Breast Cancer Incidence After a False-Positive Mammography Result

Importance

False-positive mammography results are common. However, long-term outcomes after a false-positive result remain unclear.

Objectives

To examine long-term outcomes after a false-positive mammography result and to investigate whether the association of a false-positive mammography result with cancer differs by baseline characteristics, tumor characteristics, and time since the false-positive result.

Design, Setting, and Participants

This population-based, matched cohort study was conducted in Sweden from January 1, 1991, to March 31, 2020. It included 45 213 women who received a first false-positive mammography result between 1991 and 2017 and 452 130 controls matched on age, calendar year of mammography, and screening history (no previous false-positive result). The study also included 1113 women with a false-positive result and 11 130 matched controls with information on mammographic breast density from the Karolinska Mammography Project for Risk Prediction of Breast Cancer study. Statistical analysis was performed from April 2022 to February 2023.

Exposure

A false-positive mammography result.

Main Outcomes and Measures

Breast cancer incidence and mortality.

Results

The study cohort included 497 343 women (median age, 52 years [IQR, 42-59 years]). The 20-year cumulative incidence of breast cancer was 11.3% (95% CI, 10.7%-11.9%) among women with a false-positive result vs 7.3% (95% CI, 7.2%-7.5%) among those without, with an adjusted hazard ratio (HR) of 1.61 (95% CI, 1.54-1.68). The corresponding HRs were higher among women aged 60 to 75 years at the examination (HR, 2.02; 95% CI, 1.80-2.26) and those with lower mammographic breast density (HR, 4.65; 95% CI, 2.61-8.29). In addition, breast cancer risk was higher for women who underwent a biopsy at the recall (HR, 1.77; 95% CI, 1.63-1.92) than for those without a biopsy (HR, 1.51; 95% CI, 1.43-1.60). Cancers after a false-positive result were more likely to be detected on the ipsilateral side of the false-positive result (HR, 1.92; 95% CI, 1.81-2.04) and were more common during the first 4 years of follow-up (HR, 2.57; 95% CI, 2.33-2.85 during the first 2 years; HR, 1.93; 95% CI, 1.76-2.12 at >2 to 4 years). No statistical difference was found for different tumor characteristics (except for larger tumor size). Furthermore, associated with the increased risk of breast cancer, women with a false-positive result had an 84% higher rate of breast cancer death than those without (HR, 1.84; 95% CI, 1.57-2.15).

Conclusions and Relevance

This study suggests that the risk of developing breast cancer after a false-positive mammography result differs by individual characteristics and follow-up. These findings can be used to develop individualized risk-based breast cancer screening after a false-positive result.

False-positive recalls in the prospective Malmö Breast Tomosynthesis Screening Trial

OBJECTIVES

To evaluate the total number of false-positive recalls, including radiographic appearances and false-positive biopsies, in the Malmö Breast Tomosynthesis Screening Trial (MBTST).

METHODS

The prospective, population-based MBTST, with 14,848 participating women, was designed to compare one-view digital breast tomosynthesis (DBT) to two-view digital mammography (DM) in breast cancer screening. False-positive recall rates, radiographic appearances, and biopsy rates were analyzed. Comparisons were made between DBT, DM, and DBT + DM, both in total and in trial year 1 compared to trial years 2 to 5, with numbers, percentages, and 95% confidence intervals (CI).

RESULTS

The false-positive recall rate was higher with DBT, 1.6% (95% CI 1.4; 1.8), compared to screening with DM, 0.8% (95% CI 0.7; 1.0). The proportion of the radiographic appearance of stellate distortion was 37.3% (91/244) with DBT, compared to 24.0% (29/121) with DM. The false-positive recall rate with DBT during trial year 1 was 2.6% (95% CI 1.8; 3.5), then stabilized at 1.5% (95% CI 1.3; 1.8) during trial years 2 to 5. The percentage of stellate distortion with DBT was 50% (19/38) trial year 1 compared to 35.0% (72/206) trial years 2 to 5.

CONCLUSIONS

The higher false-positive recall rate with DBT compared to DM was mainly due to an increased detection of stellate findings. The proportion of these findings, as well as the DBT false-positive recall rate, was reduced after the first trial year.

CLINICAL RELEVANCE STATEMENT

Assessment of false-positive recalls gives information on potential benefits and side effects in DBT screening.

KEY POINTS

• The false-positive recall rate in a prospective digital breast tomosynthesis screening trial was higher compared to digital mammography, but still low compared to other trials. • The higher false-positive recall rate with digital breast tomosynthesis was mainly due to an increased detection of stellate findings; the proportion of these findings was reduced after the first trial year.

Global Radiomic Features from Mammography for Predicting Difficult-To-Interpret Normal Cases

This work aimed to investigate whether global radiomic features (GRFs) from mammograms can predict difficult-to-interpret normal cases (NCs). Assessments from 537 readers interpreting 239 normal mammograms were used to categorise cases as 120 difficult-to-interpret and 119 easy-to-interpret based on cases having the highest and lowest difficulty scores, respectively. Using lattice- and squared-based approaches, 34 handcrafted GRFs per image were extracted and normalised. Three classifiers were constructed: (i) CC and (ii) MLO using the GRFs from corresponding craniocaudal and mediolateral oblique images only, based on the random forest technique for distinguishing difficult- from easy-to-interpret NCs, and (iii) CC + MLO using the median predictive scores from both CC and MLO models. Useful GRFs for the CC and MLO models were recognised using a scree test. The CC and MLO models were trained and validated using the leave-one-out-cross-validation. The models' performances were assessed by the AUC and compared using the DeLong test. A Kruskal-Wallis test was used to examine if the 34 GRFs differed between difficult- and easy-to-interpret NCs and if difficulty level based on the traditional breast density (BD) categories differed among 115 low-BD and 124 high-BD NCs. The CC + MLO model achieved higher performance (0.71 AUC) than the individual CC and MLO model alone (0.66 each), but statistically non-significant difference was found (all p > 0.05). Six GRFs were identified to be valuable in describing difficult-to-interpret NCs. Twenty features, when compared between difficult- and easy-to-interpret NCs, differed significantly (p < 0.05). No statistically significant difference was observed in difficulty between low- and high-BD NCs (p = 0.709). GRF mammographic analysis can predict difficult-to-interpret NCs.

Cumulative risks of false positive recall and screen-detected breast cancer after multiple screening examinations

Women tend to make a decision about participation in breast cancer screening and adhere to this for future invitations. Therefore, our study aimed to provide high-quality information on cumulative risks of false-positive (FP) recall and screen-detected breast cancer over multiple screening examinations. Individual Dutch screening registry data (2005-2018) were gathered on subsequent screening examinations of 92 902 women age 49 to 51 years in 2005. Survival analyses were used to calculate cumulative risks of a FP and a true-positive (TP) result after seven examinations. Data from 66 472 women age 58 to 59 years were used to extrapolate to 11 examinations. Participation, detection and additional FP rates were calculated for women who previously received FP results compared to women with true negative (TN) results. After 7 examinations, the cumulative risk of a TP result was 3.7% and the cumulative risk of a FP result was 9.1%. After 11 examinations, this increased to 7.1% and 13.5%, respectively. Following a FP result, participation was lower (71%-81%) than following a TN result (>90%). In women with a FP result, more TP results (factor 1.59 [95% CI: 1.44-1.72]), more interval cancers (factor 1.66 [95% CI: 1.41-1.91]) and more FP results (factor 1.96 [95% CI: 1.87-2.05]) were found than in women with TN results. In conclusion, due to a low recall rate in the Netherlands, the cumulative risk of a FP recall is relatively low, while the cumulative risk of a TP result is comparable. Breast cancer diagnoses and FP results were more common in women with FP results than in women with TN results, while participation was lower.

Choice of Assessment and Subsequent Risk of Breast Cancer among Women with False-Positive Mammography Screening

Women with false-positive mammography screening results have a two- to four-fold higher risk of breast cancer. This study aimed to investigate if the subsequent risk of breast cancer after a false-positive mammography screening is associated with the received diagnostic assessment. The study population consisted of women who underwent false-positive mammography screening from January 2010 to June 2019. They were categorised into seven groups depending on the elements in the assessment (standard care: additional mammography, ultrasound, and if they had a relevant biopsy). Risks of interval cancer, next-round screen-detected cancer, and long-term breast cancer for non-standard care assessments were compared to standard care assessments using Binomial and Cox regression models. We included 44,279 women with a false-positive result. Invasive assessments that lacked an ultrasound or additional mammography were not more associated with an increased risk of subsequent cancers compared to that of 'all three elements'. The few assessments that included 'only ultrasound' or 'only mammography' resulted in higher relative risks of next-round screen-detected cancer of 1.52 (95% CI: 0.93-2.47) and 1.67 (95% CI: 0.54-5.16), respectively, compared to that of standard care. The increased subsequent risk of breast cancer among women with a previous false-positive result was not found to be correlated with the choice of elements in the assessment process.

The Potential of Adding Mammography to Handheld Ultrasound or Automated Breast Ultrasound to Reduce Unnecessary Biopsies in BI-RADS Ultrasound Category 4a: A Multicenter Hospital-Based Study in China

The appropriate management strategies for BI-RADS category 4a lesions among handheld ultrasound (HHUS) remain a matter of debate. We aimed to explore the role of automated breast ultrasound (ABUS) or the second-look mammography (MAM) adjunct to ultrasound (US) of 4a masses to reduce unnecessary biopsies. Women aged 30 to 69 underwent HHUS and ABUS from 2016 to 2017 at five high-level hospitals in China, with those aged 40 or older also accepting MAM. Logistic regression analysis assessed image variables correlated with false-positive lesions in US category 4a. Unnecessary biopsies, invasive cancer (IC) yields, and diagnostic performance among different biopsy thresholds were compared. A total of 1946 women (44.9 ± 9.8 years) were eligible for analysis. The false-positive rate of category 4a in ABUS was almost 65.81% (77/117), which was similar to HHUS (67.55%; 127/188). Orientation, architectural distortion, and duct change were independent factors associated with the false-positive lesions in 4a of HHUS, whereas postmenopausal, calcification, and architectural distortion were significant features of ABUS (all p < 0.05). For HHUS, both unnecessary biopsy rate and IC yields were significantly reduced when changing biopsy thresholds by adding MAM for US 4a in the total population (scenario #1:BI-RADS 3, 4, and 5; scenario #2: BI-RADS 4 and 5) compared with the current scenario (all p < 0.05). Notably, scenario #1 reduced false-positive biopsies without affecting IC yields when compared to the current scenario for ABUS (p < 0.001; p = 0.125). The higher unnecessary biopsy rate of category 4a by ABUS was similar to HHUS. However, the second-look MAM adjunct to ABUS has the potential to safely reduce false-positive biopsies compared with HHUS.

Computer-aided breast cancer detection and classification in mammography: A comprehensive review

Cancer is the second cause of mortality worldwide and it has been identified as a perilous disease. Breast cancer accounts for ∼20% of all new cancer cases worldwide, making it a major cause of morbidity and mortality. Mammography is an effective screening tool for the early detection and management of breast cancer. However, the identification and interpretation of breast lesions is challenging even for expert radiologists. For that reason, several Computer-Aided Diagnosis (CAD) systems are being developed to assist radiologists to accurately detect and/or classify breast cancer. This review examines the recent literature on the automatic detection and/or classification of breast cancer in mammograms, using both conventional feature-based machine learning and deep learning algorithms. The review begins with a comparison of algorithms developed specifically for the detection and/or classification of two types of breast abnormalities, micro-calcifications and masses, followed by the use of sequential mammograms for improving the performance of the algorithms. The available Food and Drug Administration (FDA) approved CAD systems related to triage and diagnosis of breast cancer in mammograms are subsequently presented. Finally, a description of the open access mammography datasets is provided and the potential opportunities for future work in this field are highlighted. The comprehensive review provided here can serve both as a thorough introduction to the field but also provide indicative directions to guide future applications.

A Review of Computer-Aided Breast Cancer Diagnosis Using Sequential Mammograms

Radiologists assess the results of mammography, the key screening tool for the detection of breast cancer, to determine the presence of malignancy. They, routinely, compare recent and prior mammographic views to identify changes between the screenings. In case a new lesion appears in a mammogram, or a region is changing rapidly, it is more likely to be suspicious, compared to a lesion that remains unchanged and it is usually benign. However, visual evaluation of mammograms is challenging even for expert radiologists. For this reason, various Computer-Aided Diagnosis (CAD) algorithms are being developed to assist in the diagnosis of abnormal breast findings using mammograms. Most of the current CAD systems do so using only the most recent mammogram. This paper provides a review of the development of methods to emulate the radiological approach and perform automatic segmentation and/or classification of breast abnormalities using sequential mammogram pairs. It begins with demonstrating the importance of utilizing prior views in mammography, through the review of studies where the performance of expert and less-trained radiologists was compared. Following, image registration techniques and their application to mammography are presented. Subsequently, studies that implemented temporal analysis or subtraction of temporally sequential mammograms are summarized. Finally, a description of the open access mammography datasets is provided. This comprehensive review can serve as a thorough introduction to the use of prior information in breast cancer CAD systems but also provides indicative directions to guide future applications.

External Validation of a Mammography-Derived AI-Based Risk Model in a U.S. Breast Cancer Screening Cohort of White and Black Women

Despite the demonstrated potential of artificial intelligence (AI) in breast cancer risk assessment for personalizing screening recommendations, further validation is required regarding AI model bias and generalizability. We performed external validation on a U.S. screening cohort of a mammography-derived AI breast cancer risk model originally developed for European screening cohorts. We retrospectively identified 176 breast cancers with exams 3 months to 2 years prior to cancer diagnosis and a random sample of 4963 controls from women with at least one-year negative follow-up. A risk score for each woman was calculated via the AI risk model. Age-adjusted areas under the ROC curves (AUCs) were estimated for the entire cohort and separately for White and Black women. The Gail 5-year risk model was also evaluated for comparison. The overall AUC was 0.68 (95% CIs 0.64−0.72) for all women, 0.67 (0.61−0.72) for White women, and 0.70 (0.65−0.76) for Black women. The AI risk model significantly outperformed the Gail risk model for all women p < 0.01 and for Black women p < 0.01, but not for White women p = 0.38. The performance of the mammography-derived AI risk model was comparable to previously reported European validation results; non-significantly different when comparing White and Black women; and overall, significantly higher than that of the Gail model.

Reducing Unnecessary Biopsies Using Digital Breast Tomosynthesis and Ultrasound in Dense and Nondense Breasts

Aim: To compare digital breast tomosynthesis (DBT) and ultrasound in women recalled for assessment after a positive screening mammogram and assess the potential for each of these tools to reduce unnecessary biopsies. Methods: This data linkage study included 538 women recalled for assessment from January 2017 to December 2019. The association between the recalled mammographic abnormalities and breast density was analysed using the chi-square independence test. Relative risks and the number of recalled cases requiring DBT and ultrasound assessment to prevent one unnecessary biopsy were compared using the McNemar test. Results: Breast density significantly influenced recall decisions (p < 0.001). Ultrasound showed greater potential to decrease unnecessary biopsies than DBT: in entirely fatty (21% vs. 5%; p = 0.04); scattered fibroglandular (23% vs. 10%; p = 0.003); heterogeneously dense (34% vs. 7%; p < 0.001) and extremely dense (39% vs. 9%; p < 0.001) breasts. The number of benign cases needing assessment to prevent one unnecessary biopsy was significantly lower with ultrasound than DBT in heterogeneously dense (1.8 vs. 7; p < 0.001) and extremely dense (1.9 vs. 5.1; p = 0.03) breasts. Conclusion: Women with dense breasts are more likely to be recalled for assessment and have a false-positive biopsy. Women with dense breasts benefit more from ultrasound assessment than from DBT.

Breast cancer risk predictions by birth cohort and ethnicity in a population-based screening mammography program

OBJECTIVES

To examine whether birth cohorts affect the risk of breast cancer for East Asian, First Nations, African, South Asian and Caucasian ethnicities in British Columbia (BC).

METHODS

We used Cox PH models adjusted for well-known risk factors, such as age, breast density, mammographic features on false positives, and family history, to examine risk of breast cancer among East Asian, First Nations, African and South Asian ethnicities, relative to Caucasian, across three birth cohorts.

RESULTS

There were 813,280 participants and 11,166 in situ and invasive breast cancer diagnoses. East Asians screened in BC were found to have a lower risk of breast cancer in the birth cohort born pre-1946 compared to Caucasian, but there was no statistically significant decrease for East Asians born after 1946. First Nations had an increased risk of breast cancer compared with Caucasian for all birth cohorts ranging from 1.1 to 2.0x the risk, which was statistically significant for those born after 1965. South Asians showed a statistically significant decrease in risk ranging from 0.58 to 0.81x lower compared with Caucasians for all birth cohorts.

CONCLUSION

Risk of breast cancer for South Asians living in BC was found to be lower than Caucasians for each birth cohort examined, while East Asians had a comparable risk of breast cancer, First Nations had a consistently higher risk than Caucasians.

ADVANCES IN KNOWLEDGE

When accounting for birth cohort, compared to Caucasians, South Asians have a decreased risk, First Nations have an increased risk, and East Asians have a similar risk of breast cancer.

Diagnostic efficacy of the new prospective biomarker's combination CA 15-3 and CA-62 for early-stage breast cancer detection: Results of the blind prospective-retrospective clinical study

BACKGROUND

Combination of different cancer markers is often used for predicting tumor growth, for the response to cancer therapy, and for increase in the positive diagnosis ratio in the malignant tumors.

OBJECTIVE

Evaluation of the diagnostic efficacy of CA 15-3 and CA-62 cancer markers combination for early stages of breast cancer (BC) detection.

METHODS

This retrospective blind study was performed on 2 clinically validated Sets that included serum measurements of CA 15-3 ELISA and CLIA-CA-62 assays in 488 serum samples with TNM classification. A study included 300 BC patients (254 at Stages I and II, 20 with ductal carcinoma in situ (DCIS), and 26 Stages III and IV patients), 47 patients with breast benign diseases, and 141 healthy controls.

RESULTS

Sensitivity for DCIS & Stage I breast cancer detection was 75% at 100% Specificity (AUC = 0.895) using a following combination of two antigens: 10 < CA15-3 < 46 U/ml and CA-62 ⩾ 6300 U/ml, which allows eliminating false positive results.

CONCLUSIONS

The results obtained in a blind study demonstrate that a combination of CA15-3 with CA-62 yields 75% Sensitivity at 100% Specificity for DCIS and Stage I breast cancer detection, which has a potential to be integrated into existing screening programs.

Mammographic Breast Density, Benign Breast Disease, and Subsequent Breast Cancer Risk in 3.9 Million Korean Women

Background Mammographic breast density and benign breast disease are strong risk factors for breast cancer. Accordingly, women with both risk factors may have a markedly high risk for developing breast cancer. Purpose To investigate the risk of breast cancer associated with the combination of mammographic density and benign breast disease in Korean women, where population-based mammographic breast cancer screening is provided for all women aged at least 40 years. Materials and Methods This is a retrospective analysis of data from a nationwide breast cancer screening program linked with the national cancer registry. The study included Korean women between 40-74 years of age screened for breast cancer between January 2009 and December 2010 and observed up to December 2020 (median follow-up of 10.6 years). Benign breast disease and breast density were extracted from mammography screening results. Cox proportional hazard regression analysis was used to calculate adjusted hazard ratios (HRs) for breast cancer risk. Results In this study, 3 911 348 women (mean age, 53 years ± 9 [SD]) were analyzed. During follow-up (median, 10.6 years), 58 321 women developed breast cancer. At screening, 10 729 (18.4%) cases of benign breast disease were detected among women who developed breast cancer. Women with extremely dense breasts (Breast Imaging Reporting and Data System [BI-RADS] density category D) and benign breast disease had a greater risk of breast cancer when compared with women presenting with fatty breast (BI-RADS density category A) and those without benign breast disease (HR, 2.75; 95% CI: 2.63, 2.88; P < .001). Women with benign breast disease and fatty breasts (HR, 1.49; 95% CI: 1.40, 1.58; P < .001) and women with extremely dense breasts and without benign breast disease (HR, 2.28; 95% CI: 2.20, 2.35; P < .001) also had an elevated breast cancer risk compared with women with fatty breast tissue. Conclusion Women with dense breasts and benign breast disease at screening mammography had an elevated risk of future breast cancer. © RSNA, 2022 Online supplemental material is available for this article.

Mammographic microcalcifications and risk of breast cancer

BACKGROUND

Mammographic microcalcifications are considered early signs of breast cancer (BC). We examined the association between microcalcification clusters and the risk of overall and subtype-specific BC. Furthermore, we studied how mammographic density (MD) influences the association between microcalcification clusters and BC risk.

METHODS

We used a prospective cohort (n = 53,273) of Swedish women with comprehensive information on BC risk factors and mammograms. The total number of microcalcification clusters and MD were measured using a computer-aided detection system and the STRATUS method, respectively. Cox regressions and logistic regressions were used to analyse the data.

RESULTS

Overall, 676 women were diagnosed with BC. Women with ≥3 microcalcification clusters had a hazard ratio [HR] of 2.17 (95% confidence interval [CI] = 1.57-3.01) compared to women with no clusters. The estimated risk was more pronounced in premenopausal women (HR = 2.93; 95% CI = 1.67-5.16). For postmenopausal women, microcalcification clusters and MD had a similar influence on BC risk. No interaction was observed between microcalcification clusters and MD. Microcalcification clusters were significantly associated with in situ breast cancer (odds ratio: 2.03; 95% CI = 1.13-3.63).

CONCLUSIONS

Microcalcification clusters are an independent risk factor for BC, with a higher estimated risk in premenopausal women. In postmenopausal women, microcalcification clusters have a similar association with BC as baseline MD.

Ten years follow-up of histologically benign calcifications in the breast after vacuum-assisted stereotactic biopsy (VASB): Is additional mammographic follow-up warranted?

Objective

This study assessed the short-term and the long-term breast cancer rate in patients with benign histopathologic results after a vacuum-assisted stereotactic biopsy (VASB) for calcifications.

Methods

In a retrospective cohort study, all consecutive patients who had a benign diagnosis after VASB to analyze breast calcifications. Data of breast cancer development at short-term (four years) and long-term follow-up was gathered. Breast cancer rates in our cohort were compared to the breast cancer incidence in the general population.

Results

Of 1376 patients who underwent VASB to analyze breast calcifications, 823 had a benign histopathologic diagnosis. During short-term follow-up, eight patients developed breast cancer. During the mean long-term follow-up period of 9.3 ± 3.1 years, 22 patients were diagnosed with ipsilateral breast cancer. The incidence rate of breast cancer after benign biopsy was comparable to the rate in the general population.

Conclusion

In patients with VASB-confirmed benign calcifications of the breast, we found no excess incidence of ipsilateral breast cancer during ten years follow-up. Therefore, in patients with an increased risk of breast cancer (due to a history of breast cancer or familial risk) annual mammography should be sufficient. Patients with a population-based risk may be monitored via biennial mammography by the national screening program. More frequent screening would provide no benefit.

•

1% developed ipsilateral breast cancer in four-year follow-up after biopsy.

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3% developed ipsilateral breast cancer in ten-years follow-up after biopsy.

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No statistically significant excess incidence of breast cancer after benign calcifications.

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A benign histopathologic result after VASB can be considered a safe decision tool.

Mammographic features of benign breast lesions and risk of subsequent breast cancer in women attending breast cancer screening

OBJECTIVES

To evaluate the mammographic features in women with benign breast disease (BBD) and the risk of subsequent breast cancer according to their mammographic findings.

METHODS

We analyzed data from a Spanish cohort of women screened from 1995 to 2015 and followed up until December 2017 (median follow-up, 5.9 years). We included 10,650 women who had both histologically confirmed BBD and mammographic findings. We evaluated proliferative and nonproliferative BBD subtypes, and their mammographic features: architectural distortion, asymmetries, calcifications, masses, and multiple findings. The adjusted hazard ratios (aHR) and 95% confidence intervals (95% CI) for breast cancer were estimated using a Cox proportional hazards model. We plotted the adjusted cumulative incidence curves.

RESULTS

Calcifications were more frequent in proliferative disease with atypia (43.9%) than without atypia (36.8%) or nonproliferative disease (22.2%; p value < 0.05). Masses were more frequent in nonproliferative lesions (59.1%) than in proliferative lesions without atypia (35.1%) or with atypia (30.0%; p value < 0.05). Multiple findings and architectural distortion were more likely in proliferative disease (16.1% and 4.7%) than in nonproliferative disease (12.8% and 1.9%). Subsequent breast cancer occurred in 268 (2.5%) women. Compared with women who had masses, the highest risk of subsequent breast cancer was found in those with architectural distortions (aHR, 2.21; 95% CI, 1.16-4.22), followed by those with multiple findings (aHR, 1.89; 95% CI, 1.34-2.66), asymmetries (aHR, 1.66; 95% CI, 0.84-3.28), and calcifications (aHR, 1.60; 95% CI, 1.21-2.12).

CONCLUSION

BBD subtypes showed distinct mammographic findings. The risk of subsequent breast cancer was high in those who have shown architectural distortion, multiple findings, asymmetries, and calcifications than in women with masses.

KEY POINTS

• The presence of mammographic findings in women attending breast cancer screening helps clinicians to assess women with benign breast disease (BBD). • Calcifications were frequent in BBDs with atypia, which are the ones with a high breast cancer risk, while masses were common in low-risk BBDs. • The excess risk of subsequent breast cancer in women with BBD was higher in those who showed architectural distortion compared to those with masses.

Developing and validating an individualized breast cancer risk prediction model for women attending breast cancer screening

BACKGROUND

Several studies have proposed personalized strategies based on women's individual breast cancer risk to improve the effectiveness of breast cancer screening. We designed and internally validated an individualized risk prediction model for women eligible for mammography screening.

METHODS

Retrospective cohort study of 121,969 women aged 50 to 69 years, screened at the long-standing population-based screening program in Spain between 1995 and 2015 and followed up until 2017. We used partly conditional Cox proportional hazards regression to estimate the adjusted hazard ratios (aHR) and individual risks for age, family history of breast cancer, previous benign breast disease, and previous mammographic features. We internally validated our model with the expected-to-observed ratio and the area under the receiver operating characteristic curve.

RESULTS

During a mean follow-up of 7.5 years, 2,058 women were diagnosed with breast cancer. All three risk factors were strongly associated with breast cancer risk, with the highest risk being found among women with family history of breast cancer (aHR: 1.67), a proliferative benign breast disease (aHR: 3.02) and previous calcifications (aHR: 2.52). The model was well calibrated overall (expected-to-observed ratio ranging from 0.99 at 2 years to 1.02 at 20 years) but slightly overestimated the risk in women with proliferative benign breast disease. The area under the receiver operating characteristic curve ranged from 58.7% to 64.7%, depending of the time horizon selected.

CONCLUSIONS

We developed a risk prediction model to estimate the short- and long-term risk of breast cancer in women eligible for mammography screening using information routinely reported at screening participation. The model could help to guiding individualized screening strategies aimed at improving the risk-benefit balance of mammography screening programs.

Risk of breast cancer two years after a benign biopsy depends on the mammographic feature prompting recall

OBJECTIVE

We aimed to explore whether the type of mammographic feature prompting a false-positive recall (FPR) during mammography screening influences the risk and timing of breast cancer diagnosis, particularly if assessed with invasive procedures.

STUDY DESIGN

We included information on women screened and recalled for further assessment in Spain between 1994 and 2015, with follow-up until 2017, categorizing FPRs by the assessment (noninvasive or invasive) and mammographic feature prompting the recall.

MAIN OUTCOME MEASURES

Breast cancer rates in the first two years after FPR (first period) and after two years (second period).

RESULTS

The study included 99,825 women with FPRs. In both periods, the breast cancer rate was higher in the invasive assessment group than in the noninvasive group (first period 12 ‰ vs 1.9 ‰, p < 0.001; second period 4.4‰ vs 3.1‰, p < 0.001). During the first period, the invasive assessment group showed diverse breast cancer rates for each type of mammographic feature, with a higher rate for asymmetric density (31.9‰). When the second period was compared with the first, the breast cancer rate decreased in the invasive assessment group (from 12‰ to 4.4‰, p < 0.001) and increased in the noninvasive assessment group (from 1.9‰ to 3.1‰, p < 0.001).

CONCLUSION

In the context of mammography screening, the risk of breast cancer diagnosis during the first two years after FPR was particularly high for women undergoing invasive assessment; importantly, the risk was modified by type of mammographic feature prompting the recall. This information could help to individualize follow-up after exclusion of malignancy.

A Review of Perceptual Expertise in Radiology-How it develops, How we can test it, and Why humans still matter in the era of Artificial Intelligence

As the first step in image interpretation is detection, an error in perception can prematurely end the diagnostic process leading to missed diagnoses. Because perceptual errors of this sort-"failure to detect"-are the most common interpretive error (and cause of litigation) in radiology, understanding the nature of perceptual expertise is essential in decreasing radiology's long-standing error rates. In this article, we review what constitutes a perceptual error, the existing models of radiologic image perception, the development of perceptual expertise and how it can be tested, perceptual learning methods in training radiologists, and why understanding perceptual expertise is still relevant in the era of artificial intelligence. Adding targeted interventions, such as perceptual learning, to existing teaching practices, has the potential to enhance expertise and reduce medical error.

Association of Microcalcification Clusters with Short-term Invasive Breast Cancer Risk and Breast Cancer Risk Factors

Using for-presentation and for-processing digital mammograms, the presence of microcalcifications has been shown to be associated with short-term risk of breast cancer. In a previous article we developed an algorithm for microcalcification cluster detection from for-presentation digital mammograms. Here, we focus on digitised mammograms and use a three-step algorithm. In total, 253 incident invasive breast cancer cases (with a negative mammogram between three months and two years before diagnosis, from which we measured microcalcifications) and 728 controls (also with prior mammograms) were included in a short-term risk study. After adjusting for potential confounding variables, we found evidence of an association between the number of microcalcification clusters and short-term (within 3-24 months) invasive breast cancer risk (per cluster OR = 1.30, 95% CI = (1.11, 1.53)). Using the 728 postmenopausal healthy controls, we also examined association of microcalcification clusters with reproductive factors and other established breast cancer risk factors. Age was positively associated with the presence of microcalcification clusters (p = 4 × 10^-04). Of ten other risk factors that we studied, life time breastfeeding duration had the strongest evidence of association with the presence of microcalcifications (positively associated, unadjusted p = 0.001). Developing algorithms, such as ours, which can be applied on both digitised and digital mammograms (in particular for presentation images), is important because large epidemiological studies, for deriving markers of (clinical) risk prediction of breast cancer and prognosis, can be based on images from these different formats.

Analysis of Perceptual Expertise in Radiology - Current Knowledge and a New Perspective

Radiologists rely principally on visual inspection to detect, describe, and classify findings in medical images. As most interpretive errors in radiology are perceptual in nature, understanding the path to radiologic expertise during image analysis is essential to educate future generations of radiologists. We review the perceptual tasks and challenges in radiologic diagnosis, discuss models of radiologic image perception, consider the application of perceptual learning methods in medical training, and suggest a new approach to understanding perceptional expertise. Specific principled enhancements to educational practices in radiology promise to deepen perceptual expertise among radiologists with the goal of improving training and reducing medical error.

A systematic review and quality assessment of individualised breast cancer risk prediction models

Background

Individualised breast cancer risk prediction models may be key for planning risk-based screening approaches. Our aim was to conduct a systematic review and quality assessment of these models addressed to women in the general population.

Methods

We followed the Cochrane Collaboration methods searching in Medline, EMBASE and The Cochrane Library databases up to February 2018. We included studies reporting a model to estimate the individualised risk of breast cancer in women in the general population. Study quality was assessed by two independent reviewers. Results are narratively summarised.

Results

We included 24 studies out of the 2976 citations initially retrieved. Twenty studies were based on four models, the Breast Cancer Risk Assessment Tool (BCRAT), the Breast Cancer Surveillance Consortium (BCSC), the Rosner & Colditz model, and the International Breast Cancer Intervention Study (IBIS), whereas four studies addressed other original models. Four of the studies included genetic information. The quality of the studies was moderate with some limitations in the discriminative power and data inputs. A maximum AUROC value of 0.71 was reported in the study conducted in a screening context.

Conclusion

Individualised risk prediction models are promising tools for implementing risk-based screening policies. However, it is a challenge to recommend any of them since they need further improvement in their quality and discriminatory capacity.

Near Field Breast Tumor Detection Using Ultra-Narrow Band Probe with Machine Learning Techniques

In this work, we propose a near-field microwave sensing modality that uses a single probe combined with a classification algorithm to help in detecting the presence of tumors in the human female breast. The concept employs a near-field resonant probe with an ultra-narrow frequency response. The resonant probe is highly sensitive to the changes in the electromagnetic properties of the breast tissues such that the presence of the tumor is gauged by determining the changes in the magnitude and phase response of the sensor's reflection coefficient. A key feature of our proposed detection concept is the simultaneous sensing of tissue property changes to the two female breasts since the right and left healthy breasts are morphologically and materially identical. Once the probe response is recorded for both breasts, the Principle Component Analysis (PCA) method is employed to emphasize the difference in the probe responses. For validation of the concept, tumors embedded in a realistic breast phantoms were simulated. To provide additional confidence in the detection modality introduced here, experimental results of three different sizes of metallic spheres, mimicking tumors, inserted inside chicken and beef meat were detected using an electrically-small probe operating at 200 MHz. The results obtained from the numerical tests and experiments strongly suggest that the detection modality presented here might lead to an inexpensive and portable early and regular screening for breast tumor.

Errors in Mammography Cannot be Solved Through Technology Alone

Mammography has been the frontline screening tool for breast cancer for decades. However, high error rates in the form of false negatives (FNs) and false positives (FPs) have persisted despite technological improvements. Radiologists still miss between 10% and 30% of cancers while 80% of woman recalled for additional views have normal outcomes, with 40% of biopsied lesions being benign. Research show that the majority of cancers missed is actually visible and looked at, but either go unnoticed or are deemed to be benign. Causal agents for these errors include human related characteristics resulting in contributory search, perception and decision-making behaviours. Technical, patient and lesion factors are also important relating to positioning, compression, patient size, breast density and presence of breast implants as well as the nature and subtype of the cancer itself, where features such as architectural distortion and triple-negative cancers remain challenging to detect on screening. A better understanding of these causal agents as well as the adoption of technological and educational interventions, which audits reader performance and provide immediate perceptual feedback, should help. This paper reviews the current status of our knowledge around error rates in mammography and explores the factors impacting it. It also presents potential solutions for maximizing diagnostic efficacy thus benefiting the millions of women who undergo this procedure each year.

Biomarkers expression in benign breast diseases and risk of subsequent breast cancer: a case-control study

Women with benign breast diseases (BBD) have a high risk of breast cancer. However, no biomarkers have been clearly established to predict cancer in these women. Our aim was to explore whether estrogen receptor (ER), progesterone receptor (PR), and Ki67 expression stratify risk of breast cancer in screened women with BBD. We conducted a nested case-control study. Women with breast cancer and prior BBDs (86 cases) were matched to women with prior BBDs who were free from breast cancer (172 controls). The matching factors were age at BBD diagnosis, type of BBD, and follow-up time since BBD diagnosis. ER, PR, and Ki67 expression were obtained from BBDs' specimens. Conditional logistic regression was used to estimate odds ratios (ORs), and 95% confidence intervals (CIs) of breast cancer risk according to ER, PR, and Ki67 expression. Women with >90% of ER expression had a higher risk of breast cancer (OR = 2.63; 95% CI: 1.26-5.51) than women with ≤70% of ER expression. Similarly, women with >80% of PR expression had a higher risk of breast cancer (OR = 2.22; 95% CI: 1.15-4.27) than women with ≤40% of PR expression. Women with proliferative disease and ≥1% of Ki67 expression had a nonsignificantly increased risk of breast cancer (OR = 1.16; 95% CI: 0.46-2.90) than women with <1% of Ki67 expression. A high expression of ER and PR in BBD is associated with an increased risk of subsequent breast cancer. In proliferative disease, high Ki67 expression may also have an increased risk. This information is helpful to better characterize BBD and is one more step toward personalizing the clinical management of these women.

[Variations in the diagnostic confirmation process between breast cancer mass screening units]

OBJECTIVE

To analyse variations in the diagnostic confirmation process between screening units, variations in the outcome of each episode and the relationship between the use of the different diagnostic confirmation tests and the lesion detection rate.

METHOD

Observational study of variability of the standardised use of diagnostic and lesion detection tests in 34 breast cancer mass screening units participating in early-detection programmes in three Spanish regions from 2002-2011.

RESULTS

The diagnostic test variation ratio in percentiles 25-75 ranged from 1.68 (further appointments) to 3.39 (fine-needle aspiration). The variation ratio in detection rates of benign lesions, ductal carcinoma in situ and invasive cancer were 2.79, 1.99 and 1.36, respectively. A positive relationship between rates of testing and detection rates was found with fine-needle aspiration-benign lesions (R(2): 0.53), fine-needle aspiration-invasive carcinoma (R(2): 0 28), core biopsy-benign lesions (R(2): 0.64), core biopsy-ductal carcinoma in situ (R(2): 0.61) and core biopsy-invasive carcinoma (R(2): 0.48).

CONCLUSIONS

Variation in the use of invasive tests between the breast cancer screening units participating in early-detection programmes was found to be significantly higher than variations in lesion detection. Units which conducted more fine-needle aspiration tests had higher benign lesion detection rates, while units that conducted more core biopsies detected more benign lesions and cancer.