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

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

Patient Characteristics Impact Performance of AI Algorithm in Interpreting Negative Screening Digital Breast Tomosynthesis Studies.

Background Artificial intelligence (AI) is increasingly used to manage radiologists' workloads. The impact of patient characteristics on AI performance has not been well studied. Purpose To understand the impact of patient characteristics (race and ethnicity, age, and breast density) on the performance of an AI algorithm interpreting negative screening digital breast tomosynthesis (DBT) examinations. Materials and Methods This retrospective cohort study identified negative screening DBT examinations from an academic institution from January 1, 2016, to December 31, 2019. All examinations had 2 years of follow-up without a diagnosis of atypia or breast malignancy and were therefore considered true negatives. A subset of unique patients was randomly selected to provide a broad distribution of race and ethnicity. DBT studies in this final cohort were interpreted by a U.S. Food and Drug Administration-approved AI algorithm, which generated case scores (malignancy certainty) and risk scores (1-year subsequent malignancy risk) for each mammogram. Positive examinations were classified based on vendor-provided thresholds for both scores. Multivariable logistic regression was used to understand relationships between the scores and patient characteristics. Results A total of 4855 patients (median age, 54 years [IQR, 46-63 years]) were included: 27% (1316 of 4855) White, 26% (1261 of 4855) Black, 28% (1351 of 4855) Asian, and 19% (927 of 4855) Hispanic patients. False-positive case scores were significantly more likely in Black patients (odds ratio [OR] = 1.5 [95% CI: 1.2, 1.8]) and less likely in Asian patients (OR = 0.7 [95% CI: 0.5, 0.9]) compared with White patients, and more likely in older patients (71-80 years; OR = 1.9 [95% CI: 1.5, 2.5]) and less likely in younger patients (41-50 years; OR = 0.6 [95% CI: 0.5, 0.7]) compared with patients aged 51-60 years. False-positive risk scores were more likely in Black patients (OR = 1.5 [95% CI: 1.0, 2.0]), patients aged 61-70 years (OR = 3.5 [95% CI: 2.4, 5.1]), and patients with extremely dense breasts (OR = 2.8 [95% CI: 1.3, 5.8]) compared with White patients, patients aged 51-60 years, and patients with fatty density breasts, respectively. Conclusion Patient characteristics influenced the case and risk scores of a Food and Drug Administration-approved AI algorithm analyzing negative screening DBT examinations. © RSNA, 2024.

Evolving Treatment Paradigms for Low-Risk Ductal Carcinoma In Situ: Imaging Needs.

Ductal carcinoma in situ (DCIS) is a nonobligate precursor to invasive cancer that classically presents as asymptomatic calcifications on screening mammography. The increase in DCIS diagnoses with organized screening programs has raised concerns about overdiagnosis, while a patientcentric push for more personalized care has increased awareness about DCIS overtreatment. The standard of care for most new DCIS diagnoses is surgical excision, but nonsurgical management via active monitoring is gaining attention, and multiple clinical trials are ongoing. Imaging, along with demographic and pathologic information, is a critical component of active monitoring efforts. Commonly used imaging modalities including mammography, ultrasound, and MRI, as well as newer modalities such as contrast-enhanced mammography and dedicated breast PET, can provide prognostic information to risk stratify patients for DCIS active monitoring eligibility. Furthermore, radiologists will be responsible for closely surveilling patients on active monitoring and identifying if invasive progression occurs. Active monitoring is a paradigm shift for DCIS care, but the success or failure will rely heavily on the interpretations and guidance of radiologists.

Surveillance Imaging after Primary Diagnosis of Ductal Carcinoma in Situ.

Background Guidelines recommend annual surveillance imaging after diagnosis of ductal carcinoma in situ (DCIS). Guideline adherence has not been characterized in a contemporary cohort. Purpose To identify uptake and determinants of surveillance imaging in women who underwent treatment for DCIS. Materials and Methods A stratified random sample of women who underwent breast-conserving surgery for primary DCIS between 2008 and 2014 was retrospectively selected from 1330 facilities in the United States. Imaging examinations were recorded from date of diagnosis until first distant recurrence, death, loss to follow-up, or end of study (November 2018). Imaging after treatment was categorized into 10 12-month periods starting 6 months after diagnosis. Primary outcome was per-period receipt of asymptomatic surveillance imaging (mammography, MRI, or US). Secondary outcome was diagnosis of ipsilateral invasive breast cancer. Multivariable logistic regression with repeated measures and generalized estimating equations was used to model receipt of imaging. Rates of diagnosis with ipsilateral invasive breast cancer were compared between women who did and those who did not undergo imaging in the 6-18-month period after diagnosis using inverse probability-weighted Kaplan-Meier estimators. Results A total of 12 559 women (median age, 60 years; IQR, 52-69 years) were evaluated. Uptake of surveillance imaging was 75% in the first period and decreased over time (P < .001). Across the first 5 years after treatment, 52% of women participated in consistent annual surveillance. Surveillance was lower in Black (adjusted odds ratio [OR], 0.80; 95% CI: 0.74, 0.88; P < .001) and Hispanic (OR, 0.82; 95% CI: 0.72, 0.94; P = .004) women than in White women. Women who underwent surveillance in the first period had a higher 6-year rate of diagnosis of invasive cancer (1.6%; 95% CI: 1.3, 1.9) than those who did not (1.1%; 95% CI: 0.7, 1.4; difference: 0.5%; 95% CI: 0.1, 1.0; P = .03). Conclusion Half of women did not consistently adhere to imaging surveillance guidelines across the first 5 years after treatment, with racial disparities in adherence rates. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Rahbar and Dontchos in this issue.

Breast Cancer Risk Assessment and Screening Practices Reported Via an Online Survey.

BACKGROUND: Breast cancer screening guidelines differ between organizations, and significant variations in practice patterns exist. Previous evidence suggests that provider-level factors are the greatest contributors to risk assessment and screening practice variability. This study aimed to characterize provider factors associated with breast cancer risk assessment and screening practice patterns, and to assess perceived barriers to providing risk assessment. METHODS: An online survey was distributed to providers at a single academic institution and to providers publicly via social media (January to August 2022). Respondents in the United States who care for adult women at risk for the development of breast cancer were included. RESULTS: Most of the respondents in the 143 completed surveys were white/Caucasian (79%) females (90%) age 50 years or younger (79%), and whereas 97% discuss breast cancer screening with their patients, only 90% order screening mammograms. Risk factor assessment was common (93%), typically performed at the first visit (51%). Additional training in genetics or risk assessment was uncommon (17%), although the majority were interested but did not have the time or resources (55%). Although most (64%) did not perceive barriers to providing risk assessment or appropriate screening, the most common barriers were time (77%) and education (55%). Barriers were more common among family practice or obstetrics and gynecology (OB/GYN) providers and those who worked in an academic setting (all p < 0.05). CONCLUSIONS: Breast cancer risk assessment and screening practices are highly variable. Although time is the major barrier to providing risk assessment, providers also need education. Primary care organizations could partner with breast cancer-focused societies for additional resources.

Ductal Carcinoma in Situ: State-of-the-Art Review.

Ductal carcinoma in situ (DCIS) is a nonobligate precursor of invasive cancer, and its detection, diagnosis, and management are controversial. DCIS incidence grew with the expansion of screening mammography programs in the 1980s and 1990s, and DCIS is viewed as a major driver of overdiagnosis and overtreatment. For pathologists, the diagnosis and classification of DCIS is challenging due to undersampling and interobserver variability. Understanding the progression from normal breast tissue to DCIS and, ultimately, to invasive cancer is limited by a paucity of natural history data with multiple proposed evolutionary models of DCIS initiation and progression. Although radiologists are familiar with the classic presentation of DCIS as asymptomatic calcifications at mammography, the expanded pool of modalities, advanced imaging techniques, and image analytics have identified multiple potential biomarkers of histopathologic characteristics and prognosis. Finally, there is growing interest in the nonsurgical management of DCIS, including active surveillance, to reduce overtreatment and provide patients with more personalized management options. However, current biomarkers are not adept at enabling identification of occult invasive disease at biopsy or accurately predicting the risk of progression to invasive disease. Several active surveillance trials are ongoing and are expected to better identify women with low-risk DCIS who may avoid surgery.

Prediction of Upstaging in Ductal Carcinoma in Situ Based on Mammographic Radiomic Features.

Background Improving diagnosis of ductal carcinoma in situ (DCIS) before surgery is important in choosing optimal patient management strategies. However, patients may harbor occult invasive disease not detected until definitive surgery. Purpose To assess the performance and clinical utility of mammographic radiomic features in the prediction of occult invasive cancer among women diagnosed with DCIS on the basis of core biopsy findings. Materials and Methods In this Health Insurance Portability and Accountability Act-compliant retrospective study, digital magnification mammographic images were collected from women who underwent breast core-needle biopsy for calcifications that was performed at a single institution between September 2008 and April 2017 and yielded a diagnosis of DCIS. The database query was directed at asymptomatic women with calcifications without a mass, architectural distortion, asymmetric density, or palpable disease. Logistic regression with regularization was used. Differences across training and internal test set by upstaging rate, age, lesion size, and estrogen and progesterone receptor status were assessed by using the Kruskal-Wallis or χ2 test. Results The study consisted of 700 women with DCIS (age range, 40-89 years; mean age, 59 years ± 10 [standard deviation]), including 114 with lesions (16.3%) upstaged to invasive cancer at subsequent surgery. The sample was split randomly into 400 women for the training set and 300 for the testing set (mean ages: training set, 59 years ± 10; test set, 59 years ± 10; P = .85). A total of 109 radiomic and four clinical features were extracted. The best model on the test set by using all radiomic and clinical features helped predict upstaging with an area under the receiver operating characteristic curve of 0.71 (95% CI: 0.62, 0.79). For a fixed high sensitivity (90%), the model yielded a specificity of 22%, a negative predictive value of 92%, and an odds ratio of 2.4 (95% CI: 1.8, 3.2). High specificity (90%) corresponded to a sensitivity of 37%, positive predictive value of 41%, and odds ratio of 5.0 (95% CI: 2.8, 9.0). Conclusion Machine learning models that use radiomic features applied to mammographic calcifications may help predict upstaging of ductal carcinoma in situ, which can refine clinical decision making and treatment planning. © RSNA, 2022.

Biogeochemical Niches of Fe-Cycling Communities Influencing Heavy Metal Transport along the Rio Tinto, Spain.

In the mining-impacted Rio Tinto, Spain, Fe-cycling microorganisms influence the transport of heavy metals (HMs) into the Atlantic Ocean. However, it remains largely unknown how spatial and temporal hydrogeochemical gradients along the Rio Tinto shape the composition of Fe-cycling microbial communities and how this in turn affects HM mobility. Using a combination of DNA- and RNA-based 16S rRNA (gene) amplicon sequencing and hydrogeochemical analyses, we explored the impact of pH, Fe(III), Fe(II), and Cl- on Fe-cycling microorganisms. We showed that the water column at the acidic (pH 2.2) middle course of the river was colonized by Fe(II) oxidizers affiliated with Acidithiobacillus and Leptospirillum. At the upper estuary, daily fluctuations of pH (2.7 to 3.7) and Cl- (6.9 to 16.6 g/L) contributed to the establishment of a unique microbial community, including Fe(II) oxidizers belonging to Acidihalobacter, Marinobacter, and Mariprofundus, identified at this site. Furthermore, DNA- and RNA-based profiles of the benthic community suggested that acidophilic and neutrophilic Fe(II) oxidizers (e.g., Acidihalobacter, Marinobacter, and Mariprofundus), Fe(III) reducers (e.g., Thermoanaerobaculum), and sulfate-reducing bacteria drive the Fe cycle in the estuarine sediments. RNA-based relative abundances of Leptospirillum at the middle course as well as abundances of Acidihalobacter and Mariprofundus at the upper estuary were higher than DNA-based results, suggesting a potentially higher level of activity of these taxa. Based on our findings, we propose a model of how tidal water affects the composition and activity of the Fe-cycling taxa, playing an important role in the transport of HMs (e.g., As, Cd, Cr, and Pb) along the Rio Tinto. IMPORTANCE The estuary of the Rio Tinto is a unique environment in which extremely acidic, heavy metal-rich, and especially iron-rich river water is mixed with seawater. Due to the mixing events, the estuarine water is characterized by a low pH, almost seawater salinity, and high concentrations of bioavailable iron. The unusual hydrogeochemistry maintains unique microbial communities in the estuarine water and in the sediment. These communities include halotolerant iron-oxidizing microorganisms which typically inhabit acidic saline environments and marine iron-oxidizing microorganisms which, in contrast, are not typically found in acidic environments. Furthermore, highly saline estuarine water favored the prosperity of acidophilic heterotrophs, typically inhabiting brackish and saline environments. The Rio Tinto estuarine sediment harbors a diverse microbial community with both acidophilic and neutrophilic members that can mediate the iron cycle and, in turn, can directly impact the mobility and transport of heavy metals in the Rio Tinto estuary.

Implementation of Abbreviated Breast MRI for Screening: AJR Expert Panel Narrative Review.

Abbreviated breast MRI (AB-MRI) is being rapidly adopted to harness the high sensitivity of screening MRI while addressing issues related to access, cost, and workflow. The successful implementation of an AB-MRI program requires collaboration across administrative, operational, financial, technical, and clinical providers. Institutions must be thoughtful in defining patient eligibility for AB-MRI and providing recommendations for screening intervals, as existing practices are heterogeneous. Similarly, there is no universally accepted AB-MRI protocol, though guiding principles should harmonize abbreviated and full protocols while being mindful of scan duration and amount of time patients spend on the MRI table. The interpretation of AB-MRI will be a new experience for many radiologists and may require a phased rollout and a careful audit of performance metrics over time to ensure benchmark metrics are achieved. AB-MRI finances, which are driven by patient self-payment, will require buy-in from hospital administration with the recognition that downstream revenues will be needed to support initial costs. Finally, successful startup of an AB-MRI program requires active engagement with the larger community of patients and referring providers. As AB-MRI becomes more widely accepted and available, best practices and community standards will continue to evolve to ensure high-quality patient care.

Mixed-Methods Study to Predict Upstaging of DCIS to Invasive Disease on Mammography.

BACKGROUND. The incidence of ductal carcinoma in situ (DCIS) has steadily increased, as have concerns regarding overtreatment. Active surveillance is a novel treatment strategy that avoids surgical excision, but identifying patients with occult invasive disease who should be excluded from active surveillance is challenging. Radiologists are not typically expected to predict the upstaging of DCIS to invasive disease, though they might be trained to perform this task. OBJECTIVE. The purpose of this study was to determine whether a mixed-methods two-stage observer study can improve radiologists' ability to predict upstaging of DCIS to invasive disease on mammography. METHODS. All cases of DCIS calcifications that underwent stereotactic biopsy between 2010 and 2015 were identified. Two cohorts were randomly generated, each containing 150 cases (120 pure DCIS cases and 30 DCIS cases upstaged to invasive disease at surgery). Nine breast radiologists reviewed the mammograms in the first cohort in a blinded fashion and scored the probability of upstaging to invasive disease. The radiologists then reviewed the cases and results collectively in a focus group to develop consensus criteria that could improve their ability to predict upstaging. The radiologists reviewed the mammograms from the second cohort in a blinded fashion and again scored the probability of upstaging. Statistical analysis compared the performances between rounds 1 and 2. RESULTS. The mean AUC for reader performance in predicting upstaging in round 1 was 0.623 (range, 0.514-0.684). In the focus group, radiologists agreed that upstaging was better predicted when an associated mass, asymmetry, or architectural distortion was present; when densely packed calcifications extended over a larger area; and when the most suspicious features were focused on rather than the most common features. Additionally, radiologists agreed that BI-RADS descriptors do not adequately characterize risk of invasion, and that microinvasive disease and smaller areas of DCIS will have poor prediction estimates. Reader performance significantly improved in round 2 (mean AUC, 0.765; range, 0.617-0.852; p = .045). CONCLUSION. A mixed-methods two-stage observer study identified factors that helped radiologists significantly improve their ability to predict upstaging of DCIS to invasive disease. CLINICAL IMPACT. Breast radiologists can be trained to better predict upstaging of DCIS to invasive disease, which may facilitate discussions with patients and referring providers.

Overall health at diagnosis predicts the risk of complications within the first year after breast cancer diagnosis.

PURPOSE: Breast cancer patients with overall poor health are at a greater risk of both complications during treatment and mortality from competing causes. We sought to determine the association of pre-existing comorbidities on treatment-related complications and overall survival. METHODS: We identified women ages 40-90 years old from our institutional registry with stage I-II invasive breast cancer from 2005 to 2014. Recursive partitioning was used to stratify women based on pre-existing comorbidities as low, moderate, or high risk of treatment-associated complications. Cox proportional hazards model was constructed to estimate the association of risk with overall survival. RESULTS: 2077 women were studied. Mean age was 60 (IQR 51-68). Over half (54%) had ≥ 1 comorbid condition, and 29% experienced at least one adverse medical event within 1 year of diagnosis. Risk categories included low (no comorbidities or hypertension), moderate (combinations of comorbidities excluding congestive heart failure), and high (congestive heart failure in isolation or in combination with other conditions). High-risk women had a lower 10-year OS compared to moderate- or low-risk women (89% vs 90% vs 96%, log-rank p < 0.001). After adjustment, being at moderate (HR 2.20, 95% CI 1.30-3.72, p = 0.003) or high risk (HR 5.07, 95% CI 1.66-15.52, p = 0.004) of adverse sequelae was associated with reduced OS compared to those at low risk of these adverse medical events. CONCLUSIONS: Following breast cancer diagnosis, overall poor health was associated with a greater risk of mortality and complications within the first year of treatment, which was driven by a pre-existing diagnosis of congestive heart failure.

Awareness of implicit bias mitigates discrimination in radiology resident selection.

OBJECTIVES: Implicit bias is common and is thought to drive discriminatory behaviour. Having previously demonstrated discrimination against specific applicant demographics by academic radiology departments in a simulated resident selection process, the authors sought to better understand the relationship between implicit bias and discrimination, as well as the potential and mechanisms for their mitigation. METHODS: A total of 51 faculty reviewers at three academic radiology departments, who had participated in a 2017 audit study in which they were shown to treat applicants differently based on race or ethnicity and physical appearance, were invited to complete testing for implicit racial and weight bias using the Implicit Association Test in 2019. Respondents were also surveyed regarding awareness of their own personal racial and weight biases, as well as any prior participation in formal diversity training. Comparisons were made between implicit bias scores and applicant ratings, as well as between diversity training and self-awareness of bias. RESULTS: A total of 31 out of 51 faculty reviewers (61%) completed and submitted results of race and weight Implicit Association Tests. A total of 74% (23/31) reported implicit anti-obese bias, concordant with discrimination demonstrated in the resident selection simulation, in which obese applicants were rated 0.40 standard deviations (SDs) lower than non-obese applicants (P < .001). A total of 71% (22/31) reported implicit anti-Black bias, discordant with application ratings, which were 0.47 SDs higher for Black than for White applicants (P < .001). A total of 84% (26/31) of participants reported feeling self-aware of potential racial bias at the time of application review, significantly higher than the 23% (7/31) reporting self-awareness of potential anti-obese bias (P < .001). Participation in formal diversity training was not associated with implicit anti-Black or anti-fat bias, nor with self-reported awareness of potential racial or weight-based bias (all P > .2). CONCLUSIONS: These findings suggest that implicit bias, as measured by the Implicit Association Test, does not inevitably lead to discrimination, and that personal awareness of implicit biases may allow their mitigation.

Growth Dynamics of Mammographic Calcifications: Differentiating Ductal Carcinoma in Situ from Benign Breast Disease.

Background Most ductal carcinoma in situ (DCIS) lesions are first detected on screening mammograms as calcifications. However, false-positive biopsy rates for calcifications range from 30% to 87%. Improved methods to differentiate benign from malignant calcifications are thus needed. Purpose To quantify the growth rates of DCIS and benign breast disease that manifest as mammographic calcifications. Materials and Methods All calcifications (n = 2359) for which a stereotactic biopsy was performed from 2008 through 2015 at Duke University Medical Center were retrospectively identified. Mammograms from all cases of DCIS (n = 404) were reviewed for calcifications that were visible on mammograms taken at least 6 months before biopsy. Women with at least one prior mammogram with visible calcifications were age- and race-matched 1:2 to women with a benign breast biopsy and calcifications visible on prior mammograms. The long axis of the calcifications was measured on all mammograms. Multivariable adjusted linear mixed-effects models estimated the association of calcification growth rates with patholo findings. Hierarchical clustering accounted for matching benign and DCIS groups. Results A total of 74 DCIS calcifications and 148 benign calcifications were included for final analysis. The median patient age was 62 years (interquartile range, 51-71 years). No significant difference in breast density (P > .05) or number of available mammograms (P > .05) was detected between groups. Calcifications associated with DCIS were larger than those associated with benign breast disease at biopsy (median, 10 mm vs 6 mm, respectively; P < .001). After adjustment, the relative annual increase in the long-axis length of DCIS calcifications was greater than that of benign breast calcifications (96% [95% confidence interval: 72%, 224%] vs 68% [95% confidence interval: 56%, 80%] per year, respectively; P < .001). Conclusion Ductal carcinoma in situ calcifications are more extensive at diagnosis and grow faster in extent than those associated with benign breast disease. The rate of calcification change may help to discriminate benign from malignant calcifications. © RSNA, 2019 Online supplemental material is available for this article.

Machine learning-based prediction of future breast cancer using algorithmically measured background parenchymal enhancement on high-risk screening MRI.

BACKGROUND: Preliminary work has demonstrated that background parenchymal enhancement (BPE) assessed by radiologists is predictive of future breast cancer in women undergoing high-risk screening MRI. Algorithmically assessed measures of BPE offer a more precise and reproducible means of measuring BPE than human readers and thus might improve the predictive performance of future cancer development. PURPOSE: To determine if algorithmically extracted imaging features of BPE on screening breast MRI in high-risk women are associated with subsequent development of cancer. STUDY TYPE: Case-control study. POPULATION: In all, 133 women at high risk for developing breast cancer; 46 of these patients developed breast cancer subsequently over a follow-up period of 2 years. FIELD STRENGTH/SEQUENCE: 5 T or 3.0 T T1 -weighted precontrast fat-saturated and nonfat-saturated sequences and postcontrast nonfat-saturated sequences. ASSESSMENT: Automatic features of BPE were extracted with a computer algorithm. Subjective BPE scores from five breast radiologists (blinded to clinical outcomes) were also available. STATISTICAL TESTS: Leave-one-out crossvalidation for a multivariate logistic regression model developed using the automatic features and receiver operating characteristic (ROC) analysis were performed to calculate the area under the curve (AUC). Comparison of automatic features and subjective features was performed using a generalized regression model and the P-value was obtained. Odds ratios for automatic and subjective features were compared. RESULTS: The multivariate model discriminated patients who developed cancer from the patients who did not, with an AUC of 0.70 (95% confidence interval: 0.60-0.79, P < 0.001). The imaging features remained independently predictive of subsequent development of cancer (P < 0.003) when compared with the subjective BPE assessment of the readers. DATA CONCLUSION: Automatically extracted BPE measurements may potentially be used to further stratify risk in patients undergoing high-risk screening MRI. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019;50:456-464.

A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features.

BACKGROUND: Recent studies showed preliminary data on associations of MRI-based imaging phenotypes of breast tumours with breast cancer molecular, genomic, and related characteristics. In this study, we present a comprehensive analysis of this relationship. METHODS: We analysed a set of 922 patients with invasive breast cancer and pre-operative MRI. The MRIs were analysed by a computer algorithm to extract 529 features of the tumour and the surrounding tissue. Machine-learning-based models based on the imaging features were trained using a portion of the data (461 patients) to predict the following molecular, genomic, and proliferation characteristics: tumour surrogate molecular subtype, oestrogen receptor, progesterone receptor and human epidermal growth factor status, as well as a tumour proliferation marker (Ki-67). Trained models were evaluated on the set of the remaining 461 patients. RESULTS: Multivariate models were predictive of Luminal A subtype with AUC = 0.697 (95% CI: 0.647-0.746, p < .0001), triple negative breast cancer with AUC = 0.654 (95% CI: 0.589-0.727, p < .0001), ER status with AUC = 0.649 (95% CI: 0.591-0.705, p < .001), and PR status with AUC = 0.622 (95% CI: 0.569-0.674, p < .0001). Associations between individual features and subtypes we also found. CONCLUSIONS: There is a moderate association between tumour molecular biomarkers and algorithmically assessed imaging features.

Surgical Upstaging Rates for Vacuum Assisted Biopsy Proven DCIS: Implications for Active Surveillance Trials.

PURPOSE: This study was designed to determine invasive cancer upstaging rates at surgical excision following vacuum-assisted biopsy of ductal carcinoma in situ (DCIS) among women meeting eligibility for active surveillance trials. METHODS: Patients with vacuum-assisted, biopsy-proven DCIS at a single center from 2008 to 2015 were retrospectively reviewed. Imaging and pathology reports were interrogated for the imaging appearance, tumor grade, hormone receptor status, and presence of comedonecrosis. Subsequent surgical reports were reviewed for upstaging to invasive disease. Cases were classified by eligibility criteria for the COMET, LORIS, and LORD DCIS active surveillance trials. RESULTS: Of 307 DCIS diagnoses, 15 (5%) were low, 95 (31%) intermediate, and 197 (64%) high nuclear grade. The overall upstage rate to invasive disease was 17% (53/307). Eighty-one patients were eligible for the COMET Trial, 74 for the LORIS trial, and 10 for the LORD Trial, although LORIS trial eligibility also included real-time, multiple central pathology review, including elements not routinely reported. The upstaging rates to invasive disease were 6% (5/81), 7% (5/74), and 10% (1/10) for the COMET, LORIS, and LORD trials, respectively. Among upstaged cancers (n = 5), four tumors were Stage IA invasive ductal carcinoma and one was Stage IIA invasive lobular carcinoma; all were node-negative. CONCLUSIONS: DCIS upstaging rates in women eligible for active surveillance trials are low (6-10%), and in this series, all those with invasive disease were early-stage, node-negative. The careful patient selection for DCIS active surveillance trials has a low risk of missing occult invasive cancer and additional studies will determine clinical outcomes.

Can algorithmically assessed MRI features predict which patients with a preoperative diagnosis of ductal carcinoma in situ are upstaged to invasive breast cancer?

PURPOSE: To assess the ability of algorithmically assessed magnetic resonance imaging (MRI) features to predict the likelihood of upstaging to invasive cancer in newly diagnosed ductal carcinoma in situ (DCIS). MATERIALS AND METHODS: We identified 131 patients at our institution from 2000-2014 with a core needle biopsy-confirmed diagnosis of pure DCIS, a 1.5 or 3T preoperative bilateral breast MRI with nonfat-saturated T1 -weighted MRI sequences, no preoperative therapy before breast MRI, and no prior history of breast cancer. A fellowship-trained radiologist identified the lesion on each breast MRI using a bounding box. Twenty-nine imaging features were then computed automatically using computer algorithms based on the radiologist's annotation. RESULTS: The rate of upstaging of DCIS to invasive cancer in our study was 26.7% (35/131). Out of all imaging variables tested, the information measure of correlation 1, which quantifies spatial dependency in neighboring voxels of the tumor, showed the highest predictive value of upstaging with an area under the curve (AUC) = 0.719 (95% confidence interval [CI]: 0.609-0.829). This feature was statistically significant after adjusting for tumor size (P < 0.001). CONCLUSION: Automatically assessed MRI features may have a role in triaging which patients with a preoperative diagnosis of DCIS are at highest risk for occult invasive disease. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017;46:1332-1340.