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Breast cancer is one of the most diffuse diseases and its incidence rate is increasing. Mammography is the gold standard exam for screening breast cancer. Nevertheless, to provide better conditions for visualization and detection of tumors, in particular to young women, techniques exploiting the X-ray phase contrast to generate images have been studied and proposed for clinical use. As every imaging modality clinically implemented, the capabilities and limitations of an X-ray phase-contrast system dedicated to breast imaging should be evaluated by a phantom. Although for mammography, there are several commercial phantoms, for tomographic X-ray phase-contrast imaging systems dedicated to breast screening, they are absent. Therefore, this study aimed to design a breast phantom for application in phase-contrast computed tomography (PC-CT) imaging. A breast phantom dedicated to X-ray phase-contrast imaging was designed. The phantom has a cylindrical shape and is composed by PMMA, to mimic the breast of a young woman, with some inserts filled with tissue substitutes to normal and pathological breast tissues, such as ethanol and glycerol. These materials were chosen due to the similarity in the attenuation and scattering properties of normal and pathological human breast tissues. A comparison between tomographic X-ray absorption imaging and tomographic X-ray phase-contrast imaging showed a significant increase in edges definition, even with materials with similar attenuation properties. The results of this work reinforce the need for dedicated phantoms to exploit the features of each imaging modality more realistic. In particular, the breast phantom–designed breast screening by PC-CT allows exploiting the features of this new imaging modality.

Mammography plays an important role in screening breast cancer among females, and artificial intelligence has enabled the automated detection of diseases on medical images. This study aimed to develop a deep learning model detecting breast cancer in digital mammograms of various densities and to evaluate the model performance compared to previous studies. From 1501 subjects who underwent digital mammography between February 2007 and May 2015, craniocaudal and mediolateral view mammograms were included and concatenated for each breast, ultimately producing 3002 merged images. Two convolutional neural networks were trained to detect any malignant lesion on the merged images. The performances were tested using 301 merged images from 284 subjects and compared to a meta-analysis including 12 previous deep learning studies. The mean area under the receiver-operating characteristic curve (AUC) for detecting breast cancer in each merged mammogram was 0.952 &plusmn, 0.005 by DenseNet-169 and 0.954 &plusmn, 0.020 by EfficientNet-B5, respectively. The performance for malignancy detection decreased as breast density increased (density A, mean AUC = 0.984 vs. density D, mean AUC = 0.902 by DenseNet-169). When patients&rsquo, age was used as a covariate for malignancy detection, the performance showed little change (mean AUC, 0.953 &plusmn, 0.005). The mean sensitivity and specificity of the DenseNet-169 (87 and 88%, respectively) surpassed the mean values (81 and 82%, respectively) obtained in a meta-analysis. Deep learning would work efficiently in screening breast cancer in digital mammograms of various densities, which could be maximized in breasts with lower parenchyma density.

Simple Summary Breast cancer survivors are at risk for recurrence, and the early detection of recurrence improves survival. Therefore, imaging surveillance is performed for women who have breast-conserving surgery. The aim of our retrospective study was to compare routine mammography with contrast-enhanced mammography in the screening (asymptomatic) post-treatment setting. We confirmed that when screening women with breast conservation surgery, contrast-enhanced mammography had a higher cancer detection rate (15.4/1000) and positive predictive value of biopsies (42.9%) than full-field digital mammography (6.2/1000 and 37.5%, respectively). Abstract To investigate the value of contrast-enhanced mammography (CEM) compared to full-field digital mammography (FFDM) in screening breast cancer patients after breast-conserving surgery (BCS), this Health Insurance Portability and Accountability Act-compliant, institutional review board-approved retrospective, single-institution study included 971 CEM exams in 541 asymptomatic patients treated with BCS who underwent screening CEM between January 2013 and November 2018. Histopathology, or at least a one-year follow-up, was used as the standard of reference. Twenty-one of 541 patients (3.9%) were diagnosed with ipsi- or contralateral breast cancer: six (28.6%) cancers were seen with low-energy images (equivalent to FFDM), an additional nine (42.9%) cancers were detected only on iodine (contrast-enhanced) images, and six interval cancers were identified within 365 days of a negative screening CEM. Of the 10 ipsilateral cancers detected on CEM, four were detected on low-energy images (40%). Of the five contralateral cancers detected on CEM, two were detected on low-energy images (40%). Overall, the cancer detection rate (CDR) for CEM was 15.4/1000 (15/971), and the positive predictive value (PPV3) of the biopsies performed was 42.9% (15/35). For findings seen on low-energy images, with or without contrast, the CDR was 6.2/1000 (6/971), and the PPV3 of the biopsies performed was 37.5% (6/16). In the post-BCS screening setting, CEM has a higher CDR than FFDM.

Aim: As Breast cancer is most common in developed and developing world. Despite of seriousness & importance of breast cancer the awareness is low. This Study highlights the primary screening of breast cancer with the device Intelligent Breast Examination. Objectives: The objectives of this study are to assess the knowledge regarding awareness on screening of breast cancer with Intelligent Breast Examination (IBE), to develop a self-instructional module (SIM) on awareness on screening regarding breast cancer with IBE, and to assess its effectiveness on awareness about screening breast cancer with IBE. Materials and Methods: Evaluative research approach was used. The study used preexperimental research design. The samples (60 primary schoolteachers) were selected by the use of two sampling techniques, i.e., random sampling (for schools) and purposive sampling (study participants). Data were collected in two phases (pre and post) using structured questionnaire followed by the administration of SIM. Results: The results revealed a significant increase in mean knowledge scores (mean pre 11.16 standard deviation [SD] = 2.631 to mean post 21.2 SD = 1.842) regarding awareness on screening of breast cancer with IBE. The calculated paired t value (23.902) is greater than the table value ( t = 1.67) at 0.05 level of significance. Conclusion: The study concluded that the SIM prepared proved to be effective in increasing the awareness regarding screening with IBE in breast cancer.

Thermography is an entirely non-invasive and non-contact imaging technique that is widely used in the medicinal field. Since the early detection of cancer is very important, the computer-aided system can increase the rate of diagnosis, cure, and survival of the affected person. Considering the high cost of treatment in addition to the high prevalence of affected persons, early diagnosis is the most important step in reducing the health and social complications of this disease. Currently, mammography is the main method used for screening breast cancer. However, for young woman, mammography is not recommended due to the low contrast that results from the dense breast, and alternative techniques must be considered for this purpose. Breast cancer is the main cause of cancer-related mortality among women. Early detection of cancer-especially breast cancer-will aid the treatment process. Our goal is to develop software for detecting breast cancer automatically that uses image-processing techniques and algorithms to analyze thermal breast images to detect the signs of the disease in these images, allowing the early detection of breast cancer. A new algorithm is proposed for the extraction of the breast characteristic features based on bio-data, image analysis, and image statistics. These features have been extracted from the thermal images captured by a thermal camera, and will be used to classify the breast images as normal or suspected by using convolutional neural networks (CNNs) optimized by Bayes algorithm. By using our proposed algorithm, a 98.95% of accuracy rate was obtained for the thermal images in the dataset belonging to 140 individuals.

Accurate diagnosis is pivotal for successful treatment for breast cancer. High chances of survival are possible, if malignancy is detected at an early stage. Mammography is the most efficient and widely accepted modality for screening breast cancer. In this paper we propose a decision support system based on image retrieval which retrieves similar pathology based mammographic images to serve the physician in the diagnosis of breast cancer. The work explores how to use the retrieved similar cases as references to improve the classification performance. The rationale is that by incorporating the closeness information for decision making improves classifier performance rather than making decision from whole database. Experiments were carried out on DDSM database utilizing 4300 images of breast cancer. The results demonstrated the effectiveness of proposed system and show the vitality for clinical applications.

There is an established consensus in the breast health community that mammography is the mainstay imaging examination for screening breast cancer. However, there are varied national recommendations among stakeholders and major institutions in the topic of breast cancer screening. Lack of consensus in screening criteria includes risk stratification, age to initiate screening, and the interval of screening. The differences in practice guidelines are mainly due to variation in design and interpretation of screening trials over the past decades. As debates for and against the use of screening mammography continue to escalate, both providers and patients are often confused and wrongly perceive the recommendations as directives. The purpose of this article is to review the current guidelines, analyze the reason for the controversies in screening mammography, and shine light on the upcoming trends of future screening guidelines.

A list of organizational, management, and clinical steps that are crucial for creating an effective breast cancer screening program as part of the state medical benefits scheme in Moscow is presented. We identified key performance indicators for each stage of the screening program. Performance of radiologists and radiology technicians was evaluated, including assessing the accuracy of both single and double reading of mammograms. We streamlined an efficient system for patient routing between the stages of the screening program and developed methodological materials for patients and medical specialists. A shortened structured protocol for mammography interpretation, including second reading, was created in compliance with terms of Moscow Compulsory Medical Insurance Fund. We conceived a framework of educational activities to assess and improve skills and knowledge of radiologists and other specialists participating in the screening.

Nanoscopic synchrotron X-ray imaging was performed on scalp hair samples of patients with breast cancer and healthy individuals to investigate any structural differences as diagnostic tool. Hair strands were divided into 2-3 segments along the strands from root to tip, followed by imaging either in projection or in CT scanning with a monochromatic 6.78-keV X-ray using zone-plate optics with a resolving power of 60 nm. All the examined cancer hairs exhibited medulla loss with cancer stage-dependent pattern; complete loss, discontinuous or trace along the strands. In contrast, medullas were well retained without complete loss in the healthy hair. In the CT-scanned axial images, the cortical spindle compartments had no contrast in the healthy hair, but appeared hypointense in contrast to the surrounding hyperintense cortical membrane complex in the cancer hair. In conclusion, observation of medulla loss and cortical membrane enhancements in the hair strands of breast cancer patients demonstrated structural variations in the cancer hair, providing a new platform for further synchrotron X-ray imaging study of screening breast cancer patients.

Automated ultrasound volume scan is useful for screening breast cancer as it records the whole breast data allowing longitudinal comparisons and double reading. Unlike examinations with handheld probes, it is less operator independent. However, the number of acquired images is large, which increases radiologists' workload. In this study, we propose a mass detection method on ultrasound volume scans using convolutional neural network (CNN). Using CNN, detection performance greatly improved compared with the conventional filter based method.

Resume L’objectif de cet article est de rappeler pourquoi et comment le depistage de masse organise du cancer du sein s’est developpe puis generalise en France en 2004, et d’expliquer ce qu’est le classement BI-RADS de l’ACR. Des perspectives sur le futur du depistage seront donnees.

To evaluate the psychometric properties of a questionnaire addressing psychosocial consequences of false-positive mammographic screening.The Consequences of Screening--Breast Cancer and Lung Cancer questionnaires target psychosocial consequences of false-positive cancer screening. The Consequences of Screening--Breast Cancer questionnaire and ten items not considered lung cancer specific from the Lung Cancer questionnaire have been adapted for use in mammographic screening in Sweden, but remain psychometrically untested.Instrument development paper with psychometric cross-sectional and test-retest design.Twelve scales of a Swedish questionnaire version were tested by the Rasch model and traditional psychometric methods. Women with false-positive (Group I, n = 640) and negative (Group II, n = 802) screening mammography responded to the study questionnaire and the Nottingham Health Profile during 2009-2011.Iterative analyses resulted in nine scales demonstrating Rasch model fit, but all scales exhibited poor targeting with relatively large floor effects. Corrected item-total correlations exceeded the recommended criterion. Score differences between Groups I and II and correlations with Nottingham Health Profile sections followed an expected pattern. Cronbach's α and test-retest reliability was acceptable for group-level assessments for ten and seven scales, respectively.Five scales (Sense of dejection, Anxiety, Behavioural, Sleep and Existential values) of the Swedish questionnaire version demonstrated the best psychometric properties. Other scales should be used more cautiously. Although filling an important gap, causes of concern were identified across scales. The questionnaire should therefore be considered for group-level assessments rather than for measurement of individual degrees of psychosocial consequences.

e18052 {\rtf1\ansi\ansicpg936\cocoartf1347\cocoasubrtf570 {\fonttbl\f0\froman\fcharset0 Times-Roman;\f1\fnil\fcharset0 Calibri;\f2\fswiss\fcharset0 Helvetica; \f3\fnil\fcharset134 STHeitiSC-Light;\f4\froman\fcharset0 TimesNewRomanPSMT;} {\colortbl;\red255\green255\blue255;\red0\green0\blue0;} \paperw11900\paperh16840\margl1440\margr1440\vieww25400\viewh13600\viewkind0 \deftab720 \pard\pardeftab720 \f0\fs24 \cf2 \expnd0\expndtw0\kerning0 \outl0\strokewidth0 \strokec2 AI platform in mammagraphy to screening breast cancer: a sole center experence. \f1\fs21 \cf0 \kerning1\expnd0\expndtw0 \outl0\strokewidth0 \ \pard\pardeftab720\ri714\qj \cf0 Background: The aim of this study was to use deep convolutional neural network (DCNN) to relieve radiologists ' burden and to minimize misses and interpretation errors of suspicious lesions at digital mammography. Methods: We developed and trained the DCNN model on the training set, then validated it in an internal validation set. The labeled data were used for training the DCNN model, and the mass detection, calcification detection and benign and malignant diagnosis were interpreted respectively. After verifying the feasibility of the DCNN model in the diagnosis of breast cancer, we gradually increased the number of cases in the training set and established the verification set to test the training results. The latest training sets include 21100 atlas of our hospital between April 2010 and October 2017, among which the malignant case atlas is 1774. Each atlas contains 2 ipsilateral mammography images. The verification set includes 1307 atlas, among which the malignant case atlas is 248. Results: The DCNN model achieved high performance in identifying breast cancer patients in the validation sets tested, with sensitivity of 98.02%, specificity of 91.86% \f3 \'a3\'ac \f2 and area under the curve values of 0.9813. As for mass detection, we reached a recall rate of 89.6% on the premise of lowing the false positive to 0.168%. For the latest clinical data between October 2018 and November 2018 including 1043 atlas from 576 patients, the sensitivity was 78.6% (95%CI 62.8-89.1) versus 90.5% (95%CI 76.5 \f4 - \f2 96.9; p=0.021) and specificity was 93.4% (95%CI 91.6 \f4 - \f2 94.8) versus 82.7% (95%CI 80.2 \f4 - \f2 85 \f4 . \f2 0; p

Screens of cancer stem cells (CSCs)-specific agents present significant challenges to conventional cell assays due to the difficulty in preparing CSCs ready for drug testing. To overcome this limitation, developed is a microfluidic single-cell assay for screening breast cancer stem cell-specific agents. This assay takes advantage of the single-cell clone-forming capability of CSCs, which can be specifically inhibited by CSC-targeting agents. The single-cell assay is performed on a microfluidic chip with an array of 3840 cell-capturing units; the single-cell arrays are easily formed by flowing a cell suspension into the microchip. Achieved is a single cell-capture rate of ≈60% thus allowing more than 2000 single cells to be analyzed in a single test. Over long-term suspension culture, only a minority of cells survive and form tumorspheres. The clone-formation rate of MCF-7, MDA-MB-231, and T47D cells is 1.67%, 5.78%, and 5.24%, respectively. The clone-forming inhibition assay is conducted by exposing the single-cell arrays to a set of anticancer agents. The CSC-targeting agents show complete inhibition of single-cell clone formation while the nontargeting ones show incomplete inhibition effects. The resulting microfluidic single-cell assay with the potential to screen CSC-specific agents with high efficiency provides new tools for individualized tumor therapy.

Breast cancer is a serious health related issue for women in the world. Cancer detected at premature stages has a higher probability of being cured, whe reas at advanced stages chances of survival are ble ak. Screening programs aid in detecting potential breas t cancer at early stages of the disease. Among the various screening programs, mammography is the proven standard for screening breast cancer, because even small tumors can be detected on mammograms. In this study, a novel feature extraction technique based on dyadic wavelet transform for classificatio n of microcalcification in digital mammograms is proposed. In the feature extraction module, the hig h frequency sub-bands obtained from the decomposition of dyadic wavelet transform is used t o form innovative sub-bands. From the newly constructed sub-bands, the features such as energy and entropy are computed. In the classification module, the extracted features are fed into a Gauss ian Mixture Model (GMM) classifier and the severity of given microcalcification; benign or malignant ar e given. A classification accuracy of 95.5% is obtained using the proposed approach on DDSM database.

Accurate detection as well as classification of the breast cancer is still an unsolved question in the medical image processing techniques. We reviewed the existing Computer Aided Diagnosis (CAD)-based techniques to find that there has been enough work carried out towards both detection as well as classification of the breast cancer; however, all the existing techniques were implemented in highly controlled research environment. The prime contribution of this paper is it reviews some of the significant journals published during 2005–2016 and discusses its effectiveness thoroughly. The paper finally discusses about the open research issues that require a serious attention from the research community in order to address the existing issues. At the end, the paper makes some suggestion for carrying out future work direction in order to bridge the research gap explored from the existing system.

Purpose To evaluate diagnostic performances of CESM for breast diseases with comparison to breast MRI in China. Materials and methods Sixty-eight patients with 77 breast lesions underwent MR and CESM. Two radiologists interpreted either MRI or CESM images, separately and independently. BI-RADS 1–3 and BI-RADS 4–5 were classified into the suspicious benign and suspicious malignant groups. Diagnostic accuracy parameters were calculated. Receiver operating characteristic (ROC) curves were constructed for the two modalities. The agreement and correlation between maximum lesion diameter based on CESM and MRI, or CESM and pathology were analyzed. Results Diagnostic accuracy parameters for CESM were sensitivity 95.8 %, specificity 65.5 %, PPV 82.1 %, NPV 90.5 % and accuracy 84.4 %. The diagnostic accuracy parameters for breast MRI were sensitivity 93.8 %, specificity 82.8 %, PPV 88.2 %, NPV 92.3 %and accuracy 89.6 %. Area under the curve (AUC) of ROC was 0.96 for breast MRI and 0.88 for CESM. The Bland–Altman plots showed a mean difference of 0.7 mm with 95 % limits of agreement of 11.4 mm in tumor diameter measured using CESM and breast MRI. The differences of size measurement between CESM and breast MRI were significant, whereas no difference was observed between CESM and pathology as well as between breast MRI and pathology. The better correlation with pathological results was found in CESM than breast MRI. Conclusion Our study demonstrates that CESM possesses better diagnostic performances than breast MRI in terms of diagnostic sensitivity and lesion size assessment. And CESM is a good alternative method of screening breast cancer in high-risk people.

Breast cancer is one of the significant health problems in the world. Early detection can improve the patient recover rate. Mammography is one of the most effective methods for detecting and screening breast cancer. This paper presents Hough transform-based feature extraction method for the classification of mammograms. Hough transform is generally used to separate certain features according to a defined shape within an image. Here, Hough transform is used to detect features of mammogram image and it is classified using back propagation neural network. This method is tested on 208 mammogram images provided by MIAS.

Objectives: We evaluated a single free breast cancer screening program for breast cancer rate per 1,000 mammograms and compared it to the national cancer rate. We aimed to determine compliance rate in previously noncompliant women, to determine recall rate, and to find the percentage of diagnostic imaging done as a first test on a previously unscreened population. Methods: A retrospective chart review of 523 free breast cancer screenings was performed from 2010-2013. Baseline demographics, mammogram screenings, and breast cancer diagnoses were assessed. A p-value of

This study investigates the relationship between the number of screening mammograms read by radiologists and the screening breast cancer detection rate. Cancer detection rates for incident screens (all women aged >= 40 years) were compared by increasing categories of reader volume using Poisson regression. Data from New South Wales (NSW) for a 2 year period (2000-2001) were obtained from the BreastScreen NSW programme. Cancer detection rates increased with the number of mammograms read in the programme, reaching a plateau of approximately 40 per 10,000 after 1375 mammograms per year. No significant differences in cancer detection were evident above 875 mammograms (compared to below 875 mammograms) per year (RR = 0.79, 95% CI 0.63-0.99). (c) 2005 Elsevier Ltd. All rights reserved.

Medical infrared imaging is obtained by measuring the self-emitted infrared radiance from the human body. Infrared emission is related to surface temperature and temperature is one of the most important physiological parameters related to health. Though recent applications such as security identification and oriental medicine have provided new fields of biomedical applications, infrared thermography has had ups and downs in its usages in cancer detection. Some of the main difficulties include finding proper applications and efficient diagnostic algorithms. In this study, infrared thermal imaging was used to detect regional metastasis of breast cancer. Our measurements were done for 110 women. From 63 individuals of a Healthy Group and a Benign Breast Disease Group, we developed algorithms for differentiating malignant regional metastasis based on temperature difference and asymmetry of temperature distribution. Testing with 47 cancer patients, we achieved a positive predictive value of $87.5\%$ and a negative predictive value of $95.6\%$ . The results were better than for mammogram examination. A proper analysis of infrared imaging proved to be a highly informative and sensitive method for differentiating regional cancer metastasis from normal regions.

Before adopting a new diagnostic procedure, which is more convenient and less expensive than the standard existing procedure, it is essentially important to assess whether the diagnostic accuracy of the new procedure is non-inferior (or equivalent) to that of the standard procedure. In this paper, we consider the situation where test responses are on an ordinal scale with more than two categories. We give two definitions of non-inferiority, one in terms of the probability of correctly identifying the case for a randomly selected pair of a case and a non-case over all possible cut-off points, and the other in terms of both the sensitivity and specificity directly. On the basis of large sample theory, we develop two simple test procedures for detecting non-inferiority. We further conduct Monte Carlo simulation to evaluate the finite sample performance of these test procedures. We note that the two asymptotic test procedures proposed here can actually perform reasonably well in a variety of situations even when the numbers of studied subjects from the diseased and non-diseased populations are not large. To illustrate the use of the proposed test procedures, we include an example of determining whether the diagnostic accuracy of using a digitized film is non-inferior to that of using a plain film for screening breast cancer. Finally, we note that the extension of these results to accommodate the case of detecting (two-sided) equivalence is simply straightforward.

Breast cancer stem cells (BrCSCs)have been first identified in solid tumors. To be more exact, they are defined as tumorigenic cancer cells which exhibit the capacity to form tumors distinct from the majority non-tumorigenic cancer cells. However it is only a minor subset that cancer stem cells are highly enriched in. It is still unknown how to find the definitive research subject - "a single breast cancer stem cell" - that can cause a new tumor by itself. Since recent studies suggest that BrCSCs have a higher proportion in suspension culture and have a greater resistance to chemotherapy regimens, we propose a novel strategy to isolate and identify BrCSCs:suspension culture combined with anticancer regimens. This double sorting method by means of the unique properties of BrCSCs may be helpful to screen genuine cancer stem cells(CSCs).

Breast cancer can exist for long periods as non-invasive or invasive but non-metastatic disease. Early detection of breast cancer, for example by mammography screening, significantly improves a woman ’ s chances of survival. If breast cancer is diagnosed and treated while it is confined to the breast, the cure rate can approach 100%. However, mammography screening is far from perfect in terms of sensitivity and specificity of cancer detection. There is a significant clinical need for accurate and highly sensitive tests for early detection of breast cancer. Target applications are screening of high-risk groups, particularly younger women (under the age of 50) from high-incidence families, and breast cancer patients to detect the primary metastasis and migratory route, to direct treatment and the early detection of recurrence when screening breast cancer patients.

Mammography is the keystone of breast cancer screening. Yet high sensitivity is achieved at the cost of low specificity - only one-third of patients recalled will have breast cancer. Computer-aided detection (CAD) is a potentially valuable tool for assisting the breast radiologist to improve positive prediction values. However, to date, CAD has not reliably altered screening outcomes and the large proportion of false positives remains a drawback. We describe a novel method to improve CAD performance called Cartesian Genetic Programming (CGP); a machine-based learning algorithm, akin to genetic evolution.

The purpose of this study was to predict intention to obtain a mammogram on a mobile medical van, using Ajzen and Fishbein's theory of reasoned action. Verification of intention through behavior follow-up was conducted on 199 women ages 40 and older through use of a medical center's mobile medical van. Using regression equations, the results showed that (a) the model failed to predict intention when using the mobile van for screening breast cancer, yet (b) intention was significantly predicted in regard to obtaining a mammogram within the next two years. Attitude was a significant predictor of intention over and above subjective norm, whereas subjective norm was not a significant predictor of intention over and above attitude. The test for interaction between attitude and subjective norm was a significant predictor of intention. In view of this, the attitudinal component should receive greater emphasis with regard to designing health promotion activities aimed toward encouraging women to particip...

One way to improve the cost-benefit ratio for breast cancer screening in younger women is to identify those at high-risk of breast cancer and manage them in an optimal manner. The purpose of this study is to evaluate the sensitivity and specificity of Electrical Impedance Scanning (EIS) for identifying young women who are at risk for having breast cancer and should be followed with directed imaging technologies.A prospective, observational, two-arm, multi-site clinical trial was performed on women aged 30-45 years. The 'Sensitivity Arm' included Clinical Breast Examinations (CBE) and EIS (T-Scan 2000ED) on 189 women prior to scheduled breast biopsy. The 'Specificity Arm' included 1361 asymptomatic women visiting clinics for routine annual well-woman examination. Sensitivity and specificity were determined. Relative probability for a woman with a positive EIS examination was computed and compared with other approaches commonly used to define 'high-risk' in this population.Fifty of 189 women in the Sensitivity arm had verified cancers, 19 of whom had positive EIS examination resulting in sensitivity of 38% (19/50). Of the 1361 women in the Specificity arm, 67 had positive EIS examination resulting in a specificity of 95% (1294/1361). The relative probability of a woman with a positive EIS examination was 7.68, which compares favorably with other established risk identifiers (e.g. two first-degree relatives with breast cancer or atypical ductal hyperplasia).EIS may have an important role as a screening tool for identifying young women that should be followed more closely with advanced imaging technologies for early detection of breast cancer.

Purpose: Early detection and treatment of cancer is a primary focus of health care. Many serum markers are available for breast cancer, but are not good enough for screening. Cancer antigen CA 15-3 is the most widely used biomarker for breast cancer. However, CA 15-3 has low sensitivity and specificity. This study was performed to analyze the serum proteomic pattern in breast cancer patients by surface-enhanced laser desoption/ionization timeof-flight (SELDI-TOF). Methods: We screened for potential tumor biomarkers in 42 serum samples, including samples from a group of 23 breast cancer patients at different clinical stages [stage I (n=3), stage II (n=11), stage III (n=6), and stage IV (n=1)], and a control group of 19 healthy women. Diluted serum samples were applied to a C16 hydrophobic interaction chip (H4). Complex protein profiles of different groups were compared and analyzed using the Protein Chip software 2.1 (Ciphergen Biosystems). Results: There were 7 significant protein peaks in the breast cancer group and 5 in the control group. Scoring the expression of each peak, the mean score was 8.5 in the cancer group and 3.5 in the control. The results of the combination of each peak were highly sensitive (91.2%) and specific (94.7%). These proteomic patterns did not correlate with tumor stage and hormonal receptor, c-erb B2. Conclusion: In this preliminary report, we identified protein profiles that were differentiated in breast cancer patients. After proper validation, serum proteomic pattern analysis may ultimately be applied in screening breast cancer as a stand-alone or combined with current options. (Journal of Korean Breast Cancer Society 2004;7:22-26) ꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏꠏ

Mammography is the only modality with the potential for detecting a breast cancer while it is non-palpable and at a stage of high curability. Early detection of breast cancer is important because survival is directly related to tumor size and lymph node status, and prognosis is best for small lesions without axillary node metastasis. Many studies have indicated that screening mammography is tremendously underused. This article focuses on the effectiveness of mammography and the importance of detecting a breast cancer at an early stage. Health care providers have a responsibility to inform their clients about the benefits of mammography. In addition, women need to be taught breast self-examination and undergo regular clinical breast examinations by a health care professional. The American Cancer Society guidelines for screening breast cancer are given.

To the Editor:— I should like to briefly comment on the article entitled "Theremography and Mass Screening of Occult Breast Cancer"204:419, 1968. This paper has left many of my colleagues with the impression that thermography is of no value as a screening procedure in occult carcinoma of the female breast, and I believe this impression to be totally incorrect. Several published articles1-3and papers4,5have been presented which clearly demonstrate that thermography is of value in the diagnosis of breast carcinoma. An extensive survey5concluded that 80% of investigators with considerable experience in breast thermography "retain their enthusiasm and believe that thermography should play a significant role in screening for breast cancer." These investigators "appear to be in agreement that a very large proportion of breast cancers are detectable by thermography (possibly eight or nine out of ten)."5Hitchcock's major concern was with