Evaluation of an artificial intelligence model for identification of mass effect and vasogenic edema on computed tomography of the head.

BACKGROUND AND PURPOSE: Mass effect and vasogenic edema are critical findings on CT of the head. This study compared the accuracy of an artificial intelligence model (Annalise Enterprise CTB) to consensus neuroradiologist interpretations in detecting mass effect and vasogenic edema. MATERIALS AND METHODS: A retrospective standalone performance assessment was conducted on datasets of non-contrast CT head cases acquired between 2016 and 2022 for each finding. The cases were obtained from patients aged 18 years or older from five hospitals in the United States. The positive cases were selected consecutively based on the original clinical reports using natural language processing and manual confirmation. The negative cases were selected by taking the next negative case acquired from the same CT scanner after positive cases. Each case was interpreted independently by up to three neuroradiologists to establish consensus interpretations. Each case was then interpreted by the AI model for the presence of the relevant finding. The neuroradiologists were provided with the entire CT study. The AI model separately received thin (≤1.5mm) and/or thick (>1.5 and ≤5mm) axial series. RESULTS: The two cohorts included 818 cases for mass effect and 310 cases for vasogenic edema. The AI model identified mass effect with sensitivity 96.6% (95% CI, 94.9-98.2) and specificity 89.8% (95% CI, 84.7-94.2) for the thin series, and 95.3% (95% CI, 93.5-96.8) and 93.1% (95% CI, 89.1-96.6) for the thick series. It identified vasogenic edema with sensitivity 90.2% (95% CI, 82.0-96.7) and specificity 93.5% (95% CI, 88.9-97.2) for the thin series, and 90.0% (95% CI, 84.0-96.0) and 95.5% (95% CI, 92.5-98.0) for the thick series. The corresponding areas under the curve were at least 0.980. CONCLUSIONS: The assessed AI model accurately identified mass effect and vasogenic edema in this CT dataset. It could assist the clinical workflow by prioritizing interpretation of abnormal cases, which could benefit patients through earlier identification and subsequent treatment. ABBREVIATIONS: AI = artificial intelligence; AUC = area under the curve; CADt = computer assisted triage devices; FDA = Food and Drug Administration; NPV = negative predictive value; PPV = positive predictive value; SD = standard deviation.

Missed Screening Mammography Appointments: Patient Sociodemographic Characteristics and Mammography Completion After 1 Year.

OBJECTIVE: Patients who miss screening mammogram appointments without notifying the health care system (no-show) risk care delays. We investigate sociodemographic characteristics of patients who experience screening mammogram no-shows at a community health center and whether and when the missed examinations are completed. METHODS: We included patients with screening mammogram appointments at a community health center between January 1, 2021, and December 31, 2021. Language, race, ethnicity, insurance type, residential ZIP code tabulation area (ZCTA) poverty, appointment outcome (no-show, same-day cancelation, completed), and dates of completed screening mammograms after no-show appointments with ≥1-year follow-up were collected. Multivariable analyses were used to assess associations between patient characteristics and appointment outcomes. RESULTS: Of 6,159 patients, 12.1% (743 of 6,159) experienced no-shows. The no-show group differed from the completed group by language, race and ethnicity, insurance type, and poverty level (all P < .05). Patients with no-shows more often had: primary language other than English (32.0% [238 of 743] versus 26.7% [1,265 of 4,741]), race and ethnicity other than White non-Hispanic (42.3% [314 of 743] versus 33.6% [1,595 of 4,742]), Medicaid or means-tested insurance (62.0% [461 of 743] versus 34.4% [1,629 of 4,742]), and residential ZCTAs with ≥20% poverty (19.5% [145 of 743] versus 14.1% [670 of 4,742]). Independent predictors of no-shows were Black non-Hispanic race and ethnicity (adjusted odds ratio [aOR], 1.52; 95% confidence interval [CI], 1.12-2.07; P = .007), Medicaid or other means-tested insurance (aOR, 2.75; 95% CI, 2.29-3.30; P < .001), and ZCTAs with ≥20% poverty (aOR, 1.76; 95% CI, 1.14-2.72; P = .011). At 1-year follow-up, 40.6% (302 of 743) of patients with no-shows had not completed screening mammogram. DISCUSSION: Screening mammogram no-shows is a health equity issue in which socio-economically disadvantaged and racially and ethnically minoritized patients are more likely to experience missed appointments and continued delays in screening mammogram completion.

Incidence, Timing, and Long-Term Outcomes of COVID-19 Vaccine-Related Lymphadenopathy on Screening Mammography.

OBJECTIVE: To determine the incidence, timing, and long-term outcomes of unilateral axillary lymphadenopathy ipsilateral to vaccine site (UIAL) on screening mammography after COVID-19 vaccination. METHODS: This retrospective, multisite study included consecutive patients undergoing screening mammography February 8, 2021, to January 31, 2022, with at least 1 year of follow-up. UIAL was typically considered benign (BI-RADS 1 or 2) in the setting of recent (≤6 weeks) vaccination or BI-RADS 0 (ultrasound recommended) when accompanied by a breast finding or identified >6 weeks postvaccination. Vaccination status and manufacturer were obtained from regional registries. Lymphadenopathy rates in vaccinated patients with and without UIAL were compared using Pearson's χ2 test. RESULTS: There were 44,473 female patients (mean age 60.4 ± 11.4 years) who underwent screening mammography at five sites, and 40,029 (90.0%) received at least one vaccine dose. Ninety-four (0.2%) presented with UIAL, 1 to 191 days postvaccination (median 13.5 [interquartile range: 5.0-31.0]). Incidence declined from 2.1% to 0.9% to ≤0.5% after 1, 2, and 3 weeks and persisted up to 36 weeks (P < .001). UIAL did not vary across manufacturer (P = .15). Of 94, 77 (81.9%) were BI-RADS 1 or 2 at screening. None were diagnosed with malignancy at 1-year follow-up. Seventeen (18.1%) were BI-RADS 0 at screening. At diagnostic workup, 13 (76.5%) were BI-RADS 1 or 2, 2 (11.8%) were BI-RADS 3, and 2 (11.8%) were BI-RADS 4. Both BI-RADS 4 patients had malignant status and ipsilateral breast malignancies. Of BI-RADS 3 patients, at follow-up, one was biopsied yielding benign etiology, and one was downgraded to BI-RADS 2. DISCUSSION: Isolated UIAL on screening mammography performed within 6 months of COVID-19 vaccination can be safely assessed as benign.

Comparison of the Diagnostic Accuracy of Mammogram-based Deep Learning and Traditional Breast Cancer Risk Models in Patients Who Underwent Supplemental Screening with MRI.

Background Access to supplemental screening breast MRI is determined using traditional risk models, which are limited by modest predictive accuracy. Purpose To compare the diagnostic accuracy of a mammogram-based deep learning (DL) risk assessment model to that of traditional breast cancer risk models in patients who underwent supplemental screening with MRI. Materials and Methods This retrospective study included consecutive patients undergoing breast cancer screening MRI from September 2017 to September 2020 at four facilities. Risk was assessed using the Tyrer-Cuzick (TC) and National Cancer Institute Breast Cancer Risk Assessment Tool (BCRAT) 5-year and lifetime models as well as a DL 5-year model that generated a risk score based on the most recent screening mammogram. A risk score of 1.67% or higher defined increased risk for traditional 5-year models, a risk score of 20% or higher defined high risk for traditional lifetime models, and absolute scores of 2.3 or higher and 6.6 or higher defined increased and high risk, respectively, for the DL model. Model accuracy metrics including cancer detection rate (CDR) and positive predictive values (PPVs) (PPV of abnormal findings at screening [PPV1], PPV of biopsies recommended [PPV2], and PPV of biopsies performed [PPV3]) were compared using logistic regression models. Results This study included 2168 women who underwent 4247 high-risk screening MRI examinations (median age, 54 years [IQR, 48-60 years]). CDR (per 1000 examinations) was higher in patients at high risk according to the DL model (20.6 [95% CI: 11.8, 35.6]) than according to the TC (6.0 [95% CI: 2.9, 12.3]; P < .01) and BCRAT (6.8 [95% CI: 2.9, 15.8]; P = .04) lifetime models. PPV1, PPV2, and PPV3 were higher in patients identified as high risk by the DL model (PPV1, 14.6%; PPV2, 32.4%; PPV3, 36.4%) than those identified as high risk with the TC (PPV1, 5.0%; PPV2, 12.7%; PPV3, 13.5%; P value range, .02-.03) and BCRAT (PPV1, 5.5%; PPV2, 11.1%; PPV3, 12.5%; P value range, .02-.05) lifetime models. Conclusion Patients identified as high risk by a mammogram-based DL risk assessment model showed higher CDR at breast screening MRI than patients identified as high risk with traditional risk models. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Bae in this issue.

Long-term Cardiovascular Risk in Astronauts: Comparing NASA Mission Astronauts With a Healthy Cohort From the Cooper Center Longitudinal Study.

OBJECTIVE: To determine the long-term cardiovascular disease risk of astronauts with spaceflight exposure compared with a well-matched cohort. METHODS: National Aeronautics and Space Administration (NASA) astronauts are selected into their profession based upon education, unique skills, and health and are exposed to cardiovascular disease risk factors during spaceflight. The Cooper Center Longitudinal Study (CCLS) is a generally healthy cohort from a preventive medicine clinic in Dallas, Texas. Using a matched cohort design, astronauts who were selected beginning April 1, 1959, (and each subsequent selection class through 2009) and exposed to spaceflight were matched to CCLS participants who met astronaut selection criteria; 1514 CCLS participants matched to 303 astronauts in a 5-to-1 ratio on sex, date of birth, and age. The outcome of cardiovascular mortality through December 31, 2016, was determined by death certificate or National Death Index. RESULTS: There were 11 deaths caused by cardiovascular disease (CVD) among astronauts and 46 among CCLS participants. There was no evidence of increased mortality risk in astronauts (hazard ratio [HR]=1.10; 95% confidence interval [CI], 0.50 to 2.45) with adjustment for baseline cardiovascular covariates. However, the secondary outcome of CVD events showed an increased adjusted risk in astronauts (HR=2.41; 95% CI, 1.26 to 4.63). CONCLUSION: No increased risk of CVD mortality was observed in astronauts with spaceflight exposure compared with a well-matched cohort, but there was evidence of increased total CVD events. Given that the duration of spaceflight will increase, particularly on missions to Mars, continued surveillance and mitigation of CVD risk is needed to ensure the safety of those who venture into space.

Impact of a Deep Learning Model for Predicting Mammographic Breast Density in Routine Clinical Practice.

OBJECTIVE: Legislation in 38 states requires patient notification of dense mammographic breast tissue because increased density is a marker of breast cancer risk and can limit mammographic sensitivity. Because radiologist density assessments vary widely, our objective was to implement and measure the impact of a deep learning (DL) model on mammographic breast density assessments in clinical practice. METHODS: This institutional review board-approved prospective study identified consecutive screening mammograms performed across three clinical sites over two periods: 2017 period (January 1, 2017, through September 30, 2017) and 2019 period (January 1, 2019, through September 30, 2019). The DL model was implemented at sites A (academic practice) and B (community practice) in 2018 for all screening mammograms. Site C (community practice) was never exposed to the DL model. Prospective densities were evaluated, and multivariable logistic regression models evaluated the odds of a dense mammogram classification as a function of time and site. RESULTS: We identified 85,124 consecutive screening mammograms across the three sites. Across time intervals, odds of a dense classification decreased at sites exposed to the DL model, site A (adjusted odds ratio [aOR], 0.93; 95% confidence interval [CI], 0.86-0.99; P = .024) and site B (aOR, 0.81 [95% CI, 0.70-0.93]; P = .003), and odds increased at the site unexposed to the model (site C) (aOR, 1.13 [95% CI, 1.01-1.27]; P = .033). DISCUSSION: A DL model reduces the odds of screening mammograms categorized as dense. Accurate density assessments could help health care systems more appropriately use limited supplemental screening resources and help better inform traditional clinical risk models.

Impact of a Same-Day Breast Biopsy Program on Disparities in Time to Biopsy for Patients With Serious Mental Illness.

PURPOSE: The aim of this study was to investigate disparities in time between breast biopsy recommendation and completion and the impact of a same-day biopsy (SDB) program for patients with serious mental illness (SMI), with a focus on more vulnerable individuals with public payer insurance. METHODS: In August 2017, the authors' academic breast imaging center started routinely offering needle biopsies on the day of recommendation. Primary outcomes were the proportion of biopsies performed as SDBs and days from biopsy recommendation to completion over a 2.5-year pre- versus postintervention period, comparing all patients with SMI versus those without, and public payer-insured patients <65 years of age with SMI (SMI-PP) versus without SMI (non-SMI-PP). Multivariable proportional odds and logistic regression models were fit to assess association of SMI status, age, race/ethnicity, language, and insurance with days to biopsy and SDB within each period. RESULTS: There were 2,026 biopsies preintervention and 2,361 biopsies postintervention. Preintervention, 8.43% of patients with SMI (7 of 83) underwent SDB compared with 15.59% of those without SMI (303 of 1,943) (P = .076), and 2.7% of the SMI-PP subgroup (1 of 37) underwent SDB compared with 15.88% of the non-SMI-PP subgroup (47 of 296) (P = .031). Adjusted for age, race/ethnicity, and language, disparities persisted in odds for undergoing SDB (adjusted odds ratio, 0.13; 95% confidence interval, 0.02-0.92; P = .04) and having longer days to biopsy (adjusted odds ratio, 2.35; 95% confidence interval, 1.26-4.37; P = .01) for the SMI-PP subgroup compared with the non-SMI-PP subgroup in the preintervention period. There was no evidence of these disparities postintervention for the SMI-PP subgroup. SDB proportion increased from 15.3% (310 of 2,026) to 36.09% (852 of 2,361) (P < .001) across all patients. CONCLUSIONS: A same-day breast biopsy program mitigates disparities in time to biopsy for patients with SMI and helps improve breast cancer care equity for this vulnerable population.

Multilevel Body Composition Analysis on Chest Computed Tomography Predicts Hospital Length of Stay and Complications After Lobectomy for Lung Cancer: A Multicenter Study.

OBJECTIVE: To investigate the impact of thoracic body composition on outcomes after lobectomy for lung cancer. SUMMARY AND BACKGROUND DATA: Preoperative identification of patients at risk for adverse outcomes permits treatment modification. The impact of body composition on lung resection outcomes has not been investigated in a multicenter setting. METHODS: A total of 958 consecutive patients undergoing lobectomy for lung cancer at 3 centers from 2014 to 2017 were retrospectively analyzed. Muscle and adipose tissue cross-sectional area at the fifth, eighth, and tenth thoracic vertebral body was quantified. Prospectively collected outcomes from a national database were abstracted to characterize the association between sums of muscle and adipose tissue and hospital length of stay (LOS), number of any postoperative complications, and number of respiratory postoperative complications using multivariate regression. A priori determined covariates were forced expiratory volume in 1 second and diffusion capacity of the lungs for carbon monoxide predicted, age, sex, body mass index, race, surgical approach, smoking status, Zubrod and American Society of Anesthesiologists scores. RESULTS: Mean patient age was 67 years, body mass index 27.4 kg/m2 and 65% had stage i disease. Sixty-three percent underwent minimally invasive lobectomy. Median LOS was 4 days and 34% of patients experienced complications. Muscle (using 30 cm2 increments) was an independent predictor of LOS (adjusted coefficient 0.972; P = 0.002), any postoperative complications (odds ratio 0.897; P = 0.007) and postoperative respiratory complications (odds ratio 0.860; P = 0.010). Sarcopenic obesity was also associated with LOS and adverse outcomes. CONCLUSIONS: Body composition on preoperative chest computed tomography is an independent predictor of LOS and postoperative complications after lobectomy for lung cancer.

Disparities in Same-Day Diagnostic Imaging in Breast Cancer Screening: Impact of an Immediate-Read Screening Mammography Program Implemented During the COVID-19 Pandemic.

BACKGROUND. The need for second visits between screening mammography and diagnostic imaging contributes to disparities in the time to breast cancer diagnosis. During the COVID-19 pandemic, an immediate-read screening mammography program was implemented to reduce patient visits and decrease time to diagnostic imaging. OBJECTIVE. The purpose of this study was to measure the impact of an immediate-read screening program with focus on disparities in same-day diagnostic imaging after abnormal findings are made at screening mammography. METHODS. In May 2020, an immediate-read screening program was implemented whereby a dedicated breast imaging radiologist interpreted all screening mammograms in real time; patients received results before discharge; and efforts were made to perform any recommended diagnostic imaging during the visit (performed by different radiologists). Screening mammographic examinations performed from June 1, 2019, through October 31, 2019 (preimplementation period), and from June 1, 2020, through October 31, 2020 (postimplementation period), were retrospectively identified. Patient characteristics were recorded from the electronic medical record. Multivariable logistic regression models incorporating patient age, race and ethnicity, language, and insurance type were estimated to identify factors associated with same-day diagnostic imaging. Screening metrics were compared between periods. RESULTS. A total of 8222 preimplementation and 7235 postimplementation screening examinations were included; 521 patients had abnormal screening findings before implementation, and 359 after implementation. Before implementation, 14.8% of patients underwent same-day diagnostic imaging after abnormal screening mammograms. This percentage increased to 60.7% after implementation. Before implementation, patients who identified their race as other than White had significantly lower odds than patients who identified their race as White of undergoing same-day diagnostic imaging after receiving abnormal screening results (adjusted odds ratio, 0.30; 95% CI, 0.10-0.86; p = .03). After implementation, the odds of same-day diagnostic imaging were not significantly different between patients of other races and White patients (adjusted odds ratio, 0.92; 95% CI, 0.50-1.71; p = .80). After implementation, there was no significant difference in race and ethnicity between patients who underwent and those who did not undergo same-day diagnostic imaging after receiving abnormal results of screening mammography (p > .05). The rate of abnormal interpretation was significantly lower after than it was before implementation (5.0% vs 6.3%; p < .001). Cancer detection rate and PPV1 (PPV based on positive findings at screening examination) were not significantly different before and after implementation (p > .05). CONCLUSION. Implementation of the immediate-read screening mammography program reduced prior racial and ethnic disparities in same-day diagnostic imaging after abnormal screening mammograms. CLINICAL IMPACT. An immediate-read screening program provides a new paradigm for improved screening mammography workflow that allows more rapid diagnostic workup with reduced disparities in care.

Screening Mammography Recovery After COVID-19 Pandemic Facility Closures: Associations of Facility Access and Racial and Ethnic Screening Disparities.

BACKGROUND. Screening mammography facilities closed during the COVID-19 pandemic in spring 2020. Recovery of screening volumes has varied across patient subgroups and facilities. OBJECTIVE. We compared screening mammography volumes and patient and facility characteristics between periods before COVID-19 and early and later postclosure recovery periods. METHODS. This retrospective study included screening mammograms performed in the same 2-month period (May 26-July 26) in 2019 (pre-COVID-19), 2020 (early recovery), and 2021 (late recovery after targeted interventions to expand access) and across multiple facility types (urban, suburban, community health center). Suburban sites had highest proportion of White patients and the greatest scheduling flexibility and expanded appointments during initial reopening. Findings were compared across years. RESULTS. For White patients, volumes decreased 36.6% from 6550 in 2019 (4384 in 2020) and then increased 61.0% to 6579 in 2021; for patients with races other than White, volumes decreased 53.9% from 1321 in 2019 (609 in 2020) and then increased 136.8% to 1442 in 2021. The percentage of mammograms in patients with races other than White was 16.8% in 2019, 12.2% in 2020, and 18.0% in 2021. The proportion performed at the urban center was 55.3% in 2019, 42.2% in 2020, and 45.9% in 2021; the proportion at suburban sites was 34.0% in 2019, 49.2% in 2020, and 43.5% in 2021. Pre-COVID-19 volumes were reached by the sixth week after reopening for suburban sites but were not reached during early recovery for the other sites. The proportion that were performed on Saturday for suburban sites was similar across periods, whereas the proportion performed on Saturday for the urban site was 7.6% in 2019, 5.3% in 2020, and 8.8% in 2021; the community health center did not offer Saturday appointments during recovery. CONCLUSION. After reopening, screening shifted from urban to suburban settings, with a disproportionate screening decrease in patients with races other than White. Initial delayed access at facilities serving underserved populations exacerbated disparities. Interventions to expand access resulted in late recovery volumes exceeding prepandemic volumes in patients with races other than White. CLINICAL IMPACT. Interventions to support equitable access across facilities serving diverse patient populations may mitigate potential widening disparities in breast cancer diagnosis during the pandemic.

A Clinical Prediction Tool for MRI in Emergency Department Patients with Spinal Infection.

INTRODUCTION: Patients with pyogenic spinal Infection (PSI) are often not diagnosed at their initial presentation, and diagnostic delay is associated with increased morbidity and medical-legal risk. We derived a decision tool to estimate the risk of spinal infection and inform magnetic resonance imaging (MRI) decisions. METHODS: We conducted a two-part prospective observational cohort study that collected variables from spine pain patients over a six-year derivation phase. We fit a multivariable regression model with logistic coefficients rounded to the nearest integer and used them for variable weighting in the final risk score. This score, SIRCH (spine infection risk calculation heuristic), uses four clinical variables to predict PSI. We calculated the statistical performance, MRI utilization, and model fit in the derivation phase. In the second phase we used the same protocol but enrolled only confirmed cases of spinal infection to assess the sensitivity of our prediction tool. RESULTS: In the derivation phase, we evaluated 134 non-PSI and 40 PSI patients; median age in years was 55.5 (interquartile range [IQR] 38-70 and 51.5 (42-59), respectively. We identified four predictors for our risk score: historical risk factors; fever; progressive neurological deficit; and C-reactive protein (CRP) ≥ 50 milligrams per liter (mg/L). At a threshold SIRCH score of ≥ 3, the predictive model's sensitivity, specificity, and positive predictive value were, respectively, as follows: 100% (95% confidence interval [CI], 100-100%); 56% (95% CI, 48-64%), and 40% (95% CI, 36-46%). The area under the receiver operator curve was 0.877 (95% CI, 0.829-0.925). The SIRCH score at a threshold of ≥ 3 would prompt significantly fewer MRIs compared to using an elevated CRP (only 99/174 MRIs compared to 144/174 MRIs, P <0.001). In the second phase (49 patient disease-only cohort), the sensitivities of the SIRCH score and CRP use (laboratory standard cut-off 3.5 mg/L) were 92% (95% CI, 84-98%), and 98% (95% CI, 94-100%), respectively. CONCLUSION: The SIRCH score provides a sensitive estimate of spinal infection risk and prompts fewer MRIs than elevated CRP (cut-off 3.5 mg/L) or clinician suspicion.

Observer agreement in the choice of lumbar spine injection for pain management.

BACKGROUND AND PURPOSE: Lumbar spine MRI can help guide the choice of corticosteroid injection in pain management. We investigated whether patient-reported symptom information from a questionnaire could improve agreement in the choice of type, level, and side of injection. MATERIALS AND METHODS: In this prospective observational study, 120 patients (median age 64, 70 men) were recruited from patients referred for pain management. After informed consent, they completed electronic questionnaires that obtained symptom information for later use during MRI reviews. In 3 research arms, 6 radiologists chose injections that would ideally deliver corticosteroid to the presumed sources of pain in (1) MRI studies reviewed with symptom information from questionnaires, (2) MRI studies reviewed without symptom information, and (3) MRI reports. Blinded to questionnaire results, radiologists providing clinical care and interviewing patients chose ideal therapeutic injections to establish reference standards. Injections were categorized by type, level, and side and compared using percent agreement and kappa statistics. Interreading agreement was analyzed. RESULTS: Compared to the reference standard, kappa agreements for injection types, levels, and sides were almost perfect when MRIs were reviewed knowing symptoms (0.85-0.93), fair without symptoms (0.23-0.35) (all P < .001) and fair in MRI reports (0.24-0.36) (all P < .001). Interreading kappa agreements were almost perfect knowing symptoms (0.82-0.90), but only moderate without symptoms (0.42-0.49) (all P < .001). CONCLUSIONS: Radiologists reviewing lumbar spine MRI converged on the type, level, and side of ideal therapeutic injection whether they obtained symptom information from direct patient interview or electronic questionnaire. Observer agreement was significantly lower without symptom information.

Shear-Wave Elastography of the Breast: Impact of Technical Image Quality Parameters on Diagnostic Accuracy.

OBJECTIVE. The purpose of this study is to determine the impact of shear-wave elastography (SWE) image quality parameters on the diagnostic performance of elasticity measurements in classifying breast lesions. MATERIALS AND METHODS. This retrospective study included 281 breast lesions that underwent SWE and ultrasound-guided biopsy performed between October 1, 2017, and August 31, 2018. Three readers who were blinded to pathologic outcomes independently scored the image quality of each SWE image (with low quality denoted by a score of 0 and high quality indicated by a score of 1) on the basis of five parameters: B-mode visualization of the lesion on a dual-panel display, SWE red pattern (denoting high stiffness) in the near field of the FOV, appearance of the surrounding tissue, FOV placement, and ROI placement for the maximum (Emax), minimum (Emin), mean (Emean), and SD (ESD) of Young modulus elasticity measurements. Using ROC analysis, we compared the performance of Emax, Emean, and ESD in diagnosing malignancy on low- and high-quality images on the basis of consensus (i.e., majority) scores for each individual quality parameter as well as two models combining a few of the quality parameters. RESULTS. Three quality parameters (B-mode visualization of the lesion, presence of a near-field red pattern, and the appearance of the surrounding tissue) showed moderate-to-substantial interobserver agreement. SWE images were considered high quality (n = 167) if both B-mode visualization and near-field red pattern received a consensus score of 1, and they were considered low quality (n = 114) if either parameter received a consensus score of 0. High-quality images had a statistically higher AUC value than low-quality images when Emax (p < .001), Emean (p = .002), and ESD (p < .001) were used as classifiers of malignancy. CONCLUSION. Quality parameters can support radiologists who are performing and interpreting breast SWE images. These quality parameters have the potential to improve the accuracy of SWE in differentiating malignant from benign breast lesions.

Impact of Patient-Reported Symptom Information on Agreement in the MRI Diagnosis of Presumptive Lumbar Spine Pain Generator.

BACKGROUND. Lumbar spine MRI is associated with a high prevalence of interpretive errors by radiologists. Treating physicians can obtain symptom information, correlate symptoms with MRI findings, and distinguish presumptive pain generators from incidental abnormalities. OBJECTIVE. The purpose of this study was to capture symptom information using a patient questionnaire, review lumbar spine MRI examinations with and without symptom information, diagnose pain generators, and compare MRI diagnoses with clinical reference diagnoses. METHODS. In this prospective study, 120 participants (70 men and 50 women; median age, 64 years; interquartile range, 49.5-74 years) were recruited from patients referred for lumbar spine injections between February and June 2019. Participants completed electronic questionnaires regarding their symptoms before receiving the injections. For three research arms, six radiologists diagnosed pain generators in MRI studies reviewed with symptom information from questionnaires, MRI studies reviewed without symptom information, and MRI reports. Interreading agreement was analyzed. Blinded to the questionnaire results, the radiologists who performed injections obtained patient histories, correlated symptoms with MRI findings, and diagnosed presumptive pain generators. These diagnoses served as clinical reference standards. Pain generators were categorized by type, level, and side and were compared using kappa statistics. Diagnostic certainty was recorded using numeric values (0-100) and was compared using Wilcoxon rank-sum test RESULTS. When compared with the reference standard, agreement for the type, level, and side of pain generator was almost perfect in MRI examinations reviewed with symptom information (κ = 0.82-0.90), fair to moderate in MRI examinations reviewed without symptom information (κ = 0.28-0.51) (all p < .001), and fair to moderate in MRI reports (κ = 0.27-0.45) (all p < .001). Interreading agreement was almost perfect when MRI examinations were reviewed with symptom information (κ = 0.82-0.90) but was only moderate without symptom information (κ = 0.42-0.56) (all p < .001). Diagnostic certainty levels were highest for radiologists performing injections (mean [± SD], 90.0 ± 9.9) and were significantly higher for MRI review with symptom information versus without symptom information (means for reading 1, 84.6 ± 13.1 vs 62.9 ± 20.7; p < .001). CONCLUSION. In lumbar spine MRI, presumptive pain generators diagnosed using symptom information from electronic questionnaires showed almost perfect agreement with pain generators diagnosed using symptom information from direct patient interviews. CLINICAL IMPACT. Patient-reported symptom information from a brief questionnaire can be correlated with MRI findings to distinguish presumptive pain generators from incidental abnormalities.

Potential of using mammography screening appointments to identify high-risk women: cross sectional survey results from the national health interview survey.

PURPOSE: Mammography screening encounters may represent ideal opportunities to identify high-risk women for risk-based screening. During mammography appointments, radiology practices evaluate breast density and ascertain known breast cancer risk factors. Our purpose was to evaluate the potential for mammographic screening encounters to identify high-risk women by estimating the (1) proportion of high-risk women who report that they have undergone mammographic screening and the (2) proportion of high-risk women who receive recommendations for breast MRI screening. METHODS: Women ages 30-85 without breast cancer histories were included from the 2015 National Health Interview Survey, a nationally representative cross-sectional household survey (response rate 80%). Breast Cancer Risk Assessment Tool was used to determine high-risk (lifetime risk>20%). Among high-risk women, primary outcome was proportion reporting mammography screening, secondary outcome was receipt of a breast MRI recommendation after recent mammogram, accounting for complex survey design. RESULTS: 14,958 women were included. 1.0% were high-risk of whom: 91.9% ever had a mammogram, 68.0% had a mammogram within the last year, 81.5% had a mammogram within the last 2 years. 6.4% were recommended to undergo breast MRI. Among high-risk women, women with dense breast tissue were more likely (OR 496.0, 95%CI 52.6,4674.0) and older women were less likely (OR 0.91, 95%CI 0.84,0.99) to receive recommendations for breast MRI. CONCLUSIONS: Among high-risk women, 92% reported undergoing at least one mammogram in their lives. 94% did not receive recommendations for breast MRI screening and 32% did not have a mammogram within the last year. To identify high-risk women, breast imaging centers should consider determining lifetime breast cancer risk during mammography screening visits.

The Adoption and Impact on Performance of an Automated Outcomes Feedback Application for Tomosynthesis Screening Mammography.

OBJECTIVE: To evaluate a tomosynthesis screening mammography automated outcomes feedback application's adoption and impact on performance. METHODS: This prospective intervention study evaluated a feedback application that provided mammographers subsequent imaging and pathology results for patients that radiologists had personally recalled from screening. Deployed to 13 academic and 5 private practice attending radiologists, adoption was studied from March 29, 2018, to March 20, 2019. Radiologists indicated if reviewed feedback would influence future clinical decisions. For a subset of eight academic radiologists consistently interpreting screening mammograms during the study, performance metrics were compared pre-intervention (January 1, 2016, to September 30, 2017) and post-intervention (October 1, 2017 to June 30, 2018). Abnormal interpretation rate, positive predictive value of biopsies performed, sensitivity, specificity, and cancer detection rate were compared using Pearson's χ2 test. Logistic regression models were fit, adjusting for age, race, breast density, prior comparison, breast cancer history, and radiologist. RESULTS: The 18 radiologists reviewed 68.5% (1,398 of 2,042) of available feedback cases and indicated that 17.4% of cases (243 of 1,398) could influence future decisions. For the eight academic radiologist subset, after multivariable adjustment with comparison to pre-intervention, average abnormal interpretation rate decreased (from 7.5% to 6.7%, adjusted odds ratio [aOR] 0.86, P < .01), positive predictive value of biopsies performed increased (from 40.6% to 51.3%, aOR 1.48, P = .011), and specificity increased (from 93.0% to 93.9%, aOR 1.17, P < .01) post-intervention. There was no difference in cancer detection rate per 1,000 examinations (from 5.8 to 6.1, aOR 1.01, P = .91) or sensitivity (from 81.2% to 78.7%, aOR 0.84, P = .47). CONCLUSIONS: Radiologists used a screening mammography automated outcomes feedback application. Its use decreased false-positive examinations, without evidence of reduced cancer detection.

Impact of Background Parenchymal Enhancement on Diagnostic Performance in Screening Breast MRI.

RATIONALE AND OBJECTIVES: To evaluate the impact of background parenchymal enhancement (BPE) on diagnostic performance in screening breast magnetic resonance imaging (MRI). MATERIALS AND METHODS: Consecutive screening breast MRIs performed at our institution from 2011 to 2014 were reviewed in a HIPAA-compliant manner with institutional review board approval. BPE was extracted from radiology reports and examinations grouped into minimal/mild (lower) or moderate/marked (higher) BPE. Performance measures were compared between the two groups with Pearson's χ2 test and with logistic regression to adjust for possible confounders of age, screening indication, mammographic density, available prior MRI, and examination year, using lower BPE as the reference group. RESULTS: For 4686 screening MRIs performed in 2446 women, BPE was reported as minimal or mild for 3975 (85%) examinations and moderate or marked for 711(15%). Following logistic regression to adjust for multiple confounders, abnormal interpretation rate (AIR) significantly differed between the two BPE groups. AIR was 13% (89/711) in the higher BPE group versus 7% (295/3975) in the lower BPE group with an adjusted odds ratio of 1.37 (95% confidence interval: 1.03, 1.82). After adjustment, all other performance metrics, including cancer detection rate, positive predictive value, sensitivity, and specificity did not significantly differ between the two BPE groups (P > 0.05). CONCLUSION: Higher BPE on screening MRI is associated with higher abnormal interpretation rate, with no impact on cancer detection rate, sensitivity, or specificity.

Frequency and Cancer Yield of BI-RADS Category 3 Lesions Detected at High-Risk Screening Breast MRI.

OBJECTIVE. The purpose of this study was to evaluate the frequency and cancer yield of BI-RADS category 3 lesions in baseline versus nonbaseline (those with at least one prior) MRI screening examinations. MATERIALS AND METHODS. Consecutive MRI screening examinations performed from 2011 through 2015 were reviewed. Pearson and Wilcoxon tests were used to examine differences in age, breast density, screening indication, background parenchymal enhancement, and cancer yield between baseline and nonbaseline MRI BI-RADS category 3 assessments. Multivariate logistic regression models based on generalized estimating equations were used to assess the odds of receiving a BI-RADS 3 assessment as a function of the variables. RESULTS. Of 6672 MRI screening examinations of 3214 patients, 202 examinations (3%) were assessed BI-RADS category 3. Among baseline examinations, 8% (82/983) were assessed BI-RADS 3, compared with 2% (120/5689) of nonbaseline examinations (p < 0.001). Among the total BI-RADS 3 examinations, 6% (13/202) yielded malignancy of the lesion that had been assessed BI-RADS 3; 12 of 13 cancers were stage 0 or I at diagnosis. The cancer yield of BI-RADS 3 at baseline examinations was 2% (2/82), compared with 9% (11/120) for nonbaseline examinations (p = 0.056). Ten of 13 examinations were upgraded at or before 6-month follow-up MRI. CONCLUSION. Baseline screening breast MRI examinations are associated with a significantly higher rate of BI-RADS category 3 assessments than are nonbaseline examinations. Most cancers diagnosed at follow-up of BI-RADS 3 lesions are in an early stage and are diagnosed at or before the 6-month follow-up examination. When used judiciously, short-interval follow-up MRI is an appropriate method for identifying early-stage breast cancer while avoiding unnecessary biopsies with benign findings.

The Effect of Prior Comparison MRI on Interpretive Performance of Screening Breast MRI.

OBJECTIVE: To evaluate the effect of prior comparison MRI on interpretive performance of screening breast MRI. METHODS: After institutional review board approval, all screening breast MRI examinations performed from January 2011 through December 2014 were retrospectively reviewed. Screening performance metrics were estimated and compared for exams with and without a prior comparison MRI, using logistic regression models to adjust for age and screening indication (BRCA mutation or thoracic radiation versus breast cancer history versus high-risk lesion history versus breast cancer family history). RESULTS: Most exams, 4509 (87%), had a prior comparison MRI (incidence round), while 661 (13%) did not (prevalence round). Abnormal interpretation rate (6% vs 20%, P < 0.01), biopsy rate (3% vs 9%, P < 0.01), and false-positive biopsy recommendation rate per 1000 exams (21 vs 71, P < 0.01) were significantly lower in the incidence rounds compared to the prevalence rounds, while specificity was significantly higher (95% vs 81%, P < 0.01). There was no difference in cancer detection rate (CDR) per 1000 exams (12 vs 20, P = 0.1), positive predictive value of biopsies performed (PPV3) (35% vs 23%, P = 0.1), or sensitivity (86% vs 76%, P = 0.4). CONCLUSION: Presence of a prior comparison significantly improves incidence round screening breast MRI examination performance compared with prevalence round screening. Consideration should be given to updating the BI-RADS breast MRI screening benchmarks and auditing prevalence and incidence round examinations separately.

Obesity and breast cancer screening: Cross-sectional survey results from the behavioral risk factor surveillance system.

BACKGROUND: Postmenopausal obese women demonstrate an elevated breast cancer risk and experience increased breast cancer morbidity and mortality compared with women with a normal body mass index (BMI). However, to the authors' knowledge, prior studies have yielded inconclusive results regarding the effects of obesity on mammography screening adherence. Using national cross-sectional survey data, the objective of the current study was to assess the current association between increasing BMI and use of mammography screening. METHODS: Cross-sectional survey data from the 2016 Behavioral Risk Factor Surveillance System, a state-based national telephone survey of noninstitutionalized adults in the United States, was used to identify the association between mammography screening use and increasing incremental BMI categories, including normal (18.5-24.9 kg/m2 ), overweight (25-29.9 kg/m2 ), obese class I (30-34.9 kg/m2 ), obese class II (35-39.9 kg/m2 ), and obese class III (>40 kg/m2 ), with adjustments for potential confounders. A multivariable logistic regression model was used to evaluate the effect of each BMI category on self-reported mammography use, using unadjusted and adjusted odds ratios. Effect modification by race/ethnicity was determined by testing interaction terms using Wald tests. RESULTS: Of 116,343 survey respondents, 33.5% (38,984 respondents) had a normal BMI, 32.6% (37,969 respondents) were overweight, 19.3% (22,416 respondents) were classified as obese class I, 8.4% (9791 respondents) were classified as obese class II, and 6.2% (7183 respondents) were classified as obese class III. There was no statistically significant difference (P < .05) observed with regard to mammography use between women with a normal BMI and obese women from each obese class (classes I-III) when compared individually. There also was no evidence of effect modification by race (P = .53). CONCLUSIONS: In contrast to prior reports, the results of the current study demonstrated no association between obesity and adherence to screening mammography. These findings may relate to the increasing social acceptance of obesity among women from all racial/ethnic groups and the removal of weight-related facility-level barriers over time.