While breast cancer continues to affect the lives of millions, contemporary writers and artists have responded to the ravages of the disease in creative expression. Mary K. DeShazer's book looks ...specifically at breast cancer memoirs and photographic narratives, a category she refers to as mammographies, signifying both the imaging technology by which most Western women discover they have this disease and the documentary imperatives that drive their written and visual accounts of it. Mammographies argues that breast cancer narratives of the past ten years differ from their predecessors in their bold address of previously neglected topics such as the link between cancer and environmental carcinogens, the ethics and efficacy of genetic testing and prophylactic mastectomy, and the shifting politics of prosthesis and reconstruction.
Update on therapeutic options, emphasises combination therapies, mechanisms of resistance and the future impact of pharmacogenetics on clinical efficacy. Outlines effective treatments as part of an ...integrated programme of patient management. Suitable for specialists in oncology, clinical scientists, general physicians, doctors in training.
Purpose
To compare the interpretive performance of synthetic mammography (SM), reconstructed from digital breast tomosynthesis (DBT), and full-field digital mammography (FFDM) in a diagnostic ...setting, covering different conditions of breast density and mammographic signs.
Methods
A retrospective analysis was conducted on 231 patients, who underwent FFDM and DBT (from which SM images were reconstructed) between September 2014–September 2015. The study included 250 suspicious breast lesions, all biopsy proven: 148 (59.2%) malignant and 13 (5.2%) high-risk lesions were confirmed by surgery, 89 (35.6%) benign lesions had radiological follow-up. Two breast radiologists, blinded to histology, independently reviewed all cases. Readings were performed with SM alone, then with FFDM, collecting data on: probability of malignancy for each finding, lesion conspicuity, mammographic features and dimensions of detected lesions.
Results
Agreement between readers was good for BI-RADS classification (Cohen’s k-coefficient = 0.93 ± 0.02) and for lesion dimension (Wilcoxon’s
p
= 0.76). Visibility scores assigned to SM and FFDM for each lesion were similar for non-dense and dense breasts, however, there were significant differences (
p
= 0.0009) in distribution of mammographic features subgroups. SM and FFDM had similar sensitivities in non-dense (respectively 94 vs. 91%) and dense breasts (88 vs. 80%) and for all mammographic signs (93 vs. 87% for asymmetric densities, 96 vs. 75% for distortion, 92 vs. 85% for microcalcifications, and both 94% for masses). Based on all data, there was a significant difference in sensitivity for SM (92%) vs. FFDM (87%),
p
= 0.02, whereas the two modalities yielded similar results for specificity (SM: 60%, FFDM: 62%,
p
= 0.21).
Conclusions
SM alone showed similar interpretive performance to FFDM, confirming its potential role as an alternative to FFDM in women having tomosynthesis, with the added advantage of halving the patient’s dose exposure.
Breast density, as assessed by mammography, reflects breast tissue composition. Breast epithelium and stroma attenuate x-rays more than fat and thus appear light on mammograms while fat appears dark. ...In this review, we provide an overview of selected areas of current knowledge about the relationship between breast density and susceptibility to breast cancer. We review the evidence that breast density is a risk factor for breast cancer, the histological and other risk factors that are associated with variations in breast density, and the biological plausibility of the associations with risk of breast cancer. We also discuss the potential for improved risk prediction that might be achieved by using alternative breast imaging methods, such as magnetic resonance or ultrasound. After adjustment for other risk factors, breast density is consistently associated with breast cancer risk, more strongly than most other risk factors for this disease, and extensive breast density may account for a substantial fraction of breast cancer. Breast density is associated with risk of all of the proliferative lesions that are thought to be precursors of breast cancer. Studies of twins have shown that breast density is a highly heritable quantitative trait. Associations between breast density and variations in breast histology, risk of proliferative breast lesions, and risk of breast cancer may be the result of exposures of breast tissue to both mitogens and mutagens. Characterization of breast density by mammography has several limitations, and the uses of breast density in risk prediction and breast cancer prevention may be improved by other methods of imaging, such as magnetic resonance or ultrasound tomography.
Neuroendocrine Tumors of the Breast Rosen, Lauren Elizabeth; Gattuso, Paolo
Archives of pathology & laboratory medicine (1976)
141, Issue:
11
Journal Article
Peer reviewed
Open access
Primary neuroendocrine tumors of the breast are a rare and underrecognized subtype of mammary carcinoma. Neuroendocrine tumors of the breast occur predominately in postmenopausal women. The tumors ...are subclassified into well-differentiated and poorly differentiated neuroendocrine tumors, and invasive breast carcinoma with neuroendocrine features. Well-differentiated tumors show architectural similarity to carcinoids of other sites but lack characteristic neuroendocrine nuclei. Poorly differentiated neuroendocrine tumors are morphologically identical to small cell carcinoma of the lung. Neuroendocrine differentiation, seen in up to 30% of invasive breast carcinomas, is most commonly associated with mucinous and solid papillary carcinomas. The diagnosis of neuroendocrine differentiation requires expression of the neuroendocrine markers synaptophysin or chromogranin. The main differential diagnosis is a metastatic neuroendocrine tumor from an extramammary site. Neuroendocrine tumors of the breast are treated similarly to other invasive breast carcinomas. Although no consensus has been reached on the prognosis, most studies suggest a poor outcome.
Purpose
Digital breast tomosynthesis (DBT) has the potential to overcome limitations of conventional mammography. This study investigated the effects of addition of DBT on interval and detected ...cancers in population-based screening.
Methods
Oslo Tomosynthesis Screening Trial (OTST) was a prospective, independent double-reading trial inviting women 50–69 years biennially, comparing full-field digital mammography (FFDM) plus DBT with FFDM alone. Performance indicators and characteristics of screen-detected and interval cancers were compared with two previous FFDM rounds.
Results
24,301 consenting women underwent FFDM + DBT screening over a 2-year period. Results were compared with 59,877 FFDM examinations during prior rounds. Addition of DBT resulted in a non-significant increase in sensitivity (76.2%, 378/496, vs. 80.8%, 227/281,
p
= 0.151) and a significant increase in specificity (96.4%, 57229/59381 vs. 97.5%, 23427/24020,
p
< .001). Number of recalls per screen-detected cancer decreased from 6.7 (2530/378) to 3.6 (820/227) with DBT (
p
< .001). Cancer detection per 1000 women screened increased (6.3, 378/59877, vs. 9.3, 227/24301,
p
< .001). Interval cancer rate per 1000 screens for FFDM + DBT remained similar to previous FFDM rounds (2.1, 51/24301 vs. 2.0, 118/59877,
p
= 0.734). Interval cancers post-DBT were comparable to prior rounds but significantly different in size, grade, and node status from cancers detected only using DBT. 39.6% (19/48) of interval cancers had positive nodes compared with only 3.9% (2/51) of additional DBT-only-detected cancers.
Conclusions
DBT-supplemented screening resulted in significant increases in screen-detected cancers and specificity. However, no significant change was observed in the rate, size, node status, or grade of interval cancers.
ClinicalTrials.gov: NCT01248546.
To investigate the relationship between mammographic density measured in four quadrants of a breast with the location of the occurred cancer.
One hundred and ten women diagnosed with unilateral ...breast cancer that could be determined in one specific breast quadrant were retrospectively studied. Women with previous cancer/breast surgery were excluded. The craniocaudal (CC) and mediolateral oblique (MLO) mammography of the contralateral normal breast were used to separate a breast into 4 quadrants: Upper-Outer (UO), Upper-Inner (UI), Lower-Outer (LO), and Lower-Inner (LI). The breast area (BA), dense area (DA), and percent density (PD) in each quadrant were measured by using the fuzzy-C-means segmentation. The BA, DA, and PD were compared between patients who had cancer occurring in different quadrants.
The upper-outer quadrant had the highest BA (37 ± 15 cm
) and DA (7.1 ± 2.9 cm
), with PD = 20.0 ± 5.8%. The order of BA and DA in the 4 separated quadrants were: UO > UI > LO > LI, and almost all pair-wise comparisons showed significant differences. For tumor location, 67 women (60.9%) had tumor in UO, 16 (14.5%) in UI, 7 (6.4%) in LO, and 20 (18.2%) in LI quadrant, respectively. The estimated odds and the 95% confidence limits of tumor development in the UO, UI, LO and LI quadrants were 1.56 (1.06, 2.29), 0.17 (0.10, 0.29), 0.07 (0.03, 0.15), and 0.22 (0.14, 0.36), respectively. In these 4 groups of women, the order of quadrant BA and DA were all the same (UO > UI > LO > LI), and there was no significant difference in BA, DA or PD among them (all p > 0.05).
Breast cancer was most likely to occur in the UO quadrant, which was also the quadrant with highest BA and DA; but for women with tumors in other quadrants, the density in that quadrant was not the highest. Therefore, there was no direct association between quadrant density and tumor occurrence.
Mammographic risk scoring has commonly been automated by extracting a set of handcrafted features from mammograms, and relating the responses directly or indirectly to breast cancer risk. We present ...a method that learns a feature hierarchy from unlabeled data. When the learned features are used as the input to a simple classifier, two different tasks can be addressed: i) breast density segmentation, and ii) scoring of mammographic texture. The proposed model learns features at multiple scales. To control the models capacity a novel sparsity regularizer is introduced that incorporates both lifetime and population sparsity. We evaluated our method on three different clinical datasets. Our state-of-the-art results show that the learned breast density scores have a very strong positive relationship with manual ones, and that the learned texture scores are predictive of breast cancer. The model is easy to apply and generalizes to many other segmentation and scoring problems.
Purpose
To develop a new quantitative global kinetic breast magnetic resonance imaging (MRI) features analysis scheme and assess its feasibility to assess tumor response to neoadjuvant chemotherapy.
...Materials and Methods
A dataset involving breast MR images acquired from 151 cancer patients before neoadjuvant chemotherapy was used. Among them, 63 patients had complete response (CR) and 88 had partial response (PR) to chemotherapy based on the RECIST criterion. A computer‐aided detection (CAD) scheme was applied to segment breast region depicted on the breast MR images and computed a total of 10 kinetic image features to represent parenchyma enhancement either from the entire two breasts or the bilateral asymmetry between the two breasts. To classify between CR and PR cases, we tested an attribution selected classifier that integrates with an artificial neural network and a Wrapper Subset Evaluator. The classifier was trained and tested using a leave‐one‐case‐out (LOCO)‐based cross‐validation method. The area under a receiver operating characteristic curve (AUC) was computed to assess classifier performance.
Results
From the pool of initial 10 features, four features were selected by more than 90% times in the LOCO cross‐validation iterations. Among them, three represent the bilateral asymmetry of kinetic features between two breasts. Using the classifier yielded AUC = 0.83 ± 0.04, which is significantly higher than using each individual feature to classify between CR and PR cases (P < 0.05).
Conclusion
This study demonstrated that quantitative analysis of global kinetic features computed from breast MRI‐acquired prechemotherapy has potential to generate a useful clinical marker that is associated with tumor response to neoadjuvant chemotherapy. J. Magn. Reson. Imaging 2016;44:1099–1106.
Paget’s disease of the nipple Sandoval-Leon, Ana C.; Drews-Elger, Katherine; Gomez-Fernandez, Carmen R. ...
Breast cancer research and treatment,
08/2013, Volume:
141, Issue:
1
Journal Article
Peer reviewed
Paget’s disease of the breast is a disorder of the nipple–areola complex that, while rare, is often associated with an underlying carcinoma. It is characterized by eczematoid changes of the nipple. ...Two theories have been proposed to explain the pathogenesis of Paget’s disease. The Epidermotropic, which is the most accepted theory, suggests that Paget’s cells originate from ductal cancer cells that had migrated from the underlying breast parenchyma. It is supported by the predominance of breast cancer markers found in Paget’s disease. This article provides an overview of Paget’s disease of the breast with special attention to immunohistochemistry and raises the question of new therapeutic approaches.
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