD) mammography, dynamic contrast agent-enhanced (DCE) magnetic resonance (MR) imaging, and biannual automated breast (AB) ultrasonography (US) in women with BRCA1 and BRCA2 mutations. Materials and Methods This prospective multicenter trial enrolled 296 carriers of the BRCA mutation (153 BRCA1 and 128 BRCA2 carriers, and 15 women with first-degree untested relatives) between September 2010 and November 2012, with follow-up until November 2015. Participants underwent 2 years of intensified surveillance including biannual AB US, and routine yearly DCE MR imaging and FFD mammography. The surveillance performance for each modality and possible combinations were determined. Results Breast cancer was screening-detected in 16 women (age range, 33-58 years). Three interval cancers were detected by self-examination, all in carriers of the BRCA1 mutation under age 43 years. One cancer was detected in a carrier of the BRCA1 mutation with a palpable abnormality in the contralateral breast. One incidental breast cancer was detected in a prophylactic mastectomy specimen. Respectively, sensitivity of DCE MR imaging, FFD mammography, and AB US was 68.1% (14 of 21; 95% confidence interval [CI]: 42.9%, 85.8%), 37.2% (eight of 21; 95% CI: 19.8%, 58.7%), and 32.1% (seven of 21; 95% CI: 16.1%, 53.8%); specificity was 95.0% (643 of 682; 95% CI: 92.7%, 96.5%), 98.1% (638 of 652; 95% CI: 96.7%, 98.9%), and 95.1% (1030 of 1088; 95% CI: 93.5%, 96.3%); cancer detection rate was 2.0% (14 of 702), 1.2% (eight of 671), and 1.0% (seven of 711) per 100 women-years; and positive predictive value was 25.2% (14 of 54), 33.7% (nine of 23), and 9.5% (seven of 68). DCE MR imaging and FFD mammography combined yielded the highest sensitivity of 76.3% (16 of 21; 95% CI: 53.8%, 89.9%) and specificity of 93.6% (643 of 691; 95% CI: 91.3%, 95.3%). AB US did not depict additional cancers. FFD mammography yielded no additional cancers in women younger than 43 years, the mean age at diagnosis. In carriers of the BRCA2 mutation, sensitivity of FFD mammography with DCE MR imaging surveillance was 90.9% (10 of 11; 95% CI: 72.7%, 100%) and 60.0% (six of 10; 95% CI: 30.0%, 90.0%) in carriers of the BRCA1 mutation because of the high interval cancer rate in carriers of the BRCA1 mutation. Conclusion AB US may not be of added value to yearly FFD mammography and DCE MR imaging surveillance of carriers of the BRCA mutation. Study results suggest that carriers of the BRCA mutation younger than 40 years may not benefit from FFD mammography surveillance in addition to DCE MR imaging. © RSNA, 2017 Online supplemental material is available for this article.

Sonographic Phenotypes of Molecular Subtypes of Invasive Ductal Cancer in Automated 3-D Breast Ultrasound.

Our aim was to investigate whether Breast Imaging Reporting and Data System-Ultrasound (BI-RADS-US) lexicon descriptors can be used as imaging biomarkers to differentiate molecular subtypes (MS) of invasive ductal carcinoma (IDC) in automated breast ultrasound (ABUS). We included 125 IDCs diagnosed between 2010 and 2014 and imaged with ABUS at two institutes retrospectively. IDCs were classified as luminal A or B, HER2 enriched or triple negative based on reports of histopathologic analysis of surgical specimens. Two breast radiologists characterized all IDCs using the BI-RADS-US lexicon and specific ABUS features. Univariate and multivariate analyses were performed. A multinomial logistic regression model was built to predict the MSs from the imaging characteristics. BI-RADS-US descriptor margins and the retraction phenomenon are significantly associated with MSs (both p < 0.001) in both univariate and multivariate analysis. Posterior acoustic features and spiculation pattern severity were only significantly associated in univariate analysis (p < 0.001). Luminal A IDCs tend to have more prominent retraction patterns than luminal B IDCs. HER2-enriched and triple-negative IDCs present significantly less retraction than the luminal subtypes. The mean accuracy of MS prediction was 0.406. Overall, several BI-RADS-US descriptors and the coronal retraction phenomenon and spiculation pattern are associated with MSs, but prediction of MSs on ABUS is limited.

Compressed Sensing for Breast MRI: Resolving the Trade-Off Between Spatial and Temporal Resolution.

OBJECTIVE: Ultrafast dynamic contrast-enhanced magnetic resonance imaging of the breast enables assessment of the contrast inflow dynamics while providing images with diagnostic spatial resolution. However, the slice thickness of common ultrafast techniques still prevents multiplanar reconstruction. In addition, some temporal blurring of the enhancement characteristics occurs in case view-sharing is used. We evaluate a prototype compressed-sensing volume-interpolated breath-hold examination (CS-VIBE) sequence for ultrafast breast MRI that improves through plane spatial resolution and avoids temporal blurring while maintaining an ultrafast temporal resolution (less than 5 seconds per volume). Image quality (IQ) of the new sequence is compared with an ultrafast view-sharing sequence (time-resolved angiography with interleaved stochastic trajectories [TWIST]), and assessment of lesion morphology is compared with a regular T1-weighted 3D Dixon sequence (VIBE-DIXON) with an acquisition time of 91 seconds. MATERIALS AND METHODS: From April 2016 to October 2016, 30 women were scanned with the CS-VIBE sequence, replacing the routine ultrafast TWIST sequence in a hybrid breast MRI protocol. The need for informed consent was waived. All MRI scans were performed on a 3T MAGNETOM Skyra system (Siemens Healthcare, Erlangen, Germany) using a 16-channel bilateral breast coil. Two reader studies were conducted involving 5 readers. In the first study, overall IQ of CS-VIBE and TWIST in the axial plane was independently rated for 23 women for whom prior MRI examinations with TWIST were available. In addition, the presence of several types of artifacts was rated on a 5-point scale. The second study was conducted in women (n = 16) with lesions. In total, characteristics of 31 lesions (5 malignant and 26 benign) were described independently for CS-VIBE and VIBE-DIXON, according to the BI-RADS MRI-lexicon. In addition, a lesion conspicuity score was given. RESULTS: Using CS-VIBE, a much higher through-plane spatial resolution was achieved in the same acquisition time as with TWIST, without affecting in-plane IQ (P = 0.260). Time-resolved angiography with interleaved stochastic trajectories showed slightly more motion artifacts and infolding and ghosting artifacts compared with CS-VIBE, whereas CS-VIBE showed more breathing and pulsation artifacts. For morphologic assessment, intrareader agreement between CS-VIBE and the more time-consuming VIBE-DIXON was slight to almost perfect, and generally higher than interreader agreement. Mean sensitivity (84.0% and 92.0% for CS-VIBE and VIBE-DIXON, P = 0.500) and specificity (60.0% and 55.4% for CS-VIBE and VIBE-DIXON, P = 0.327) were comparable for both sequences. CONCLUSIONS: Compressed-sensing volume-interpolated breath-hold examination allows an increase of the through-plane spatial resolution of ultrafast dynamic contrast-enhanced magnetic resonance imaging compared with TWIST at a comparable in-plane IQ. Morphological assessment of lesions using CS-VIBE is comparable to VIBE-DIXON, which takes 18 times longer. Consequently, CS-VIBE enables 3D evaluation of breast lesions in ultrafast breast MRI.

Improvements of an objective model of compressed breasts undergoing mammography: Generation and characterization of breast shapes.

PURPOSE: To develop a set of accurate 2D models of compressed breasts undergoing mammography or breast tomosynthesis, based on objective analysis, to accurately characterize mammograms with few linearly independent parameters, and to generate novel clinically realistic paired cranio-caudal (CC) and medio-lateral oblique (MLO) views of the breast. METHODS: We seek to improve on an existing model of compressed breasts by overcoming detector size bias, removing the nipple and non-mammary tissue, pairing the CC and MLO views from a single breast, and incorporating the pectoralis major muscle contour into the model. The outer breast shapes in 931 paired CC and MLO mammograms were automatically detected with an in-house developed segmentation algorithm. From these shapes three generic models (CC-only, MLO-only, and joint CC/MLO) with linearly independent components were constructed via principal component analysis (PCA). The ability of the models to represent mammograms not used for PCA was tested via leave-one-out cross-validation, by measuring the average distance error (ADE). RESULTS: The individual models based on six components were found to depict breast shapes with accuracy (mean ADE-CC = 0.81 mm, ADE-MLO = 1.64 mm, ADE-Pectoralis = 1.61 mm), outperforming the joint CC/MLO model (P ≤ 0.001). The joint model based on 12 principal components contains 99.5% of the total variance of the data, and can be used to generate new clinically realistic paired CC and MLO breast shapes. This is achieved by generating random sets of 12 principal components, following the Gaussian distributions of the histograms of each component, which were obtained from the component values determined from the images in the mammography database used. CONCLUSION: Our joint CC/MLO model can successfully generate paired CC and MLO view shapes of the same simulated breast, while the individual models can be used to represent with high accuracy clinical acquired mammograms with a small set of parameters. This is the first step toward objective 3D compressed breast models, useful for dosimetry and scatter correction research, among other applications.

Segmentation of malignant lesions in 3D breast ultrasound using a depth-dependent model.

PURPOSE: Automated 3D breast ultrasound (ABUS) has been proposed as a complementary screening modality to mammography for early detection of breast cancers. To facilitate the interpretation of ABUS images, automated diagnosis and detection techniques are being developed, in which malignant lesion segmentation plays an important role. However, automated segmentation of cancer in ABUS is challenging since lesion edges might not be well defined. In this study, the authors aim at developing an automated segmentation method for malignant lesions in ABUS that is robust to ill-defined cancer edges and posterior shadowing. METHODS: A segmentation method using depth-guided dynamic programming based on spiral scanning is proposed. The method automatically adjusts aggressiveness of the segmentation according to the position of the voxels relative to the lesion center. Segmentation is more aggressive in the upper part of the lesion (close to the transducer) than at the bottom (far away from the transducer), where posterior shadowing is usually visible. The authors used Dice similarity coefficient (Dice) for evaluation. The proposed method is compared to existing state of the art approaches such as graph cut, level set, and smart opening and an existing dynamic programming method without depth dependence. RESULTS: In a dataset of 78 cancers, our proposed segmentation method achieved a mean Dice of 0.73 ± 0.14. The method outperforms an existing dynamic programming method (0.70 ± 0.16) on this task (p = 0.03) and it is also significantly (p < 0.001) better than graph cut (0.66 ± 0.18), level set based approach (0.63 ± 0.20) and smart opening (0.65 ± 0.12). CONCLUSIONS: The proposed depth-guided dynamic programming method achieves accurate breast malignant lesion segmentation results in automated breast ultrasound.

Consistency of breast density categories in serial screening mammograms: A comparison between automated and human assessment.

Reliable breast density measurement is needed to personalize screening by using density as a risk factor and offering supplemental screening to women with dense breasts. We investigated the categorization of pairs of subsequent screening mammograms into density classes by human readers and by an automated system. With software (VDG) and by four readers, including three specialized breast radiologists, 1000 mammograms belonging to 500 pairs of subsequent screening exams were categorized into either two or four density classes. We calculated percent agreement and the percentage of women that changed from dense to non-dense and vice versa. Inter-exam agreement (IEA) was calculated with kappa statistics. Results were computed for each reader individually and for the case that each mammogram was classified by one of the four readers by random assignment (group reading). Higher percent agreement was found with VDG (90.4%, CI 87.9-92.9%) than with readers (86.2-89.2%), while less plausible changes from non-dense to dense occur less often with VDG (2.8%, CI 1.4-4.2%) than with group reading (4.2%, CI 2.4-6.0%). We found an IEA of 0.68-0.77 for the readers using two classes and an IEA of 0.76-0.82 using four classes. IEA is significantly higher with VDG compared to group reading. The categorization of serial mammograms in density classes is more consistent with automated software than with a mixed group of human readers. When using breast density to personalize screening protocols, assessment with software may be preferred over assessment by radiologists.

Automated quality assessment in three-dimensional breast ultrasound images.

Automated three-dimensional breast ultrasound (ABUS) is a valuable adjunct to x-ray mammography for breast cancer screening of women with dense breasts. High image quality is essential for proper diagnostics and computer-aided detection. We propose an automated image quality assessment system for ABUS images that detects artifacts at the time of acquisition. Therefore, we study three aspects that can corrupt ABUS images: the nipple position relative to the rest of the breast, the shadow caused by the nipple, and the shape of the breast contour on the image. Image processing and machine learning algorithms are combined to detect these artifacts based on 368 clinical ABUS images that have been rated manually by two experienced clinicians. At a specificity of 0.99, 55% of the images that were rated as low quality are detected by the proposed algorithms. The areas under the ROC curves of the single classifiers are 0.99 for the nipple position, 0.84 for the nipple shadow, and 0.89 for the breast contour shape. The proposed algorithms work fast and reliably, which makes them adequate for online evaluation of image quality during acquisition. The presented concept may be extended to further image modalities and quality aspects.

Multiplanar Reconstructions of 3D Automated Breast Ultrasound Improve Lesion Differentiation by Radiologists.

RATIONALE AND OBJECTIVES: To investigate the value of multiplanar reconstructions (MPRs) of automated three-dimensional (3D) breast ultrasound (ABUS) compared to transverse evaluation only, in differentiation of benign and malignant breast lesions. MATERIALS AND METHODS: Five breast radiologists evaluated ABUS scans of 96 female patients with biopsy-proven abnormalities (36 malignant and 60 benign). They classified the most suspicious lesion based on the breast imaging reporting and data system (BI-RADS) lexicon using the transverse scans only. A likelihood-of-malignancy (LOM) score (0-100) and a BI-RADS final assessment were assigned. Thereafter, the MPR was provided and readers scored the cases again. In addition, they rated the presence of spiculation and retraction in the coronal plane on a five-point scale called Spiculation and Retraction Severity Index (SRSI). Reader performance was analyzed with receiver-operating characteristics analysis. RESULTS: The area under the curve increased from 0.82 to 0.87 (P = .01) after readers were shown the reconstructed planes. The SRSI scores are highly correlated (Spearman's r) with the final LOM scores (range, r = 0.808-0.872) and ΔLOM scores (range, r = 0.525-0.836). Readers downgraded 3%-18% of the biopsied benign lesions to BI-RADS 2 after MPR evaluation. Inter-reader agreement for SRSI was substantial (intraclass correlation coefficient, 0.617). Inter-reader agreement of the BI-RADS final assessment improved from 0.367 to 0.536 after MPRs were read. CONCLUSIONS: Full 3D evaluation of ABUS using MPR improves differentiation of breast lesions in comparison to evaluating only transverse planes. Results suggest that the added value of MPR might be related to visualization of spiculation and retraction patterns in the coronal reconstructions.

Computer-aided detection of breast cancers using Haar-like features in automated 3D breast ultrasound.

PURPOSE: Automated 3D breast ultrasound (ABUS) has gained interest in breast imaging. Especially for screening women with dense breasts, ABUS appears to be beneficial. However, since the amount of data generated is large, the risk of oversight errors is substantial. Computer aided detection (CADe) may be used as a second reader to prevent oversight errors. When CADe is used in this fashion, it is essential that small cancers are detected, while the number of false positive findings should remain acceptable. In this work, the authors improve their previously developed CADe system in the initial candidate detection stage. METHODS: The authors use a large number of 2D Haar-like features to differentiate lesion structures from false positives. Using a cascade of GentleBoost classifiers that combines these features, a likelihood score, highly specific for small cancers, can be efficiently computed. The likelihood scores are added to the previously developed voxel features to improve detection. RESULTS: The method was tested in a dataset of 414 ABUS volumes with 211 cancers. Cancers had a mean size of 14.72 mm. Free-response receiver operating characteristic analysis was performed to evaluate the performance of the algorithm with and without using the aforementioned Haar-like feature likelihood scores. After the initial detection stage, the number of missed cancer was reduced by 18.8% after adding Haar-like feature likelihood scores. CONCLUSIONS: The proposed technique significantly improves our previously developed CADe system in the initial candidate detection stage.

A novel approach to contrast-enhanced breast magnetic resonance imaging for screening: high-resolution ultrafast dynamic imaging.

OBJECTIVES: The use of breast magnetic resonance imaging (MRI) as screening tool has been stalled by high examination costs. Scan protocols have lengthened to optimize specificity. Modern view-sharing sequences now enable ultrafast dynamic whole-breast MRI, allowing much shorter and more cost-effective procedures. This study evaluates whether dynamic information from ultrafast breast MRI can be used to replace standard dynamic information to preserve accuracy. MATERIALS AND METHODS: We interleaved 20 ultrafast time-resolved angiography with stochastic trajectory (TWIST) acquisitions (0.9 × 1 × 2.5 mm, temporal resolution, 4.3 seconds) during contrast inflow in a regular high-resolution dynamic MRI protocol. A total of 160 consecutive patients with 199 enhancing abnormalities (95 benign and 104 malignant) were included. The maximum slope of the relative enhancement versus time curve (MS) obtained from the TWIST and curve type obtained from the regular dynamic sequence as defined in the breast imaging reporting and data system (BIRADS) lexicon were recorded. Diagnostic performance was compared using receiver operating characteristic analysis. RESULTS: All lesions were visible on both the TWIST and standard series. Maximum slope allows discrimination between benign and malignant disease with high accuracy (area under the curve, 0.829). Types of MS were defined in analogy to BIRADS curve types: MS type 3 implies a high risk of malignancy (MS >13.3%/s; specificity, 85%), MS type 2 yields intermediate risk (MS <13.3%/s and >6.4%/s), and MS type 1 implies a low risk (MS <6.4%/s; sensitivity, 90%). This simplification provides a much higher accuracy than the much lengthier BIRADS curve type analysis does (area under the curve, 0.812 vs 0.692; P = 0.0061). CONCLUSIONS: Ultrafast dynamic breast MRI allows detection of breast lesions and classification with high accuracy using MS. This allows substantial shortening of scan protocols and hence reduces imaging costs, which is beneficial especially for screening.

Incorporating texture features in a computer-aided breast lesion diagnosis system for automated three-dimensional breast ultrasound.

We investigated the benefits of incorporating texture features into an existing computer-aided diagnosis (CAD) system for classifying benign and malignant lesions in automated three-dimensional breast ultrasound images. The existing system takes into account 11 different features, describing different lesion properties; however, it does not include texture features. In this work, we expand the system by including texture features based on local binary patterns, gray level co-occurrence matrices, and Gabor filters computed from each lesion to be diagnosed. To deal with the resulting large number of features, we proposed a combination of feature-oriented classifiers combining each group of texture features into a single likelihood, resulting in three additional features used for the final classification. The classification was performed using support vector machine classifiers, and the evaluation was done with 10-fold cross validation on a dataset containing 424 lesions (239 benign and 185 malignant lesions). We compared the classification performance of the CAD system with and without texture features. The area under the receiver operating characteristic curve increased from 0.90 to 0.91 after adding texture features ([Formula: see text]).