Deep learning of mammogram images to reduce unnecessary breast biopsies: a preliminary study.

BACKGROUND: Patients with a Breast Imaging Reporting and Data System (BI-RADS) 4 mammogram are currently recommended for biopsy. However, 70-80% of the biopsies are negative/benign. In this study, we developed a deep learning classification algorithm on mammogram images to classify BI-RADS 4 suspicious lesions aiming to reduce unnecessary breast biopsies. MATERIALS AND METHODS: This retrospective study included 847 patients with a BI-RADS 4 breast lesion that underwent biopsy at a single institution and included 200 invasive breast cancers, 200 ductal carcinoma in-situ (DCIS), 198 pure atypias, 194 benign, and 55 atypias upstaged to malignancy after excisional biopsy. We employed convolutional neural networks to perform 4 binary classification tasks: (I) benign vs. all atypia + invasive + DCIS, aiming to identify the benign cases for whom biopsy may be avoided; (II) benign + pure atypia vs. atypia-upstaged + invasive + DCIS, aiming to reduce excision of atypia that is not upgraded to cancer at surgery; (III) benign vs. each of the other 3 classes individually (atypia, DCIS, invasive), aiming for a precise diagnosis; and (IV) pure atypia vs. atypia-upstaged, aiming to reduce unnecessary excisional biopsies on atypia patients. RESULTS: A 95% sensitivity for the "higher stage disease" class was ensured for all tasks. The specificity value was 33% in Task I, and 25% in Task II, respectively. In Task III, the respective specificity value was 30% (vs. atypia), 30% (vs. DCIS), and 46% (vs. invasive tumor). In Task IV, the specificity was 35%. The AUC values for the 4 tasks were 0.72, 0.67, 0.70/0.73/0.72, and 0.67, respectively. CONCLUSION: Deep learning of digital mammograms containing BI-RADS 4 findings can identify lesions that may not need breast biopsy, leading to potential reduction of unnecessary procedures and the attendant costs and stress.

Organizational Breast Cancer Data Mart: A Solution for Assessing Outcomes of Imaging and Treatment.

PURPOSE: In the United States, a comprehensive national breast cancer registry (CR) does not exist. Thus, care and coverage decisions are based on data from population subsets, other countries, or models. We report a prototype real-world research data mart to assess mortality, morbidity, and costs for breast cancer diagnosis and treatment. METHODS: With institutional review board approval and Health Insurance Portability and Accountability Act (HIPPA) compliance, a multidisciplinary clinical and research data warehouse (RDW) expert group curated demographic, risk, imaging, pathology, treatment, and outcome data from the electronic health records (EHR), radiology (RIS), and CR for patients having breast imaging and/or a diagnosis of breast cancer in our institution from January 1, 2004, to December 31, 2020. Domains were defined by prebuilt views to extract data denormalized according to requirements from the existing RDW using an export, transform, load pattern. Data dictionaries were included. Structured query language was used for data cleaning. RESULTS: Five-hundred eighty-nine elements (EHR 311, RIS 211, and CR 67) were mapped to 27 domains; all, except one containing CR elements, had cancer and noncancer cohort views, resulting in a total of 53 views (average 12 elements/view; range, 4-67). EHR and RIS queries returned 497,218 patients with 2,967,364 imaging examinations and associated visit details. Cancer biology, treatment, and outcome details for 15,619 breast cancer cases were imported from the CR of our primary breast care facility for this prototype mart. CONCLUSION: Institutional real-world data marts enable comprehensive understanding of care outcomes within an organization. As clinical data sources become increasingly structured, such marts may be an important source for future interinstitution analysis and potentially an opportunity to create robust real-world results that could be used to support evidence-based national policy and care decisions for breast cancer.

Addition of Contrast-enhanced Mammography to Tomosynthesis for Breast Cancer Detection in Women with a Personal History of Breast Cancer: Prospective TOCEM Trial Interim Analysis.

Background Digital breast tomosynthesis (DBT) is often inadequate for screening women with a personal history of breast cancer (PHBC). The ongoing prospective Tomosynthesis or Contrast-Enhanced Mammography, or TOCEM, trial includes three annual screenings with both DBT and contrast-enhanced mammography (CEM). Purpose To perform interim assessment of cancer yield, stage, and recall rate when CEM is added to DBT in women with PHBC. Materials and Methods From October 2019 to December 2022, two radiologists interpreted both examinations: Observer 1 reviewed DBT first and then CEM, and observer 2 reviewed CEM first and then DBT. Effects of adding CEM to DBT on incremental cancer detection rate (ICDR), cancer type and node status, recall rate, and other performance characteristics of the primary radiologist decisions were assessed. Results Among the participants (mean age at entry, 63.6 years ± 9.6 [SD]), 1273, 819, and 227 women with PHBC completed year 1, 2, and 3 screening, respectively. For observer 1, year 1 cancer yield was 20 of 1273 (15.7 per 1000 screenings) for DBT and 29 of 1273 (22.8 per 1000 screenings; ICDR, 7.1 per 1000 screenings [95% CI: 3.2, 13.4]) for DBT plus CEM (P < .001). Year 2 plus 3 cancer yield was four of 1046 (3.8 per 1000 screenings) for DBT and eight of 1046 (7.6 per 1000 screenings; ICDR, 3.8 per 1000 screenings [95% CI: 1.0, 7.6]) for DBT plus CEM (P = .001). Year 1 recall rate for observer 1 was 103 of 1273 (8.1%) for (incidence) DBT alone and 187 of 1273 (14.7%) for DBT plus CEM (difference = 84 of 1273, 6.6% [95% CI: 5.3, 8.1]; P < .001). Year 2 plus 3 recall rate was 40 of 1046 (3.8%) for DBT and 92 of 1046 (8.8%) for DBT plus CEM (difference = 52 of 1046, 5.0% [95% CI: 3.7, 6.3]; P < .001). In 18 breasts with cancer detected only at CEM after integration of both observers, 13 (72%) cancers were invasive (median tumor size, 0.6 cm) and eight of nine (88%) with staging were N0. Among 1883 screenings with adequate reference standard, there were three interval cancers (one at the scar, two in axillae). Conclusion CEM added to DBT increased early breast cancer detection each year in women with PHBC, with an accompanying approximately 5.0%-6.6% recall rate increase. Clinical trial registration no. NCT04085510 © RSNA, 2024 Supplemental material is available for this article.

Upgrade Rates of Variant Lobular Carcinoma In Situ Compared to Classic Lobular Carcinoma In Situ Diagnosed in Core Needle Biopsies: A 10-Year Single Institution Retrospective Study.

The primary aim of this study was to determine the upgrade rates of variant lobular carcinoma in situ (V-LCIS, ie, combined florid [F-LCIS] and pleomorphic [P-LCIS]) compared with classic LCIS (C-LCIS) when diagnosed on core needle biopsy (CNB). The secondary goal was to determine the rate of progression/development of invasive carcinoma on long-term follow-up after primary excision. After institutional review board approval, our institutional pathology database was searched for patients with "pure" LCIS diagnosed on CNB who underwent subsequent excision. Radiologic findings were reviewed, radiologic-pathologic (rad-path) correlation was performed, and follow-up patient outcome data were obtained. One hundred twenty cases of LCIS were identified on CNB (C-LCIS = 97, F-LCIS = 18, and P-LCIS = 5). Overall upgrade rates after excision for C-LCIS, F-LCIS, and P-LCIS were 14% (14/97), 44% (8/18), and 40% (2/5), respectively. Of the total cases, 79 (66%) were deemed rad-path concordant. Of these, the upgrade rate after excision for C-LCIS, F-LCIS, and P-LCIS was 7.5% (5 of 66), 40% (4 of 10), and 0% (0 of 3), respectively. The overall upgrade rate for V-LCIS was higher than for C-LCIS (P = .004), even for the cases deemed rad-path concordant (P value: .036). Most upgraded cases (23 of 24) showed pT1a disease or lower. With an average follow-up of 83 months, invasive carcinoma in the ipsilateral breast was identified in 8/120 (7%) cases. Six patients had died: 2 of (contralateral) breast cancer and 4 of other causes. Because of a high upgrade rate, V-LCIS diagnosed on CNB should always be excised. The upgrade rate for C-LCIS (even when rad-path concordant) is higher than reported in many other studies. Rad-path concordance read, surgical consultation, and individualized decision making are recommended for C-LCIS cases. The risk of developing invasive carcinoma after LCIS diagnosis is small (7% with ∼7-year follow-up), but active surveillance is required to diagnose early-stage disease.

Assessment of Background Parenchymal Enhancement at Dynamic Contrast-enhanced MRI in Predicting Breast Cancer Recurrence Risk.

Background Background parenchymal enhancement (BPE) at dynamic contrast-enhanced (DCE) MRI of cancer-free breasts increases the risk of developing breast cancer; implications of quantitative BPE in ipsilateral breasts with breast cancer are largely unexplored. Purpose To determine whether quantitative BPE measurements in one or both breasts could be used to predict recurrence risk in women with breast cancer, using the Oncotype DX recurrence score as the reference standard. Materials and Methods This HIPAA-compliant retrospective single-institution study included women diagnosed with breast cancer between January 2007 and January 2012 (development set) and between January 2012 and January 2017 (internal test set). Quantitative BPE was automatically computed using an in-house-developed computer algorithm in both breasts. Univariable logistic regression was used to examine the association of BPE with Oncotype DX recurrence score binarized into high-risk (recurrence score >25) and low- or intermediate-risk (recurrence score ≤25) categories. Models including BPE measures were assessed for their ability to distinguish patients with high risk versus those with low or intermediate risk and the actual recurrence outcome. Results The development set included 127 women (mean age, 58 years ± 10.2 [SD]; 33 with high risk and 94 with low or intermediate risk) with an actual local or distant recurrence rate of 15.7% (20 of 127) at a minimum 10 years of follow-up. The test set included 60 women (mean age, 57.8 years ± 11.6; 16 with high risk and 44 with low or intermediate risk). BPE measurements quantified in both breasts were associated with increased odds of a high-risk Oncotype DX recurrence score (odds ratio range, 1.27-1.66 [95% CI: 1.02, 2.56]; P < .001 to P = .04). Measures of BPE combined with tumor radiomics helped distinguish patients with a high-risk Oncotype DX recurrence score from those with a low- or intermediate-risk score, with an area under the receiver operating characteristic curve of 0.94 in the development set and 0.79 in the test set. For the combined models, the negative predictive values were 0.97 and 0.93 in predicting actual distant recurrence and local recurrence, respectively. Conclusion Ipsilateral and contralateral DCE MRI measures of BPE quantified in patients with breast cancer can help distinguish patients with high recurrence risk from those with low or intermediate recurrence risk, similar to Oncotype DX recurrence score. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Zhou and Rahbar in this issue.

Screening mammographic performance by race and age in the National Mammography Database: 29,479,665 screening mammograms from 13,181,241 women.

PURPOSE: There are insufficient large-scale studies comparing the performance of screening mammography in women of different races. This study aims to compare the screening performance metrics across racial and age groups in the National Mammography Database (NMD). METHODS: All screening mammograms performed between January 1, 2008, and December 31, 2021, in women aged 30-100 years from 746 mammography facilities in 46 U.S. states in the NMD were included. Patients were stratified by 10-year age intervals and 5 racial groups (African American, American Indian, Asian, White, unknown). Incidence of risk factors (breast density, personal history, family history of breast cancer, age), and time since prior exams were compared. Five screening mammography metrics were calculated: recall rate (RR), cancer detection rate (CDR), positive predictive values for recalls (PPV1), biopsy recommended (PPV2) and biopsy performed (PPV3). RESULTS: 29,479,655 screening mammograms performed in 13,181,241 women between January 1, 2008, and December 31, 2021, from the NMD were analyzed. The overall mean performance metrics were RR 10.00% (95% CI 9.99-10.02), CDR 4.18/1000 (4.16-4.21), PPV1 4.18% (4.16-4.20), PPV2 25.84% (25.72-25.97), PPV3 25.78% (25.66-25.91). With advancing age, RR significantly decreases, while CDR, PPV1, PPV2, and PPV3 significantly increase. Incidence of personal/family history of breast cancer, breast density, age, prior mammogram availability, and time since prior mammogram were mostly similar across all races. Compared to White women, African American women had significantly higher RR, but lower CDR, PPV1, PPV2 and PPV3. CONCLUSIONS: Benefits of screening mammography increase with age, including for women age > 70 and across all races. Screening mammography is effective; with lower RR and higher CDR, PPV2, and PPV3 with advancing age. African American women have poorer outcomes from screening mammography (higher RR and lower CDR), compared to White and all women in the NMD. Racial disparity can be partly explained by higher rate of African American women lost to follow up.

Local texture statistics augment the power spectrum in modeling radiographic judgments of breast density.

Purpose: Anatomical "noise" is an important limitation of full-field digital mammography. Understanding its impact on clinical judgments is made difficult by the complexity of breast parenchyma, which results in image texture not fully captured by the power spectrum. While the number of possible parameters for characterizing anatomical noise is quite large, a specific set of local texture statistics has been shown to be visually salient, and human sensitivity to these statistics corresponds to their informativeness in natural scenes. Approach: We evaluate these local texture statistics in addition to standard power-spectral measures to determine whether they have additional explanatory value for radiologists' breast density judgments. We analyzed an image database consisting of 111 disease-free mammographic screening exams (4 views each) acquired at the University of Pittsburgh Medical Center. Each exam had a breast density score assigned by the examining radiologist. Power-spectral descriptors and local image statistics were extracted from images of breast parenchyma. Model-selection criteria and accuracy were used to assess the explanatory and predictive value of local image statistics for breast density judgments. Results: The model selection criteria show that adding local texture statistics to descriptors of the power spectra produce better explanatory and predictive models of radiologists' judgments of breast density. Thus, local texture statistics capture, in some form, non-Gaussian aspects of texture that radiologists are using. Conclusions: Since these local texture statistics are expected to be impacted by imaging factors like modality, dose, and image processing, they suggest avenues for understanding and optimizing observer performance.

[Cost-effectiveness of treatment of type 2 diabetes mellitus in México]. / Costo-efectividad del tratamiento de la diabetes mellitus tipo 2 en México.

Background: Type 2 Diabetes Mellitus (DM2) is a public health and socioeconomic problem, generating direct medical costs for its treatment. Objective: To analyze the cost-effectiveness of monotherapy and bitherapy treatments in patients with DM2. Methods: Cost-effectiveness, observational, ambispective, cross-sectional and analytical analysis of files in a first level medical unit. The data in the cost matrix was executed with the Office Excel 2010 program; the most prescribed drug was identified and compared with monotherapy and bitherapy. Results: The annual direct medical costs of the total population were drug cost $118,561.70MN, hospitalization cost $243,756.00MN, consultation cost $327,414.00MN and clinical trial cost $2416.79MN, obtaining an annual total of $692,148.58MN. metformin was the most indicated in monotherapy (88.4%) and as standard therapy it has higher cost-effectiveness compared to glibenclamide. In bitherapy it was metformin/glibenclamide (35.7%) versus the therapeutics of metformin/NPH insulin, metformin/insulin glargine and metformin/dapagliflozin, which had a better cost-effective result, with an incremental cost effectiveness of -$1,128,428.50MN, -$34,365.00 MN, -$119,848.97MN respectively. Conclusions: Metformin presented a better cost-effectiveness ratio in monotherapy, while in bitherapy it was the metformin/NPH insulin association.

Introducción: La Diabetes Mellitus tipo 2 (DM2) es un problema de salud pública y socioeconómico, tanto por su alta incidencia como por la generación de los costos médicos directos para su tratamiento. Objetivo: Analizar el costo-efectividad de los tratamientos en monoterapia y biterapia en pacientes con DM2. Métodos: Análisis costo-efectividad, observacional, ambispectivo, transversal y analítico. Análisis de expedientes en una unidad médica de primer nivel. Se ejecutaron los datos en la matriz de costos con el programa Office Excel 2010; se identificó el fármaco más prescrito, se comparó con monoterapia y biterapia. Resultados: Los costos médicos directos anuales del total de la población fueron: costo del medicamento $118,561.70MN, costo por hospitalización $243,756.00MN, costo por consultas $ 327,414.00MN y costo por estudios clínicos $2416.79MN, obteniendo un total anual de $692,148.58MN. La metformina fue la más indicada en monoterapia (88.4%) y como terapéutica estándar tiene mayor costo-efectividad comparada con la glibenclamida. En biterapia fue metformina/glibenclamida (35.7%) versus las terapéuticas de metformina/insulina NPH, metformina/insulina glargina y metformina/dapagliflozina, las cuales tuvieron un resultando más costo-efectivo, con un costo efectividad incremental de -$1,128,428.50MN, -$34,365.00MN, -$119,848.97MN respectivamente. Conclusiones: La metformina presento mejor relación costo efectividad en monoterapia, mientras que en biterapia fue la asociación metformina/Insulina NPH.

Visual adaptation to medical images: a comparison of digital mammography and tomosynthesis.

Purpose: Radiologists and other image readers spend prolonged periods inspecting medical images. The visual system can rapidly adapt or adjust sensitivity to the images that an observer is currently viewing, and previous studies have demonstrated that this can lead to pronounced changes in the perception of mammogram images. We compared these adaptation effects for images from different imaging modalities to explore both general and modality-specific consequences of adaptation in medical image perception. Approach: We measured perceptual changes induced by adaptation to images acquired by digital mammography (DM) or digital breast tomosynthesis (DBT), which have both similar and distinct textural properties. Participants (nonradiologists) adapted to images from the same patient acquired from each modality or for different patients with American College of Radiology-Breast Imaging Reporting and Data System (BI-RADS) classification of dense or fatty tissue. The participants then judged the appearance of composite images formed by blending the two adapting images (i.e., DM versus DBT or dense versus fatty in each modality). Results: Adaptation to either modality produced similar significant shifts in the perception of dense and fatty textures, reducing the salience of the adapted component in the test images. In side-by-side judgments, a modality-specific adaptation effect was not observed. However, when the images were directly fixated during adaptation and testing, so that the textural differences between the modalities were more visible, significantly different changes in the sensitivity to the noise in the images were observed. Conclusions: These results confirm that observers can readily adapt to the visual properties or spatial textures of medical images in ways that can bias their perception of the images, and that adaptation can also be selective for the distinctive visual features of images acquired by different modalities.

Prospective Multicenter Diagnostic Performance of Technologist-Performed Screening Breast Ultrasound After Tomosynthesis in Women With Dense Breasts (the DBTUST).

PURPOSE: To assess diagnostic performance of digital breast tomosynthesis (DBT) alone or combined with technologist-performed handheld screening ultrasound (US) in women with dense breasts. METHODS: In an institutional review board-approved, Health Insurance Portability and Accountability Act-compliant multicenter protocol in western Pennsylvania, 6,179 women consented to three rounds of annual screening, interpreted by two radiologist observers, and had appropriate follow-up. Primary analysis was based on first observer results. RESULTS: Mean participant age was 54.8 years (range, 40-75 years). Across 17,552 screens, there were 126 cancer events in 125 women (7.2/1,000; 95% CI, 5.9 to 8.4). In year 1, DBT-alone cancer yield was 5.0/1,000, and of DBT+US, 6.3/1,000, difference 1.3/1,000 (95% CI, 0.3 to 2.1; P = .005). In years 2 + 3, DBT cancer yield was 4.9/1,000, and of DBT+US, 5.9/1,000, difference 1.0/1,000 (95% CI, 0.4 to 1.5; P < .001). False-positive rate increased from 7.0% for DBT in year 1 to 11.5% for DBT+US and from 5.9% for DBT in year 2 + 3 to 9.7% for DBT+US (P < .001 for both). Nine cancers were seen only by double reading DBT and one by double reading US. Ten interval cancers (0.6/1,000 [95% CI, 0.2 to 0.9]) were identified. Despite reduction in specificity, addition of US improved receiver operating characteristic curves, with area under receiver operating characteristic curve increasing from 0.83 for DBT alone to 0.92 for DBT+US in year 1 (P = .01), with smaller improvements in subsequent years. Of 6,179 women, across all 3 years, 172/6,179 (2.8%) unique women had a false-positive biopsy because of DBT as did another 230/6,179 (3.7%) women because of US (P < .001). CONCLUSION: Overall added cancer detection rate of US screening after DBT was modest at 19/17,552 (1.1/1,000; CI, 0.5- to 1.6) screens but potentially overcomes substantial increases in false-positive recalls and benign biopsies.

Barriers to Implementation of Contrast-Enhanced Mammography in Clinical Practice: AJR Expert Panel Narrative Review.

Accumulating evidence shows that contrast-enhanced mammography (CEM) has higher diagnostic performance than digital mammography and ultrasound and comparable diagnostic performance to MRI for various indications. CEM also offers certain practical advantages for patients. Nevertheless, the clinical implementation of CEM has been limited because of a range of factors. This AJR Expert Panel Narrative Review explores such factors hindering CEM implementation. These factors include the following: the risks of iodinated contrast media, increased radiation exposure, indications for which CEM is not the preferred test or for which further evidence is needed, workflow adjustments needed when performing CEM examinations, incomplete availability of CEM-guided biopsy systems, and reimbursement challenges. Considerations that currently mitigate or are expected to mitigate these factors are also highlighted.

Contrast-enhanced Mammography-guided Biopsy: Initial Trial and Experience.

OBJECTIVE: Evaluate lesion visibility and radiologist confidence during contrast-enhanced mammography (CEM)-guided biopsy. METHODS: Women with BI-RADS ≥4A enhancing breast lesions were prospectively recruited for 9-g vacuum-assisted CEM-guided biopsy. Breast density, background parenchymal enhancement (BPE), lesion characteristics (enhancement and conspicuity), radiologist confidence (scale 1-5), and acquisition times were collected. Signal intensities in specimens were analyzed. Patient surveys were collected. RESULTS: A cohort of 28 women aged 40-81 years (average 57) had 28 enhancing lesions (7/28, 25% malignant). Breast tissue was scattered (10/28, 36%) or heterogeneously dense (18/28, 64%) with minimal (12/28, 43%), mild (7/28, 25%), or moderate (9/28, 32%) BPE on CEM. Twelve non-mass enhancements, 11 masses, 3 architectural distortions, and 2 calcification groups demonstrated weak (12/28, 43%), moderate (14/28, 50%), or strong (2/28, 7%) enhancement. Specimen radiography demonstrated lesion enhancement in 27/28 (96%). Radiologists reported complete lesion removal on specimen radiography in 8/28 (29%). Average time from contrast injection to specimen radiography was 18 minutes (SD = 5) and, to post-procedure mammogram (PPM), 34 minutes (SD = 10). Contrast-enhanced mammography PPM was performed in 27/28 cases; 13/19 (68%) of incompletely removed lesions on specimen radiography showed residual enhancement; 6/19 (32%) did not. Across all time points, average confidence was 2.2 (SD = 1.2). Signal intensities of enhancing lesions were similar to iodine. Patients had an overall positive assessment. CONCLUSION: Lesion enhancement persisted through PPM and was visible on low energy specimen radiography, with an average "confident" score. Contrast-enhanced mammography-guided breast biopsy is easily implemented clinically. Its availability will encourage adoption of CEM.

Impact of the COVID-19 Pandemic on Breast Imaging: An Analysis of the National Mammography Database.

PURPOSE: The aim of this study was to quantify the initial decline and subsequent rebound in breast cancer screening metrics throughout the coronavirus disease 2019 (COVID-19) pandemic. METHODS: Screening and diagnostic mammographic examinations, biopsies performed, and cancer diagnoses were extracted from the ACR National Mammography Database from March 1, 2019, through May 31, 2021. Patient (race and age) and facility (regional location, community type, and facility type) demographics were collected. Three time periods were used for analysis: pre-COVID-19 (March 1, 2019, to May 31, 2019), peak COVID-19 (March 1, 2020, to May 31, 2020), and COVID-19 recovery (March 1, 2021, to May 31, 2021). Analysis was performed at the facility level and overall between time periods. RESULTS: In total, 5,633,783 screening mammographic studies, 1,282,374 diagnostic mammographic studies, 231,390 biopsies, and 69,657 cancer diagnoses were analyzed. All peak COVID-19 metrics were less than pre-COVID-19 volumes: 36.3% of pre-COVID-19 for screening mammography, 57.9% for diagnostic mammography, 47.3% for biopsies, and 48.7% for cancer diagnoses. There was some rebound during COVID-19 recovery as a percentage of pre-COVID-19 volumes: 85.3% of pre-COVID-19 for screening mammography, 97.8% for diagnostic mammography, 91.5% for biopsies, and 92.0% for cancer diagnoses. Across various metrics, there was a disproportionate negative impact on older women, Asian women, facilities in the Northeast, and facilities affiliated with academic medical centers. CONCLUSIONS: COVID-19 had the greatest impact on screening mammography volumes, which have not returned to pre-COVID-19 levels. Cancer diagnoses declined significantly in the acute phase and have not fully rebounded, emphasizing the need to increase outreach efforts directed at specific patient population and facility types.

Utilization and Cancer Yield of Probably Benign Assessment Category in the National Mammography Database: 2009 to 2018.

PURPOSE: Data on utilization rate and cancer yield of BI-RADS® category 3 in routine clinical practice in diagnostic mammography are sparse. The aim of this study was to determine utilization rate and cancer yield of BI-RADS 3 in diagnostic mammography in the ACR National Mammography Database (NMD). METHODS: Retrospective analysis of NMD mammograms from January 1, 2009, to June 30, 2018, was performed. BI-RADS 3 utilization rate in diagnostic setting was calculated and stratified by patient, facility, and examination-level variables. Patient-level cancer yield was calculated among women with BI-RADS 3 assessment and adequate follow-up (imaging follow-up ≥24 months or biopsy). Logistic regression was performed to assess the odds of utilization of BI-RADS 3, with respect to facility, examination, and patient variables, and the odds of malignancy among patients with probably benign findings. Chi-square and t tests were used to determine significance (P < .05). RESULTS: Data from 19,443,866 mammograms from 500 NMD facilities across 31 states were analyzed, of which 3,039,952 were diagnostic mammograms. Utilization rate of BI-RADS 3 was 15.5% (470,155 of 3,039,952) in the diagnostic setting. There was a statistically significant difference in BI-RADS 3 utilization rate across all collected variables (P < .001). Patient-level cancer yield at 2-year follow-up was 0.91% (2,009 of 220,672; 95% confidence interval [CI], 0.87%-0.95%) in the diagnostic setting. Patient and examination variables associated with significantly higher likelihood of malignancy included calcifications (odds ratio, 4.27; 95% CI, 2.43-7.51), patient age > 70 years (odds ratio, 3.77; 95% CI, 2.49-5.7), and presence of prior comparisons (odds ratio, 1.23; 95% CI, 1.07-1.42). CONCLUSIONS: In the NMD, BI-RADS 3 assessment was common in diagnostic mammography (15.5%), with an overall cancer yield of 0.91%, less than the benchmark of 2%. Utilization trends in diagnostic mammography warrant further research for optimization of use.

Personalising therapy for early-stage oestrogen receptor-positive breast cancer in older women.

Age is one of the most important risk factors for the development of breast cancer. Nearly a third of all breast cancer cases occur in older women (aged ≥70 years), with most cases being oestrogen receptor-positive (ER+). Such tumours are often indolent and unlikely to be the ultimate cause of death for older women, particularly when considering other comorbidities. This Review focuses on unique clinical considerations for screening, detection, and treatment regimens for older women who develop ER+ breast cancers-specifically, we focus on recent trends for de-implementation of screening, staging, surgery, and adjuvant therapies along the continuum of care. Additionally, we also review emerging basic and translational research that will further uncover the unique underlying biology of these tumours, which develop in the context of systemic age-related inflammation and changing hormone profiles. With prevailing trends of clinical de-implementation, new insights into mechanistic biology might provide an opportunity for precision medicine approaches to treat patients with well tolerated, low-toxicity agents to extend patients' lives with a higher quality of life, prevent tumour recurrences, and reduce cancer-related burdens.

Deep learning of longitudinal mammogram examinations for breast cancer risk prediction.

Information in digital mammogram images has been shown to be associated with the risk of developing breast cancer. Longitudinal breast cancer screening mammogram examinations may carry spatiotemporal information that can enhance breast cancer risk prediction. No deep learning models have been designed to capture such spatiotemporal information over multiple examinations to predict the risk. In this study, we propose a novel deep learning structure, LRP-NET, to capture the spatiotemporal changes of breast tissue over multiple negative/benign screening mammogram examinations to predict near-term breast cancer risk in a case-control setting. Specifically, LRP-NET is designed based on clinical knowledge to capture the imaging changes of bilateral breast tissue over four sequential mammogram examinations. We evaluate our proposed model with two ablation studies and compare it to three models/settings, including 1) a "loose" model without explicitly capturing the spatiotemporal changes over longitudinal examinations, 2) LRP-NET but using a varying number (i.e., 1 and 3) of sequential examinations, and 3) a previous model that uses only a single mammogram examination. On a case-control cohort of 200 patients, each with four examinations, our experiments on a total of 3200 images show that the LRP-NET model outperforms the compared models/settings.

Communicating With Breast Imaging Patients During the COVID-19 Pandemic: Impact on Patient Care and Physician Wellness.

OBJECTIVE: Assess the impact of COVID-19 on patient-breast radiologist interactions and evaluate the relationship between safety measure-constrained communication and physician wellbeing. METHODS: A 41-question survey on the perceived effect of COVID-19 on patient care was distributed from June 2020 to September 2020 to members of the Society of Breast Imaging and the National Consortium of Breast Centers. Non-radiologists and international members were excluded. Anxiety and psychological distress scores were calculated. A multivariable logistic model was used to identify demographic and mental health factors associated with responses. RESULTS: Five hundred twenty-five surveys met inclusion criteria (23% response rate). Diminished ability to fulfill patients' emotional needs was reported by 46% (221/479), a response associated with younger age (OR, 0.8 per decade; P < 0.01), higher anxiety (OR, 2.3; P < 0.01), and higher psychological distress (OR, 2.2; P = 0.04). Personal protective equipment made patient communication more difficult for 88% (422/478), a response associated with younger age (OR, 0.8 per decade; P = 0.008), female gender (OR, 1.9; P < 0.01), and greater anxiety (OR, 2.6; P = 0.001). The inability to provide the same level of care as prior to COVID-19 was reported by 37% (177/481) and was associated with greater anxiety (OR, 3.4; P < 0.001) and psychological distress (OR, 1.7; P = 0.03). CONCLUSION: The majority of breast radiologists reported that COVID-19 has had a negative impact on patient care. This perception was more likely among younger radiologists and those with higher levels of anxiety and psychological distress.

A machine and human reader study on AI diagnosis model safety under attacks of adversarial images.

While active efforts are advancing medical artificial intelligence (AI) model development and clinical translation, safety issues of the AI models emerge, but little research has been done. We perform a study to investigate the behaviors of an AI diagnosis model under adversarial images generated by Generative Adversarial Network (GAN) models and to evaluate the effects on human experts when visually identifying potential adversarial images. Our GAN model makes intentional modifications to the diagnosis-sensitive contents of mammogram images in deep learning-based computer-aided diagnosis (CAD) of breast cancer. In our experiments the adversarial samples fool the AI-CAD model to output a wrong diagnosis on 69.1% of the cases that are initially correctly classified by the AI-CAD model. Five breast imaging radiologists visually identify 29%-71% of the adversarial samples. Our study suggests an imperative need for continuing research on medical AI model's safety issues and for developing potential defensive solutions against adversarial attacks.