Publications by authors named "Christoph I Lee"

134 Publications

A Warning about Warning Signals for Interpreting Mammograms.

Radiology 2021 Nov 9:212092. Epub 2021 Nov 9.

From the Section for Mammographic Screening, Cancer Registry of Norway, PO Box 5313, Majorstuen, Oslo 0304, Norway (S.H.); Department of Health and Care Sciences, UiT-The Arctic University of Norway, Tromsø, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); and Department of Health Systems and Population Health, University of Washington School of Public Health, Seattle, Wash (C.I.L.).

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http://dx.doi.org/10.1148/radiol.2021212092DOI Listing
November 2021

Preoperative MRI in breast cancer: effect of breast density on biopsy rate and yield.

Breast Cancer Res Treat 2021 Oct 22. Epub 2021 Oct 22.

Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA.

Purpose: Preoperative 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.

Methods: We 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.

Results: Among 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%).

Conclusion: For 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 Registration: ClinicalTrials.gov: NCT02980848; registered 2017.
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http://dx.doi.org/10.1007/s10549-021-06418-xDOI Listing
October 2021

A Continuously Benchmarked and Crowdsourced Challenge for Rapid Development and Evaluation of Models to Predict COVID-19 Diagnosis and Hospitalization.

JAMA Netw Open 2021 10 1;4(10):e2124946. Epub 2021 Oct 1.

Department of Biomedical Informatics and Medical Education, University of Washington, Seattle.

Importance: Machine learning could be used to predict the likelihood of diagnosis and severity of illness. Lack of COVID-19 patient data has hindered the data science community in developing models to aid in the response to the pandemic.

Objectives: To describe the rapid development and evaluation of clinical algorithms to predict COVID-19 diagnosis and hospitalization using patient data by citizen scientists, provide an unbiased assessment of model performance, and benchmark model performance on subgroups.

Design, Setting, And Participants: This diagnostic and prognostic study operated a continuous, crowdsourced challenge using a model-to-data approach to securely enable the use of regularly updated COVID-19 patient data from the University of Washington by participants from May 6 to December 23, 2020. A postchallenge analysis was conducted from December 24, 2020, to April 7, 2021, to assess the generalizability of models on the cumulative data set as well as subgroups stratified by age, sex, race, and time of COVID-19 test. By December 23, 2020, this challenge engaged 482 participants from 90 teams and 7 countries.

Main Outcomes And Measures: Machine learning algorithms used patient data and output a score that represented the probability of patients receiving a positive COVID-19 test result or being hospitalized within 21 days after receiving a positive COVID-19 test result. Algorithms were evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC) scores. Ensemble models aggregating models from the top challenge teams were developed and evaluated.

Results: In the analysis using the cumulative data set, the best performance for COVID-19 diagnosis prediction was an AUROC of 0.776 (95% CI, 0.775-0.777) and an AUPRC of 0.297, and for hospitalization prediction, an AUROC of 0.796 (95% CI, 0.794-0.798) and an AUPRC of 0.188. Analysis on top models submitting to the challenge showed consistently better model performance on the female group than the male group. Among all age groups, the best performance was obtained for the 25- to 49-year age group, and the worst performance was obtained for the group aged 17 years or younger.

Conclusions And Relevance: In this diagnostic and prognostic study, models submitted by citizen scientists achieved high performance for the prediction of COVID-19 testing and hospitalization outcomes. Evaluation of challenge models on demographic subgroups and prospective data revealed performance discrepancies, providing insights into the potential bias and limitations in the models.
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http://dx.doi.org/10.1001/jamanetworkopen.2021.24946DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8506231PMC
October 2021

Data Quality, Data Sharing, and Moving Artificial Intelligence Forward.

JAMA Netw Open 2021 Aug 2;4(8):e2119345. Epub 2021 Aug 2.

Department of Radiology, University of Washington School of Medicine, Seattle.

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http://dx.doi.org/10.1001/jamanetworkopen.2021.19345DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8612009PMC
August 2021

Receipt of Screening Mammography by Insured Women Diagnosed With Breast Cancer and Impact on Outcomes.

J Natl Compr Canc Netw 2021 07 30. Epub 2021 Jul 30.

Department of Radiology, University of Washington School of Medicine; and.

Background: The purpose of this study was to determine factors associated with receipt of screening mammography by insured women before breast cancer diagnosis, and subsequent outcomes.

Patients And Methods: Using claims data from commercial and federal payers linked to a regional SEER registry, we identified women diagnosed with breast cancer from 2007 to 2017 and determined receipt of screening mammography within 1 year before diagnosis. We obtained patient and tumor characteristics from the SEER registry and assigned each woman a socioeconomic deprivation score based on residential address. Multivariable logistic regression models were used to evaluate associations of patient and tumor characteristics with late-stage disease and nonreceipt of mammography. We used multivariable Cox proportional hazards models to identify predictors of subsequent mortality.

Results: Among 7,047 women, 69% (n=4,853) received screening mammography before breast cancer diagnosis. Compared with women who received mammography, those with no mammography had a higher proportion of late-stage disease (34% vs 10%) and higher 5-year mortality (18% vs 6%). In multivariable modeling, late-stage disease was most associated with nonreceipt of mammography (odds ratio [OR], 4.35; 95% CI, 3.80-4.98). The Cox model indicated that nonreceipt of mammography predicted increased risk of mortality (hazard ratio [HR], 2.00; 95% CI, 1.64-2.43), independent of late-stage disease at diagnosis (HR, 5.00; 95% CI, 4.10-6.10), Charlson comorbidity index score ≥1 (HR, 2.75; 95% CI, 2.26-3.34), and negative estrogen receptor/progesterone receptor status (HR, 2.09; 95% CI, 1.67-2.61). Nonreceipt of mammography was associated with younger age (40-49 vs 50-59 years; OR, 1.69; 95% CI, 1.45-1.96) and increased socioeconomic deprivation (OR, 1.05 per decile increase; 95% CI, 1.03-1.07).

Conclusions: In a cohort of insured women diagnosed with breast cancer, nonreceipt of screening mammography was significantly associated with late-stage disease and mortality, suggesting that interventions to further increase uptake of screening mammography may improve breast cancer outcomes.
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http://dx.doi.org/10.6004/jnccn.2020.7801DOI Listing
July 2021

Prioritizing breast imaging services during the COVID pandemic: A survey of breast imaging facilities within the Breast Cancer Surveillance Consortium.

Prev Med 2021 10 30;151:106540. Epub 2021 Jun 30.

Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA; Division of Biostatistics, Department of Public Health Sciences, University of California Davis, Davis, CA, USA.

The COVID-19 pandemic disrupted breast cancer screening and diagnostic imaging in the United States. We sought to evaluate how medical facilities prioritized breast imaging services during periods of reduced capacity or upon re-opening after closures. In fall 2020, we surveyed 77 breast imaging facilities within the Breast Cancer Surveillance Consortium in the United States. The survey ascertained the pandemic's impact on clinical practices during March-September 2020. Nearly all facilities (97%) reported closing or operating at reduced capacity at some point during this period. All facilities were open by August 2020, though 14% were still operating at reduced capacity in September 2020. During periods of re-opening or reduced capacity, 93% of facilities reported prioritizing diagnostic breast imaging over breast cancer screening. For diagnostic imaging, facilities prioritized based on rescheduling canceled appointments (89%), specific indication for diagnostic imaging (89%), patient demand (84%), individual characteristics and risk factors (77%), and time since last imaging examination (72%). For screening mammography, facilities prioritized based on rescheduled cancelations (96%), patient demand (83%), individual characteristics and risk factors (73%), and time since last mammogram (71%). For biopsy services, more than 90% of facilities reported prioritization based on rescheduling of canceled exams, patient demand, patient characteristics and risk factors and level of suspicion on imaging. The observed patterns from this large and geographically diverse sample of facilities in the United States indicate that multiple factors were commonly used to prioritize breast imaging services during periods of reduced capacity.
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http://dx.doi.org/10.1016/j.ypmed.2021.106540DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8241650PMC
October 2021

Interval and Subsequent Round Breast Cancer in a Randomized Controlled Trial Comparing Digital Breast Tomosynthesis and Digital Mammography Screening.

Radiology 2021 07 11;300(1):66-76. Epub 2021 May 11.

From the Cancer Registry of Norway, PO 5313, Maiorstuen, 0304 Oslo, Norway (S.H., N.M., Å.S.H., A.S.D.); Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway (S.H.); Department of Radiology, University of Washington School of Medicine, Seattle, Wash (C.I.L.); Department of Health Services, University of Washington School of Public Health, Seattle, Wash (C.I.L.); Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, Australia (N.H.); Department of Radiology (H.S.A., I.S.H.), Department of Pathology (L.A.A.), and Mohn Medical Imaging and Visualization Centre (I.S.H.), Haukeland University Hospital, Bergen, Norway; and Department of Clinical Medicine (H.S.A., I.S.H.), Section for Pathology (L.A.A.), and Centre for Cancer Biomarkers CCBIO (L.A.A.), University of Bergen, Bergen, Norway.

Background Prevalent digital breast tomosynthesis (DBT) has shown higher cancer detection rates and lower recall rates compared with those of digital mammography (DM). However, data are limited on rates and histopathologic tumor characteristics of interval and subsequent round screen-detected cancers for DBT. Purpose To follow women randomized to screening with DBT or DM and to investigate rates and tumor characteristics of interval and subsequent round screen-detected cancers. Materials and Methods To-Be is a randomized controlled trial comparing the outcome of DBT and DM in organized breast cancer screening. The trial included 28 749 women, with 22 306 women returning for subsequent DBT screening 2 years later (11 201 and 11 105 originally screened with DBT and DM, respectively). Differences in rates, means, and distribution of histopathologic tumor characteristics between women prevalently screened with DBT versus DM were evaluated with Z tests, tests, and χ tests. Relative risk (RR) with 95% CIs was calculated for the cancer rates. Results Interval cancer rates were 1.4 per 1000 screens (20 of 14 380; 95% CI: 0.9, 2.1) for DBT versus 2.0 per 1000 screens (29 of 14 369; 95% CI: 1.4, 2.9; = .20) for DM. The rates of subsequent round screen-detected cancer were 8.1 per 1000 (95% CI: 6.6, 10.0) for women originally screened with DBT and 9.1 per 1000 (95% CI: 7.4, 11.0; = .43) for women screened with DM. The distribution of tumor characteristics did not differ between groups for either interval or subsequent screen-detected cancer. The RR of interval cancer was 0.69 (95% CI: 0.39, 1.22; = .20) for DBT versus DM, whereas RR of subsequent screen-detected cancer for women prevalently screened with DBT versus DM was 0.89 (95% CI: 0.67, 1.19; = .43). Conclusion Rates of interval or subsequent round screen-detected cancers and their tumor characteristics did not differ between women originally screened with digital breast tomosynthesis (DBT) versus digital mammography. The analysis suggests that the benefits of prevalent DBT screening did not come at the expense of worse downstream screening performance measures in a population-based screening program. © RSNA, 2021 See also the editorial by Taourel in this issue.
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http://dx.doi.org/10.1148/radiol.2021203936DOI Listing
July 2021

Assessment of a Risk-Based Approach for Triaging Mammography Examinations During Periods of Reduced Capacity.

JAMA Netw Open 2021 03 1;4(3):e211974. Epub 2021 Mar 1.

Department of Radiology, University of Washington School of Medicine, Seattle.

Importance: Breast cancer screening, surveillance, and diagnostic imaging services were profoundly limited during the initial phase of the coronavirus disease 2019 (COVID-19) pandemic.

Objective: To develop a risk-based strategy for triaging mammograms during periods of decreased capacity.

Design, Setting, And Participants: This population-based cohort study used data collected prospectively from mammography examinations performed in 2014 to 2019 at 92 radiology facilities in the Breast Cancer Surveillance Consortium. Participants included individuals undergoing mammography. Data were analyzed from August 10 to November 3, 2020.

Exposures: Clinical indication for screening, breast symptoms, personal history of breast cancer, age, time since last mammogram/screening interval, family history of breast cancer, breast density, and history of high-risk breast lesion.

Main Outcomes And Measures: Combinations of clinical indication, clinical history, and breast cancer risk factors that subdivided mammograms into risk groups according to their cancer detection rate were identified using classification and regression trees.

Results: The cohort included 898 415 individuals contributing 1 878 924 mammograms (mean [SD] age at mammogram, 58.6 [11.2] years) interpreted by 448 radiologists, with 1 722 820 mammograms in individuals without a personal history of breast cancer and 156 104 mammograms in individuals with a history of breast cancer. Most individuals were aged 50 to 69 years at imaging (1 113 174 mammograms [59.2%]), and 204 305 (11.2%) were Black, 206 087 (11.3%) were Asian or Pacific Islander, 126 677 (7.0%) were Hispanic or Latina, and 40 021 (2.2%) were another race/ethnicity or mixed race/ethnicity. Cancer detection rates varied widely based on clinical indication, breast symptoms, personal history of breast cancer, and age. The 12% of mammograms with very high (89.6 [95% CI, 82.3-97.5] to 122.3 [95% CI, 108.1-138.0] cancers detected per 1000 mammograms) or high (36.1 [95% CI, 33.1-39.3] to 47.5 [95% CI, 42.4-53.3] cancers detected per 1000 mammograms) cancer detection rates accounted for 55% of all detected cancers and included mammograms to evaluate an abnormal mammogram or breast lump in individuals of all ages regardless of breast cancer history, to evaluate breast symptoms other than lump in individuals with a breast cancer history or without a history but aged 60 years or older, and for short-interval follow-up in individuals aged 60 years or older without a breast cancer history. The 44.2% of mammograms with very low cancer detection rates accounted for 13.1% of detected cancers and included annual screening mammograms in individuals aged 50 to 69 years (3.8 [95% CI, 3.5-4.1] cancers detected per 1000 mammograms) and all screening mammograms in individuals younger than 50 years regardless of screening interval (2.8 [95% CI, 2.6-3.1] cancers detected per 1000 mammograms).

Conclusions And Relevance: In this population-based cohort study, clinical indication and individual risk factors were associated with cancer detection and may be useful for prioritizing mammography in times and settings of decreased capacity.
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http://dx.doi.org/10.1001/jamanetworkopen.2021.1974DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7994953PMC
March 2021

The Impact of the COVID-19 Pandemic on Journal Scholarly Activity Among Female Contributors.

J Am Coll Radiol 2021 07 26;18(7):1044-1047. Epub 2021 Jan 26.

Department of Radiology, University of Michigan School of Medicine, Ann Arbor, Michigan.

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http://dx.doi.org/10.1016/j.jacr.2021.01.011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7835095PMC
July 2021

Comparative Access to and Use of Digital Breast Tomosynthesis Screening by Women's Race/Ethnicity and Socioeconomic Status.

JAMA Netw Open 2021 02 1;4(2):e2037546. Epub 2021 Feb 1.

Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, School of Medicine.

Importance: Digital breast tomosynthesis (DBT) has reduced recall and increased cancer detection compared with digital mammography (DM), depending on women's age and breast density. Whether DBT screening access and use are equitable across groups of women based on race/ethnicity and socioeconomic characteristics is uncertain.

Objective: To determine women's access to and use of DBT screening based on race/ethnicity, educational attainment, and income.

Design, Setting, And Participants: This cross-sectional study included 92 geographically diverse imaging facilities across 5 US states, at which a total of 2 313 118 screening examinations were performed among women aged 40 to 89 years from January 1, 2011, to December 31, 2017. Data were analyzed from June 13, 2019, to August 18, 2020.

Exposures: Women's race/ethnicity, educational level, and community-level household income.

Main Outcomes And Measures: Access to DBT (on-site access) at time of screening by examination year and actual use of DBT vs DM screening by years since facility-level DBT adoption (≤5 years).

Results: Among the 2 313 118 screening examinations included in the analysis, the proportion of women who had DBT access at the time of their screening appointment increased from 11 558 of 354 107 (3.3%) in 2011 to 194 842 of 235 972 (82.6%) in 2017. In 2012, compared with White women, Black (relative risk [RR], 0.05; 95% CI, 0.03-0.11), Asian American (RR, 0.28; 95% CI, 0.11-0.75), and Hispanic (RR, 0.38; 95% CI, 0.18-0.80) women had significantly less DBT access, and women with less than a high school education had lower DBT access compared with college graduates (RR, 0.18; 95% CI, 0.10-0.32). Among women attending facilities with both DM and DBT available at the time of screening, Black women experienced lower DBT use compared with White women attending the same facility (RRs, 0.83 [95% CI, 0.82-0.85] to 0.98 [95% CI, 0.97-0.99]); women with lower educational level experienced lower DBT use (RRs, 0.79 [95% CI, 0.74-0.84] to 0.88 [95% CI, 0.85-0.91] for non-high school graduates and 0.90 [95% CI, 0.89-0.92] to 0.96 [95% CI, 0.93-0.99] for high school graduates vs college graduates); and women within the lowest income quartile experienced lower DBT use vs women in the highest income quartile (RRs, 0.89 [95% CI, 0.87-0.91] to 0.99 [95% CI, 0.98-1.00]) regardless of the number of years after facility-level DBT adoption.

Conclusions And Relevance: In this cross-sectional study, women of minority race/ethnicity and lower socioeconomic status experienced lower DBT access during the early adoption period and persistently lower DBT use when available over time. Future efforts should address racial/ethnic, educational, and financial barriers to DBT screening.
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http://dx.doi.org/10.1001/jamanetworkopen.2020.37546DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7896194PMC
February 2021

The Provocative: A Glimpse Into Radiology's Future.

J Am Coll Radiol 2021 01;18(1 Pt B):137-139

Director, Northwest Screening and Cancer Outcomes Research Enterprise, Department of Radiology, University of Washington School of Medicine, Seattle, Washington. Electronic address:

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http://dx.doi.org/10.1016/j.jacr.2020.10.007DOI Listing
January 2021

Keeping Pace With Technology Advances in Breast Cancer Screening: Synthetic 2D Images Outperform Digital Mammography.

J Natl Cancer Inst 2021 Jun;113(6):645-646

Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA.

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http://dx.doi.org/10.1093/jnci/djaa208DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8168230PMC
June 2021

Artificial intelligence in breast cancer screening: primary care provider preferences.

J Am Med Inform Assoc 2021 06;28(6):1117-1124

The Comparative Health Outcomes, Policy & Economics (CHOICE) Institute, University of Washington School of Pharmacy, Seattle, Washington, USA.

Background: Artificial intelligence (AI) is increasingly being proposed for use in medicine, including breast cancer screening (BCS). Little is known, however, about referring primary care providers' (PCPs') preferences for this technology.

Methods: We identified the most important attributes of AI BCS for ordering PCPs using qualitative interviews: sensitivity, specificity, radiologist involvement, understandability of AI decision-making, supporting evidence, and diversity of training data. We invited US-based PCPs to participate in an internet-based experiment designed to force participants to trade off among the attributes of hypothetical AI BCS products. Responses were analyzed with random parameters logit and latent class models to assess how different attributes affect the choice to recommend AI-enhanced screening.

Results: Ninety-one PCPs participated. Sensitivity was most important, and most PCPs viewed radiologist participation in mammography interpretation as important. Other important attributes were specificity, understandability of AI decision-making, and diversity of data. We identified 3 classes of respondents: "Sensitivity First" (41%) found sensitivity to be more than twice as important as other attributes; "Against AI Autonomy" (24%) wanted radiologists to confirm every image; "Uncertain Trade-Offs" (35%) viewed most attributes as having similar importance. A majority (76%) accepted the use of AI in a "triage" role that would allow it to filter out likely negatives without radiologist confirmation.

Conclusions And Relevance: Sensitivity was the most important attribute overall, but other key attributes should be addressed to produce clinically acceptable products. We also found that most PCPs accept the use of AI to make determinations about likely negative mammograms without radiologist confirmation.
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http://dx.doi.org/10.1093/jamia/ocaa292DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8200265PMC
June 2021

Screening for Breast Cancer.

Med Clin North Am 2020 Nov;104(6):1007-1021

Radiology, Harvard Medical School, Massachusetts General Hospital, 55 Fruit Street, Wang Building, Suite 219L, Boston, MA 02114, USA.

Among women, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer-related death in the world. The purpose of this article is to review the evidence regarding breast cancer screening for average-risk women. The review primarily focuses on mammographic screening but also reviews clinical breast examinations, emerging screening technologies, and opportunities to build consensus. Wherever possible, the review relies on published systematic reviews, meta-analyses, and guidelines from three major societies (US Preventive Services Task Force, American College of Radiology, and the American Cancer Society) to reflect a range of evidence-based perspectives regarding mammographic screening.
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http://dx.doi.org/10.1016/j.mcna.2020.08.003DOI Listing
November 2020

Data, Distilled.

J Am Coll Radiol 2020 Oct;17(10):1197-1198

Professor, Department of Radiology, University of Washington School of Medicine, Seattle, Washington; Adjunct Professor, Department of Health Services, University of Washington School of Public Health, Seattle, Washington; and the Hutchinson Institute for Cancer Outcomes Research, Fred Hutchinson Research Cancer Center, Seattle, Washington.

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http://dx.doi.org/10.1016/j.jacr.2020.08.009DOI Listing
October 2020

Association Between Capitated Payments and Low-Value Imaging in Primary Care.

J Gen Intern Med 2021 Dec 1;36(12):3882-3884. Epub 2020 Oct 1.

Department of Health Services, University of Washington School of Public Health, Seattle, WA, USA.

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http://dx.doi.org/10.1007/s11606-020-06265-4DOI Listing
December 2021

Comparing Screening Outcomes for Digital Breast Tomosynthesis and Digital Mammography by Automated Breast Density in a Randomized Controlled Trial: Results from the To-Be Trial.

Radiology 2020 12 15;297(3):522-531. Epub 2020 Sep 15.

From the Cancer Registry of Norway, PO Box 5313, Majorstuen, 0304 Oslo, Norway (N.M., A.S.D., S.H.); Department of Radiology, Haukeland University Hospital, Bergen, Norway (H.S.A., I.S.H.); Department of Clinical Medicine, University of Bergen, Bergen, Norway (H.S.A., I.S.H.); Department of Radiology, Seattle Cancer Care Alliance, University of Washington, Seattle, Wash (C.I.L.); Department of Translational Medicine, Diagnostic Radiology, Lund University Cancer Center, Malmö, Sweden (S.Z.); and Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway (S.H.).

Background Digital breast tomosynthesis (DBT) is considered superior to digital mammography (DM) for women with dense breasts. Purpose To identify differences in screening outcomes, including rates of recall, false-positive (FP) findings, biopsy, cancer detection rate, positive predictive value of recalls and biopsies, and histopathologic tumor characteristics by density using DBT combined with two-dimensional synthetic mammography (SM) (hereafter, DBT+SM) versus DM. Materials and Methods This randomized controlled trial comparing DBT+SM and DM was performed in Bergen as part of BreastScreen Norway, 2016-2017. Automated software measured density (Volpara Density Grade [VDG], 1-4). The outcomes were compared for DBT+SM versus DM by VDG in descriptive analyses. A stratified log-binomial regression model was used to estimate relative risk of outcomes in subgroups by screening technique. Results Data included 28 749 women, 14 380 of whom were screened with DBT+SM and 14 369 of whom were screened with DM (both groups: median age, 59 years; interquartile range [IQR], 54-64 years). The recall rate was lower for women screened with DBT+SM versus those screened with DM for VDG 1 (2.1% [81 of 3929] vs 3.3% [106 of 3212]; = .001) and VDG 2 (3.2% [200 of 6216] vs 4.3% [267 of 6280]; = .002). For DBT+SM, adjusted relative risk of recall (VDG 2: 1.8; < .001; VDG 3: 2.4; < .001; VDG 4: 1.8; = .02) and screen-detected breast cancer (VDG 2: 2.4; = .004; VDG 3: 2.8; = .01; VDG 4: 2.8; = .05) increased with VDG, whereas no differences were observed for DM (relative risk of recall for VDG 2: 1.3; = .06; VDG 3: 1.1; = .41; VDG 4: 1.1; = .71; and relative risk of screen-detected breast cancer for VDG 2: 1.7; = .13; VDG 3: 2.1; = .06; VDG 4: 2.2; = .15). Conclusion Screening with digital breast tomosynthesis combined with synthetic two-dimensional mammograms (DBT+SM) versus digital mammography (DM) yielded lower recall rates for women with Volpara Density Grade (VDG) 1 and VDG 2. Adjusted relative risk of recall and screen-detected breast cancer increased with denser breasts for DBT+SM but not for DM. © RSNA, 2020 See also the editorial by Sechopoulos and Athanasiou in this issue.
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http://dx.doi.org/10.1148/radiol.2020201150DOI Listing
December 2020

Risk for Upgrade to Malignancy After Breast Core Needle Biopsy Diagnosis of Lobular Neoplasia: A Systematic Review and Meta-Analysis.

J Am Coll Radiol 2020 Oct 27;17(10):1207-1219. Epub 2020 Aug 27.

Department of Radiology, University of Washington, Seattle Cancer Care Alliance, Seattle, Washington. Electronic address:

Purpose: Lobular neoplasia (LN) detected on breast core needle biopsy is frequently managed with surgical excision because of concern for undersampled malignancy. The authors performed a systematic review and meta-analysis to estimate the risk for upgrade to malignancy in the setting of imaging-concordant classic LN diagnosed on core biopsy.

Methods: PubMed and Embase were searched for original articles published from 1998 to 2020 that reported rates of upgrade to malignancy for classic LN, including atypical lobular hyperplasia (ALH) and classic lobular carcinoma in situ (LCIS). Two reviewers extracted study data and assessed the following quality criteria: exclusion of variant LCIS, exclusion of imaging-discordant lesions, and outcome reporting for ≥70% of lesions. For studies meeting all criteria, pooled risks for upgrade to any malignancy (invasive carcinoma or ductal carcinoma in situ) and invasive malignancy for all LN, ALH, and LCIS were estimated using random-effects models.

Results: For 65 full-text articles included in the review, the risk for upgrade to any malignancy ranged from 0% to 45%. Among the 16 studies that met all quality criteria for the meta-analysis, pooled risks for upgrade to any malignancy were 3.1% (95% confidence interval [CI], 1.8%-5.2%) for all LN, 2.5% (95% CI, 1.6%-3.9%) for ALH, and 5.8% (95% CI, 2.9%-11.3%) for LCIS. Risks for upgrade to invasive malignancy were 1.3% (95% CI, 0.7%-2.4%) for all LN, 0.4% (95% CI, 0.0%-4.2%) for ALH, and 3.5% (95% CI, 2.0%-5.9%) for LCIS.

Conclusions: The risk for upgrade to malignancy for LN found on breast biopsy is low. Imaging surveillance can likely be offered as an alternative to surgical management for LN, particularly for ALH.
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http://dx.doi.org/10.1016/j.jacr.2020.07.036DOI Listing
October 2020

The Value of Patient and Tumor Factors in Predicting Preoperative Breast MRI Outcomes.

Radiol Imaging Cancer 2020 07 10;2(4):e190099. Epub 2020 Jul 10.

Department of Radiology, University of Washington School of Medicine, Seattle Cancer Care Alliance, 1144 Eastlake Ave E, Room LG2-211, Seattle, WA 98109 (H.R., D.S.H., S.H.C., S.C.P., C.I.L.); Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, Calif (A.A., M.v.d.S.); and Departments of Applied Mathematics and Theoretical Physics and Public Health, University of Cambridge, Cambridge, England (M.v.d.S.).

Purpose: To identify patient and tumor features that predict true-positive, false-positive, and negative breast preoperative MRI outcomes.

Materials And Methods: Using a breast MRI database from a large regional cancer center, the authors retrospectively identified all women with unilateral breast cancer who underwent preoperative MRI from January 2005 to February 2015. A total of 1396 women with complete data were included. Patient features (ie, age, breast density) and index tumor features (ie, type, grade, hormone receptor, human epidermal growth factor receptor type 2/, Ki-67) were extracted and compared with preoperative MRI outcomes (ie, true positive, false positive, negative) using univariate (ie, Fisher exact) and multivariate machine learning approaches (ie, least absolute shrinkage and selection operator, AutoPrognosis). Overall prediction performance was summarized using the area under the receiver operating characteristic curve (AUC), calculated using internal validation techniques (bootstrap and cross-validation) to account for model training.

Results: At the examination level, 181 additional cancers were identified among 1396 total preoperative MRI examinations (median patient age, 56 years; range, 25-94 years), resulting in a positive predictive value for biopsy of 43% (181 true-positive findings of 419 core-needle biopsies). In univariate analysis, no patient or tumor feature was associated with a true-positive outcome ( > .05), although greater mammographic density ( = .022) and younger age (< 50 years, = .025) were associated with false-positive examinations. Machine learning approaches provided weak performance for predicting true-positive, false-positive, and negative examinations (AUC range, 0.50-0.57).

Conclusion: Commonly used patient and tumor factors driving expert opinion for the use of preoperative MRI provide limited predictive value for determining preoperative MRI outcomes in women. © RSNA, 2020See also the commentary by Grimm in this issue.
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http://dx.doi.org/10.1148/rycan.2020190099DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7398118PMC
July 2020

Screening Performance of Digital Breast Tomosynthesis vs Digital Mammography in Community Practice by Patient Age, Screening Round, and Breast Density.

JAMA Netw Open 2020 07 1;3(7):e2011792. Epub 2020 Jul 1.

Department of Radiology, University of Washington, Seattle Cancer Care Alliance, Seattle.

Importance: Digital mammography (DM) and digital breast tomosynthesis (DBT) are used for routine breast cancer screening. There is minimal evidence on performance outcomes by age, screening round, and breast density in community practice.

Objective: To compare DM vs DBT performance by age, baseline vs subsequent screening round, and breast density category.

Design, Setting, And Participants: This comparative effectiveness study assessed 1 584 079 screening examinations of women aged 40 to 79 years without prior history of breast cancer, mastectomy, or breast augmentation undergoing screening mammography at 46 participating Breast Cancer Surveillance Consortium facilities from January 2010 to April 2018.

Exposures: Age, Breast Imaging Reporting and Data System breast density category, screening round, and modality.

Main Outcomes And Measures: Absolute rates and relative risks (RRs) of screening recall and cancer detection.

Results: Of 1 273 492 DM and 310 587 DBT examinations analyzed, 1 028 891 examinations (65.0%) were of white non-Hispanic women; 399 952 women (25.2%) were younger than 50 years; and 671 136 women (42.4%) had heterogeneously dense or extremely dense breasts. Adjusted differences in DM vs DBT performance were largest on baseline examinations: for example, per 1000 baseline examinations in women ages 50 to 59, recall rates decreased from 241 examinations for DM to 204 examinations for DBT (RR, 0.84; 95% CI, 0.73-0.98), and cancer detection rates increased from 5.9 with DM to 8.8 with DBT (RR, 1.50; 95% CI, 1.10-2.08). On subsequent examinations, women aged 40 to 79 years with heterogeneously dense breasts had improved recall rates and improved cancer detection with DBT. For example, per 1000 examinations in women aged 50 to 59 years, the number of recall examinations decreased from 102 with DM to 93 with DBT (RR, 0.91; 95% CI, 0.84-0.98), and cancer detection increased from 3.7 with DM to 5.3 with DBT (RR, 1.42; 95% CI, 1.23-1.64). Women aged 50 to 79 years with scattered fibroglandular density also had improved recall and cancer detection rates with DBT. Women aged 40 to 49 years with scattered fibroglandular density and women aged 50 to 79 years with almost entirely fatty breasts benefited from improved recall rates without change in cancer detection rates. No improvements in recall or cancer detection rates were observed in women with extremely dense breasts on subsequent examinations for any age group.

Conclusions And Relevance: This study found that improvements in recall and cancer detection rates with DBT were greatest on baseline mammograms. On subsequent screening mammograms, the benefits of DBT varied by age and breast density. Women with extremely dense breasts did not benefit from improved recall or cancer detection with DBT on subsequent screening rounds.
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http://dx.doi.org/10.1001/jamanetworkopen.2020.11792DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7388021PMC
July 2020

Optimal Screening in Breast Cancer Survivors With Dense Breasts on Mammography.

J Clin Oncol 2020 11 24;38(33):3833-3840. Epub 2020 Jul 24.

Department of Radiology, University of Washington School of Medicine, Seattle Cancer Care Alliance, Seattle, WA.

Journal Journal of Clinical Oncology,
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http://dx.doi.org/10.1200/JCO.20.01641DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7676885PMC
November 2020

National Quality Improvement Participation Among US Radiation Oncology Facilities: Compliance with Guideline-Concordant Palliative Radiation Therapy for Bone Metastases.

Int J Radiat Oncol Biol Phys 2020 11 11;108(3):564-571. Epub 2020 May 11.

Department of Radiology, University of Washington, Seattle, Washington.

Purpose: To characterize the participation of radiation oncology (RO) in reporting quality metrics through the Centers for Medicare and Medicaid Services' (CMS) Hospital Compare database and to describe the association of hospital characteristics with RO-specific quality metrics.

Methods And Materials: Data from the CMS Hospital Compare, International Atomic Energy Agency's Directory of Radiotherapy Centre, 2010 US Census, and CMS Inpatient Prospective Payment System were linked to create an integrated data set of geographic information, facility characteristics, and quality measures, focusing on the use of external beam radiation therapy (EBRT) for bony metastases.

Results: Of 4829 hospitals in the Hospital Compare database, 2030 had access to radiation therapy. Among these, 814 (40%) reported on the rate of guideline-concordant EBRT for bony metastases, a RO-specific quality measure. A total of 33,614 eligible cases of bony metastases treated with EBRT were sampled. Participation in quality reporting varied significantly by geography, population type, teaching status, hospital ownership, hospital type, and hospital size. The median rate of guideline-concordant palliative EBRT utilization was 89%. Nine percent of 814 centers had a compliance rate of less than 50%. On multivariable analysis, increasing number of cases sampled (odds ratio 0.93, P = .028), increasing hospital star-rating, and above-average patient experience rating (odds ratio 0.58, P = .024) remained significantly associated with decreased odds of falling into the lowest quartile of guideline-concordant EBRT utilization.

Conclusions: RO participation in a large, national quality improvement effort is nascent and reveals potential quality gaps between hospitals offering palliative EBRT for bone metastases. More robust RO-specific quality measures are needed.
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http://dx.doi.org/10.1016/j.ijrobp.2020.04.047DOI Listing
November 2020

New mammography screening performance metrics based on the entire screening episode.

Cancer 2020 07 6;126(14):3289-3296. Epub 2020 May 6.

Department of Medicine, University of California San Francisco, San Francisco, California, USA.

Background: Established mammography screening performance metrics use the initial screening mammography assessment because they were developed for radiologist performance auditing, yet these metrics are frequently used to inform health policy and screening decision making. The authors have developed new performance metrics based on the final assessment that consider the entire screening episode, including diagnostic workup.

Methods: The authors used data from 2,512,577 screening episodes during 2005-2017 at 146 facilities in the United States participating in the Breast Cancer Surveillance Consortium. Screening performance metrics based on the final assessment of the screening episode were compared with conventional metrics defined with the initial assessment. Results were also stratified by breast density and breast cancer risk.

Results: The cancer detection rates were similar for the final assessment (4.1 per 1000; 95% confidence interval [CI], 3.8-4.3 per 1000) and the initial assessment (4.1 per 1000; 95% CI, 3.9-4.3 per 1000). The interval cancer rate was 12% higher when it was based on the final assessment (0.77 per 1000; 95% CI, 0.71-0.83 per 1000) versus the initial assessment (0.69 per 1000; 95% CI, 0.64-0.74 per 1000), and this resulted in a modest difference in sensitivity (84.1% [95% CI, 83.0%-85.1%] vs 85.7% [95% CI, 84.8%-86.6%], respectively). Absolute differences in the interval cancer rate between final and initial assessments increased with breast density and breast cancer risk (eg, a difference of 0.29 per 1000 for women with extremely dense breasts and a 5-year risk >2.49%).

Conclusions: Established screening performance metrics underestimate the interval cancer rate of a mammography screening episode, particularly for women with dense breasts or an elevated breast cancer risk. Women, clinicians, policymakers, and researchers should use final-assessment performance metrics to support informed screening decisions.
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http://dx.doi.org/10.1002/cncr.32939DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7319901PMC
July 2020

The Role of Social Determinants of Health in Self-Reported Access to Health Care Among Women Undergoing Screening Mammography.

J Womens Health (Larchmt) 2020 11 5;29(11):1437-1446. Epub 2020 May 5.

Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, USA.

Social determinants of health (SDOH) contribute to health care disparities, with social and economic barriers often leading to difficulties in obtaining necessary care. We evaluated barriers to receiving health care, focusing on caretaker responsibilities, health insurance and cost, and transportation. We included women ages ≥40 years receiving screening mammography across three Breast Cancer Surveillance Consortium registries from 2012 to 2017. Women self-reported social and financial barriers to receiving health care in the 12 months before their screening mammogram. We evaluated woman- and census-based community-level factors associated with reporting a barrier using multivariate logistic regression. We assessed interaction with urban versus nonurban residence using Wald tests. Among 393,430 women, 3.6% reported a barrier with a higher proportion in urban versus nonurban settings (3.9% [ = 11,977] vs. 2.2% [ = 1,655], respectively;  < 0.001). Among women reporting a barrier, health care cost and/or no insurance was the most common (49.3%), and no transportation was the least common (7.8%). Compared with white women, odds of reporting barriers were higher among black (adjusted odds ratio [aOR] = 1.30, 95% confidence interval [CI]: 1.16-1.44), Hispanic (aOR = 1.66, 95% CI: 1.53-1.80), and other race (aOR = 1.84, 95% CI: 1.65-2.04) women. Barriers were less likely in women with higher median household income (aOR = 0.69, 95% CI: 0.61-0.79) or higher average health insurance costs (aOR = 0.85, 95% CI: 0.74-0.98), but were more likely in high diversity index areas (aOR = 1.28, 95% CI: 1.11-1.48). Social and financial barriers exist based on race/ethnicity and SDOH related to income, insurance costs, and place of residence among women undergoing screening mammography. Breast imaging facilities could utilize information on these barriers to improve biennial screening adherence or ensure that women with abnormal findings obtain appropriate follow-up care through targeted interventions.
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http://dx.doi.org/10.1089/jwh.2019.8267DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7703148PMC
November 2020

Assessment of Radiologist Performance in Breast Cancer Screening Using Digital Breast Tomosynthesis vs Digital Mammography.

JAMA Netw Open 2020 03 2;3(3):e201759. Epub 2020 Mar 2.

Division of Biostatistics, Department of Public Health Sciences, University of California Davis School of Medicine, Davis.

Importance: Many US radiologists have screening mammography recall rates above the expert-recommended threshold of 12%. The influence of digital breast tomosynthesis (DBT) on the distribution of radiologist recall rates is uncertain.

Objective: To evaluate radiologists' recall and cancer detection rates before and after beginning interpretation of DBT examinations.

Design, Setting, And Participants: This cohort study included 198 radiologists from 104 radiology facilities in the Breast Cancer Surveillance Consortium who interpreted 251 384 DBT and 2 000 681 digital mammography (DM) screening examinations from 2009 to 2017, including 126 radiologists (63.6%) who interpreted DBT examinations during the study period and 72 (36.4%) who exclusively interpreted DM examinations (to adjust for secular trends). Data were analyzed from April 2018 to July 2019.

Exposures: Digital breast tomosynthesis and DM screening examinations.

Main Outcomes And Measures: Recall rate and cancer detection rate.

Results: A total of 198 radiologists interpreted 2 252 065 DM and DBT examinations (2 000 681 [88.8%] DM examinations; 251 384 [11.2%] DBT examinations; 710 934 patients [31.6%] aged 50-59 years; 1 448 981 [64.3%] non-Hispanic white). Among the 126 radiologists (63.6%) who interpreted DBT examinations, 83 (65.9%) had unadjusted DM recall rates of no more than 12% before using DBT, with a median (interquartile range) recall rate of 10.0% (7.5%-13.0%). On DBT examinations, 96 (76.2%) had an unadjusted recall rate of no more than 12%, with a median (interquartile range) recall rate of 8.8% (6.3%-11.3%). A secular trend in recall rate was observed, with the multivariable-adjusted risk of recall on screening examinations declining by 1.2% (95% CI, 0.9%-1.5%) per year. After adjusting for examination characteristics and secular trends, recall rates were 15% lower on DBT examinations compared with DM examinations interpreted before DBT use (relative risk, 0.85; 95% CI, 0.83-0.87). Adjusted recall rates were significantly lower on DBT examinations compared with DM examinations interpreted before DBT use for 45 radiologists (35.7%) and significantly higher for 18 (14.3%); 63 (50.0%) had no statistically significant change. The unadjusted cancer detection rate on DBT was 5.3 per 1000 examinations (95% CI, 5.0-5.7 per 1000 examinations) compared with 4.7 per 1000 examinations (95% CI, 4.6-4.8 per 1000 examinations) on DM examinations interpreted before DM use (multivariable-adjusted risk ratio, 1.21; 95% CI, 1.11-1.33).

Conclusions And Relevance: In this study, DBT was associated with an overall decrease in recall rate and an increase in cancer detection rate. However, our results indicated that there is wide variability among radiologists, including a subset of radiologists who experienced increased recall rates on DBT examinations. Radiology practices should audit radiologist DBT screening performance and consider additional DBT training for radiologists whose performance does not improve as expected.
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http://dx.doi.org/10.1001/jamanetworkopen.2020.1759DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7292996PMC
March 2020

Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms.

JAMA Netw Open 2020 03 2;3(3):e200265. Epub 2020 Mar 2.

Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, New York.

Importance: Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives.

Objective: To evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms.

Design, Setting, And Participants: In this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016.

Main Outcomes And Measurements: Algorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated.

Results: Overall, 144 231 screening mammograms from 85 580 US women (952 cancer positive ≤12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166 578 examinations from 68 008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity.

Conclusions And Relevance: While no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation.
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http://dx.doi.org/10.1001/jamanetworkopen.2020.0265DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7052735PMC
March 2020

Identifying Effective Supplemental Screening Strategies for Women with a Personal History of Breast Cancer.

Radiology 2020 04 25;295(1):64-65. Epub 2020 Feb 25.

From the Department of Radiology, University of Washington School of Medicine, 1144 Eastlake Ave E, LG-212, Seattle, WA 98109.

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http://dx.doi.org/10.1148/radiol.2020200015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7104698PMC
April 2020

Consensus Reads: The More Sets of Eyes Interpreting a Mammogram, the Better for Women.

Radiology 2020 04 11;295(1):42-43. Epub 2020 Feb 11.

From the Department of Screening, Cancer Registry of Norway, Oslo, Norway (S.H); Department of Health Sciences, Oslo Metropolitan University, Oslo, Norway (S.H.); and Department of Radiology, University of Washington, Seattle, Wash (C.I.L.).

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http://dx.doi.org/10.1148/radiol.2020192746DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7104699PMC
April 2020

Facility Variability in Examination Indication Among Women With Prior Breast Cancer: Implications and the Need for Standardization.

J Am Coll Radiol 2020 Jun 28;17(6):755-764. Epub 2020 Jan 28.

Department of Radiology, University of Washington School of Medicine, Seattle, Washington.

Objective: We sought to identify and characterize examinations in women with a personal history of breast cancer likely performed for asymptomatic surveillance.

Methods: We included surveillance mammograms (1997-2017) in asymptomatic women with a personal history of breast cancer diagnosed at age ≥18 years (1996-2016) from 103 Breast Cancer Surveillance Consortium facilities. We examined facility-level variability in examination indication. We modeled the relative risk (RR) and 95% confidence intervals (CIs) at the examination level of a (1) nonscreening indication and (2) surveillance interval ≤9 months using Poisson regression with fixed effects for facility, stage, diagnosis age, surgery, examination year, and time since diagnosis.

Results: Among 244,855 surveillance mammograms, 69.5% were coded with a screening indication, 12.7% short-interval follow-up, and 15.3% as evaluation of a breast problem. Within a facility, the proportion of examinations with a screening indication ranged from 6% to 100% (median 86%, interquartile range 79%-92%). Facilities varied the most for examinations in the first 5 years after diagnosis, with 39.4% of surveillance mammograms having a nonscreening indication. Within a facility, breast conserving surgery compared with mastectomy (RR = 1.64; 95% CI = 1.60-1.68) and less time since diagnosis (1 year versus 5 years; RR = 1.69; 95% CI = 1.66-1.72; 3 years versus 5 years = 1.20; 95% CI = 1.18-1.23) were strongly associated with a nonscreening indication with similar results for ≤9-month surveillance interval. Screening indication and >9-month surveillance intervals were more common in more recent years.

Conclusion: Variability in surveillance indications across facilities in the United States supports including indications beyond screening in studies evaluating surveillance mammography effectiveness and demonstrates the need for standardization.
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http://dx.doi.org/10.1016/j.jacr.2019.12.020DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7275918PMC
June 2020
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