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Background Although several clinical breast cancer risk models are used to guide screening and prevention, they have only moderate discrimination. Purpose To compare selected existing mammography artificial intelligence (AI) algorithms and the Breast Cancer Surveillance Consortium (BCSC) risk model for prediction of 5-year risk. Materials and Methods This retrospective case-cohort study included data in women with a negative screening mammographic examination (no visible evidence of cancer) in 2016, who were followed until 2021 at Kaiser Permanente Northern California. Women with prior breast cancer or a highly penetrant gene mutation were excluded. Of the 324 009 eligible women, a random subcohort was selected, regardless of cancer status, to which all additional patients with breast cancer were added. The index screening mammographic examination was used as input for five AI algorithms to generate continuous scores that were compared with the BCSC clinical risk score. Risk estimates for incident breast cancer 0 to 5 years after the initial mammographic examination were calculated using a time-dependent area under the receiver operating characteristic curve (AUC). Results The subcohort included 13 628 patients, of whom 193 had incident cancer. Incident cancers in eligible patients (additional 4391 of 324 009) were also included. For incident cancers at 0 to 5 years, the time-dependent AUC for BCSC was 0.61 (95% CI: 0.60, 0.62). AI algorithms had higher time-dependent AUCs than did BCSC, ranging from 0.63 to 0.67 (Bonferroni-adjusted P < .0016). Time-dependent AUCs for combined BCSC and AI models were slightly higher than AI alone (AI with BCSC time-dependent AUC range, 0.66-0.68; Bonferroni-adjusted P < .0016). Conclusion When using a negative screening examination, AI algorithms performed better than the BCSC risk model for predicting breast cancer risk at 0 to 5 years. Combined AI and BCSC models further improved prediction. © RSNA, 2023 Supplemental material is available for this article.

BackgroundMachine learning (ML) approaches facilitate risk prediction model development using high-dimensional predictors and higher-order interactions at the cost of model interpretability and transparency. We compared the relative predictive performance of statistical and ML models to guide modeling strategy selection for surveillance mammography outcomes in women with a personal history of breast cancer (PHBC).MethodsWe cross-validated seven risk prediction models for two surveillance outcomes, failure (breast cancer within 12 months of a negative surveillance mammogram) and benefit (surveillance-detected breast cancer). We included 9,447 mammograms (495 failures, 1,414 benefits, and 7,538 nonevents) from years 1996 to 2017 using a 1:4 matched case-control samples of women with PHBC in the Breast Cancer Surveillance Consortium. We assessed model performance of conventional regression, regularized regressions (LASSO and elastic-net), and ML methods (random forests and gradient boosting machines) by evaluating their calibration and, among well-calibrated models, comparing the area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CI).ResultsLASSO and elastic-net consistently provided well-calibrated predicted risks for surveillance failure and benefit. The AUCs of LASSO and elastic-net were both 0.63 (95% CI, 0.60-0.66) for surveillance failure and 0.66 (95% CI, 0.64-0.68) for surveillance benefit, the highest among well-calibrated models.ConclusionsFor predicting breast cancer surveillance mammography outcomes, regularized regression outperformed other modeling approaches and balanced the trade-off between model flexibility and interpretability.ImpactRegularized regression may be preferred for developing risk prediction models in other contexts with rare outcomes, similar training sample sizes, and low-dimensional features.

The bacterial pathogen Staphylococcus aureus employs a thick cell wall for protection against physical and chemical insults. This wall requires continuous maintenance to ensure strength and barrier integrity, but also to permit bacterial growth and division. The main cell wall component is peptidoglycan. Accordingly, the bacteria produce so-called peptidoglycan hydrolases (PGHs) that cleave glycan strands to facilitate growth, cell wall remodelling, separation of divided cells and release of exported proteins into the extracellular milieu. A special class of PGHs contains so-called ‘cysteine, histidine-dependent amidohydrolase/peptidase’ (CHAP) domains. In the present study, we profiled the roles of 11 CHAP PGHs encoded by the core genome of S. aureus USA300 LAC. Mutant strains lacking individual CHAP PGHs were analysed for growth, cell morphology, autolysis, and invasion and replication inside human lung epithelial cells. The results show that several investigated CHAP PGHs contribute to different extents to extracellular and intracellular growth and replication of S. aureus, septation of dividing cells, daughter cell separation once the division process is completed, autolysis and biofilm formation. In particular, the CHAP PGHs Sle1 and SAUSA300_2253 control intracellular staphylococcal replication and the resistance to β-lactam antibiotics like oxacillin. This makes the S. aureus PGHs in general, and the Sle1 and SAUSA300_2253 proteins in particular, attractive targets for future prophylactic or therapeutic anti-staphylococcal interventions. Alternatively, these cell surface-exposed enzymes, or particular domains of these enzymes, could be applied in innovative anti-staphylococcal therapies.

AIM: Financial incentives improve response to electronic health surveys, yet little is known about how unconditional incentives (guaranteed regardless of survey completion), conditional incentives, and various combinations of incentives influence response rates. We compared electronic health survey completion with two different financial incentive structures. METHODS: We invited women aged 30-64 years enrolled in a U.S. healthcare system and overdue for Pap screening to complete a web-based survey after receiving a mailed human papillomavirus (HPV) self-sampling kit in a pragmatic trial. HPV kit returners (n = 272) and non-returners (n = 1,083) were allocated to one of two different incentive structures: (1) Unconditional: $5 pre-incentive only (n = 653); (2) Combined: $2 pre-incentive plus $10 post-incentive conditional on completion (n = 702). Chi-square tests evaluated whether survey completion differed by incentive structure within kit return groups or was modified by kit return status. For each incentive-by-kit status group, the cost-per-survey response was calculated as: ([number invited*pre-incentive amount] + [number responses*post-incentive amount]) / number responses. RESULTS: Overall, survey response was higher in kit returners vs. kit non-returners (42.6% vs. 11.0%, P < 0.01), and survey response was higher in the combined (20.1%) vs. unconditional (14.4%) incentive group (P = 0.01). Kit return status did not modify the association between incentive type and survey response (P = 0.52). Among respondents, time to survey completion did not differ by incentive type among either kit returners or non-returners. Among returners, the cost-per-survey response was similar between groups ($13.57 unconditional; $14.15 combined); among non-returners, the cost was greater in the unconditional ($57.78) versus the combined ($25.22) group. CONCLUSION: A combined incentive can be cost-effective for increasing survey response in health services research, particularly in hard-to-reach populations.

ObjectiveWhen multiple surveillance mammograms are performed within an annual interval, the current guidance for one-year follow-up to determine breast cancer status results in shared follow-up periods in which a single breast cancer diagnosis can be attributed to multiple preceding examinations, posing a challenge for standardized performance assessment. We assessed the impact of using follow-up periods that eliminate the artifactual inflation of second breast cancer diagnoses.Materials and methodsWe evaluated surveillance mammograms from 2007-2016 in women with treated breast cancer linked with tumor registry and pathology outcomes. Second breast cancers included ductal carcinoma in situ or invasive breast cancer diagnosed during one-year follow-up. The cancer detection rate, interval cancer rate, sensitivity, and specificity were compared using different follow-up periods: standard one-year follow-up per the American College of Radiology versus follow-up that was shortened at the next surveillance mammogram if less than one year (truncated follow-up). Performance measures were calculated overall and by indication (screening, evaluation for breast problem, and short interval follow-up).ResultsOf 117971 surveillance mammograms, 20% (n = 23533) were followed by another surveillance mammogram within one year. Standard follow-up identified 1597 mammograms that were associated with second breast cancers. With truncated follow-up, the breast cancer status of 179 mammograms (11.2%) was revised, resulting in 1418 mammograms associated with unique second breast cancers. The interval cancer rate decreased with truncated versus standard follow-up (3.6 versus 4.9 per 1000 mammograms, respectively), with a difference (95% confidence interval [CI]) of -1.3 (-1.6, -1.1). The overall sensitivity increased to 70.4% from 63.7%, for the truncated versus standard follow-up, with a difference (95% CI) of 6.6% (5.6%, 7.7%). The specificity remained stable at 98.1%.ConclusionTruncated follow-up, if less than one year to the next surveillance mammogram, enabled second breast cancers to be associated with a single preceding mammogram and resulted in more accurate estimates of diagnostic performance for national benchmarks.

Aim: Extracellular vesicles (EVs) have gained significant attention for their diagnostic and therapeutic potential in various diseases, including liver disorders. This study focuses on optimizing the isolation and characterization of small EVs from plasma and serum samples in patients with liver diseases, aiming to advance our understanding and potential clinical applications of EVs. Methods: Blood samples were collected from patients with end-stage liver disease (ESLD) enlisted in the TransplantLines Cohort and Biobank Study, and healthy donors. We employed differential ultracentrifugation (DUC) to evaluate three distinct protocols: a 3-step DUC, a washing step omitted [samples without washing (WW)], and a contaminant-depleted plasma (CDP) protocol. RNA isolation methodologies were compared, involving the use of TRI-reagent or the commercial AllPrep DNA/RNA kit. Further insights into EV composition were obtained through proteomic analyses, comparing samples subjected to traditional cell lysis (L) with those processed without lysis (NL). Results: We successfully isolated EVs from both plasma and serum samples as confirmed by the presence of specific EV markers, including CD9, CD63, CD81, and tumor susceptibility gene 101 (TSG-101). While some contaminants remained, such as albumin and lipoproteins, the protocol selected to continue EVs analysis was the 3-step protocol. Transmission electron microscopy (TEM) and nanotracking analysis (NTA) further confirmed EVs presence. RNA extraction was achieved using TRI-reagent, but not with the commercial kit highlighting the importance of selecting an appropriate method for RNA isolation. Finally, proteomics analysis showed that lysed samples were significantly more enriched in proteins compared to non-lysed samples, although protein variability was still present in both groups. Conclusions: Optimizing EV isolation techniques is essential for harnessing their potential in liver disease diagnosis and therapy. Further refinement of purification methods, a deep characterization of our cohort and understanding the variability and cargo within EVs will be crucial for future biomarker discovery and therapeutic applications in liver-related diseases.

Objective: Little is known about women's confidence in their breast cancer screening. We sought to characterize breast cancer screening confidence by imaging modality and clinically assessed breast density. Materials and Methods: We undertook a cross-sectional survey of women ages 40-74 years who received digital mammography (DM), digital breast tomosynthesis (DBT), and/or breast magnetic resonance imaging (MRI) with a normal screening exam in the prior year. The main outcome was women's confidence (Very, Somewhat, A little, Not at all) in their breast cancer screening detecting any cancer. Multivariable logistic regression identified correlates of being very confident in breast cancer screening by screening modality group: Group 1) DM vs. DBT and Group 2) DM or DBT alone vs. with supplemental MRI. Results: Overall, 2329 of 7439 (31.3%) invitees participated, with 30%-61% being very confident in their screening across modality and density subgroups. Having dense versus nondense breasts was associated with lower odds of being very confident (Group 1: odds ratio [OR]: 0.58; 95% confidence interval [CI]: 0.46-0.79; Group 2: OR: 0.56; 95% CI: 0.40-0.79). There were no differences by modality within Group 1, but for Group 2, women undergoing MRI had higher odds of being very confident (OR: 1.69; 95% CI: 1.21-2.37). Other correlates of greater screening confidence were as follows: Group 1-being offered a screening test choice and cost not influencing modality received, and Group 2-decision satisfaction and worry. Conclusions: Women with dense breasts had lower screening confidence regardless of screening modality and those undergoing MRI had higher confidence regardless of density. The importance of informing women about screening options is underscored by observed associations between screening choice, decision satisfaction, and screening confidence. ClinicalTrials.gov: NCT02980848.

Abstract Background Currently recommended cancer screening programs address only part of the overall population cancer burden. Even populations deemed high‐risk for certain individual cancers experience a considerable potential burden of other cancers. However, few published cancer screening trials report the incidence of untargeted cancers. Methods The National Lung Screening Trial (NLST), initiated in 2002–2004, was a randomized controlled trial of lung cancer screening in adults with ≥30 pack‐years of smoking. Active follow‐up for incident invasive cancers continued through 2009. Results Among 53,229 NLST subjects (median follow‐up 6.5 years after randomization), the incidence of lung cancer was 615 per 100,000 person‐years (32% of 6142 overall first primary incident invasive cancers), and that of non‐lung cancer was 1327 per 100,000 (68%). Non‐lung cancer incidence exceeded that for lung cancer in all 5‐year age categories and all quintiles of smoking pack‐years. Besides lung cancer, the most common cancers were prostate, breast, colon/rectum, bladder, and head/neck; 23% were smoking‐related cancers, and 54% were cancer types lacking recommended population‐based screening modalities (32% excluding prostate). Non‐lung cancer comprised 48% of 1793 cancer deaths. Conclusions In the NLST, only 32% of first primary cancer incidence after study entry was lung, compared with 68% non‐lung. Even in a population at high risk for lung cancer, a single‐cancer screening test misses most cancers. Thus, in combination with existing single‐cancer screening modalities, multi‐cancer screening tests—which address many of the incident non‐lung cancers in this trial—have potential to address a currently inaccessible portion of cancer morbidity and mortality.

PurposeWe evaluated self-report of decision quality and regret with breast cancer surgical treatment by pre-operative breast MRI use in women recently diagnosed with breast cancer.MethodsWe conducted a survey with 957 women aged 18 + with stage 0-III breast cancer identified in the Breast Cancer Surveillance Consortium. Participants self-reported receipt of pre-operative breast MRI. Primary outcomes were process measures in the Breast Cancer Surgery Decision Quality Instrument (BCS-DQI) (continuous outcome) and Decision Regret Scale (dichotomized outcome as any/none). Generalized estimating equations with linear and logit link were used to estimate adjusted associations between breast MRI and primary outcomes. All analyses were also stratified by breast density.ResultsSurvey participation rate was 27.9% (957/3430). Study population was primarily > 60 years, White, college educated, and diagnosed with early-stage breast cancer. Pre-operative breast MRI was reported in 46% of women. A higher proportion of women who were younger age (

Sensorimotor impairment is a prevalent condition requiring effective rehabilitation strategies. This study introduces a novel wearable device for Mindful Sensorimotor Training (MiSMT) designed for sensory and motor rehabilitation. Our MiSMT device combines motor training using myoelectric pattern recognition along sensory training using two tactile displays. This device offers a comprehensive solution, integrating electromyography and haptic feedback, lacking in existing devices. The device features eight electromyography channels, a rechargeable battery, and wireless Bluetooth or Wi-Fi connectivity for seamless communication with a computer or mobile device. Its flexible material allows for adaptability to various body parts, ensuring ease of use in diverse patients. The two tactile displays, with 16 electromagnetic actuators each, provide touch and vibration sensations up to 250 Hz. In this proof-of-concept study, we show improved two-point discrimination after 5 training sessions in participants with intact limbs (p=0.047). We also demonstrated successful acquisition, processing, and decoding of myoelectric signals in offline and online evaluations. In conclusion, the MiSMT device presents a promising tool for sensorimotor rehabilitation by combining motor execution and sensory training benefits. Further studies are required to assess its effectiveness in individuals with sensorimotor impairments. Integrating mindful sensory and motor training with innovative technology can enhance rehabilitation outcomes and improve the quality of life for those with sensorimotor impairments.

BackgroundEstimating advanced breast cancer risk in women undergoing annual or biennial mammography could identify women who may benefit from less or more intensive screening. We developed an actionable model to predict cumulative 6-year advanced cancer (prognostic pathologic stage II or higher) risk according to screening interval.MethodsWe included 931 186 women aged 40-74 years in the Breast Cancer Surveillance Consortium undergoing 2 542 382 annual (prior mammogram within 11-18 months) or 752 049 biennial (prior within 19-30 months) screening mammograms. The prediction model includes age, race and ethnicity, body mass index, breast density, family history of breast cancer, and prior breast biopsy subdivided by menopausal status and screening interval. We used fivefold cross-validation to internally validate model performance. We defined higher than 95th percentile as high risk (>0.658%), higher than 75th percentile to 95th or less percentile as intermediate risk (0.380%-0.658%), and 75th or less percentile as low to average risk (

ObjectiveWomen are increasingly informed about their breast density due to state density reporting laws. However, accuracy of personal breast density knowledge remains unclear. We compared self-reported with clinically assessed breast density and assessed knowledge of density implications and feelings about future screening.MethodsFrom December 2017 to January 2020, we surveyed women aged 40 to 74 years without prior breast cancer, with a normal screening mammogram in the prior year, and ≥1 recorded breast density measures in four Breast Cancer Surveillance Consortium registries with density reporting laws. We measured agreement between self-reported and BI-RADS breast density categorized as "ever-dense" if heterogeneously or extremely dense within the past 5 years or "never-dense" otherwise, knowledge of dense breast implications, and feelings about future screening.ResultsSurvey participation was 28% (1,528 of 5,408), and 59% (896 of 1,528) of participants had ever-dense breasts. Concordance between self-report versus clinical density was 76% (677 of 896) among women with ever-dense breasts and 14% (89 of 632) among women with never-dense breasts, and 34% (217 of 632) with never-dense breasts reported being told they had dense breasts. Desire for supplemental screening was more frequent among those who reported having dense breasts 29% (256 of 893) or asked to imagine having dense breasts 30% (152 of 513) versus those reporting nondense breasts 15% (15 of 102) (P = .003, P = .002, respectively). Women with never-dense breasts had 6.3-fold higher odds (95% confidence interval:3.39-11.80) of accurate knowledge in states reporting density to all compared to states reporting only to women with dense breasts.DiscussionStandardized communications of breast density results to all women may increase density knowledge and are needed to support informed screening decisions.

Background The COVID-19 pandemic reduced mammography use, potentially delaying breast cancer diagnoses. Purpose To examine breast biopsy recommendations and breast cancers diagnosed before and during the COVID-19 pandemic by mode of detection (screen detected vs symptomatic) and women's characteristics. Materials and Methods In this secondary analysis of prospectively collected data, monthly breast biopsy recommendations after mammography, US, or both with subsequent biopsy performed were examined from 66 facilities of the Breast Cancer Surveillance Consortium between January 2019 and September 2020. The number of monthly and cumulative biopsies recommended and performed and the number of subsequent cancers diagnosed during the pandemic period (March 2020 to September 2020) were compared with data from the prepandemic period using Wald χ2 tests. Analyses were stratified by mode of detection and race or ethnicity. Results From January 2019 to September 2020, 17 728 biopsies were recommended and performed, with 6009 cancers diagnosed. From March to September 2020, there were substantially fewer breast biopsy recommendations with cancer diagnoses when compared with the same period in 2019 (1650 recommendations in 2020 vs 2171 recommendations in 2019 [24% fewer], P < .001), predominantly due to fewer screen-detected cancers (722 cancers in 2020 vs 1169 cancers in 2019 [38% fewer], P < .001) versus symptomatic cancers (895 cancers in 2020 vs 965 cancers in 2019 [7% fewer], P = .27). The decrease in cancer diagnoses was largest in Asian (67 diagnoses in 2020 vs 142 diagnoses in 2019 [53% fewer], P = .06) and Hispanic (82 diagnoses in 2020 vs 145 diagnoses in 2019 [43% fewer], P = .13) women, followed by Black women (210 diagnoses in 2020 vs 287 diagnoses in 2019 [27% fewer], P = .21). The decrease was smallest in non-Hispanic White women (1128 diagnoses in 2020 vs 1357 diagnoses in 2019 [17% fewer], P = .09). Conclusion There were substantially fewer breast biopsies with cancer diagnoses during the COVID-19 pandemic from March to September 2020 compared with the same period in 2019, with Asian and Hispanic women experiencing the largest declines, followed by Black women. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Heller in this issue.

IntroductionLimited evidence exists on the impact of age and comorbidity on biopsy rates and findings among older women.Materials and methodsWe used data from 170,657 women ages 66-94 enrolled in the United States Breast Cancer Surveillance Consortium (BCSC). We estimated one-year rates of biopsy by type (any, fine-needle aspiration (FNA), core or surgical) and yield of the most invasive biopsy finding (benign, ductal carcinoma in situ (DCIS) and invasive breast cancer) by age and comorbidity. Statistical significance was assessed using Wald statistics comparing coefficients estimated from logistic regression models adjusted for age, comorbidity, BCSC registry, and interaction between age and comorbidity.ResultsOf 524,860 screening mammograms, 9830 biopsies were performed following 7930 exams (1.5%) within one year, specifically 5589 core biopsies (1.1%), 3422 (0.7%) surgical biopsies and 819 FNAs (0.2%). Biopsy rates per 1000 screens decreased with age (66-74:15.7, 95%CI:14.8-16.8), 75-84:14.5(13.5-15.6), 85-94:13.2(11.3,15.4), ptrend

PurposeThe aim of this study was to describe the current state of science regarding independent external validation of artificial intelligence (AI) technologies for screening mammography.MethodsA systematic review was performed across five databases (Embase, PubMed, IEEE Explore, Engineer Village, and arXiv) through December 10, 2020. Studies that used screening examinations from real-world settings to externally validate AI algorithms for mammographic cancer detection were included. The main outcome was diagnostic accuracy, defined by area under the receiver operating characteristic curve (AUC). Performance was also compared between radiologists and either stand-alone AI or combined radiologist and AI interpretation. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool.ResultsAfter data extraction, 13 studies met the inclusion criteria (148,361 total patients). Most studies (77% [n = 10]) evaluated commercially available AI algorithms. Studies included retrospective reader studies (46% [n = 6]), retrospective simulation studies (38% [n = 5]), or both (15% [n = 2]). Across 5 studies comparing stand-alone AI with radiologists, 60% (n = 3) demonstrated improved accuracy with AI (AUC improvement range, 0.02-0.13). All 5 studies comparing combined radiologist and AI interpretation with radiologists alone demonstrated improved accuracy with AI (AUC improvement range, 0.028-0.115). Most studies had risk for bias or applicability concerns for patient selection (69% [n = 9]) and the reference standard (69% [n = 9]). Only two studies obtained ground-truth cancer outcomes through regional cancer registry linkage.ConclusionsTo date, external validation efforts for AI screening mammographic technologies suggest small potential diagnostic accuracy improvements but have been retrospective in nature and suffer from risk for bias and applicability concerns.

PurposePreoperative breast MRI is used to evaluate for additional cancer and extent of disease for newly diagnosed breast cancer, yet benefits and harms of preoperative MRI are not well-documented. We examined whether preoperative MRI yields additional biopsy and cancer detection by extent of breast density.MethodsWe followed women in the Breast Cancer Surveillance Consortium with an incident breast cancer diagnosed from 2005 to 2017. We quantified breast biopsies and cancers detected within 6 months of diagnosis by preoperative breast MRI receipt, overall and by breast density, accounting for MRI selection bias using inverse probability weighted logistic regression.ResultsAmong 19,324 women with newly diagnosed breast cancer, 28% had preoperative MRI, 11% additional biopsy, and 5% additional cancer detected. Four times as many women with preoperative MRI underwent additional biopsy compared to women without MRI (22.6% v. 5.1%). Additional biopsy rates with preoperative MRI increased with increasing breast density (27.4% for extremely dense compared to 16.2% for almost entirely fatty breasts). Rates of additional cancer detection were almost four times higher for women with v. without MRI (9.9% v. 2.6%). Conditional on additional biopsy, age-adjusted rates of additional cancer detection were lowest among women with extremely dense breasts, regardless of imaging modality (with MRI: 35.0%; 95% CI 27.0-43.0%; without MRI: 45.1%; 95% CI 32.6-57.5%).ConclusionFor women with dense breasts, preoperative MRI was associated with much higher biopsy rates, without concomitant higher cancer detection. Preoperative MRI may be considered for some women, but selecting women based on breast density is not supported by evidence.Trial registrationClinicalTrials.gov: NCT02980848; registered 2017.