Mammographic predictors of the presence and size of invasive carcinomas associated with malignant microcalcification lesions without a mass

Clinical and Imaging Characteristics of Contrast-enhanced Mammography and MRI to Distinguish Microinvasive Carcinoma from Ductal Carcinoma In situ

RATIONALE AND OBJECTIVES

The prognosis of ductal carcinoma in situ with microinvasion (DCISM) is more similar to that of small invasive ductal carcinoma (IDC) than to pure ductal carcinoma in situ (DCIS). It is particularly important to accurately distinguish between DCISM and DCIS. The present study aims to compare the clinical and imaging characteristics of contrast-enhanced mammography (CEM) and magnetic resonance imaging (MRI) between DCISM and pure DCIS, and to identify predictive factors of microinvasive carcinoma, which may contribute to a comprehensive understanding of DCISM in clinical diagnosis and support surveillance strategies, such as surgery, radiation, and other treatment decisions.

MATERIALS AND METHODS

Forty-seven female patients diagnosed with DCIS were included in the study from May 2019 to August 2023. Patients were further divided into two groups based on pathological diagnosis: DCIS and DCISM. Clinical and imaging characteristics of these two groups were analyzed statistically. The independent clinical risk factors were selected using multivariate logistic regression and used to establish the logistic model [Logit(P)]. The diagnostic performance of independent predictors was assessed and compared using receiver operating characteristic (ROC) analysis and DeLong's test.

RESULTS

In CEM, the maximum cross-sectional area (CSAmax), the percentage signal difference between the enhancing lesion and background in the craniocaudal and mediolateral oblique projection (%RSCC, and %RSMLO) were found to be significantly higher for DCISM compared to DCIS (p = 0.001; p < 0.001; p = 0.008). Additionally, there were noticeable statistical differences in the patterns of enhancement morphological distribution (EMD) and internal enhancement pattern (IEP) between DCIS and DCISM (p = 0.047; p = 0.008). In MRI, only CSAmax (p = 0.012) and IEP (p = 0.020) showed significant statistical differences. The multivariate regression analysis suggested that CSAmax (in CEM or MR) and %RSCC were independent predictors of DCISM (all p < 0.05). The area under the curve (AUC) of CSAmax (CEM), %RSCC (CEM), Logit(P) (CEM), and CSAmax (MR) were 0.764, 0.795, 0.842, and 0.739, respectively. There were no significant differences in DeLong's test for these values (all p > 0.10). DCISM was significantly associated with high nuclear grade, comedo type, high axillary lymph node (ALN) metastasis, and high Ki-67 positivity compared to DCIS (all p < 0.05).

CONCLUSION

The tumor size (CSAmax), enhancement index (%RS), and internal enhancement pattern (IEP) were highly indicative of DCISM. DCISM tends to express more aggressive pathological features, such as high nuclear grade, comedo-type necrosis, ALN metastasis, and Ki-67 overexpression. As with MRI, CEM has the capability to help predict when DCISM is accompanying DCIS.

Mammographic features and risk of breast cancer death among women with invasive screen-detected cancer in BreastScreen Norway 1996-2020

OBJECTIVES

We explored associations between mammographic features and risk of breast cancer death among women with small (<15 mm) and large (≥15 mm) invasive screen-detected breast cancer.

METHODS

We included data from 17,614 women diagnosed with invasive breast cancer as a result of participation in BreastScreen Norway, 1996-2020. Data on mammographic features (mass, spiculated mass, architectural distortion, asymmetric density, density with calcification and calcification alone), tumour diameter and cause of death was obtained from the Cancer Registry of Norway. Cox regression was used to estimate hazard ratios (HR) with 95% confidence intervals (CI) for breast cancer death by mammographic features using spiculated mass as reference, adjusting for age, tumour diameter and lymph node status. All analyses were dichotomised by tumour diameter (small versus large).

RESULTS

Mean age at diagnosis was 60.8 (standard deviation, SD=5.8) for 10,160 women with small tumours and 60.0 (SD=5.8) years for 7454 women with large tumours. The number of breast cancer deaths was 299 and 634, respectively. Mean time from diagnosis to death was 8.7 (SD=5.0) years for women with small tumours and 7.2 (4.6) years for women with large tumours. Using spiculated mass as reference, adjusted HR for breast cancer death among women with small tumours was 2.48 (95% CI 1.67-3.68) for calcification alone, while HR for women with large tumours was 1.30 (95% CI 1.02-1.66) for density with calcification.

CONCLUSIONS

Small screen-detected invasive cancers presenting as calcification and large screen-detected cancers presenting as density with calcification were associated with the highest risk of breast cancer death.

CLINICAL RELEVANCE STATEMENT

Small tumours (<15 mm) presented as calcification alone and large tumours (≥ 15 mm) presented as density with calcification were associated with the highest risk of breast cancer death among women with screen-detected invasive breast cancer diagnosed 1996-2020.

KEY POINTS

• Women diagnosed with invasive screen-detected breast cancer 1996-2020 were analysed. • Small screen-detected cancers presenting as calcification alone resulted in the highest risk of breast cancer death. • Large screen-detected cancers presenting as density with calcification resulted in the highest risk of breast cancer death.

Clinicopathological and prognostic value of calcification morphology descriptors in ductal carcinoma in situ of the breast: a systematic review and meta-analysis

BACKGROUND

Calcifications on mammography can be indicative of breast cancer, but the prognostic value of their appearance remains unclear. This systematic review and meta-analysis aimed to evaluate the association between mammographic calcification morphology descriptors (CMDs) and clinicopathological factors.

METHODS

A comprehensive literature search in Medline via Ovid, Embase.com, and Web of Science was conducted for articles published between 2000 and January 2022 that assessed the relationship between CMDs and clinicopathological factors, excluding case reports and review articles. The risk of bias and overall quality of evidence were evaluated using the QUIPS tool and GRADE. A random-effects model was used to synthesize the extracted data. This systematic review is reported according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA).

RESULTS

Among the 4715 articles reviewed, 29 met the inclusion criteria, reporting on 17 different clinicopathological factors in relation to CMDs. Heterogeneity between studies was present and the overall risk of bias was high, primarily due to small, inadequately described study populations. Meta-analysis demonstrated significant associations between fine linear calcifications and high-grade DCIS [pooled odds ratio (pOR), 4.92; 95% confidence interval (CI), 2.64-9.17], (comedo)necrosis (pOR, 3.46; 95% CI, 1.29-9.30), (micro)invasion (pOR, 1.53; 95% CI, 1.03-2.27), and a negative association with estrogen receptor positivity (pOR, 0.33; 95% CI, 0.12-0.89).

CONCLUSIONS

CMDs detected on mammography have prognostic value, but there is a high level of bias and variability between current studies. In order for CMDs to achieve clinical utility, standardization in reporting of CMDs is necessary.

CRITICAL RELEVANCE STATEMENT

Mammographic calcification morphology descriptors (CMDs) have prognostic value, but in order for CMDs to achieve clinical utility, standardization in reporting of CMDs is necessary.

SYSTEMATIC REVIEW REGISTRATION

CRD42022341599 KEY POINTS: • Mammographic calcifications can be indicative of breast cancer. • The prognostic value of mammographic calcifications is still unclear. • Specific mammographic calcification morphologies are related to lesion aggressiveness. • Variability between studies necessitates standardization in calcification evaluation to achieve clinical utility.

Ductal Carcinoma in Situ: A Detailed Review of Current Practices

Ductal carcinoma in situ is a challenge for breast surgeons, beginning with its difficult radiological detection and continuing with its contentious multimodal treatment and management. It is becoming more common as a result of widespread screening mammography and usually manifests as a cluster of calcifications. Patients are usually asymptomatic or present with a small, palpable lump. It is, however, a premalignant lesion that has the potential to progress to invasive carcinoma and is treated similarly with multimodal therapy. Treatment options currently include total or simple mastectomy with sentinel lymph node biopsy or lumpectomy with radiation. Tamoxifen and human epidermal growth factor receptor two suppression therapy are examples of adjuvant therapy. A review of consensus guidelines and literature was performed, in which we included the available online literature on the concerned topic from 2000-2022. This article is not a complete review of all the available literature; rather, it is a comprehensive review of the topic and its current management guidelines.

Detection of microcalcifications in photon-counting dedicated breast-CT using a deep convolutional neural network: Proof of principle

OBJECTIVE

In this study, we investigate the feasibility of a deep Convolutional Neural Network (dCNN), trained with mammographic images, to detect and classify microcalcifications (MC) in breast-CT (BCT) images.

METHODS

This retrospective single-center study was approved by the local ethics committee. 3518 icons generated from 319 mammograms were classified into three classes: "no MC" (1121), "probably benign MC" (1332), and "suspicious MC" (1065). A dCNN was trained (70% of data), validated (20%), and tested on a "real-world" dataset (10%). The diagnostic performance of the dCNN was tested on a subset of 60 icons, generated from 30 mammograms and 30 breast-CT images, and compared to human reading. ROC analysis was used to calculate diagnostic performance. Moreover, colored probability maps for representative BCT images were calculated using a sliding-window approach.

RESULTS

The dCNN reached an accuracy of 98.8% on the "real-world" dataset. The accuracy on the subset of 60 icons was 100% for mammographic images, 60% for "no MC", 80% for "probably benign MC" and 100% for "suspicious MC". Intra-class correlation between the dCNN and the readers was almost perfect (0.85). Kappa values between the two readers (0.93) and the dCNN were almost perfect (reader 1: 0.85 and reader 2: 0.82). The sliding-window approach successfully detected suspicious MC with high image quality. The diagnostic performance of the dCNN to classify benign and suspicious MC was excellent with an AUC of 93.8% (95% CI 87, 4%-100%).

CONCLUSION

Deep convolutional networks can be used to detect and classify benign and suspicious MC in breast-CT images.

Calcinosis universalis and breast cancer: A distinctive association

Calcinosis universalis (CU) is characterised by diffuse deposition of insoluble calcium salt in the skin, subcutaneous tissue or organs. Calcium deposits in the breast may be associated with an increased risk for developing breast cancer. We present a case of a 65-year-old woman diagnosed with CU secondary to undifferentiated connective tissue disease. She developed progressive calcification of her skin, which did not improve with oral medications aimed at reducing the calcification. Investigations to look for possible causes of calcification were all unremarkable. During follow-up, calcification was also found in both her breasts. Initial mammography was reported as fibroadenoma. However, 3 years later, she returned with metastatic breast cancer which presented with a massive pleural effusion of the right lung. Calcinosis universalis should now be considered as a risk factor for breast cancer.

Improved automated early detection of breast cancer based on high resolution 3D micro-CT microcalcification images

Background

The detection of suspicious microcalcifications on mammography represents one of the earliest signs of a malignant breast tumor. Assessing microcalcifications’ characteristics based on their appearance on 2D breast imaging modalities is in many cases challenging for radiologists. The aims of this study were to: (a) analyse the association of shape and texture properties of breast microcalcifications (extracted by scanning breast tissue with a high resolution 3D scanner) with malignancy, (b) evaluate microcalcifications’ potential to diagnose benign/malignant patients.

Methods

Biopsy samples of 94 female patients with suspicious microcalcifications detected during a mammography, were scanned using a micro-CT scanner at a resolution of 9 μm. Several preprocessing techniques were applied on 3504 extracted microcalcifications. A high amount of radiomic features were extracted in an attempt to capture differences among microcalcifications occurring in benign and malignant lesions. Machine learning algorithms were used to diagnose: (a) individual microcalcifications, (b) samples. For the samples, several methodologies to combine individual microcalcification results into sample results were evaluated.

Results

We could classify individual microcalcifications with 77.32% accuracy, 61.15% sensitivity and 89.76% specificity. At the sample level diagnosis, we achieved an accuracy of 84.04%, sensitivity of 86.27% and specificity of 81.39%.

Conclusions

By studying microcalcifications’ characteristics at a level of details beyond what is currently possible by using conventional breast imaging modalities, our classification results demonstrated a strong association between breast microcalcifications and malignancies. Microcalcification’s texture features extracted in transform domains, have higher discriminating power to classify benign/malignant individual microcalcifications and samples compared to pure shape-features.

Microcalcifications, mammographic breast density, and risk of breast cancer: a cohort study

BACKGROUND

Breast density and microcalcifications are strongly associated with the risk of breast cancer. However, few studies have evaluated the combined association between these two factors and breast cancer risk. We investigated the association between breast density, microcalcifications, and risk of breast cancer.

METHODS

This cohort study included 3,910,815 women aged 40-74 years who were screened for breast cancer between 2009 and 2010 and followed up until 2020. The National Health Insurance Service database includes national health-screening results from the national breast cancer screening program, which is an organized screening program provided every 2 years for all women aged 40 years or older. Breast density was assessed based on the Breast Imaging Reporting and Data System (BI-RADS) 4^th edition, mostly through visual assessment by radiologists. The presence or absence of microcalcifications was obtained from the mammographic results. Cox proportional hazard regression for breast cancer risk was used to estimate hazard ratios (aHRs) adjusted for breast cancer risk factors.

RESULTS

A total of 58,315 women developed breast cancer during a median follow-up of 10.8 years. Women with breast cancer had a higher proportion of microcalcifications than women without breast cancer (0.9% vs. 0.3%). After adjusting for breast density, women with microcalcification had a 3.07-fold (95% confidence interval [CI] 2.82-3.35) increased risk of breast cancer compared to women without microcalcification. The combined association between microcalcification and breast density dramatically increased the risk of breast cancer, corresponding to a higher level of breast density. Among postmenopausal women, the highest risk group was women with BI-RADS 4 and microcalcification. These women had more than a sevenfold higher risk than women with BI-RADS 1 and non-microcalcification (aHR, 7.26; 95% CI 5.01-10.53).

CONCLUSION

Microcalcification is an independent risk factor for breast cancer, and its risk is elevated when combined with breast density.

Mammographic microcalcifications and risk of breast cancer

BACKGROUND

Mammographic microcalcifications are considered early signs of breast cancer (BC). We examined the association between microcalcification clusters and the risk of overall and subtype-specific BC. Furthermore, we studied how mammographic density (MD) influences the association between microcalcification clusters and BC risk.

METHODS

We used a prospective cohort (n = 53,273) of Swedish women with comprehensive information on BC risk factors and mammograms. The total number of microcalcification clusters and MD were measured using a computer-aided detection system and the STRATUS method, respectively. Cox regressions and logistic regressions were used to analyse the data.

RESULTS

Overall, 676 women were diagnosed with BC. Women with ≥3 microcalcification clusters had a hazard ratio [HR] of 2.17 (95% confidence interval [CI] = 1.57-3.01) compared to women with no clusters. The estimated risk was more pronounced in premenopausal women (HR = 2.93; 95% CI = 1.67-5.16). For postmenopausal women, microcalcification clusters and MD had a similar influence on BC risk. No interaction was observed between microcalcification clusters and MD. Microcalcification clusters were significantly associated with in situ breast cancer (odds ratio: 2.03; 95% CI = 1.13-3.63).

CONCLUSIONS

Microcalcification clusters are an independent risk factor for BC, with a higher estimated risk in premenopausal women. In postmenopausal women, microcalcification clusters have a similar association with BC as baseline MD.

Predictors of mammographic microcalcifications

We examined the association between established risk factors for breast cancer and microcalcification clusters and their asymmetry. A cohort study of 53 273 Swedish women aged 30 to 80 years, with comprehensive information on breast cancer risk factors and mammograms, was conducted. Total number of microcalcification clusters and the average mammographic density area were measured using a Computer Aided Detection system and the STRATUS method, respectively. A polygenic risk score for breast cancer, including 313 single nucleotide polymorphisms, was calculated for those women genotyped (N = 7387). Odds ratios (ORs) and 95% confidence intervals (CIs), with adjustment for potential confounders, were estimated. Age was strongly associated with microcalcification clusters. Both high mammographic density (>40 cm2 ), and high polygenic risk score (80-100 percentile) were associated with microcalcification clusters, OR = 2.08 (95% CI = 1.93-2.25) and OR = 1.22 (95% CI = 1.06-1.48), respectively. Among reproductive risk factors, life-time breastfeeding duration >1 year was associated with microcalcification clusters OR = 1.22 (95% CI = 1.03-1.46). The association was confined to postmenopausal women. Among lifestyle risk factors, women with a body mass index ≥30 kg/m2 had the lowest risk of microcalcification clusters OR = 0.79 (95% CI = 0.73-0.85) and the association was stronger among premenopausal women. Our results suggest that age, mammographic density, genetic predictors of breast cancer, having more than two children, longer duration of breast-feeding are significantly associated with increased risk of microcalcification clusters. However, most lifestyle risk factors for breast cancer seem to protect against presence of microcalcification clusters. More research is needed to study biological mechanisms behind microcalcifications formation.

Convolutional neural networks for breast cancer detection in mammography: A survey

Despite its proven record as a breast cancer screening tool, mammography remains labor-intensive and has recognized limitations, including low sensitivity in women with dense breast tissue. In the last ten years, Neural Network advances have been applied to mammography to help radiologists increase their efficiency and accuracy. This survey aims to present, in an organized and structured manner, the current knowledge base of convolutional neural networks (CNNs) in mammography. The survey first discusses traditional Computer Assisted Detection (CAD) and more recently developed CNN-based models for computer vision in mammography. It then presents and discusses the literature on available mammography training datasets. The survey then presents and discusses current literature on CNNs for four distinct mammography tasks: (1) breast density classification, (2) breast asymmetry detection and classification, (3) calcification detection and classification, and (4) mass detection and classification, including presenting and comparing the reported quantitative results for each task and the pros and cons of the different CNN-based approaches. Then, it offers real-world applications of CNN CAD algorithms by discussing current Food and Drug Administration (FDA) approved models. Finally, this survey highlights the potential opportunities for future work in this field. The material presented and discussed in this survey could serve as a road map for developing CNN-based solutions to improve mammographic detection of breast cancer further.

Technique and clinical applications of dual-energy contrast-enhanced digital mammography (CEDM) in breast cancer evaluation: a pictorial essay

This pictorial essay illustrates an overview of the basic technique used in acquiring dual-energy contrast-enhanced digital mammography (CEDM) images and its potential clinical applications in regular practice. CEDM may be used as a low-cost alternative to magnetic resonance imaging (MRI), as a problem-solving tool in clinical practice and for therapeutic planning of breast cancer, which may include high-risk screening, dense breast evaluation, mammographically equivocal lesions, local staging, treatment response evaluation, and post treatment follow-up. We share our experience of CEDM at a tertiary care cancer hospital.

Recent advances in X-ray imaging of breast tissue: From two- to three-dimensional imaging

Breast cancer is a globally widespread disease whose detection has already been significantly improved by the introduction of screening programs. Nevertheless, mammography suffers from low soft tissue contrast and the superposition of diagnostically relevant anatomical structures as well as from low values for sensitivity and specificity especially for dense breast tissue. In recent years, two techniques for X-ray breast imaging have been developed that bring advances for the early detection of breast cancer. Grating-based phase-contrast mammography is a new imaging technique that is able to provide three image modalities simultaneously (absorption-contrast, phase-contrast and dark-field signal). Thus, an enhanced detection and delineation of cancerous structures in the phase-contrast image and an improved visualization and characterization of microcalcifications in the dark-field image is possible. Furthermore, latest studies about this approach show that dose-compatible imaging with polychromatic X-ray sources is feasible. In order to additionally overcome the limitations of projection-based imaging, efforts were also made towards the development of breast computed tomography (BCT), which recently led to the first clinical installation of an absorption-based BCT system. Further research combining the benefits of both imaging technologies is currently in progress. This review article summarizes the latest advances in phase-contrast imaging for the female breast (projection-based and three-dimensional view) with special focus on possible clinical implementations in the future.

Missed Breast Cancer: Effects of Subconscious Bias and Lesion Characteristics

Medical errors are a substantial cause of morbidity and mortality and the third leading cause of death in the United States. Errors resulting in missed breast cancer are the most common reason for medical malpractice lawsuits against all physicians. Missed breast cancers are breast malignancies that are detectable at retrospective review of a previously obtained mammogram that was prospectively reported as showing negative, benign, or probably benign findings. Investigators in prior studies have found that up to 35% of both interval cancers and screen-detected cancers could be classified as missed. As such, in conjunction with having awareness of the most common misleading appearances of breast cancer, it is important to understand the cognitive processes and unconscious biases that can impact image interpretation, thereby helping to decrease the number of missed breast cancers. The various cognitive processes that lead to unconscious bias in breast imaging, such as satisfaction of search, inattention blindness, hindsight, anchoring, premature closing, and satisfaction of reporting, are outlined in this pictorial review of missed breast cancers. In addition, strategies for reducing the rates of these missed cancers are highlighted. The most commonly missed and misinterpreted lesions, including stable lesions, benign-appearing masses, one-view findings, developing asymmetries, subtle calcifications, and architectural distortion, also are reviewed. This information will help illustrate why and how breast cancers are missed and aid in the development of appropriate minimization strategies in breast imaging. ^©RSNA, 2020.

Calcification Microstructure Reflects Breast Tissue Microenvironment

Microcalcifications are important diagnostic indicators of disease in breast tissue. Tissue microenvironments differ in many aspects between normal and cancerous cells, notably extracellular pH and glycolytic respiration. Hydroxyapatite microcalcification microstructure is also found to differ between tissue pathologies, including differential ion substitutions and the presence of additional crystallographic phases. Distinguishing between tissue pathologies at an early stage is essential to improve patient experience and diagnostic accuracy, leading to better disease outcome. This study explores the hypothesis that microenvironment features may become immortalised within calcification crystallite characteristics thus becoming indicators of tissue pathology. In total, 55 breast calcifications incorporating 3 tissue pathologies (benign - B2, ductal carcinoma in-situ - B5a and invasive malignancy - B5b) from archive formalin-fixed paraffin-embedded core needle breast biopsies were analysed using X-ray diffraction. Crystallite size and strain were determined from 548 diffractograms using Williamson-Hall analysis. There was an increased crystallinity of hydroxyapatite with tissue malignancy compared to benign tissue. Coherence length was significantly correlated with pathology grade in all basis crystallographic directions (P < 0.01), with a greater difference between benign and in situ disease compared to in-situ disease and invasive malignancy. Crystallite size and non-uniform strain contributed to peak broadening in all three pathologies. Furthermore, crystallite size and non-uniform strain normal to the basal planes increased significantly with malignancy (P < 0.05). Our findings support the view that tissue microenvironments can influence differing formation mechanisms of hydroxyapatite through acidic precursors, leading to differential substitution of carbonate into the hydroxide and phosphate sites, causing significant changes in crystallite size and non-uniform strain.

Metachronous vulvar ectopic breast cancer, a case report and literature review

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When two or more primary tumors arise at the same time, they are considered synchronous.

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A metachronous tumor in a new primary that develops after an initial cancer diagnosis.

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The diagnosis of vulvar breast cancer is primarily histopathologic, based on morphology and immunostaining.

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Identifying a cancer as a metastasis versus as synchronous/metachronous significantly impacts staging and treatment.

A multi-context CNN ensemble for small lesion detection

In this paper, we propose a novel method for the detection of small lesions in digital medical images. Our approach is based on a multi-context ensemble of convolutional neural networks (CNNs), aiming at learning different levels of image spatial context and improving detection performance. The main innovation behind the proposed method is the use of multiple-depth CNNs, individually trained on image patches of different dimensions and then combined together. In this way, the final ensemble is able to find and locate abnormalities on the images by exploiting both the local features and the surrounding context of a lesion. Experiments were focused on two well-known medical detection problems that have been recently faced with CNNs: microcalcification detection on full-field digital mammograms and microaneurysm detection on ocular fundus images. To this end, we used two publicly available datasets, INbreast and E-ophtha. Statistically significantly better detection performance were obtained by the proposed ensemble with respect to other approaches in the literature, demonstrating its effectiveness in the detection of small abnormalities.

Stereotactic Vacuum-Assisted Breast Biopsy in Ductal Carcinoma in situ: Residual Microcalcifications and Intraoperative Findings

Purpose

The question of overtreatment of ductal carcinoma in situ (DCIS) was raised because a significant proportion of especially low-grade DCIS lesions never progress to invasive cancer. The rationale for the present study was to analyze the value of stereotactic vacuum-assisted biopsy (VAB) for complete removal of DCIS, focusing on the relationship between the absence of residual microcalcifications after stereotactic VAB and the histopathological diagnosis of the definitive surgical specimen.

Patients and Methods

Data of 58 consecutive patients diagnosed with DCIS by stereotactic VAB in a single breast center between 2012 and 2017 were analyzed. Patient records from the hospital information system were retrieved, and mammogram reports and images as well as histopathology reports were evaluated. The extent of microcalcifications before and after biopsy as well as the occurrence of DCIS in biopsy and definitive surgical specimens were analyzed and correlated.

Results

There was no correlation between the absence of residual microcalcifications in the post-biopsy mammogram and the absence of residual DCIS in the final surgical specimen (p = 0.085). Upstaging to invasive cancer was recorded in 4 cases (13%) but occurred only in the group that had high-grade DCIS on biopsy. Low-grade DCIS was never upgraded to high-grade DCIS in the definitive specimen.

Conclusions

The radiological absence of microcalcifications after stereotactic biopsy does not rule out residual DCIS in the final surgical specimen. Since upstaging to invasive cancer is seen in a substantial proportion of high-grade DCIS, the surgical excision of high-grade DCIS should remain the treatment of choice.

Association of Microcalcification Clusters with Short-term Invasive Breast Cancer Risk and Breast Cancer Risk Factors

Using for-presentation and for-processing digital mammograms, the presence of microcalcifications has been shown to be associated with short-term risk of breast cancer. In a previous article we developed an algorithm for microcalcification cluster detection from for-presentation digital mammograms. Here, we focus on digitised mammograms and use a three-step algorithm. In total, 253 incident invasive breast cancer cases (with a negative mammogram between three months and two years before diagnosis, from which we measured microcalcifications) and 728 controls (also with prior mammograms) were included in a short-term risk study. After adjusting for potential confounding variables, we found evidence of an association between the number of microcalcification clusters and short-term (within 3-24 months) invasive breast cancer risk (per cluster OR = 1.30, 95% CI = (1.11, 1.53)). Using the 728 postmenopausal healthy controls, we also examined association of microcalcification clusters with reproductive factors and other established breast cancer risk factors. Age was positively associated with the presence of microcalcification clusters (p = 4 × 10^-04). Of ten other risk factors that we studied, life time breastfeeding duration had the strongest evidence of association with the presence of microcalcifications (positively associated, unadjusted p = 0.001). Developing algorithms, such as ours, which can be applied on both digitised and digital mammograms (in particular for presentation images), is important because large epidemiological studies, for deriving markers of (clinical) risk prediction of breast cancer and prognosis, can be based on images from these different formats.

Classification of breast microcalcifications using dual-energy mammography

The potential of dual-energy mammography for microcalcification classification was investigated with simulation and phantom studies. Classification of type I/II calcifications was performed using the tissue attenuation ratio as a performance metric. The simulation and phantom studies were carried out using breast phantoms of 50% fibroglandular and 50% adipose tissue composition and thicknessess ranging from 3 to 6 cm. The phantoms included models of microcalcifications ranging in size between 200 and 900    μ m . The simulation study was carried out with fixed MGD of 1.5 mGy using various low- and high-kVp spectra, aluminum filtration thicknesses, and exposure distribution ratios to predict an optimized imaging protocol for the phantom study. Attenuation ratio values were calculated for microcalcification signals of different types at two different voltage settings. ROC analysis showed that classification performance as indicated by the area under the ROC curve was always greater than 0.95 for 1.5 mGy deposited mean glandular dose. This study provides encouraging first results in classifying malignant and benign microcalcifications based solely on dual-energy mammography images.

Improving the Automated Detection of Calcifications Using Adaptive Variance Stabilization

In this paper, we analyze how stabilizing the variance of intensity-dependent quantum noise in digital mammograms can significantly improve the computerized detection of microcalcifications (MCs). These lesions appear on mammograms as tiny deposits of calcium smaller than 20 pixels in diameter. At this scale, high frequency image noise is dominated by quantum noise, which in raw mammograms can be described with a square-root noise model. Under this assumption, we derive an adaptive variance stabilizing transform (VST) that stabilizes the noise to unitary standard deviation in all the images. This is achieved by estimating the noise characteristics from the image at hand. We tested the adaptive VST as a preprocessing stage for four existing computerized MC detection methods on three data sets acquired with mammographic units from different manufacturers. In all the test cases considered, MC detection performance on transformed mammograms was statistically significantly higher than on unprocessed mammograms. Results were also superior in comparison with a "fixed" (nonparametric) VST previously proposed for digital mammograms.

The importance of early detection of calcifications associated with breast cancer in screening

PURPOSE

The aim of this study was to assess how often women with undetected calcifications in prior screening mammograms are subsequently diagnosed with invasive cancer.

METHODS

From a screening cohort of 63,895 women, exams were collected from 59,690 women without any abnormalities, 744 women with a screen-detected cancer and a prior negative exam, 781 women with a false positive exam based on calcifications, and 413 women with an interval cancer. A radiologist identified cancer-related calcifications, selected by a computer-aided detection system, on mammograms taken prior to screen-detected or interval cancer diagnoses. Using this ground truth and the pathology reports, the sensitivity for calcification detection and the proportion of lesions with visible calcifications that developed into invasive cancer were determined.

RESULTS

The screening sensitivity for calcifications was 45.5%, at a specificity of 99.5%. A total of 68.4% (n = 177) of cancer-related calcifications that could have been detected earlier were associated with invasive cancer when diagnosed.

CONCLUSIONS

Screening sensitivity for detection of malignant calcifications is low. Improving the detection of these early signs of cancer is important, because the majority of lesions with detectable calcifications that are not recalled immediately but detected as interval cancer or in the next screening round are invasive at the time of diagnosis.

Radiopathological features predictive of involved margins in ductal carcinoma in situ

INTRODUCTION Ductal carcinoma in situ (DCIS) usually manifests as microcalcification on mammography but may be uncalcified. Consequently, a quarter of patients undergoing excision of a presumed pure DCIS require further surgery to re-excise margins. Patients at highest risk of margin involvement may benefit from additional preoperative assessment. METHODS A retrospective review was carried out of patients treated for screen detected, biopsy proven DCIS in a single centre over a ten-year period (1999-2009). Logistic regression analysis identified factors predictive of need for further surgery to clear margins. RESULTS Overall, 248 patients underwent surgery for DCIS (low/intermediate grade: 82, high grade: 155) and 49 (19.8%) required further surgery. High grade disease was associated with greater mammographic extent (mean: 32mm [range: 5-120mm] vs 25mm [range: 2-100mm]), p=0.009) and higher incidence of mastectomy (38% vs 24%, p=0.034). Factors predictive of involvement of surgical margins necessitating further surgery included negative oestrogen receptor status (OR: 5.2, 95% CI: 2.1-12.8, p<0.001) and mammographic extent (odds ratio [OR]: 1.6, 95% confidence interval [CI]: 1.2-2.1, p=0.004). Once size exceeded 30mm, more than 50% of patients required secondary breast surgery for margins. CONCLUSIONS Reoperation rates for DCIS increase with preoperative size on mammography and negative oestrogen receptor status on core biopsy. Patients with these risk features should be counselled accordingly and consideration should be given to the role of additional preoperative imaging.

Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning

Microcalcification is an effective indicator of early breast cancer. To improve the diagnostic accuracy of microcalcifications, this study evaluates the performance of deep learning-based models on large datasets for its discrimination. A semi-automated segmentation method was used to characterize all microcalcifications. A discrimination classifier model was constructed to assess the accuracies of microcalcifications and breast masses, either in isolation or combination, for classifying breast lesions. Performances were compared to benchmark models. Our deep learning model achieved a discriminative accuracy of 87.3% if microcalcifications were characterized alone, compared to 85.8% with a support vector machine. The accuracies were 61.3% for both methods with masses alone and improved to 89.7% and 85.8% after the combined analysis with microcalcifications. Image segmentation with our deep learning model yielded 15, 26 and 41 features for the three scenarios, respectively. Overall, deep learning based on large datasets was superior to standard methods for the discrimination of microcalcifications. Accuracy was increased by adopting a combinatorial approach to detect microcalcifications and masses simultaneously. This may have clinical value for early detection and treatment of breast cancer.

The Diagnostic Value of Micropure Imaging in Breast Suspicious Microcalcificaion

RATIONALE AND OBJECTIVES

The purpose of this study was to evaluate the diagnostic value of Micropure Imaging (MI) in breast lesions differentiation by comparison with B-mode ultrasonography (B-US) and Doppler ultrasonography (DU).

MATERIALS AND METHODS

A total of 135 consecutive patients (all females) with 135 suspicious lesions were examined and skin marked by MI before mammotome biopsies. All patients (age range, 20-86 years; mean age, 42.5 ± 15.6 years) were the first onset, not in the pregnancy or lactation and had no history of radiation or chemotherapy. The maximum diameter of lesions ranged from 0.2 to 9.6 cm (average 1.98 ± 1.3 cm). Their final diagnoses were obtained by histologic examination. The study protocol was approved by the hospital review board; each patient gave written informed consent.

RESULTS

One hundred thirty-five breast lesions were classified into 90 nonmalignant and 45 malignant types. To 86 breast lesions with microcalcification, MI showed more microcalcification and coincided better with pathology results than B-US did (P < .05). The specificity of MI was higher than that of B-US and DU; the sensitivity of DU was significantly higher than that of B-US and MI (P < .001). By combining B-US, DU, and MI, the detection accuracy was 86.7%. Receiver-operator characteristic curves showed the area under the curve of B-US, DU, and MI was 0.865, 0.934, and 0.923(P = .000), respectively. Moreover, the interobserver agreements of MI were the highest, 0.922 (observer 1 vs. observer 2), 0.866 (observer 1 vs. observer 3), and 0.916 (observer 2 vs. observer 3).

CONCLUSIONS

MI as an adjunct ultrasound modality holds some promise in locating and differentiating breast lesions.

Differentiation of ductal carcinoma in-situ from benign micro-calcifications by dedicated breast computed tomography

PURPOSE

Compare conspicuity of ductal carcinoma in-situ (DCIS) to benign calcifications on unenhanced (bCT), contrast-enhanced dedicated breast CT (CEbCT) and mammography (DM).

METHODS AND MATERIALS

The institutional review board approved this HIPAA-compliant study. 42 women with Breast Imaging Reporting and Data System 4 or 5 category micro-calcifications had breast CT before biopsy. Three subjects with invasive disease at surgery were excluded. Two breast radiologists independently compared lesion conspicuity scores (CS) for CEbCT, to bCT and DM. Enhancement was measured in Hounsfield units (HU). Mean CS ± standard deviations are shown. Receiver operating characteristic analysis (ROC) measured radiologists' discrimination performance by comparing CS to enhancement alone. Statistical measurements were made using ANOVA F-test, Wilcoxon rank-sum test and robust linear regression analyses.

RESULTS

39 lesions (17 DCIS, 22 benign) were analyzed. DCIS (8.5 ± 0.9, n=17) was more conspicuous than benign micro-calcifications (3.6 ± 2.9, n=22; p<0.0001) on CEbCT. DCIS was equally conspicuous on CEbCT and DM (8.5 ± 0.9, 8.7 ± 0.8, n=17; p=0.85) and more conspicuous when compared to bCT (5.3 ± 2.6, n=17; p<0.001). All DCIS enhanced; mean enhancement (90HU ± 53HU, n=17) was higher compared to benign lesions (33 ± 30HU, n=22) (p<0.0001). ROC analysis of the radiologists' CS showed high discrimination performance (AUC=0.94) compared to enhancement alone (AUC=0.85) (p<0.026).

CONCLUSION

DCIS is more conspicuous than benign micro-calcifications on CEbCT. DCIS visualization on CEbCT is equal to mammography but improved compared to bCT. Radiologists' discrimination performance using CEBCT is significantly higher than enhancement values alone. CEbCT may have an advantage over mammography by reducing false positive examinations when calcifications are analyzed.

Microcalcifications in 1657 Patients with Pure Ductal Carcinoma in Situ of the Breast: Correlation with Clinical, Histopathologic, Biologic Features, and Local Recurrence

PURPOSE

This study was designed to determine the relationship of microcalcification morphology and distribution with clinical, histopathologic, biologic features, and local recurrence (LR) in patients with pure ductal carcinoma in situ (DCIS) of the breast.

METHODS

All patients with pure DCIS who underwent preoperative mammography at our institution from 1996 through 2009 were identified. Mammographic findings were classified according to the ACR BI-RADS lexicon. Associations between mammographic findings and clinical, histopathologic, biologic characteristics, and LR were analyzed. Statistical inference used multiple logistic regression and Cox proportional hazards regression adjusted for age and confounding due to bias from nonrandomized selection of radiation therapy.

RESULTS

We identified 1657 patients with microcalcifications visualized on mammography. The mean age at diagnosis was 55 years (SD, 11). The mean follow-up was 7 years (range 1-16). Ipsilateral LR was 4 % in segmentectomy (987) and 1.5 % in mastectomy (670) patients. Increased LR risk was seen in patients with dense breast tissue (p < 0.05) and larger DCIS size (p < 0.01). Radiation therapy was associated with a 2.8-fold decrease in the LR risk. Fine linear (branching) microcalcifications were associated with 5.2-fold increase in LR. Extremely dense breast tissue was associated with positive/close margins (p = 0.04) and multicentricity (p < 0.01). Younger women were more likely to have extremely dense breast tissue (p < 0.0001), multicentric disease (p < 0.0004), and undergo mastectomy (p < 0.0001).

CONCLUSIONS

Dense breast tissue, large DCIS size, and fine linear (branching) microcalcifications were associated with increased LR, yet overall LR rates remained low. Extremely dense breast tissue was a risk factor for multicentricity and positive margins in DCIS.

Ductal carcinoma arising from ectopic breast tissue following microcalcification observed on screening mammography: a case report and review of the literature

Ectopic breast tissue can occur anywhere along the incompletely regressed mammary ridge. Among the various types of breast choristoma, ectopic breast tissue, which has only glandular tissue without a nipple or areola, is most commonly detected in axillary areas. However, ectopic breast cancer is often not detected until significant clinical symptoms have been revealed, or diagnosis is delayed. Furthermore, an examination of ectopic breast tissue tends to be omitted from a screening mammography. Especially, the microcalcifications of ectopic breast tissue are difficult to delineate on mammography. Herein, the authors report a case of ectopic breast carcinoma that showed clustered microcalcifications on screening mammography, and discuss the interpretation and implications of microcalcification in ectopic breast tissue.

Breast US in patients with breast cancer presenting as only microcalcifications on mammography: can US differentiate ductal carcinoma in situ from invasive cancer?

PURPOSE

To retrospectively review sonographic findings of breast cancers presenting as only microcalcifications on mammography and to evaluate factors essential for differentiating ductal carcinoma in situ (DCIS) from invasive cancers.

METHODS

We retrospectively reviewed the medical records of 620 consecutive patients with confirmed breast cancer according to surgery performed between March 2008 and October 2011 at our institution. Of these, 53 lesions from 52 patients who had only microcalcifications without a mass or other associated findings on mammography were selected. Sonographic findings of microcalcification areas were analyzed and correlated with the histopathological findings.

RESULTS

Of the 53 lesions, 26 (49.18 %) were classified as invasive cancer and 27 (50.9 %) as DCIS. Ultrasonography (US) showed only echogenic calcifications in five (9.4 %), calcifications within hypoechoic parenchymal thickening in 14 (26.4 %), calcifications within ductal changes in three (5.7 %), and calcifications within a mass in 14 (26.4 %). Seventeen (32.1 %) lesions were not visible on US. Negative findings in US were more frequently observed for DCIS (n = 15, 55.6 %) than for invasive cancers (n = 2, 7.7 %) (p < 0.001). Masses (n = 11, 42.3 % of invasive cancer; n = 3, 11.1 % of DCIS; p = 0.01) were more frequently observed in invasive cancers than in DCIS.

CONCLUSIONS

US findings of breast cancers presenting as only mammographic microcalcifications were significantly different between DCIS and invasive cancers. Targeted US of microcalcifications might be helpful for predicting invasive cancers and for determining the clinical preoperative work-up, including axillary staging.

[Does molecular biology play any role in ductal carcinoma in situ?]

The natural history of ductal carcinoma in situ (DCIS) is not fully elucidated, but it is recognized that DCIS is the true precursor of invasive carcinoma. Studies could show that DCIS is as heterogeneous as invasive ductal carcinoma, yet, they were unable to predict which DCIS will progress to invasion. Several biomarkers were also demonstrated to have a certain prognostic value. However, except for estrogen receptors and HER2, biomarkers are not yet widely used in clinical practice since their predictive value has not proven to be better than the grade and the classical classifying systems of DCIS. Identifying biomarkers for risk of invasiveness in DCIS could be of great value to help high risk patients through the management of their disease and to avoid overtreatment in low risk patients.

Impact on breast cancer diagnosis in a multidisciplinary unit after the incorporation of mammography digitalization and computer-aided detection systems

OBJECTIVE

The purpose of this article is to evaluate the impact on the diagnosis of breast cancer of implementing full-field digital mammography (FFDM) in a multidisciplinary breast pathology unit and, 1 year later, the addition of a computer-aided detection (CAD) system.

MATERIALS AND METHODS

A total of 13,453 mammograms performed between January and July of the years 2004, 2006, and 2007 were retrospectively reviewed using conventional mammography, digital mammography, and digital mammography plus CAD techniques. Mammograms were classified into two subsets: screening and diagnosis. Variables analyzed included cancer detection rate, rate of in situ carcinoma, tumor size at detection, biopsy rate, and positive predictive value of biopsy.

RESULTS

FFDM increased the cancer detection rate, albeit not statistically significantly. The detection rate of in situ carcinoma increased significantly using FFDM plus CAD compared with conventional technique (36.8% vs 6.7%; p = 0.05 without Bonferroni statistical correction) for the screening dataset. Relative to conventional mammography, tumor size at detection decreased with digital mammography (T1, 61.5% vs 88%; p = 0.018) and with digital mammography plus CAD (T1, 79.7%; p = 0.03 without Bonferroni statistical correction). Biopsy rates in the general population increased significantly using CAD (10.6/1000 for conventional mammography, 14.7/1000 for digital mammography, and 17.9/1000 for digital mammography plus CAD; p = 0.02). The positive predictive value of biopsy decreased slightly, but not significantly, for both subsets.

CONCLUSION

The incorporation of new techniques has improved the performance of the breast unit by increasing the overall detection rates and earlier detection (smaller tumors), both leading to an increase in interventionism.

The role of breast MR imaging in pre-operative determination of invasive disease for ductal carcinoma in situ diagnosed by needle biopsy

OBJECTIVES

To evaluate whether magnetic resonance (MR) imaging features can predict the presence of occult invasion in cases of biopsy-proven pure ductal carcinoma in situ (DCIS).

METHODS

We retrospectively reviewed 92 biopsy-proven pure DCIS in 92 women who underwent MR imaging. The following MR imaging findings were compared between confirmed DCIS and invasive breast cancer (IBC): lesion size, type, morphological and kinetic assessments by ACR BI-RADS MRI, and findings of fat-suppressed T2-weighted (FS-T2W) imaging.

RESULTS

Sixty-eight of 92 (74%) were non-mass-like enhancements (NMLE) and 24 were mass lesions on MR imaging. Twenty-one of 68 (31%) NMLE and 13 of 24 (54%) mass lesions were confirmed as IBC. In NMLE lesions, large lesions (P = 0.007) and higher signal intensities (SI) on FS-T2W images (P = 0.032) were significantly associated with IBC. Lesion size remained a significant independent predictor of invasion in multivariate analysis (P = 0.032), and combined with FS-T2W SIs showed slightly higher observer performances (area under the curve, AUC, 0.71) than lesion size alone (AUC 0.68). There were no useful findings that enabled the differentiation of mass-type lesions.

CONCLUSIONS

Breast MR imaging is potentially useful to predict the presence of occult invasion in biopsy-proven DCIS with NMLE.

KEY POINTS

MR mammography permits more precise lesion assessment including ductal carcinoma in situ A correct diagnosis of occult invasion before treatment is important for clinicians This study showed the potential of MR mammography to diagnose occult invasion Treatment and/or aggressive biopsy can be given with greater confidence MR mammography can lead to more appropriate management of patients.

Independent predictors of breast malignancy in screen-detected microcalcifications: biopsy results in 2545 cases

Background:

Mammographic microcalcifications are associated with many benign lesions, ductal carcinoma in situ (DCIS) and invasive cancer. Careful assessment criteria are required to minimise benign biopsies while optimising cancer diagnosis. We wished to evaluate the assessment outcomes of microcalcifications biopsied in the setting of population-based breast cancer screening.

Methods:

Between January 1992 and December 2007, cases biopsied in which microcalcifications were the only imaging abnormality were included. Patient demographics, imaging features and final histology were subjected to statistical analysis to determine independent predictors of malignancy.

Results:

In all, 2545 lesions, with a mean diameter of 21.8 mm (s.d. 23.8 mm) and observed in patients with a mean age of 57.7 years (s.d. 8.4 years), were included. Using the grading system adopted by the RANZCR, the grade was 3 in 47.7% 4 in 28.3% and 5 in 24.0%. After assessment, 1220 lesions (47.9%) were malignant (809 DCIS only, 411 DCIS with invasive cancer) and 1325 (52.1%) were non-malignant, including 122 (4.8%) premalignant lesions (lobular carcinoma in situ, atypical lobular hyperplasia and atypical ductal hyperplasia). Only 30.9% of the DCIS was of low grade.

Mammographic extent of microcalcifications >15 mm, imaging grade, their pattern of distribution, presence of a palpable mass and detection after the first screening episode showed significant univariate associations with malignancy. On multivariate modeling imaging grade, mammographic extent of microcalcifications >15 mm, palpable mass and screening episode were retained as independent predictors of malignancy. Radiological grade had the largest effect with lesions of grade 4 and 5 being 2.2 and 3.3 times more likely to be malignant, respectively, than grade 3 lesions.

Conclusion:

The radiological grading scheme used throughout Australia and parts of Europe is validated as a useful system of stratifying microcalcifications into groups with significantly different risks of malignancy. Biopsy assessment of appropriately selected microcalcifications is an effective method of detecting invasive breast cancer and DCIS, particularly of non-low-grade subtypes.

3-5 BI-RADs Microcalcifications: Correlation between MRI and Histological Findings

Purpose. To evaluate the correlation between MRI and histopathological findings in patients with mammographically detected 3–5 BI-RAD (Breast Imaging Reporting And Data Systems) microcalcifications and to allow a better surgical planning. Materials and Method. 62 female Patients (age 50 ± 12) with screening detected 3–5 BI-RAD microcalcifications underwent dynamic 3 T contrast-enhanced breast MRI. After 30-day (range 24–36 days) period, 55 Patients underwent biopsy using stereotactic vacuum-assisted biopsy (VAB), 5 Patients underwent stereotactic mammographically guided biopsy, and 2 Patients underwent MRI-guided VAB. Results. Microhistology examination demonstrated 36 malignant lesions and 26 benign lesions. The analysis of MRI findings identified 8 cases of MRI BI-RADS 5, 23 cases of MRI BI-RADS 4, 11 cases of MRI BI-RADS 3, 4 cases type A and 7 cases type B, and 20 cases of MRI BI-RADS 1-2. MRI sensitivity, specificity, positive predictive value, and negative predictive value were 88.8%, 76.9%, 84.2%, and 83.3%, respectively.

The influence of clinicopathological features on the predictive accuracy of conventional breast imaging in determining the extent of screen-detected high-grade pure ductal carcinoma in situ

INTRODUCTION

The extent of calcified ductal carcinoma in situ (DCIS) detected by screening mammography is a determinant for treatment with breast conserving surgery (BCS). However, DCIS may be uncalcified and almost a quarter of patients with DCIS treated initially by BCS either require a second operation or are found to have unexpected invasive disease following surgery. Identification of these cases might guide selective implementation of additional diagnostic procedures.

METHODS

A retrospective review of patients with a preoperative diagnosis of pure high-grade DCIS at the Southampton and Salisbury Breast Screening Unit over a ten-year period was carried out. Mammograms were reviewed independently by a consultant radiologist and additional factors including the Breast Imaging Reporting and Data System (BI-RADS(®)) breast density score, DCIS extent and disease location within the breast recorded.

RESULTS

Unexpected invasive disease was found in 35 of 144 patients (24%). Within our unit the re-excision rate for all screen-detected DCIS is currently 23% but for patients included in this study with high-grade DCIS the re-excision rate was 39% (34/87). The extent of DCIS (p=0.008) and lack of expression of the oestrogen receptor (ER) predicted the requirement for re-excision in both univariate (p=0.004) and multivariate analysis (p=0.005).

CONCLUSIONS

High-grade DCIS may be focally uncalcified, leading to underestimation of disease extent, which might be related to ER status. Invasive foci associated with high-grade DCIS are often mammographically occult. Exploration of additional biomarkers and targeted use of further diagnostic techniques may improve the preoperative staging of DCIS.

[Impact of mammographic breast density on computer-assisted detection (CAD) in a breast imaging department]

OBJECTIVE

To evaluate whether breast density influences the sensitivity of a computer-assisted detection (CAD) system for the detection of breast cancer.

MATERIAL AND METHODS

We prospectively studied 8750 digital mammograms with an associated CAD system. We used BI-RADS criteria to classify breast density. We calculated the overall sensitivity of the radiologist and of the CAD system, as well as the sensitivity for each projection and type of finding in relation to the mammographic density of the breast. Finally, we analyzed the interval carcinomas. We used SPSS 11 for all statistical analyses.

RESULTS

The overall sensitivity of the CAD system was 88.5% (95% CI: 83.2-92.7%), and the overall sensitivity of the radiologist was 93.5% (95% CI: 84.4%-95.5%). The sensitivity of the craniocaudal view was 81.6% (95% CI: 76.5-90.7%) vs 76.5% (95% CI: 69.3-89.3%) for the mediolateral oblique view. The sensitivity for microcalcifications was 98.6% (95% CI: 96.5-99.7%), and the sensitivity for masses 83.4% (95% CI: 81.2-91.7%). We detected discrepancies smaller than 20% both for microcalcifications present in the four types of densities and for masses with densities 1 and 2. In masses with density 3 the discrepancy was 20.8% and in those with density 4 it was 55%. The CAD system failed to mark only 9.1% (9/94) of the cancers presenting as masses. Half of the interval carcinomas were found in type 4 density and 75% manifested as masses, asymmetries, and distortions. The CAD system had marked 35.7% of the carcinomas.

CONCLUSIONS

The craniocaudal view was more sensitive, although this difference was not statistically significant. The sensitivity of CAD was high for microcalcifications in all four density types; however, CAD's sensitivity for masses was low in density types 3 and 4. The CAD system only failed to mark 9.1% of the cancers presenting as masses but was not sensitive for the other two radiological findings included in this marking. Half of the interval carcinomas occurred in type 4 densities and 35.7% had been marked by the CAD system.

Correlation between mammographic and sonographic findings and prognostic factors in patients with node-negative invasive breast cancer

OBJECTIVES

The purpose of this study was to correlate sonographic and mammographic findings with prognostic factors in patients with node-negative invasive breast cancer.

METHODS

Sonographic and mammographic findings in 710 consecutive patients (age range 21-81 years; mean age 49 years) with 715 node-negative invasive breast cancers were retrospectively evaluated. Pathology reports relating to tumour size, histological grade, lymphovascular invasion (LVI), extensive intraductal component (EIC), oestrogen receptor (ER) status and HER-2/neu status were reviewed and correlated with the imaging findings. Statistical analysis was performed using logistic regression analysis and intraclass correlation coefficient (ICC).

RESULTS

On mammography, non-spiculated masses with calcifications were associated with all poor prognostic factors: high histological grade, positive LVI, EIC, HER-2/neu status and negative ER. Other lesions were associated with none of these poor prognostic factors. Hyperdense masses on mammography, the presence of mixed echogenicity, posterior enhancement, calcifications in-or-out of masses and diffusely increased vascularity on sonography were associated with high histological grade and negative ER. Associated calcifications on both mammograms and sonograms were correlated with EIC and HER-2/neu overexpression. The ICC value for the disease extent was 0.60 on mammography and 0.70 on sonography.

CONCLUSION

Several sonographic and mammographic features can have a prognostic value in the subsequent treatment of patients with node-negative invasive breast cancer. Radiologists should pay more attention to masses that are associated with calcifications because on both mammography and sonography associated calcifications were predictors of positive EIC and HER-2/neu overexpression.

A dose- and time-controllable syngeneic animal model of breast cancer microcalcification

The development of novel diagnostic agents for the detection of breast cancer microcalcifications requires a reliable animal model. Based on previous work from our group, we hypothesized that a single systemic injection of recombinant bone morphogenetic protein-2 (rBMP-2) could be used to create such a model. The cDNA encoding mature human BMP-2 was expressed in BL21(DE3) bacteria, purified to homogeneity, and refolded as a dimer. Bioactivity was confirmed using a C2C12 alkaline phosphatase assay. rBMP-2 was radiolabeled with (99m)Tc, and its biodistribution and clearance were quantified after both intravenous (IV) and intraperitoneal (IP) injection. Fischer 344 rats bearing syngeneic R3230 breast tumors received a single intraperitoneal injection of rBMP-2 at a specified dose. Tumor microcalcification was quantified over time using micro-single photon emission computed tomography (SPECT) and microcomputed tomography (CT). rBMP-2 could be expressed in E. coli at high levels, isolated at >95% purity, and refolded to a bioactive dimer. Beta-phase half-life was 30.5 min after IV administration and 47.6 min after IP administration. Renal excretion was the primary mode of clearance. A single IP injection of >or=50 microg rBMP-2 when tumors were not yet palpable resulted in dose-dependent microcalcification in 8 of 8 R3230 tumors. No calcification was found in control tumors or in normal tissues and organs of animals injected with rBMP-2. Tumor calcification increased progressively between weeks 2 and 4 post-rBMP-2 injection. A single IP injection of rBMP-2 in rats bearing a syngeneic breast cancer will produce dose-dependent and time-dependent microcalcifications. This animal model lays the foundation for the development of novel diagnostic radiotracers for breast cancer.

Positive predictive value of specific mammographic findings according to reader and patient variables

PURPOSE

To evaluate the risk of cancer (positive predictive value [PPV]) associated with specific findings (mass, calcifications, architectural distortion, asymmetry) in mammographic examinations with abnormal results, to determine the distribution of these findings in examinations in which the patients received a diagnosis of cancer and examinations in which the patients did not, and to analyze PPV variation according to radiologist and patient factors.

MATERIALS AND METHODS

HIPAA-compliant institutional review board approval was obtained. PPV of mammographic findings was evaluated in a prospective cohort of 10,262 women who underwent 10,641 screening or diagnostic mammographic examinations with abnormal results between January 1998 and December 2002 in the San Francisco Mammography Registry. The cohort was linked with the Surveillance Epidemiology and End Results program to determine cancer status among these women. PPVs were calculated for each finding and were stratified according to patient characteristics, cancer type, and radiologist reader.

RESULTS

Cases of breast cancer (n = 1552) were identified (invasive, n = 1287; ductal carcinoma in situ, n = 270); in five, both kinds of breast cancer were recorded. Overall, of the number of interpretations, masses were most frequently noted in 56%, followed by calcifications in 29%, asymmetry in 12%, and architectural distortion in 4%. Masses, calcifications, architectural distortion, and developing asymmetry demonstrated similar PPVs in screening examinations (9.7%, 12.7%, 10.2%, and 7.4%, respectively), whereas one-view-only and focal asymmetry demonstrated lower PPVs (3.6% and 3.7%, respectively) and were a frequent reason for an abnormal result (42%). Overall, one (5%) in 20 invasive cancers was identified with asymmetry, one (6%) in 16 invasive cancers was identified with architectural distortion, one (21%) in five invasive cancers was identified with calcifications, and two (68%) in three invasive cancers were identified with a mass.

CONCLUSION

Five percent of invasive cancers were identified with asymmetry, and asymmetry is more weakly associated with cancer in screening examinations than are mass, calcifications, and architectural distortion.