Breast Microcalcifications Research Articles

Raman Spectroscopy Reveals That Biochemical Composition of Breast Microcalcifications Correlates with Histopathologic Features.

Breast microcalcifications are a common mammographic finding. Microcalcifications are considered suspicious signs of breast cancer and a breast biopsy is required, however, cancer is diagnosed in only a few patients. Reducing unnecessary biopsies and rapid characterization of breast microcalcifications are unmet clinical needs. In this study, 473 microcalcifications detected on breast biopsy specimens from 56 patients were characterized entirely by Raman mapping and confirmed by X-ray scattering. Microcalcifications from malignant samples were generally more homogeneous, more crystalline, and characterized by a less substituted crystal lattice compared with benign samples. There were significant differences in Raman features corresponding to the phosphate and carbonate bands between the benign and malignant groups. In addition to the heterogeneous composition, the presence of whitlockite specifically emerged as marker of benignity in benign microcalcifications. The whole Raman signature of each microcalcification was then used to build a classification model that distinguishes microcalcifications according to their overall biochemical composition. After validation, microcalcifications found in benign and malignant samples were correctly recognized with 93.5% sensitivity and 80.6% specificity. Finally, microcalcifications identified in malignant biopsies, but located outside the lesion, reported malignant features in 65% of in situ and 98% of invasive cancer cases, respectively, suggesting that the local microenvironment influences microcalcification features. This study confirms that the composition and structural features of microcalcifications correlate with breast pathology and indicates new diagnostic potentialities based on microcalcifications assessment. SIGNIFICANCE: Raman spectroscopy could be a quick and accurate diagnostic tool to precisely characterize and distinguish benign from malignant breast microcalcifications detected on mammography.

Breast microcalcifications detection based on fusing features with DTCWT.

Breast cancer is a common disease in women. Early detection and early treatment can reduce breast cancer mortality. Studies have shown that breast cancer microcalcifications is one of the important clinical manifestations of early breast cancer, and sometimes even the only manifestation. When the mammography image shows typical malignant microcalcification, it can be diagnosed as breast cancer without any other signs of malignancy. In the aided diagnosis of microcalcifications, it is a crucial step to automatically find and locate regions of interest containing microcalcifications. However, the existing feature extraction method for microcalcifications only extracts features in the time domain or wavelet domain, and does not completely represent all the information of the region of interest. An extraction method based on the combination of Dual-Tree Complex Wavelet Transform (DTCWT) and texture features is proposed in the paper. First, the processing operations including denoising, enhancement, and edge detection were performed on mammograms. Sub-image segmentation is then performed. DTCWT features and texture features are extracted for each sub-image.DTCWT features are combined with texture features, and then genetic algorithm is used for feature optimization. The features are classified by the Extreme Learning Machine (ELM) to achieve rapid detection and automatic extraction of ROI with microcalcifications. The experimental results verify that the feature model proposed in this paper has the highest detection rate for ROI regions. The ROI region extracted by the proposed feature model was used as subsequent experimental data. Three different methods were used to detect the microcalcifications, including Top-hat, wavelet transform, and methods combining Top-Hat and wavelet transform. The method was applied to 100 mammograms from the mammograms database of women in Northeast China. In the automatic extraction of ROI, the accuracy, sensitivity, specificity, positive accuracy and negative accuracy of the proposed model combined with DTCWT were 95.92%, 96.71%, 92.20%, 93.65%, 96.33%, respectively. When the Top-hat algorithm was used for microcalcifications detection, the sensitivity reached 89.6%, and the false positive detection rate was 2.6. When the wavelet transform algorithm was used for microcalcifications detection, the sensitivity was 91.1%, and the false positive detection rate was 3.28. When the combined algorithm was used for microcalcifications detection, the sensitivity was 86.7%, and the false positive detection rate decreased to 1.35. The proposed model combined with DTCWT features achieves better result in the automatic extraction of ROI. Moreover, in the subsequent detection of microcalcifications based on three methods, the three methods achieved better results in sensitivity and false positive detection rate, respectively.

Ultrasonography Findings of Breast Microcalcifications without Accompanying Mass and Evaluation of Ultrasound-Guided Biopsy Results

Objective: Ultrasonography guided core needle biopsy is a real-time, inexpensive method with higher patient comfort. The aim of this study was to evaluate ultrasonography findings of microcalcifications without accompanying mass and also to investigate the accuracy of ultrasonography guided core needle biopsy results.
 Materials and Methods: The study included a total of 54 patients, with microcalcifications observed on mammography and no accompanying mass, who underwent ultrasonography guided core needle biopsy and surgical excision. Core needle biopsy specimen x-rays were obtained from 23 patients. In 11 patients, the location of microcalcification was confirmed by mammography following the administration of contrast agent under ultrasonography guidance. Ultrasonography findings of microcalcifications were identified. The results of ultrasonography guided core needle biopsy were compared with the excisional pathology results.
 Results: The microcalcifications without accompanying mass were presented with punctate echogenous foci, hypoechoic area, small distortion, ductal abnormality or fibrocystic changes on ultrasonography. Hypoechoic area and distortion were seen more in malignant lesions, and fibrocystic changes and ductal abnormalities in benign lesions but the difference was not statistically significant. The agreement between ultrasonography guided core needle biopsy and the excisional pathology results was high (Kappa = 0.781). When a specimen x-ray was obtained or core needle biopsy was performed after confirming the location of the microcalcifications with the use of contrast agent, Kappa values were even higher (0.87 and 1, respectively).
 Conclusions: Microcalcifications can be seen with targeted ultrasonography imaging and ultrasonography guided core needle biopsy has high accuracy. Taking a specimen x-ray, or biopsy performed after identifying of the location of microcalcifications with a trace amount of contrast agent, can increase the accuracy of ultrasonography guided core needle biopsy.

A machine learning approach on multiscale texture analysis for breast microcalcification diagnosis

BackgroundScreening programs use mammography as primary diagnostic tool for detecting breast cancer at an early stage. The diagnosis of some lesions, such as microcalcifications, is still difficult today for radiologists. In this paper, we proposed an automatic binary model for discriminating tissue in digital mammograms, as support tool for the radiologists. In particular, we compared the contribution of different methods on the feature selection process in terms of the learning performances and selected features.ResultsFor each ROI, we extracted textural features on Haar wavelet decompositions and also interest points and corners detected by using Speeded Up Robust Feature (SURF) and Minimum Eigenvalue Algorithm (MinEigenAlg). Then a Random Forest binary classifier is trained on a subset of a sub-set features selected by two different kinds of feature selection techniques, such as filter and embedded methods. We tested the proposed model on 260 ROIs extracted from digital mammograms of the BCDR public database. The best prediction performance for the normal/abnormal and benign/malignant problems reaches a median AUC value of 98.16% and 92.08%, and an accuracy of 97.31% and 88.46%, respectively. The experimental result was comparable with related work performance.ConclusionsThe best performing result obtained with embedded method is more parsimonious than the filter one. The SURF and MinEigen algorithms provide a strong informative content useful for the characterization of microcalcification clusters.

Abstract P2-01-02: Do decreased breast microcalcifications after neoadjuvant chemotherapy predict pathological complete response?

Abstract Background: Clinical response evaluation by image examination after neoadjuvant chemotherapy for breast cancer is important for determining drug response and progression. Mammography is less correlated with pathological response assessment than magnetic resonance imaging or ultrasonography particularly because mammographic calcifications can increase or decrease in response to neoadjuvant chemotherapy. A conventional theory of breast microcalcifications is that they are likely to paradoxically change with neoadjuvant chemotherapy. Traditionally, microcalcifications have been said to occur due to necrosis. If conventional theory is correct, the increase in necrotic cells would increase microcalcifications in response to neoadjuvant chemotherapy. However, decreased microcalcifications were found as a clinical response to neoadjuvant chemotherapy in many cases. To increase the diagnostic ability of mammography after neoadjuvant chemotherapy, it is important to clarify the cases in which microcalcifications can be reduced by anticancer agents. The present study clarified characteristics of breast cancer cases with decreased microcalcifications after neoadjuvant. Methods: Consecutive patients who received neoadjuvant chemotherapy for breast cancer at our hospital from January 2013 to June 2017 were retrospectively reviewed. Characteristics of cases with microcalcifications before neoadjuvant chemotherapy and those showing a reduction after were examined. Stromal tumor-infiltrating lymphocytes were evaluated in the biopsy. before neoadjuvant chemotherapy based on the protocol of the International TILs Working Group 2014. Pathological complete response was also explored in cases exhibiting a decrease in microcalcifications by neoadjuvant chemotherapy. In addition, sensitivity, specificity, and likelihood ratios were calculated for pCR assessment in patients with segmental microcalcifications, pleomorphic/linear microcalcifications, and microcalcifications without mammographic mass. Results: A total of 184 breast cancer cases, including 8 with synchronous bilateral breast cancer, received neoadjuvant chemotherapy at our hospital. Of them, 114 cases were excluded due to unavailable mammograms at our institute before or after neoadjuvant chemotherapy. Seventy breast cancer cases were included; 37 had no malignant microcalcifications before neoadjuvant chemotherapy and 33 did. Breast cancer with microcalcifications was significantly more positive for human epidermal growth factor receptor 2 than those without microcalcifications (48% versus 22%, P = 0.018). Only 6 out of 33 cases with microcalcifications before neoadjuvant chemotherapy showed a decrease after treatment. These cases tended to have high stromal tumor-infiltrating lymphocytes (50% versus 17%), segmental microcalcifications (66% versus 33%), and pleomorphic/linear microcalcifications (83% versus 37%). The sensitivity of each group, compared with total cases of microcalcifications after neoadjuvant chemotherapy, tended to increase, although no significant difference was observed (sensitivity; patients with segmental microcalcifications, 42.9%; patients with pleomorphic/linear microcalcifications, 57.1%; and patients with microcalcifications but without mass, 50.0%). Conclusion: These results suggest that neoadjuvant chemotherapy is able to decrease malignant calcifications, particularly segmental and pleomorphic/linear microcalcifications and microcalcifications with high stromal tumor-infiltrating lymphocytes. By clarifying the mechanisms of formation and disappearance of microcalcifications, a consensus can be reached on whether microcalcifications apparent by mammography are useful for evaluating response to neoadjuvant chemotherapy. Citation Format: Atsushi Fushimi, Rei Kudo, Hiroshi Takeyama. Do decreased breast microcalcifications after neoadjuvant chemotherapy predict pathological complete response? [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P2-01-02.

Breast cancer metastasis to bone: From epithelial to mesenchymal transition to breast osteoblast-like cells.

In this review we highlighted the newest aspects concerning the physiopathology of breast cancer metastatization into the bone including: a) in situ biomarkers of breast cancer metastatic diseases, b) biological processes related to the origin of metastatic cells (epithelial to mesenchymal transition), c) the nature and the possible role of Breast Osteoblast-Like Cells in the formation of bone lesions and d) the prognostic value of breast microcalcifications for the bone metastatic disease. In addition, the more recent data about the biology of breast cancer metastatic process and the origin and function of Breast Osteoblast-Like Cells have been analyzed to propose the use of molecular imaging investigations able to identify early neoplastic lesions with high propensity to form bone metastasis in vivo.

Single-shot, high sensitivity X-ray phase contrast imaging system based on a Hartmann mask

Significant efforts are currently ongoing in X-Ray imaging to provide multimodal imaging systems, targeting better sensitivity and specificity for both biomedical or non-destructive testing (NDT) applications. X-Ray Phase Contrast Imaging (X-PCI) shows great capability to differentiate elements with similar absorption. For example, in the medical field, knowing the chemical composition of breast microcalcifications would help to differentiate malign and benign tumors. The composition can be determined from the measurement of the phase as the optical index of materials is directly related to the composition. We propose a novel, high-sensitivity X-ray quantitative phase imaging system based on a Hartmann wavefront sensor. The system provides high resolution (20?m without magnification) and high sensitivity (~100 nrad), and is compatible with tomographic experiments using both synchrotron beamlines or laboratory sources. We present here our first X-PCI prototype as well as the first images obtained. We also present an alternative design based on the same approach, providing larger field-of-view at the cost of some trade-off regarding resolution and sensitivity and the first tomographic results obtained with this imaging system.

Classification of Micro-Calcification in Breast from Mammographic Images using Transfer Learning

Early detection of cancer is most important for long term survival of patient. Now a days CADx are widely used for early identification of breast cancer automatically. CAD uses significant features to identify and categorize cancer. CADx based on Convolutional Neural Network are becoming popular now a days due to extracting relevant features automatically. CNNs can be trained from scratch for medical images due to various input sizes and tumor structures. But due to limited amount of medical images available for training ,we have used transfer learning approach.We developed a deep learning framework based on CNN to discriminate the breast tumor either benign or malignant using transfer learning. We used digital mammographic images containing both views from CBIS-DDSM database. We have achived training(100%) and validation accuracy greater than 90% with minimum training and validation loss. We have also compared the reaults with transfer learning using pretrained network alexnet and googlenet on same dataset.

An Automated Breast Micro-Calcification Detection and Classification Technique Using Temporal Subtraction of Mammograms

Radiologists worldwide use mammography as a reliable tool for breast cancer screening. However, mammography assessment is challenging even for well-trained radiologists, leading to a pressing need for Computer Aided Diagnosis (CAD) systems. In this work, a novel technique for the detection and classification of breast Micro-Calcifications (MCs), which are diagnostically significant but difficult to detect findings, is presented. The proposed method is based on the subtraction of temporally sequential mammogram pairs, after pre-processing and image registration, followed by machine-learning. The classification was performed using several features extracted from the subtracted mammograms and selected during training to optimize the accuracy of the results. Six classifiers were tested in a leave-one-patient-out, 4, 5 and 10 fold cross-validation process. This technique was evaluated on a unique dataset, consisting of temporal sequences of mammograms from 80 patients taken between 1 to 6 years apart. The resulting 320 mammograms were reviewed by 2 radiologists who precisely marked each MC location. The accuracy of classifying MCs as benign or suspicious improved from 91.42% without temporal subtraction and an Ensemble of Decision Trees (EDT), to 99.55% with the use of sequential mammograms and Support Vector Machines (SVMs) with leave-one-patient-out validation. The improvement was statistically significant (p-value <; 0.005). These results verify the accuracy and the effectiveness of the proposed technique should to be further evaluated on a larger dataset.

Quantitative chemical imaging of breast calcifications in association with neoplastic processes.

Calcifications play an essential role in early breast cancer detection and diagnosis. However, information regarding the chemical composition of calcifications identified on mammography and histology is limited. Detailed spectroscopy reveals an association between the chemical composition of calcifications and breast cancer, warranting the development of novel analytical tools to better define calcification types. Previous investigations average calcification composition across broad tissue sections with no spatially resolved information or provide qualitative visualization, which prevents a robust linking of specific spatially resolved changes in calcification chemistry with the pathologic process.Method: To visualize breast calcification chemical composition at high spatial resolution, we apply hyperspectral stimulated Raman scattering (SRS) microscopy to study breast calcifications associated with a spectrum of breast changes ranging from benign to neoplastic processes, including atypical ductal hyperplasia, ductal carcinoma in situ, and invasive ductal carcinoma. The carbonate content of individual breast calcifications is quantified using a simple ratiometric analysis.Results: Our findings reveal that intra-sample calcification carbonate content is closely associated with local pathological processes. Single calcification analysis supports previous studies demonstrating decreasing average carbonate level with increasing malignant potential. Sensitivity and specificity reach >85% when carbonate content level is used as the single differentiator in separating benign from neoplastic processes. However, the average carbonate content is limiting when trying to separate specific diagnostic categories, such as fibroadenoma and invasive ductal carcinoma. Second harmonic generation (SHG) data can provide critical information to bridge this gap.Conclusion: SRS, combined with SHG, can be a valuable tool in better understanding calcifications in carcinogenesis, diagnosis, and possible prognosis. This study not only reveals previously unknown large variations of breast microcalcifications in association with local malignancy but also corroborates the clinical value of linking microcalcification chemistry to breast malignancy. More importantly, it represents an important step in the development of a label-free imaging strategy for breast cancer diagnosis with tremendous potential to address major challenges in diagnostic discordance in pathology.

BMP-2 upregulates the AKT/mTOR pathway in breast cancer with microcalcification and indicates a poor prognosis.

As a reliable biomarker of breast cancer, breast microcalcification has been reported to be correlated with poor prognosis. Bone morphogenetic protein 2 (BMP-2) plays an important role in microcalcification of breast cancer. Studies in other tissues have shown an association between BMP-2 and AKT/mTOR pathway, while their relationship in breast cancer still remains largely undetermined. To clarify the relationship of these three factors, we collected patients of invasive breast cancer with/without microcalcification and immunohistochemical examination was performed. A total of 272 patients with primary invasive breast cancer were selected from the First Hospital of China Medical University from January 2010 to January 2012. Immunohistochemical examination of the BMP-2, p-AKT and p-mTOR was performed on 4-µm tissue microarray (TMA) sections. Then, we analyzed the relationship of BMP-2, p-AKT, and p-mTOR and their correlation with disease-free survival (DFS) in breast cancer with/without microcalcification. We found that breast cancer patients with microcalcification were correlated with HER-2 positive expression and poor prognosis. Immunohistochemical examination showed that the expressions of BMP-2 and p-mTOR were increased in breast cancer with microcalcification and the expressions of BMP-2, p-AKT, and p-mTOR were correlated with each other. Moreover, the high expressions of BMP-2, p-AKT, and p-mTOR were significantly correlated with poor prognosis. Based on the abovementioned findings, we hypothesized that the high expression of BMP-2 not only played a vital role in the formation of microcalcification, but also activated the AKT/mTOR pathway. Collectively, breast cancer patients with microcalcification were more likely to be resistant to targeted or endocrine therapy and be correlated with poor prognosis.

A mixture of views network with applications to multi-view medical imaging

This paper examines data fusion methods for multi-view data classification. We present a decision concept that explicitly takes into account the input multi-view structure, where for each case there is a different subset of relevant views. This data fusion concept, which we dub Mixture of Views, is implemented by a special purpose neural network architecture. The single view decisions are combined by a data-driven decision, into a global decision according to the relevance of each view in a given case. The method was applied to two challenging computer-aided diagnosis (CADx) tasks: the task of classifying breast microcalcifications as benign or malignant based on craniocaudal (CC) and mediolateral oblique (MLO) mammography views and segmenting Multiple Sclerosis (MS) white matter lesions. The experimental results show that our method outperforms previously suggested fusion methods.

Generalized linear model (GLM) analysis: Multivariables of microcalcification specimens obtained via X-ray guided by stereotactic wire localization biopsy.

To retrospectively explore correlation of the resected specimen volume of breast microcalcification lesions and endogenous and exogenous factors of stereotactic needle localization biopsy (SNLB). Totally 214 patients underwent SNLB for non-palpable breast lesion with microcalcification lesions. Of 211 patients, 198 patients underwent single needle localization and 13 patients underwent multi-needle localization (26 lesions). Lesion sizes, distribution characteristics, lesion localization accuracy and resected specimen volumes were recorded and analyzed using a generalized linear model (GLM). The average lesion diameter is 2.63±1.73 cm. The localization accuracy of 187 lesions were moderate, 26 were too deep and 11 were too superficial. The mean resected specimen volume (V) was 17.51±5.14 cm3. One-way ANOVA analysis showed that 3 factors, including lesion sizes, distribution characteristics and the localization accuracy were associated with resected specimen volume (F = 67.56-112.78, P < 0.001). GLM revealed that lesion sizes, single clustered distribution and accurate localization were significant factors for resected specimen volume (F = -4.82-11.36, P < 0.05). The ratio (%) of the resected specimen volume to the involved breast volume (V0) was defined as the degree of breast defect. The mean breast defect of 125 benign patients (V/V0) was 27.5% ranging from 10.1% to 42.3%. Average lesion diameter and localization accuracy are highly significant variables for the resected specimen volume. Localization accuracy as a subjective controllable variable is one of the important factors that determine the volume of lesion resection. Single clustered distribution was more susceptible localization accuracy than other characteristic distributions. Improving localization accuracy can reduce resected specimen volume, which can reduce breast defect to a certain extent.

Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM

Microcalcification is the most important landmark information for early breast cancer. At present, morphological artificial observation is the main method for clinical diagnosis of such diseases, but it is easy to cause misdiagnosis and missed diagnosis. The present study proposes an algorithm for detecting microcalcification on mammography for early breast cancer. Firstly, the contrast characteristics of mammograms are enhanced by Contourlet transformation and morphology (CTM). Secondly, split the ROI by the improved K-means algorithm. Thirdly, calculate grayscale feature, shape feature, and Histogram of Oriented Gradient (HOG) for the ROI region. The Adaptive support vector machine (ASVM) is used as a tool to classify the rough calcification point and the false calcification point. Under the guidance of a professional doctor, 280 normal images and 120 calcification images were selected for experimentation, of which 210 normal images and 90 images with calcification images were used for training classification. The remaining 100 are used to test the algorithm. It is found that the accuracy of the automatic classification results of the Adaptive support vector machine (ASVM) algorithm reaches 94%, and the experimental results are superior to similar algorithms. The algorithm overcomes various difficulties in microcalcification detection and has great clinical application value.

Do Decreased Breast Microcalcifications After Neoadjuvant Chemotherapy Predict Pathologic Complete Response?

BackgroundClinical response evaluation by image examination after neoadjuvant chemotherapy for breast cancer is important for determining drug response and progression. Mammography is less correlated with pathologic response assessment than magnetic resonance imaging or ultrasonography. The present study clarified characteristics of breast cancer patients with decreased microcalcifications after neoadjuvant chemotherapy to increase the accuracy of mammographic assessment. Patients and MethodsConsecutive patients who received neoadjuvant chemotherapy for breast cancer at our hospital from January 2013 to June 2017 were retrospectively reviewed. Characteristics of cases of microcalcifications before neoadjuvant chemotherapy and those showing a reduction after were examined. Stromal tumor-infiltrating lymphocytes were evaluated in the biopsy. Pathologic complete response was also explored in patients exhibiting a decrease in microcalcifications by neoadjuvant chemotherapy. ResultsSeventy breast cancer cases were included; 37 had no malignant microcalcifications before neoadjuvant chemotherapy and 33 did. Breast cancer with microcalcifications was significantly more positive for human epidermal growth factor receptor 2 than those without microcalcifications (48% vs. 22%, P = .018). Only 6 of 33 patients with microcalcifications before neoadjuvant chemotherapy showed a decrease after treatment. These patients tended to have high stromal tumor-infiltrating lymphocytes (50% vs. 17%), segmental microcalcifications (66% vs. 33%), and pleomorphic/linear microcalcifications (83% vs. 37%). ConclusionThese results suggest that neoadjuvant chemotherapy is able to decrease malignant calcifications, particularly segmental and pleomorphic/linear microcalcifications. By clarifying the mechanisms of formation and disappearance of microcalcifications, a consensus can be reached on whether microcalcifications apparent by mammography are useful for evaluating response to neoadjuvant chemotherapy.

Multi-view information fusion in mammograms: A comprehensive overview

In the framework of computer-aided diagnosis of breast cancer, many systems were designed for the detection, the classification and/or the content-based mammogram retrieval (CBMR); in order to serve as a second source of decision for the radiologists. Nevertheless, to improve the final decision-making, the concept of multi-view information fusion (MVIF) was recently introduced. Indeed, this concept has been successfully applied in the context of breast cancer, since screening mammography provides two views for each breast: MedioLateral-Oblique (MLO) and CranioCaudal (CC) views. As these two views are complementary, MVIF methods widely proved their effectiveness. In this paper, we review the main methods that have been proposed for MVIF in the context of the detection (abnormality vs. non abnormality), the classification (normal vs. benign vs. malignant) and the content-based retrieval of mammograms. In fact, we classified detection based on MVIF methods into two main sub-classes, including ipsilateral analysis and bilateral analysis. Besides, classification based on MVIF methods were regrouped into two sub-classes, namely classification of breast masses based on MVIF and classification of breast microcalcifications based on MVIF. Lastly, CBMR based on MVIF methods were also classified into two sub-classes: early fusion-based MVIF-CBMR and late fusion-based MVIF-CBMR.

Evaluation of photon-counting spectral mammography for classification of breast microcalcifications

The purpose of this study was to evaluate the feasibility of spectral mammography using a dual-energy method to non-invasively distinguish between type I (calcium oxalate) and type II (calcium hydroxyapatite) microcalcifications. These two types of microcalcifications are difficult to differentiate owing to their similar linear attenuation coefficients. To improve the detection efficiency regarding microcalcifications, a photon-counting detector with energy discrimination capability was used, and the microcalcifications were classified using the optimal dual-energy spectrum. Two energy bins were used to obtain dual-energy images. In this study, a photon-counting spectral mammography system was simulated using Geant4 Application for Tomographic Emission (GATE) simulation tools. A cadmium zinc telluride detector with a high quantum efficiency was used in this study. The effects of classification on the density and thickness of the breast phantom were investigated. Microcalcifications of various sizes, ranging from 130 to 550 μm, were embedded in the phantom. For statistical analysis, the microcalcifications were classified as calcium hydroxyapatite or calcium oxalate based on a score calculation using the dual-energy images. According to the results, the measured contrast-to-noise ratio (CNR) of the calcium hydroxyapatite was higher than that of calcium oxalate in a single-energy image. The calcium hydroxyapatite had a higher attenuation coefficient and slightly higher contrast compared with calcium oxalate. In addition, the microcalcifications were classified into the optimal energy bin in the dual-energy images. This classification showed better performance with a high energy of 50 kVp and an energy threshold of 30 keV. This study demonstrated that using the dual-energy method could improve classification performance because the microcalcifications are affected by the density and thickness of the breast phantom. These results indicate that the classification accuracy was improved when the difference between the low-energy image and high-energy image was used. The dual-energy method can reduce the frequency of the need for biopsies and discriminate between different types of microcalcifications in mammography. These results can improve the efficiency of early breast cancer diagnosis.

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 6cm. The phantoms included models of microcalcifications ranging in size between 200 and . The simulation study was carried out with fixed MGD of 1.5mGy 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 1.5mGy deposited mean glandular dose. This study provides encouraging first results in classifying malignant and benign microcalcifications based solely on dual-energy mammography images.

Abstract P1-20-05: Raman imaging as a tool for the chemical and spatial characterization of breast microcalcifications to improve lesion assessment

Abstract Background: microcalcifications (MC) are common findings on screening mammography and are among the earliest signs of breast cancer. At the same time, from the use of well-known radiographic risk score systems, which include MC assessment, such as Breast Imaging-Reporting and Data System (BI-RADS), only 20% of screened patients are further associated with malignancy. This leads to repeated biopsies and to unnecessary surgeries with discomfort for patients and increased costs for the healthcare system. Furthermore, the definitive diagnosis by histological and immunohistological evaluations is still laborious and time-consuming. Raman Spectroscopy (RS) is a photonic approach capable to provide detailed chemical information of analysed samples without complex tissue preparation or staining. RS has a proven ability to distinguish different crystal structures, including those commonly present in MC. In this context some studies based on RS suggested a correlation between MC chemical features and pathology. On the other hand, previous Raman-based studies mainly investigated the overall MC chemical composition (by single-point scans) while an extensive MC characterization by Raman imaging approaches (mapping) for diagnostic purposes is still lacking. The aim of this study is to assess the usefulness of Raman imaging as a quick and accurate tool for a complete spatial characterization of MC detected on screening mammography and sampled by breast biopsy in order to better distinguish malignant vs. benign lesions. Method: 30 patients with breast calcifications detected on mammography with radiological classification BI-RAD 3-5 where selected and evaluated by core biopsy. 10 μm formalin-fixed paraffin-embedded (FFPE) histological sections obtained from biopsies were dewaxed with a specifically developed protocol that allows the removal of paraffin in less than 15 minutes. All MC present in each tissue section (usually from 2 to &amp;gt;10 per section) were then characterized by a Raman microscope thus obtaining Raman maps with lateral resolution between 5 and 10 μm. After pre-processing steps the Raman maps were analysed by both clustering and multivariate analysis approaches used to produce false-colour images and to perform automated features identification. Results: Our results confirm that hydroxyapatite is the prevalent form of calcium phosphate in MC and that MC composition correlates with lesion malignancy. In addition, thanks to the Raman imaging approach used here, we report for the first time that hydroxyapatite is more homogeneously distributed in malignant lesions and that, on the contrary, benign lesions show a heterogeneous distribution of hydroxyapatite, whitlockite and calcium-carbonate, inside the lesion and in the surrounding tissue. Conclusion: These evidences suggest that the characterization of MC by Raman imaging is a potential tool for the definition of new diagnostic signatures of breast cancer, especially if we consider that these evaluations can be performed by the simple and relative fast scanning of dewaxed slices, without altering the clinical workflow and without the need of staining or antibodies. Further studies with a larger cohort will be done to validate these results. Citation Format: Vanna R, Morasso C, Piccotti F, Sorrentino L, Villani L, Torti E, Bonizzi A, Leporati F, Corsi F. Raman imaging as a tool for the chemical and spatial characterization of breast microcalcifications to improve lesion assessment [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P1-20-05.

A review of ultrasound detection methods for breast microcalcification.

Breast microcalcifications are one of the important imaging features of early breast cancer and are a key to early breast cancer diagnosis. Ultrasound imaging has been widely used in the detection and diagnosis of breast diseases because of its low cost, nonionizing radiation, and real-time capability. However, due to factors such as limited spatial resolution and speckle noise, it is difficult to detect breast microcalcifications using conventional B-mode ultrasound imaging. Recent studies show that new ultrasound technologies improved the visualization of microcalcifications over conventional B-mode ultrasound imaging. In this paper, a review of the literature on the ultrasonic detection methods of microcalcifications was conducted. The reviewed methods were broadly divided into high-frequency B-mode ultrasound imaging techniques, B-mode ultrasound image processing techniques, ultrasound elastography techniques, time reversal techniques, high spatial frequency techniques, second-order ultrasound field imaging techniques, and photoacoustic imaging techniques. The related principles and research status of these methods were introduced, and the characteristics and limitations of the various methods were discussed and analyzed. Future developments of ultrasonic breast microcalcification detection are suggested.