Deep learning networks find unique mammographic differences in previous negative mammograms between interval and screen-detected cancers: a case-case study.

BackgroundTo determine if mammographic features from deep learning networks can be applied in breast cancer to identify groups at interval invasive cancer risk due to masking beyond using traditional breast density measures.MethodsFull-field digital screening mammograms acquired in our clinics between 2006 and 2015 were reviewed. Transfer learning of a deep learning network with weights initialized from ImageNet was performed to classify mammograms that were followed by an invasive interval or screen-detected cancer within 12 months of the mammogram. Hyperparameter optimization was performed and the network was visualized through saliency maps. Prediction loss and accuracy were calculated using this deep learning network. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were generated with the outcome of interval cancer using the deep learning network and compared to predictions from conditional logistic regression with errors quantified through contingency tables.ResultsPre-cancer mammograms of 182 interval and 173 screen-detected cancers were split into training/test cases at an 80/20 ratio. Using Breast Imaging-Reporting and Data System (BI-RADS) density alone, the ability to correctly classify interval cancers was moderate (AUC = 0.65). The optimized deep learning model achieved an AUC of 0.82. Contingency table analysis showed the network was correctly classifying 75.2% of the mammograms and that incorrect classifications were slightly more common for the interval cancer mammograms. Saliency maps of each cancer case found that local information could highly drive classification of cases more than global image information.ConclusionsPre-cancerous mammograms contain imaging information beyond breast density that can be identified with deep learning networks to predict the probability of breast cancer detection

Can high-frequency ultrasound predict metastatic lymph nodes in patients with invasive breast cancer?

Aim To determine whether high-frequency ultrasound can predict the presence of metastatic axillary lymph nodes, with a high specificity and positive predictive value, in patients with invasive breast cancer. The clinical aim is to identify patients with axillary disease requiring surgery who would not normally, on clinical grounds, have an axillary dissection, so potentially improving outcome and survival rates. Materials and methods The ipsilateral and contralateral axillae of 42 consecutive patients with invasive breast cancer were scanned prior to treatment using a B-mode frequency of 13 MHz and a Power Doppler frequency of 7 MHz. The presence or absence of an echogenic centre for each lymph node detected was recorded, and measurements were also taken to determine the L/S ratio and the widest and narrowest part of the cortex. Power Doppler was also used to determine vascularity. The contralateral axilla was used as a control for each patient. Results In this study of patients with invasive breast cancer, ipsilateral lymph nodes with a cortical bulge ≥3 mm and/or at least two lymph nodes with absent echogenic centres indicated the presence of metastatic axillary lymph nodes (10 patients). The sensitivity and specificity were 52.6% and 100%, respectively, positive and negative predictive values were 100% and 71.9%, respectively, the P value was 0.001 and the Kappa score was 0.55.\ud Conclusion This would indicate that high-frequency ultrasound can be used to accurately predict metastatic lymph nodes in a proportion of patients with invasive breast cancer, which may alter patient management

Mammographic density and structural features can individually and jointly contribute to breast cancer risk assessment in mammography screening:a case-control study

BACKGROUND: Mammographic density is a well-established risk factor for breast cancer. We investigated the association between three different methods of measuring density or parenchymal pattern/texture on digitized film-based mammograms, and examined to what extent textural features independently and jointly with density can improve the ability to identify screening women at increased risk of breast cancer. METHODS: The study included 121 cases and 259 age- and time matched controls based on a cohort of 14,736 women with negative screening mammograms from a population-based screening programme in Denmark in 2007 (followed until 31 December 2010). Mammograms were assessed using the Breast Imaging-Reporting and Data System (BI-RADS) density classification, Tabár’s classification on parenchymal patterns and a fully automated texture quantification technique. The individual and combined association with breast cancer was estimated using binary logistic regression to calculate Odds Ratios (ORs) and the area under the receiver operating characteristic (ROC) curves (AUCs). RESULTS: Cases showed significantly higher BI-RADS and texture scores on average than controls (p < 0.001). All three methods were individually able to segregate women into different risk groups showing significant ORs for BI-RADS D3 and D4 (OR: 2.37; 1.32–4.25 and 3.93; 1.88–8.20), Tabár’s PIII and PIV (OR: 3.23; 1.20–8.75 and 4.40; 2.31–8.38), and the highest quartile of the texture score (3.04; 1.63–5.67). AUCs for BI-RADS, Tabár and the texture scores (continuous) were 0.63 (0.57–0–69), 0.65 (0.59–0–71) and 0.63 (0.57–0–69), respectively. Combining two or more methods increased model fit in all combinations, demonstrating the highest AUC of 0.69 (0.63-0.74) when all three methods were combined (a significant increase from standard BI-RADS alone). CONCLUSION: Our findings suggest that the (relative) amount of fibroglandular tissue (density) and mammographic structural features (texture/parenchymal pattern) jointly can improve risk segregation of screening women, using information already available from normal screening routine, in respect to future personalized screening strategies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12885-016-2450-7) contains supplementary material, which is available to authorized users

Does Gender Discrimination Impact Regular Mammography Screening? Findings from the Race Differences in Screening Mammography Study

Objective: To determine if gender discrimination, conceptualized as a negative life stressor, is a deterrent to adherence to mammography screening guidelines. Methods: African American and white women (1451) aged 40–79 years who obtained an index screening mammogram at one of five urban hospitals in Connecticut between October 1996 and January 1998 were enrolled in this study. This logistic regression analysis includes the 1229 women who completed telephone interviews at baseline and follow-up (average 29.4 months later) and for whom the study outcome, nonadherence to age-specific mammography screening guidelines, was determined. Gender discrimination was measured as lifetime experience in seven possible situations. Results: Gender discrimination, reported by nearly 38% of the study population, was significantly associated with non-adherence to mammography guidelines in women with annual family incomes of $50,000 or greater (or 1.99, 95% CI 1.33, 2.98) and did not differ across racial/ethnic groups. Conclusions: Our findings suggest that gender discrimination can adversely influence regular mammography screening in some women. With nearly half of women nonadherent to screening mammography guidelines in this study and with decreasing mammography rates nationwide, it is important to address the complexity of nonadherence across subgroups of women. Life stressors, such as experiences of gender discrimination, may have considerable consequences, potentially influencing health prevention prioritization in women

Nipple discharge: the state of the art

Over 80% of females experience nipple discharge during their life. Differently from lactational (milk production) and physiological (white, green, or yellow), which are usually bilateral and involving multiple ducts, pathologic nipple discharge (PND) is a spontaneous commonly single-duct and unilateral, clear, serous, or bloody secretion. Mostly caused by intraductal papilloma(s) or ductal ectasia, in 5-33% of cases is due to an underlying malignancy. After clinical history and physical examination, mammography is the first step after 39, but its sensitivity is low (7–26%). Ultrasound shows higher sensitivity (63–100%). Nipple discharge cytology is limited by a false negative rate over 50%. Galactography is an invasive technique that may cause discomfort and pain; it can be performed only when the duct discharge is demonstrated at the time of the study, with incomplete/failed examination rate up to 15% and a difficult differentiation between malignant and benign lesions. Ductoscopy, performed under local anesthesia in outpatients, provides a direct visualization of intraductal lesions, allowing for directed excision and facilitating a targeted surgery. Its sensitivity reaches 94%; however, it is available in only few centers and most clinicians are unfamiliar with its use. PND has recently emerged as a new indication for contrast-enhanced breast MRI, showing sensitivity superior to galactography, with an overall sensitivity up to 96%, also allowing tailored surgery. Surgery no longer can be considered the standard approach to PND. We propose a state-of-the art flowchart for the management of nipple discharge, including ductoscopy and breast MRI as best options

Determinants and influence of mammographic features on breast cancer risk

Mammographic density and mammographic microcalcifications are the key imaging features in mammography examination. Mammographic density is known as a strong risk factor for breast cancer and is the radiographic appearance of epithelial and fibrous tissue which appears white on a mammogram. While, the dark part of a mammogram represents the fatty tissue. Mammographic microcalcifications appear as small deposits of calcium and they are one of the earliest sign of breast cancer. Malignant microcalcifications are seen in both in situ and invasive lesions. In this thesis we used the data from the prospective KARMA cohort to study the association between established breast cancer risk factors with mammographic density change over time (Study I), to examine the association between annual mammographic density change and risk of breast cancer (Study II), to investigate the association between established risk factors for breast cancer and microcalcification clusters and their asymmetry (Study III), and finally to elucidate the association between microcalcification clusters, their asymmetry, and risk of overall and subtype specific breast cancer (Study IV). The lifestyle and reproductive factors were assessed using web-based questionnaires. Average mammographic density and total microcalcification clusters were measured using a Computer Aided Detection system (CAD) and the STRATUS method, respectively. In Study I, the average yearly dense area change was -1.0 cm . Body mass index (BMI) and physical activity were statistically associated with density change. Beside age, lean and physically active women had the largest decrease in mammographic density per year. In Study II, overall, 563 women were diagnosed with breast cancer and annual mammographic density change did not seem to influence the risk of breast cancer. Furthermore, density change does not seem to modify the association between baseline density and risk of breast cancer. In Study III, age, mammographic density, genetic factors related to breast cancer, having more children, longer duration of breast-feeding were significantly associated with increased risk of presence of microcalcification clusters. In Study IV, 676 women were diagnosed with breast cancer. Further, women with 33 microcalcification clusters had 2 times higher risk of breast cancer compared to women with no clusters. Microcalcification clusters were associated with both in situ and invasive breast cancer. Finally, during postmenopausal period, microcalcification clusters influence risk of breast cancer to the similar extend as baseline mammographic density. In conclusion, we have identified novel determinants of mammographic density changes and potential predictors of suspicious mammographic microcalcification clusters. Further, our results suggested that annual mammographic density change does not influence breast cancer risk, while presence of suspicious microcalcification clusters was strongly associated with breast cancer risk

Artificial intelligence in mammographic phenotyping of breast cancer risk: A narrative review

BACKGROUND: Improved breast cancer risk assessment models are needed to enable personalized screening strategies that achieve better harm-to-benefit ratio based on earlier detection and better breast cancer outcomes than existing screening guidelines. Computational mammographic phenotypes have demonstrated a promising role in breast cancer risk prediction. With the recent exponential growth of computational efficiency, the artificial intelligence (AI) revolution, driven by the introduction of deep learning, has expanded the utility of imaging in predictive models. Consequently, AI-based imaging-derived data has led to some of the most promising tools for precision breast cancer screening. MAIN BODY: This review aims to synthesize the current state-of-the-art applications of AI in mammographic phenotyping of breast cancer risk. We discuss the fundamentals of AI and explore the computing advancements that have made AI-based image analysis essential in refining breast cancer risk assessment. Specifically, we discuss the use of data derived from digital mammography as well as digital breast tomosynthesis. Different aspects of breast cancer risk assessment are targeted including (a) robust and reproducible evaluations of breast density, a well-established breast cancer risk factor, (b) assessment of a woman\u27s inherent breast cancer risk, and (c) identification of women who are likely to be diagnosed with breast cancers after a negative or routine screen due to masking or the rapid and aggressive growth of a tumor. Lastly, we discuss AI challenges unique to the computational analysis of mammographic imaging as well as future directions for this promising research field. CONCLUSIONS: We provide a useful reference for AI researchers investigating image-based breast cancer risk assessment while indicating key priorities and challenges that, if properly addressed, could accelerate the implementation of AI-assisted risk stratification to future refine and individualize breast cancer screening strategies