Diagnostic Accuracy of Screening Contrast-enhanced Mammography for Women with Extremely Dense Breasts at Increased Risk of Breast Cancer.

Background Mammogram interpretation is challenging in female patients with extremely dense breasts (Breast Imaging Reporting and Data System [BI-RADS] category D), who have a higher breast cancer risk. Contrast-enhanced mammography (CEM) has recently emerged as a potential alternative; however, data regarding CEM utility in this subpopulation are limited. Purpose To evaluate the diagnostic performance of CEM for breast cancer screening in female patients with extremely dense breasts. Materials and Methods This retrospective single-institution study included consecutive CEM examinations in asymptomatic female patients with extremely dense breasts performed from December 2012 to March 2022. From CEM examinations, low-energy (LE) images were the equivalent of a two-dimensional full-field digital mammogram. Recombined images highlighting areas of contrast enhancement were constructed using a postprocessing algorithm. The sensitivity and specificity of LE images and CEM images (ie, including both LE and recombined images) were calculated and compared using the McNemar test. Results This study included 1299 screening CEM examinations (609 female patients; mean age, 50 years ± 9 [SD]). Sixteen screen-detected cancers were diagnosed, and two interval cancers occured. Five cancers were depicted at LE imaging and an additional 11 cancers were depicted at CEM (incremental cancer detection rate, 8.7 cancers per 1000 examinations). CEM sensitivity was 88.9% (16 of 18; 95% CI: 65.3, 98.6), which was higher than the LE examination sensitivity of 27.8% (five of 18; 95% CI: 9.7, 53.5) (P = .003). However, there was decreased CEM specificity (88.9%; 1108 of 1246; 95% CI: 87.0, 90.6) compared with LE imaging (specificity, 96.2%; 1199 of 1246; 95% CI: 95.0, 97.2) (P < .001). Compared with specificity at baseline, CEM specificity at follow-up improved to 90.7% (705 of 777; 95% CI: 88.5, 92.7; P = .01). Conclusion Compared with LE imaging, CEM showed higher sensitivity but lower specificity in female patients with extremely dense breasts, although specificity improved at follow-up. © RSNA, 2024 See also the editorial by Lobbes in this issue.

Artificial intelligence in mammography: a systematic review of the external validation.

Objective: To conduct a systematic review of external validation studies on the use of different Artificial Intelligence algorithms in breast cancer screening with mammography. Data source: Our systematic review was conducted and reported following the PRISMA statement, using the PubMed, EMBASE, and Cochrane databases with the search terms "Artificial Intelligence," "Mammography," and their respective MeSH terms. We filtered publications from the past ten years (2014 - 2024) and in English. Study selection: A total of 1,878 articles were found in the databases used in the research. After removing duplicates (373) and excluding those that did not address our PICO question (1,475), 30 studies were included in this work. Data collection: The data from the studies were collected independently by five authors, and it was subsequently synthesized based on sample data, location, year, and their main results in terms of AUC, sensitivity, and specificity. Data synthesis: It was demonstrated that the Area Under the ROC Curve (AUC) and sensitivity were similar to those of radiologists when using independent Artificial Intelligence. When used in conjunction with radiologists, statistically higher accuracy in mammogram evaluation was reported compared to the assessment by radiologists alone. Conclusion: AI algorithms have emerged as a means to complement and enhance the performance and accuracy of radiologists. They also assist less experienced professionals in detecting possible lesions. Furthermore, this tool can be used to complement and improve the analyses conducted by medical professionals.

Reproductive factors and mammographic density within the International Consortium of Mammographic Density: A cross-sectional study.

BACKGROUND: Elevated mammographic density (MD) for a woman's age and body mass index (BMI) is an established breast cancer risk factor. The relationship of parity, age at first birth, and breastfeeding with MD is less clear. We examined the associations of these factors with MD within the International Consortium of Mammographic Density (ICMD). METHODS: ICMD is a consortium of 27 studies with pooled individual-level epidemiological and MD data from 11,755 women without breast cancer aged 35-85 years from 22 countries, capturing 40 country-& ethnicity-specific population groups. MD was measured using the area-based tool Cumulus. Meta-analyses across population groups and pooled analyses were used to examine linear regression associations of square-root (√) transformed MD measures (percent MD (PMD), dense area (DA), and non-dense area (NDA)) with parity, age at first birth, ever/never breastfed and lifetime breastfeeding duration. Models were adjusted for age at mammogram, age at menarche, BMI, menopausal status, use of hormone replacement therapy, calibration method, mammogram view and reader, and parity and age at first birth when not the association of interest. RESULTS: Among 10,988 women included in these analyses, 90.1% (n = 9,895) were parous, of whom 13% (n = 1,286) had ≥ five births. The mean age at first birth was 24.3 years (Standard deviation = 5.1). Increasing parity (per birth) was inversely associated with √PMD (ß: - 0.05, 95% confidence interval (CI): - 0.07, - 0.03) and √DA (ß: - 0.08, 95% CI: - 0.12, - 0.05) with this trend evident until at least nine births. Women who were older at first birth (per five-year increase) had higher √PMD (ß:0.06, 95% CI:0.03, 0.10) and √DA (ß:0.06, 95% CI:0.02, 0.10), and lower √NDA (ß: - 0.06, 95% CI: - 0.11, - 0.01). In stratified analyses, this association was only evident in women who were post-menopausal at MD assessment. Among parous women, no associations were found between ever/never breastfed or lifetime breastfeeding duration (per six-month increase) and √MD. CONCLUSIONS: Associations with higher parity and older age at first birth with √MD were consistent with the direction of their respective associations with breast cancer risk. Further research is needed to understand reproductive factor-related differences in the composition of breast tissue and their associations with breast cancer risk.

Differentiation Between Phyllodes Tumor and Fibroadenoma of the Breast: A Radiomics Prediction Model Based on Full-Field Digital Mammography & Digital Tomosynthesis.

OBJECTIVE: To assess the diagnostic performance of FFDM-based and DBT-based radiomics models to differentiate breast phyllodes tumors from fibroadenomas. METHODS: 192 patients (93 phyllodes tumors and 99 fibroadenomas) who underwent mammography were retrospectively enrolled. Radiomic features were respectively extracted from FFDM and the clearest slice of DBT images. A least absolute shrinkage and selection operator (LASSO) regression was used to select radiomics features. A combined model was constructed by radiomics and radiological signatures. Machine learning classification was done using logistic regression based on radiomics or radiological signatures (clinical model). Four radiologists were tested on phyllodes tumors and fibroadenomas with and without optimal model assistance. The area under the receiver operating characteristic (ROC) curve (AUC) was computed to assess the performance of each model or radiologist. The Delong test and McNemar's test were performed to compare the performance. RESULTS: The combined model yielded the highest performance with an AUC of 0.948 (95%CI: 0.889-1.000) in the testing set, slightly higher than the FFDM-radiomics model (AUC of 0.937, 95%CI: 0.841-0.984) and the DBT-radiomics model (AUC of 0.860, 95%CI: 0.742-0.936) and significantly superior to the clinical model (AUC of 0.719, 95%CI: 0.585-0.829). With the combined model aid, the AUCs of four radiologists were improved from 0.808 to 0.914 (p=0.079), 0.759 to 0.888 (p=0.015), 0.717 to 0.846 (p=0.004), and 0.629 to 0.803 (p=0.001). CONCLUSION: Radiomics analysis based on FFDM and DBT shows promise in differentiating phyllodes tumors from fibroadenomas.

Enhancing Radiologist Reading Performance by Ordering Screening Mammograms Based on Characteristics That Promote Visual Adaptation.

Background Mammographic background characteristics may stimulate human visual adaptation, allowing radiologists to detect abnormalities more effectively. However, it is unclear whether density, or another image characteristic, drives visual adaptation. Purpose To investigate whether screening performance improves when screening mammography examinations are ordered for batch reading according to mammographic characteristics that may promote visual adaptation. Materials and Methods This retrospective multireader multicase study was performed with mammograms obtained between September 2016 and May 2019. The screening examinations, each consisting of four mammograms, were interpreted by 13 radiologists in three distinct orders: randomly, by increasing volumetric breast density (VBD), and based on a self-supervised learning (SSL) encoding (examinations automatically grouped as "looking similar"). An eye tracker recorded radiologists' eye movements during interpretation. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of random-ordered readings were compared with those of VBD- and SSL-ordered readings using mixed-model analysis of variance. Reading time, fixation metrics, and perceived density were compared using Wilcoxon signed-rank tests. Results Mammography examinations (75 with breast cancer, 75 without breast cancer) from 150 women (median age, 55 years [IQR, 50-63]) were read. The examinations ordered by increasing VBD versus randomly had an increased AUC (0.93 [95% CI: 0.91, 0.96] vs 0.92 [95% CI: 0.89, 0.95]; P = .009), without evidence of a difference in specificity (89% [871 of 975] vs 86% [837 of 975], P = .04) and sensitivity (both 81% [794 of 975 vs 788 of 975], P = .78), and a reduced reading time (24.3 vs 27.9 seconds, P < .001), fixation count (47 vs 52, P < .001), and fixation time in malignant regions (3.7 vs 4.6 seconds, P < .001). For SSL-ordered readings, there was no evidence of differences in AUC (0.92 [95% CI: 0.89, 0.95]; P = .70), specificity (84% [820 of 975], P = .37), sensitivity (80% [784 of 975], P = .79), fixation count (54, P = .05), or fixation time in malignant regions (4.6 seconds, P > .99) compared with random-ordered readings. Reading times were significantly higher for SSL-ordered readings compared with random-ordered readings (28.4 seconds, P = .02). Conclusion Screening mammography examinations ordered from low to high VBD improved screening performance while reducing reading and fixation times. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Grimm in this issue.

Breast tumor segmentation using neural cellular automata and shape guided segmentation in mammography images.

PURPOSE: Using computer-aided design (CAD) systems, this research endeavors to enhance breast cancer segmentation by addressing data insufficiency and data complexity during model training. As perceived by computer vision models, the inherent symmetry and complexity of mammography images make segmentation difficult. The objective is to optimize the precision and effectiveness of medical imaging. METHODS: The study introduces a hybrid strategy combining shape-guided segmentation (SGS) and M3D-neural cellular automata (M3D-NCA), resulting in improved computational efficiency and performance. The implementation of Shape-guided segmentation (SGS) during the initialization phase, coupled with the elimination of convolutional layers, enables the model to effectively reduce computation time. The research proposes a novel loss function that combines segmentation losses from both components for effective training. RESULTS: The robust technique provided aims to improve the accuracy and consistency of breast tumor segmentation, leading to significant improvements in medical imaging and breast cancer detection and treatment. CONCLUSION: This study enhances breast cancer segmentation in medical imaging using CAD systems. Combining shape-guided segmentation (SGS) and M3D-neural cellular automata (M3D-NCA) is a hybrid approach that improves performance and computational efficiency by dealing with complex data and not having enough training data. The approach also reduces computing time and improves training efficiency. The study aims to improve breast cancer detection and treatment methods in medical imaging technology.

Pericardial cyst unveiling: a case of unusual chest symptoms in a young woman with a family history of cancer: a case report and review of literature.

BACKGROUND: Pericardial cysts, though rare and benign, can present with various clinical symptoms depending on their size and location in the body. The detection of these cysts typically relies on imaging studies for a conclusive diagnosis, with surgical removal being the definitive treatment. CASE PRESENTATION: This case report details the clinical journey of a 32-year-old Iranian woman with a family history of breast and lung cancer, who experienced left-sided chest pain. Utilizing a combination of clinical history review, mammography, echocardiography, and computed tomography, a precise diagnosis of a 10 cm × 3.5 cm pericardial cyst was achieved. The patient underwent median sternotomy for complete cyst excision. CONCLUSIONS: While pericardial cysts are often asymptomatic and benign, they can lead to life-threatening complications. Hence, regular follow-up is advised, and in certain instances, minimally invasive interventions or surgery may be necessary.

Artificial Intelligence Algorithm for Subclinical Breast Cancer Detection.

Importance: Early breast cancer detection is associated with lower morbidity and mortality. Objective: To examine whether a commercial artificial intelligence (AI) algorithm for breast cancer detection could estimate the development of future cancer. Design, Setting, and Participants: This retrospective cohort study of 116 495 women aged 50 to 69 years with no prior history of breast cancer before they underwent at least 3 consecutive biennial screening examinations used scores from an AI algorithm (INSIGHT MMG, version 1.1.7.2; Lunit Inc; used September 28, 2022, to April 5, 2023) for breast cancer detection and screening data from multiple, consecutive rounds of mammography performed from September 13, 2004, to December 21, 2018, at 9 breast centers in Norway. The statistical analyses were performed from September 2023 to August 2024. Exposure: Artificial intelligence algorithm score indicating suspicion for the presence of breast cancer. The algorithm provided a continuous cancer detection score for each examination ranging from 0 to 100, with increasing values indicating a higher likelihood of cancer being present on the current mammogram. Main Outcomes and Measures: Maximum AI algorithm score for cancer detection and absolute difference in score among breasts of women developing screening-detected cancer, women with interval cancer, and women who screened negative. Results: The mean (SD) age at the first study round was 58.5 (4.5) years for 1265 women with screening-detected cancer in the third round, 57.4 (4.6) years for 342 women with interval cancer after 3 negative screening rounds, and 56.4 (4.9) years for 116 495 women without breast cancer all 3 screening rounds. The mean (SD) absolute differences in AI scores among breasts of women developing screening-detected cancer were 21.3 (28.1) at the first study round, 30.7 (32.5) at the second study round, and 79.0 (28.9) at the third study round. The mean (SD) differences prior to interval cancer were 19.7 (27.0) at the first study round, 21.0 (27.7) at the second study round, and 34.0 (33.6) at the third study round. The mean (SD) differences among women who did not develop breast cancer were 9.9 (17.5) at the first study round, 9.6 (17.4) at the second study round, and 9.3 (17.3) at the third study round. Areas under the receiver operating characteristic curve for the absolute difference were 0.63 (95% CI, 0.61-0.65) at the first study round, 0.72 (95% CI, 0.71-0.74) at the second study round, and 0.96 (95% CI, 0.95-0.96) at the third study round for screening-detected cancer and 0.64 (95% CI, 0.61-0.67) at the first study round, 0.65 (95% CI, 0.62-0.68) at the second study round, and 0.77 (95% CI, 0.74-0.79) at the third study round for interval cancers. Conclusions and Relevance: In this retrospective cohort study of women undergoing screening mammography, mean absolute AI scores were higher for breasts developing vs not developing cancer 4 to 6 years before their eventual detection. These findings suggest that commercial AI algorithms developed for breast cancer detection may identify women at high risk of a future breast cancer, offering a pathway for personalized screening approaches that can lead to earlier cancer diagnosis.

Social Risk Factor Domains and Preventive Care Services in US Adults.

Importance: Growing evidence suggests that social determinants of health are associated with low uptake of preventive care services. Objective: To examine the independent associations of social risk factor domains with preventive care services among US adults. Design, Setting, and Participants: This cross-sectional study used National Health Interview Survey data on 82 432 unweighted individuals (239 055 950 weighted) from 2016 to 2018. Subpopulations were created for each of the primary outcomes: routine mammography (women aged 40-74 years), Papanicolaou test (women aged 21-65 years), colonoscopy (adults aged 45-75 years), influenza vaccine (adults aged ≥18 years), and pneumococcal vaccine (adults aged ≥65 years). Statistical analysis was performed from July to December 2023. Exposures: Six social risk domains (economic instability, lack of community, education deficit, food insecurity, social isolation, and lack of access to care) and a count of domains. Main Outcomes and Measures: Logistic regression models were used to examine the independent association between each primary outcome (mammography, Papanicolaou test, colonoscopy, influenza vaccine, and pneumococcal vaccine) and social risk factor domains, while controlling for covariates (age, sex, race and ethnicity, health insurance, and comorbidities). Results: A total of 82 432 unweighted US individuals (239 055 950 weighted individuals) were analyzed. A total of 54.3% were younger than 50 years, and 51.7% were female. All 5 screening outcomes were associated with educational deficit (mammography: odds ratio [OR], 0.73 [95% CI, 0.67-0.80]; Papanicolaou test: OR, 0.78 [95% CI, 0.72-0.85]; influenza vaccine: OR, 0.71 [95% CI, 0.67-0.74]; pneumococcal vaccine: OR, 0.68 [95% CI, 0.63-0.75]; colonoscopy: OR, 0.82 [95% CI, 0.77-0.87]) and a lack of access to care (mammography: OR, 0.32 [95% CI, 0.27-0.38]; Papanicolaou test: OR, 0.49 [95% CI, 0.44-0.54]; influenza vaccine: OR, 0.44 [95% CI, 0.41-0.47]; pneumococcal vaccine: OR, 0.30 [95% CI, 0.25-0.38]; colonoscopy: OR, 0.35 [95% CI, 0.30-0.41]). Fully adjusted models showed that every unit increase in social risk count was significantly associated with decreased odds of receiving a mammography (OR, 0.74 [95% CI, 0.71-0.77]), Papanicolaou test (OR, 0.84 [95% CI, 0.81-0.87]), influenza vaccine (OR, 0.81 [95% CI, 0.80-0.83]), pneumococcal vaccine (OR, 0.80 [95% CI, 0.77-0.83]), and colonoscopy (OR, 0.88 [95% CI, 0.86-0.90]). Conclusions and Relevance: This cross-sectional study of US adults suggests that social risk factor domains were associated with decreased odds of receiving preventive services; this association was cumulative. There is a need to address social risk factors to optimize receipt of recommended preventive services.

The message matters: changes to binary Computer Aided Detection recommendations affect cancer detection in low prevalence search.

Computer Aided Detection (CAD) has been used to help readers find cancers in mammograms. Although these automated systems have been shown to help cancer detection when accurate, the presence of CAD also leads to an over-reliance effect where miss errors and false alarms increase when the CAD system fails. Previous research investigated CAD systems which overlayed salient exogenous cues onto the image to highlight suspicious areas. These salient cues capture attention which may exacerbate the over-reliance effect. Furthermore, overlaying CAD cues directly on the mammogram occludes sections of breast tissue which may disrupt global statistics useful for cancer detection. In this study we investigated whether an over-reliance effect occurred with a binary CAD system, which instead of overlaying a CAD cue onto the mammogram, reported a message alongside the mammogram indicating the possible presence of a cancer. We manipulated the certainty of the message and whether it was presented only to indicate the presence of a cancer, or whether a message was displayed on every mammogram to state whether a cancer was present or absent. The results showed that although an over-reliance effect still occurred with binary CAD systems miss errors were reduced when the CAD message was more definitive and only presented to alert readers of a possible cancer.

Highlights of the 2023 Amendments to the MQSA Implementing Regulations.

The Mammography Quality Standards Act (MQSA) of 1992 is intended to ensure that mammography practice nationwide meets consistent baseline quality standards. Amendments to the MQSA implementing regulations ("Amendments") were published on March 10, 2023, and are effective on September 10, 2024. The Amendments address various aspects of the program, including mammography technology, enforcement, the retention and transfer of personnel records and medical records, the medical outcomes audit, and mammography reporting, including (but not limited to) reporting of breast tissue density. The amended regulations are available online, and the Food and Drug Admininstration (FDA) offers several resources for mammography facilities and other stakeholders to receive additional information, including a facility hotline, a summary document distributed to all certified mammography facilities, and a Small Entity Compliance Guide (or SECG) written in question-and-answer format, which the FDA intends to be helpful to facilities of any size.

An assessment of ChatGPT's responses to frequently asked questions about cervical and breast cancer.

BACKGROUND: Cervical cancer (CC) and breast cancer (BC) threaten women's well-being, influenced by health-related stigma and a lack of reliable information, which can cause late diagnosis and early death. ChatGPT is likely to become a key source of health information, although quality concerns could also influence health-seeking behaviours. METHODS: This cross-sectional online survey compared ChatGPT's responses to five physicians specializing in mammography and five specializing in gynaecology. Twenty frequently asked questions about CC and BC were asked on 26th and 29th of April, 2023. A panel of seven experts assessed the accuracy, consistency, and relevance of ChatGPT's responses using a 7-point Likert scale. Responses were analyzed for readability, reliability, and efficiency. ChatGPT's responses were synthesized, and findings are presented as a radar chart. RESULTS: ChatGPT had an accuracy score of 7.0 (range: 6.6-7.0) for CC and BC questions, surpassing the highest-scoring physicians (P < 0.05). ChatGPT took an average of 13.6 s (range: 7.6-24.0) to answer each of the 20 questions presented. Readability was comparable to that of experts and physicians involved, but ChatGPT generated more extended responses compared to physicians. The consistency of repeated answers was 5.2 (range: 3.4-6.7). With different contexts combined, the overall ChatGPT relevance score was 6.5 (range: 4.8-7.0). Radar plot analysis indicated comparably good accuracy, efficiency, and to a certain extent, relevance. However, there were apparent inconsistencies, and the reliability and readability be considered inadequate. CONCLUSIONS: ChatGPT shows promise as an initial source of information for CC and BC. ChatGPT is also highly functional and appears to be superior to physicians, and aligns with expert consensus, although there is room for improvement in readability, reliability, and consistency. Future efforts should focus on developing advanced ChatGPT models explicitly designed to improve medical practice and for those with concerns about symptoms.

Acceptability of de-intensified screening for women at low risk of breast cancer: a randomised online experimental survey.

BACKGROUND: Risk-stratified approaches to breast screening show promise for increasing benefits and reducing harms. But the successful implementation of such an approach will rely on public acceptability. To date, research suggests that while increased screening for women at high risk will be acceptable, any de-intensification of screening for low-risk groups may be met with less enthusiasm. We report findings from a population-based survey of women in England, approaching the age of eligibility for breast screening, to compare the acceptability of current age-based screening with two hypothetical risk-adapted approaches for women at low risk of breast cancer. METHODS: An online survey of 1,579 women aged 40-49 with no personal experience of breast cancer or mammography. Participants were recruited via a market research panel, using target quotas for educational attainment and ethnic group, and were randomised to view information about (1) standard NHS age-based screening; (2) a later screening start age for low-risk women; or (3) a longer screening interval for low-risk women. Primary outcomes were cognitive, emotional, and global acceptability. ANOVAs and multiple regression were used to compare acceptability between groups and explore demographic and psychosocial factors associated with acceptability. RESULTS: All three screening approaches were judged to be acceptable on the single-item measure of global acceptability (mean score > 3 on a 5-point scale). Scores for all three measures of acceptability were significantly lower for the risk-adapted scenarios than for age-based screening. There were no differences between the two risk-adapted scenarios. In multivariable analysis, higher breast cancer knowledge was positively associated with cognitive and emotional acceptability of screening approach. Willingness to undergo personal risk assessment was not associated with experimental group. CONCLUSION: We found no difference in the acceptability of later start age vs. longer screening intervals for women at low risk of breast cancer in a large sample of women who were screening naïve. Although acceptability of both risk-adapted scenarios was lower than for standard age-based screening, overall acceptability was reasonable. The positive associations between knowledge and both cognitive and emotional acceptability suggests clear and reassuring communication about the rationale for de-intensified screening may enhance acceptability.

Secretory Carcinoma of the Breast: Radiologic-Pathologic Correlation.

Secretory carcinoma is a rare, low-grade, special histological type of invasive breast carcinoma. Although it is the most common primary breast cancer in the pediatric population, most cases are diagnosed in adults, with a median age of 48 years (range 3 to 91 years). It most often presents as a painless and slowly growing palpable lump. Imaging findings are nonspecific. Secretory carcinomas have abundant periodic acid-Schiff positive intracytoplasmic and extracellular secretions on histopathology. Nearly all secretory carcinomas have mild to moderate nuclear pleomorphism with low mitotic activity. Over 80% (86/102) of secretory carcinomas display the translocation of t(12;15)(p13;q25), resulting in ETV6::NTRK3 gene fusion. Secretory carcinoma generally has an indolent course and has a better prognosis and overall survival than invasive breast carcinoma of no special type. A good prognosis is associated with age <20 years, tumor size <2 cm, and ≤3 axillary lymph node metastases. Metastases beyond the ipsilateral axillary lymph nodes are rare, with the most common sites involving the lung and liver. Except for the potential addition of targeted drug therapy for NTRK fusion-positive tumors, the treatment approach is otherwise similar to invasive breast carcinomas of similar receptor status.

Radiological and pathological predictors of post-operative upstaging of breast ductal carcinoma in situ (DCIS) to invasive ductal carcinoma and lymph-nodes metastasis; a potential algorithm for node surgical de-escalation.

BACKGROUND/AIM: Ductal carcinoma in situ is considered a local disease with no metastatic potential, thus sentinel lymph node biopsy (SLNB) may be deemed an overtreatment. SLNB should be reserved for patients with invasive cancer, even though the risk of upstaging rises to 25 %. We aimed to identify clinicopathological predictors of post-operative upstaging in invasive carcinoma. METHODS: We retrospectively analyzed patients with a pre-operative diagnosis of DCIS subjected to breast surgery between January 2017 to December 2021, and evaluated at the Breast Unit of PTV (Policlinico Tor Vergata, Rome). RESULTS: Out of 267 patients diagnosed with DCIS, 33(12.4 %) received a diagnosis upstaging and 9(3.37 %) patients presented with sentinel lymph node (SLN) metastasis. In multivariate analysis, grade 3 tumor (OR 1.9; 95 % CI 1.2-5.6), dense nodule at mammography (OR 1.3; 95 % CI 1.1-2.6) and presence of a solid nodule at ultrasonography (OR 1.5; 95 % CI 1.2-2.6) were independent upstaging predictors. Differently, the independent predictors for SLNB metastasis were: upstaging (OR 2.1.; 95 % CI 1.2-4.6; p = 0.0079) and age between 40 and 60yrs (OR 1.4; 95 % CI 1.4-2.7; p = 0.027). All 9 patients with SLN metastasis received a diagnosis upstaging and were aged between 40 and 60 years old. CONCLUSION: We identified pre-operative independent predictors of upstaging to invasive ductal carcinoma. The combined use of different predictors in an algorithm for surgical treatments of DCIS could reduce the numbers of unnecessary SLNB.

Identifying Local Facilitators and Barriers to Screening Mammography Within a Rural Acute Care Hospital Service Area.

INTRODUCTION: Women living in rural areas are more likely to be diagnosed with advanced-stage breast cancer than their urban counterparts. The advanced stage at diagnosis is potentially attributable to lower rates of mammogram screening. We aimed to elucidate factors affecting women in decision-making about mammogram screening in a rural area in Wisconsin served by a critical access hospital. METHODS: We conducted an observational cross-sectional mixed-methods study, collecting data from various sources using 3 methods. Virtual interviews with hospital staff, virtual focus groups with community members, and a survey of women 40 years and older occurred from September 2021 through February 2022. Qualitative data were organized into themes of facilitators and barriers to mammogram screening. Survey responses were reported descriptively. FINDINGS: Eleven hospital staff interviewed and 21 community members who joined 1 of 3 virtual focus groups voiced similar perceptions of facilitators and barriers to mammogram screening. Clinician recommendation was among facilitators, while insurance concerns were the primary barrier. Among survey respondents (N = 282), mean age was 58.7, 98% self-identified as White, and 91% saw a health care provider in the past year. Top reasons for having their first mammogram were doctor recommendation (70%), family history (19%), and personal decision (18%). Top reasons they did not have a mammogram screening at least every year were putting it off (23%), lack of problems (17%), and pandemic-related reasons (15%). CONCLUSIONS: Improving patient education and supporting clinicians to deliver screening recommendations may increase appropriate screening. Future studies should focus on reaching women not engaged with the health system.