Specificity Of Breast Cancer Diagnosis Research Articles

A circulating miR-19b-based model in diagnosis of human breast cancer.

Objective: Breast cancer (BC) is becoming the leading cause of cancer-related death in women all over the word. Identification of diagnostic biomarkers for early detection of BC is one of the most effective ways to reduce the mortality. Methods: Plasma samples from BC patients (n = 120) and normal controls (n = 50) were collected to determine the differentially expressed circulating miRNAs in BC patients. Binary logistic regression was applied to develop miRNA diagnostic models. Receiver operating characteristic (ROC) curves were applied to calculate the area under the curve (AUC). MMTV-PYMT mammary tumor mice were used to validate the expression change of those circulating miRNAs. Plasma samples from patients with other tumor types were collected to determine the specificity of the model in diagnosis of BC. Results: In the screening phase, 5 circulating miRNAs (miR-16, miR-17, miR-19b, miR-27a, and miR-106a) were identified as the most significantly upregulated miRNAs in plasma of BC patients. In consistence, the 5 miRNAs showed upregulation in the circulation of additional 80 BC patients in a tumor stage-dependent manner. Application of a tumor-burden mice model further confirmed upregulation of the 5 miRNAs in circulation. Based on these data, five models with diagnostic potential of BC were developed. Among the 5 miRNAs, miR-19b ranked at the top position with the highest specificity and the biggest contribution. In combination with miR-16 and miR-106a, a miR-19b-based 3-circulating miRNA model was selected as the best for further validation. Taken the samples together, the model showed 92% of sensitivity and 90% of specificity in diagnosis of BC. In addition, three other tumor types including prostate cancer, thyroid cancer and colorectal cancer further verified the specificity of the BC diagnostic model. Conclusion: The current study developed a miR-19b-based 3-miRNA model holding potential for diagnosis of BC using blood samples.

Breast Ultrasound Elastography is the Most Mature and Well-documented Method for the Measurement of Tissue Stiffness: A Review

Breast cancer is the most common malignancy and one of the leading causes of death in females worldwide. North America has one of the highest incidence breast cancer rates in the world, making breast cancer awareness a high priority. Only in the USA, 527 women are expected to be diagnosed with breast cancer while 110 women will die of it per day. Central to the importance of breast cancer diagnosis is the fact that almost one-third of the latter group could survive if their cancer is detected and treated early. In a worldwide context, this translates into nearly 400,000 lives that could be saved every year as a result of early detection. As such; developing technique that can help to detect and diagnose breast cancer at early stage can have a great impact on survival and quality of life of breast cancer patients. Conventional breast cancer screening and detection techniques such as clinical breast examination and X- ray mammography are known to have low sensitivity. Breast magnetic resonance imaging (MRI) is more sensitive modality for breast cancer detection, however, MRI is costly and has been shown to have low specificity for breast cancer diagnosis. Dynamic contrast-enhanced MRI has been demonstrated to provide a good sensitivity and specificity for differentiation of benign versus malignant lesions, due to altered angiogenesis mechanisms in tumors. However, in addition to being costly, requires injection of exogenous contrast agents to provide such contrast. An alternate imaging technique for breast cancer detection employs tissue stiffness as contrast mechanism. The technique is founded on the fact that alterations in breast tissue stiffness are frequently associated with pathology. Ultrasound elastography is the most mature and well-documented method for the measurement of tissue stiffness. Elastographybased imaging technique has received substantial attention in recent years for non-invasive assessment of tissue mechanical properties. These techniques take advantage of changed soft tissue elasticity in various pathologies to yield qualitative and quantitative information that can be used for diagnostic purpose. Measurements are acquired in specialized imaging modes that can detect tissue stiffness in response to an applied mechanical force. Ultrasoundbased methods are of particular interest due to its many inherent advantages, such as wide availability including at the bedside and relatively low cost. While ultrasound elastography has shown promising results for non-invasive assessment of breast stiffness is emerging.
 Faridpur Med. Coll. J. 2021;16(1):42-44

Cross-platform genomic identification and clinical validation of breast cancer diagnostic biomarkers.

Introduction: Circulating non-coding RNA is an ideal source to discover novel biomarkers for non-invasive screening. However, studies for the discovery of universal miRNAs in serum and exosomes for breast cancer early diagnosis are limited.Methods: Based on bioinformatic analysis, in vitro and in vivo studies were performed to understand the role of identified hsa-miR-423-5p in cancer proliferation, migration, cancer stem cell properties. Next, global non-coding RNA expression profiles in blood serum and exosome were performed. hsa-miR-423-5p expression from a total of 356 peripheral blood samples was evaluated and the association of hsa-miR-423-5p expression with clinical characteristics, sensitivity and specificity for breast cancer diagnosis were assessed.Results: The expression of serum and exosomal hsa-miR-423-5p is abnormally increased in breast cancer. Suppression of hsa-miR-423-5p inhibited cell proliferation and invasion in both T47D and MDA-MB-231 breast cancer cell lines, and tumor growth in vivo. Compared with 113 healthy women, quantification analysis of hsa-miR-423-5p in 224 breast cancer samples confirmed the abnormal expression. Serum hsa-miR-423-5p was significantly associated with the clinical stage (P=0.001) and Ki-67 level (P=0.004).Conclusions: A translational bioinformatics analysis procedure and validation by in vitro, in vivo, and clinical samples reveal that hsa-miR-423-5p could be used as a non-invasive breast cancer biomarker.

Abstract P6-02-04: Investigating the feasibility of performing quantitative DCE-MRI in an abbreviated breast examination

Abstract Introduction. Dynamic contrast-enhanced (DCE) MRI provides quantitative information on tissue properties that enhances the specificity of breast cancer diagnosis; however, mammography remains the standard screening protocol due to its lower cost. There is a push to develop more accessible abbreviated breast MRI scans for screening high-risk patients without compromising diagnostic power. Here we analyze the effects of the limited dynamic time course afforded by an abbreviated breast MRI exam on the diagnostic performance of quantitative DCE-MRI. Methods and Results. We evaluate the ability of five quantitative measures to retrospectively differentiate malignant (N=21) and benign (N=24) lesions using DCE-MRI data acquired with 15 second temporal resolution in a cohort of 45 patients from the ACRIN 6883 multi-site breast trial. The first two measures are the volume transfer constant (Ktrans) estimated by, respectively, fitting the standard Kety-Tofts (STK) perfusion model and the Patlak approximation of the STK model (Patlak model) to patient data sets that have been truncated into a series of abbreviated-time courses (ATCs). An ATC is defined as containing the first n time points of a time course and is referred to as “ATC n.” For the first measure, n is the inclusive set of integers from 7 to 14, and, for the second measure, n is the set of 4 and 7 since the Patlak approximation only holds during the initial enhancement. For each patient, the fitting procedure provides Ktrans values for each voxel within the region of interest (ROI). The values are averaged and statistically evaluated for performance in discriminating malignant from benign tumors. For Ktrans, the maximum AUC of 0.61 was achieved using ATC 14 (i.e., 3.50 minutes), while for the Patlak model, the maximum AUC of 0.55 was achieved using ATC 7 (i.e., 1.75 minutes). The third measure is a modified median signal enhancement ratio (SER) computed for a series of four ATCs per patient, where n is now the inclusive set of integers 8 through 11. The SER is defined as (S1-S0)/(S2-S0), where S0 is the pre-contrast signal, S1 is the peak enhancement, and S2 is the signal at the last time point. Abbreviating the time course effectively shifts S2 to an earlier time point. For each ATC for each patient, the SERs are computed for all voxels within the ROI after which the median is computed. We calculate the AUC under the ROC curve to evaluate the diagnostic performance of each ATC-derived median SER. ATC 10 (i.e., 2.5 minutes) yields a maximum AUC of 0.79 among all ATCs. The fourth and fifth measures are, respectively, the area under the enhancement phase and the slope of the washout phase of the patient DCE time courses. The fourth measure is computed by numerically integrating the time course of each voxel within a patient’s ROI between the first and seventh time points and averaging the values. Similarly, for the fifth measure, the slope is calculated between the seventh and last time points within the ROI and averaged. We find that the fourth measure yields an AUC under the ROC curve of 0.76, and the fifth measure yields an AUC of 0.77. Discussion and Conclusion. The highest AUC from the two pharmacokinetic measures is 0.61, which suggests ineffective diagnostic ability in this data set. The median SER computed using ATC10 yields an AUC of 0.79, showing promise as a quantitative diagnostic tool in the abbreviated scan setting. Inter-site variability and the low temporal resolution of this data set may explain the relatively lower performance of measures one, two, four, and five as compared to measure three. It is necessary to repeat this analysis using a state-of-the-art acquisition of DCE-MRI data to determine the specificity of the remaining quantitative measures to imaging specifications. Citation Format: Kalina P Slavkova, Julie C DiCarlo, Anum K Syed, Chengyue Wu, John Virostko, Anna G Sorace, Thomas E Yankeelov. Investigating the feasibility of performing quantitative DCE-MRI in an abbreviated breast examination [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 P6-02-04.

Abstract P2-14-18: Achiveing rapid intrao-operative diagnosis during breast cancer surgery using high-reloution full-field optical coherence imaging and dynamic cell imaging

Abstract Background: Intraoperative pathological diagnosis such as frozen section and imprint cytology is not routinely recommended in clinical practice because of time and accuracy concerns. Full-field optical coherence tomography (FF-OCT) is a new optical imaging technique that could generate sectioning tomogram from fresh tissue and provide depiction of the morphological structure and pathological changes in minutes without conventional tissue preparation, slicing, and staining, and dynamic cell imaging (DCI) added the viability information of cells/tissue, which could be more important in cancer diagnosis. This study was to evaluate the feasibility and diagnostic value of FF-OCT and DCI in breast lesions and lymph node specimens during breast cancer surgery. Methods: We evaluated normal breast tissue, benign breast lesions, breast cancer and axillary lymph node specimens from 107 patients using FF-OCT and DCI. After the optical assessment, the tissue was paraffin embedded and sent to conventional H&E diagnosis. The similar layer of OCT and H&E images were compared and diagnostic criteria were generated. The diagnostic sensitivity and specificity by two trained surgeons without pathology diagnosis experience were evaluated. Results: A total of 194 specimens were examined, including 143 breast tissue(101 malignant and 42 benign/normal) and 51 lymph nodes(26 metastatic and 25 non-metastatic). On FF-OCT and DCI, normal morphological structures such as adipose, collagen, mammary ducts, and lobules in breast tissue and lymphoid follicle and hilum in lymph nodes were easily recognized. Breast cancer characteristics on H&E imaging correspond to collagen distortion, focal hypointensity, micro-calcification, clustered or linear lively cells et etc on FF-OCT or DCI, which could also be easily distinguished. We included the previously mentioned features to build diagnosis criteria for cancer on FF-OCT and DCI. The average acquisition time is 14±11 minutes. The sensitivity and specificity for breast cancer diagnosis were 92.1% and 94.3% respectively. The sensitivity and specificity for lymph node involvement were 92.3% and 84% respectively. Conclusion: The time- and tissue-saving optical imaging technique yielded high accuracy that was comparable to that of traditional intraoperative and postoperative pathological diagnosis in breast cancer and lymph node metastasis. These results implied the promising application in the intraoperative evaluation and possible decrease of the re-excision rate for breast cancer surgery. Citation Format: Yang H, Zhang S, Guo J, Xie F, Tong F, Cao Y, Liu P, Zhou B, Cheng L, Liu M, Wang S, Peng Y, Wang C, Yang Y, Ma Y, Chen D, Shen D, Wang S. Achiveing rapid intrao-operative diagnosis during breast cancer surgery using high-reloution full-field optical coherence imaging and dynamic cell imaging [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 P2-14-18.

Classification of contrast-enhanced spectral mammography (CESM) images.

Contrast-enhanced spectral mammography (CESM) is a recently developed breast imaging technique. CESM relies on dual-energy acquisition following contrast agent injection to improve mammography sensitivity. CESM is comparable to contrast-enhanced MRI in terms of sensitivity, at a fraction of the cost. However, since lesion variability is large, even with the improved visibility provided by CESM, differentiation between benign and malignant enhancement is not accurate and a biopsy is usually performed for final assessment. Breast biopsies can be stressful to the patient and are expensive to healthcare systems. Moreover, as the biopsies results are most of the time benign, a specificity improvement in the radiologist diagnosis is required. This work presents a deep learning-based decision support system, which aims at improving the specificity of breast cancer diagnosis by CESM without affecting sensitivity. We compare two analysis approaches, fine-tuning a pretrained network and fully training a convolutional neural network, for classification of CESM breast mass as benign or malignant. Breast Imaging Reporting and Data Systems (BIRADS) is a radiological lexicon, used with breast images, to categorize lesions. We improve each classification network by incorporating BIRADS textual features as an additional input to the network. We evaluate two ways of BIRADS fusion as network input: feature fusion and decision fusion. This leads to multimodal network architectures. At classification, we also exploit information from apparently normal breast tissue in the CESM of the considered patient, leading to a patient-specific classification. We evaluate performance using fivefold cross-validation, on 129 randomly selected breast lesions annotated by an experienced radiologist. Each annotation includes a contour of the mass in the image, biopsy-proven label of benign or malignant lesion and BIRADS descriptors. At 100% sensitivity, specificity of 66% was achieved using a multimodal network, which combines inputs at feature level and patient-specific classification. The presented multimodal network may significantly reduce benign biopsies, without compromising sensitivity.

The potential of multiparametric MRI of the breast.

MRI is an essential tool in breast imaging, with multiple established indications. Dynamic contrast-enhanced MRI (DCE-MRI) is the backbone of any breast MRI protocol and has an excellent sensitivity and good specificity for breast cancer diagnosis. DCE-MRI provides high-resolution morphological information, as well as some functional information about neoangiogenesis as a tumour-specific feature. To overcome limitations in specificity, several other functional MRI parameters have been investigated and the application of these combined parameters is defined as multiparametric MRI (mpMRI) of the breast. MpMRI of the breast can be performed at different field strengths (1.5-7 T) and includes both established (diffusion-weighted imaging, MR spectroscopic imaging) and novel MRI parameters (sodium imaging, chemical exchange saturation transfer imaging, blood oxygen level-dependent MRI), as well as hybrid imaging with positron emission tomography (PET)/MRI and different radiotracers. Available data suggest that multiparametric imaging using different functional MRI and PET parameters can provide detailed information about the underlying oncogenic processes of cancer development and progression and can provide additional specificity. This article will review the current and emerging functional parameters for mpMRI of the breast for improved diagnostic accuracy in breast cancer.

The Values of Combined and Sub-Stratified Imaging Scores with Ultrasonography and Mammography in Breast Cancer Subtypes.

Background and ObjectivesThe Breast Imaging Reporting and Data System (BI-RADS) of Mammography (MG) and Ultrasonography (US) were equivalent to the “5-point score” and applied for combined and sub-stratified imaging assessments. This study evaluated the value of combined and sub-stratified imaging assessments with MG and US over breast cancer subtypes (BCS).Materials and MethodsMedical records of 5,037 cases having imaging-guided core biopsy, performed from 2009 to 2012, were retrospectively reviewed. This study selected 1,995 cases (1,457 benign and 538 invasive cancer) having both MG and US before biopsy. These cases were categorized with the “5-point score” for their MG and US, and applied for combined and sub-stratified imaging assessments. Invasive cancers were classified on the basis of BCS, and correlated with combined and sub-stratified imaging assessments.ResultsThese selected cases were evaluated by the “5-point score.” MG, US, and combined and sub-stratified imaging assessments all revealed statistically significant (P < 0.001) incidence of malignancy. The sensitivity was increased in the combined imaging score (99.8%), and the specificity was increased in the sub-stratified combined score (75.4%). In the sub-stratified combined imaging assessment, all BCS can be classified with higher scores (abnormality hierarchy), and luminal B subtype showed the most salient result (hierarchy: higher, 95%; lower, 5%).ConclusionsCombined and sub-stratified imaging assessments can increase sensitivity and specificity of breast cancer diagnosis, respectively, and Luminal B subtype shows the best identification by sub-stratified combined imaging scoring.

TU-F-18C-01: Breast Tissue Decomposition Using Spectral CT After Distortion Correction

Purpose: To investigate the feasibility of accurate breast tissue compositional characterization by using spectral-distortion-corrected dual energy images from a photon-counting spectral CT. Methods: Thirty eight postmortem breasts were imaged with a Cadmium-Zinc-Telluride (CZT)-based photon-counting spectral CT system at beam energy of 100 kVp. The energy-resolved detector sorted photons into low and high energy bins with a splitting energy of 42 keV. The estimated mean glandular dose (MGD) for each breast was approximately 2.0 mGy. Dual energy technique was used to decompose breast tissue into water, lipid, and protein contents. Two image-based methods were investigated to improve the accuracy of tissue compositional characterization. The first method simply limited the recorded spectra up to 90 keV. This reduced the pulse pile-up artifacts but it has some dose penalty. The second method corrected the spectral information of all measured photons by using a spectral distortion correction technique. Breasts were then chemically decomposed into their respective water, lipid, and protein contents, which was used as the reference standard. The accuracy of the tissue compositional measurement with spectral CT was evaluated by the root-mean-square (RMS) errors in percentage composition. Results: The errors in quantitative material decomposition were significantly reduced after the appropriate image processing methods. As compared to the chemical analysis as the reference standard, the averages of the RMS errors were estimated to be 15.5%, 3.3%, and 2.8% for the raw, energy-limited, and spectral-corrected images, respectively. Conclusion: Spectral CT can be used to accurately quantify the water, lipid, and protein contents in breast tissues by implementing a spectral distortion correction algorithm. The tissue compositional information can potentially improve the sensitivity and specificity for breast cancer diagnosis.

A Review Study on the Efficacy of Quantitative DCE-MRI in Breast Lesion Diagnosis

: Breast cancer is the most common cancer among women, for which a successful treatment depends in part, on early diagnosis. Accurate differentiation of lesions may greatly affect the outcome of treatment. In this regard, anatomical along with physiological MR imaging modalities, such as dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), diffusion weighted imaging (DWI), and magnetic resonance spectroscopy (MRS) which provide accurate, repeatable and noninvasive physiological biomarkers of tumor growth, have been increasingly used as secondary characterizing tools to ultrasonography (US) and mammography. The aim of this study is to present a thorough review on available scholarly works, especially where quantitative DCE-MRI has been employed for diagnostic decisions on breast cancer, concluding if the modality is either necessary for better sensitivity/specificity, or for a reliable surgery/treatment planning. US and mammography are the conventional screening modalities for breast cancer diagnosis. The overall sensitivity of mammography is 70%90%. However, this sensitivity is variable and can range from as high as 80%98% in women with fatty breast tissue to as low as 30%48% in women with dense breast tissue. Sonographic classification of benign and malignant tumors has a high sensitivity of as high as 98% but a low specificity of about 32%. The low specificity of mammography and US methods and the need for extracting biomarkers, has led to rising interest in employing physiological MR imaging methods for characterizing breast cancers. DCE-MR images can be analyzed in a semi-quantitative (non-model-abased) or quantitative (model-based) approach. The sensitivity and specificity of semi-quantitative DCE-MRI in detecting breast cancer reaches 88.1% and 67.7%, respectively. While it has been proved that DCE-MR imaging features could be useful to noninvasively distinguish highly aggressive breast carcinomas, they are not as useful in discriminating benign from malignant non-mass lesions (such as DCIS). On the other hand, the pharmacokinetic (or model-based) analysis of DCE-MRI can be useful in monitoring and predicting response to therapy, as the changes in tumor vascularization in responder patients precede changes in tumor size, and may better describe tumor response to therapy. Pharmacokinetic model analysis of DCE-MRI data provided a sensitivity and specificity of 100% and 78%, respectively. In comparison with semi-quantitative DCE-MRI, Quantitative DCE-MRI has significantly improved the sensitivity and specificity of breast cancer diagnosis. Other physiological MR imaging alternatives like DW-MRI and MRS have also been employed for breast cancer diagnosis. In a pooled analysis on clinical breast MRS gathered from five independent centers around the world, the sensitivity and specificity of breast MRS were 83% and 85%, respectively. In a study on 78 patients (110 lesions), the sensitivity and specificity of DWI in detection of malignant and benign lesions was reported at 86%. Although the sensitivity and specificity of imaging could be influenced by the selected values of ADC cut-off, the ADC-values may still be used to characterize breast lesions. The diagnosis of breast cancer is a very challenging issue, as early detection may allow for conservative surgery as an alternative to mastectomy. Although mammography and US allow earlier detection of cancers and have been shown to improve the survival of women with breast cancer, with a relatively high sensitivity in women older than 50 years, monitoring the breast cancer is limited by low specificity. DCE-MRI is a powerful imaging tool that has shown utility in delineating the extent of disease in patients with known breast cancer and in screening women at high risk for breast cancer. Semi-quantitative DCE-MRI has high sensitivity but only moderate specificity, because of an overlap between benign and malignant lesions, independent of breast density, tumor type, and menopausal status. Quantitative analysis of DCE-MRI is used by many researchers for detecting and evaluating breast diseases and is a means of increasing the specificity of breast MR examination. MRS of the breast has been proposed as an adjunct to the anatomical MRI examination to improve the specificity of distinguishing malignant from benign breast tumors, however, MRS alone is not suitable in lactating women. Nevertheless, many studies have reported that DWI-MRI may help to differentiate between benign and malignant lesions with good specificity. These findings suggest that incorporating MRS and DW-MRI may increase the overall specificity of quantitative analysis of breast DCE-MRI.

Measurement of breast tissue composition with dual energy cone‐beam computed tomography: A postmortem study

To investigate the feasibility of a three-material compositional measurement of water, lipid, and protein content of breast tissue with dual kVp cone-beam computed tomography (CT) for diagnostic purposes. Simulations were performed on a flat panel-based computed tomography system with a dual kVp technique in order to guide the selection of experimental acquisition parameters. The expected errors induced by using the proposed calibration materials were also estimated by simulation. Twenty pairs of postmortem breast samples were imaged with a flat-panel based dual kVp cone-beam CT system, followed by image-based material decomposition using calibration data obtained from a three-material phantom consisting of water, vegetable oil, and polyoxymethylene plastic. The tissue samples were then chemically decomposed into their respective water, lipid, and protein contents after imaging to allow direct comparison with data from dual energy decomposition. Guided by results from simulation, the beam energies for the dual kVp cone-beam CT system were selected to be 50 and 120 kVp with the mean glandular dose divided equally between each exposure. The simulation also suggested that the use of polyoxymethylene as the calibration material for the measurement of pure protein may introduce an error of -11.0%. However, the tissue decomposition experiments, which employed a calibration phantom made out of water, oil, and polyoxymethylene, exhibited strong correlation with data from the chemical analysis. The average root-mean-square percentage error for water, lipid, and protein contents was 3.58% as compared with chemical analysis. The results of this study suggest that the water, lipid, and protein contents can be accurately measured using dual kVp cone-beam CT. The tissue compositional information may improve the sensitivity and specificity for breast cancer diagnosis.

Diagnostic values of GHSR DNA methylation pattern in breast cancer

DNA methylation patterns have been recognised as cancer-specific markers with high potential for clinical applications. We aimed at identifying methylation variations that differentiate between breast cancers and other breast tissue entities to establish a signature for diagnosis. Candidate genomic loci were analysed in 117 fresh-frozen breast specimens, which included cancer, benign and normal breast tissues from patients as well as material from healthy individuals. A cancer-specific DNA methylation signature was identified by microarray analysis in a test set of samples (n = 52, p < 2.1 × 10(-4)) and its performance was assessed through bisulphite pyrosequencing in an independent validation set (n = 65, p < 1.9 × 10(-7)). The signature is associated with SFRP2 and GHSR genes, and exhibited significant hypermethylation in cancers. Normal-appearing breast tissues from cancer patients were also methylated at these loci but to a markedly lower extent. This occurrence of methylated DNA in normal breast tissue of cancer patients is indicative of an epigenetic field defect. Concerning diagnosis, receiver operating characteristic curves and the corresponding area under the curve (AUC) analysis demonstrated a very high sensitivity and specificity of 89.3 and 100 %, respectively, for the GHSR methylation pattern (AUC >0.99). To date, this represents the DNA methylation marker of the highest sensitivity and specificity for breast cancer diagnosis. Functionally, ectopic expression of GHSR in a cell line model reduced breast cancer cell invasion without affecting cell viability upon stimulation of cells with ghrelin. Our data suggest a link between epigenetic down-regulation of GHSR and breast cancer cell invasion.

The application of DCE-MRI in diagnosing breast cancer

Objective The aim of the study was to further explore the diagnostic value of breast dynamic contrast enhancement (DCE), and improve specificity of breast cancer diagnosis.

Emerging Techniques and Molecular Imaging in Breast Cancer

The sensitivity of screening mammography is limited in the evaluation of dense breasts, with as few as 45% of cancers visible in extremely dense breasts. Supplementary imaging for improved sensitivity in women with dense breasts is necessary to overcome this limitation. Emerging technologies that advance the applications of digital mammography include digital breast tomosynthesis and dedicated breast cone-beam computed tomography. Molecular imaging goes beyond structural imaging. A functional imaging technique that provides information on the biology, physiology, and metabolic pathways of cancer might help to improve the sensitivity and specificity of breast cancer diagnosis, facilitate early assessment of treatment response, and help individualize therapy options for patients. Advanced magnetic resonance, nuclear medicine, and optical imaging techniques in the realm of molecular imaging will be explored in this article.

The pressing need for better histologic-mammographic correlation of the many variations in normal breast anatomy

Mammography screening calls for a reevaluation of the working relationship between physicians dealing with the diagnosis and treatment of breast diseases. In this new era, histologic-mammographic correlation needs to be extended to correctly describe the deceptive mammographic findings that correspond to variations in normal breast tissue. Progress in histologic-mammographic correlation can only be made by overcoming the limitations inherent to the traditional histologic technique by examining a histologic specimen of greater length, width, and depth. There are several distinct advantages to using the large-section histology technique in the diagnosis of breast diseases. The subgross (three-dimensional) histology technique serves to bridge the gap that separates the pathologist and radiologist, bringing them to a common ground for a better understanding of breast morphology. These improvements in communication between the members of the diagnostic team will serve to optimize the sensitivity and specificity of breast cancer diagnosis.