Characteristics and prognosis of interval cancers after biennial screen-film or full-field digital screening mammography

Systematic review and meta-analysis of prognostic characteristics for breast cancers in populations with digital vs film mammography indicate the transition may have increased both early detection and overdiagnosis

OBJECTIVES

Film mammography has been replaced by digital mammography in breast screening programs globally. This led to a small increase in the rate of detection, but whether the detection of clinically important cancers increased is uncertain. We aimed to assess the impact on tumor characteristics of screen-detected and interval breast cancers.

STUDY DESIGN AND SETTING

We searched seven databases from inception to October 08, 2023, for publications comparing film and digital mammography within the same population of asymptomatic women at population (average) risk of breast cancer. We recorded reported tumor characteristics and assessed risk of bias using the Risk Of Bias In Non-randomised Studies - of Interventions tool. We synthesized results using meta-analyses of random effects.

RESULTS

Eighteen studies were included in the analysis from 8 countries, including 11,592,225 screening examinations (8,117,781 film; 3,474,444 digital). There were no differences in tumor size, morphology, grade, node status, receptor status, or stage in the pooled differences for screen-detected and interval invasive cancer tumor characteristics. There were statistically significant increases in screen-detected ductal carcinoma in situ (DCIS) across all grades: 0.05 (0.00-0.11), 0.14 (0.05-0.22), and 0.19 (0.05-0.33) per 1000 screens for low, intermediate, and high-grade DCIS, respectively. There were similar (non-statistically significant) increases in screen-detected invasive cancer across all grades.

CONCLUSION

The increased detection of all grades of DCIS and invasive cancer may indicate both increased early detection of more aggressive disease and increased overdiagnosis.

Interval Cancers in Understanding Screening Outcomes

Interval breast cancers are not detected at routine screening and are diagnosed in the interval between screening examinations. A variety of factors contribute to interval cancers, including patient and tumor characteristics as well as the screening technique and frequency. The interval cancer rate is an important metric by which the effectiveness of screening may be assessed and may serve as a surrogate for mortality benefit.

Genetic landscape of interval and screen detected breast cancer

Interval breast cancers (IBCs) are cancers diagnosed between screening episodes. Understanding the biological differences between IBCs and screen-detected breast-cancers (SDBCs) has the potential to improve mammographic screening and patient management. We analysed and compared the genomic landscape of 288 IBCs and 473 SDBCs by whole genome sequencing of paired tumour-normal patient samples collected as part of the UK 100,000 Genomes Project. Compared to SDBCs, IBCs were more likely to be lobular, higher grade, and triple negative. A more aggressive clinical phenotype was reflected in IBCs displaying features of genomic instability including a higher mutation rate and number of chromosomal structural abnormalities, defective homologous recombination and TP53 mutations. We did not however, find evidence to indicate that IBCs are associated with a significantly different immune response. While IBCs do not represent a unique molecular class of invasive breast cancer they exhibit a more aggressive phenotype, which is likely to be a consequence of the timing of tumour initiation. This information is relevant both with respect to treatment as well as informing the screening interval for mammography.

Radiological features of screening-detected and interval breast cancers and subsequent survival in Eastern Finnish women

Interval breast cancers are diagnosed between scheduled screenings and differ in many respects from screening-detected cancers. Studies comparing the survival of patients with interval and screening-detected cancers have reported differing results. The aim of this study was to investigate the radiological and histopathological features and growth rates of screening-detected and interval breast cancers and subsequent survival. This retrospective study included 942 female patients aged 50-69 years with breast cancers treated and followed-up at Kuopio University Hospital between January 2010 and December 2016. The screening-detected and interval cancers were classified as true, minimal-signs, missed, or occult. The radiological features were assessed on mammograms by one of two specialist breast radiologists with over 15 years of experience. A χ2 test was used to examine the association between radiological and pathological variables; an unpaired t test was used to compare the growth rates of missed and minimal-signs cancers; and the Kaplan-Meier estimator was used to examine survival after screening-detected and interval cancers. Sixty occult cancers were excluded, so a total of 882 women (mean age 60.4 ± 5.5 years) were included, in whom 581 had screening-detected cancers and 301 interval cancers. Disease-specific survival, overall survival and disease-free survival were all worse after interval cancer than after screening-detected cancer (p < 0.001), with a mean follow-up period of 8.2 years. There were no statistically significant differences in survival between the subgroups of screening-detected or interval cancers. Missed interval cancers had faster growth rates (0.47% ± 0.77%/day) than missed screening-detected cancers (0.21% ± 0.11%/day). Most cancers (77.2%) occurred in low-density breasts (< 25%). The most common lesion types were masses (73.9%) and calcifications (13.4%), whereas distortions (1.8%) and asymmetries (1.7%) were the least common. Survival was worse after interval cancers than after screening-detected cancers, attributed to their more-aggressive histopathological characteristics, more nodal and distant metastases, and faster growth rates.

Long-term trends in incidence, characteristics and prognosis of screen-detected and interval cancers in women participating in the Dutch breast cancer screening programme

BACKGROUND

No studies are available in which changes over time in characteristics and prognosis of patients with interval breast cancers (ICs) and screen-detected breast cancers (SDCs) have been compared. The aim was to study these trends between 1995 and 2018.

METHODS

All women with invasive SDCs (N = 4290) and ICs (N = 1352), diagnosed in a southern mammography screening region in the Netherlands, were included and followed until date of death or 31 December 2022.

RESULTS

The 5-year overall survival rate of women with SDCs increased from 91.4% for those diagnosed in 1995-1999 to 95.0% for those diagnosed in 2013-2018 (P < 0.001), and from 74.8 to 91.6% (P < 0.001) in the same periods for those with ICs. A similar trend was observed for the 10-year survival rates. After adjustment for changes in tumour characteristics, the hazard ratio (HR) for overall survival was 0.47 (95% confidence interval (CI): 0.38-0.59) for women with SDCs diagnosed in the period 2013-2018, compared to the women diagnosed in the period 1995-1999. For the women with ICs this HR was 0.27 (95% CI: 0.19-0.40).

CONCLUSION

The prognosis of women with ICs has improved rapidly since 1995 and is now almost similar to that of women with SDCs.

Risk Factors and Mortality Among Women With Interval Breast Cancer vs Screen-Detected Breast Cancer

Importance

The risk factors for interval breast cancer (IBC) compared with those for screen-detected breast cancer (SBC) and their association with mortality outcomes have not yet been evaluated among Korean women.

Objective

To evaluate risk factors associated with IBC and survival among Korean women with IBC compared with those with SBC.

Design, Setting, and Participants

This retrospective cohort study used data from the Korean National Health Insurance Service Database. Women who participated in a national mammographic breast cancer screening program between January 1, 2009, and December 31, 2012, were included. Mortality outcomes were calculated from the date of breast cancer diagnosis to the date of death or December 31, 2020. Data were analyzed from March 1 to June 30, 2023.

Exposure

Breast cancer diagnosed within 6 to 24 months after a negative screening result (ie, IBC) or within 6 months after a positive screening result (ie, SBC).

Main Outcomes and Measures

Risk factors and survival rates for IBC and SBC.

Results

This study included 8702 women with IBC (mean [SD] age, 53.3 [8.6] years) and 9492 women with SBC (mean [SD] age, 54.1 [9.0] years). Compared with SBC, the probability of IBC decreased as mammographic density increased. Lower body mass index, menopausal status, hormone replacement therapy (HRT) use, and lack of family history of breast cancer were associated with a higher likelihood of IBC. When stratified by detection time, younger age at breast cancer diagnosis and family history of breast cancer were associated with an increased likelihood of IBC diagnosed at 6 to 12 months but a decreased likelihood of IBC diagnosed at 12 to 24 months. Overall mortality of IBC was comparable with SBC, but total mortality and cancer-related mortality of IBC diagnosed between 6 and 12 months was higher than that of SBC.

Conclusions and Relevance

The findings of this cohort study suggest that breast density, obesity, and HRT use were associated with IBC compared with SBC. These findings also suggest that higher supplemental breast ultrasound use among Korean women, especially those with dense breasts, could be attributed to a lower incidence of IBC among women with dense breasts compared with women with SBC, due to greater detection. Finally, overall mortality of IBC was comparable with that of SBC.

Prolonged screening interval due to the COVID-19 pandemic and its association with tumor characteristics and treatment; a register-based study from BreastScreen Norway

OBJECTIVE

During the COVID-19 pandemic Norway had to suspend its national breast cancer screening program. We aimed to investigate the effect of the pandemic-induced suspension on the screening interval, and its subsequent association with the tumor characteristics and treatment of screen-detected (SDC) and interval breast cancer (IC).

METHODS

Information about women aged 50-69, participating in BreastScreen Norway, and diagnosed with a SDC (N = 3799) or IC (N = 1806) between 2018 and 2021 was extracted from the Cancer Registry of Norway. Logistic regression was used to investigate the association between COVID-19 induced prolonged screening intervals and tumor characteristics and treatment.

RESULTS

Women with a SDC and their last screening exam before the pandemic had a median screening interval of 24.0 months (interquartile range: 23.8-24.5), compared to 27.0 months (interquartile range: 25.8-28.5) for those with their last screening during the pandemic. The tumor characteristics and treatment of women with a SDC, last screening during the pandemic, and a screening interval of 29-31 months, did not differ from those of women with a SDC, last screening before the pandemic, and a screening interval of 23-25 months. ICs detected 24-31 months after screening, were more likely to be histological grade 3 compared to ICs detected 0-23 months after screening (odds ratio: 1.40, 95% confidence interval: 1.06-1.84).

CONCLUSIONS

Pandemic-induced prolonged screening intervals were not associated with the tumor characteristics and treatment of SDCs, but did increase the risk of a histopathological grade 3 IC. This study provides insights into the possible effects of extending the screening interval.

New Approaches and Recommendations for Risk-Adapted Breast Cancer Screening

Population-based breast cancer screening using mammography as the gold standard imaging modality has been in clinical practice for over 40 years. However, the limitations of mammography in terms of sensitivity and high false-positive rates, particularly in high-risk women, challenge the indiscriminate nature of population-based screening. Additionally, in light of expanding research on new breast cancer risk factors, there is a growing consensus that breast cancer screening should move toward a risk-adapted approach. Recent advancements in breast imaging technology, including contrast material-enhanced mammography (CEM), ultrasound (US) (automated-breast US, Doppler, elastography US), and especially magnetic resonance imaging (MRI) (abbreviated, ultrafast, and contrast-agent free), may provide new opportunities for risk-adapted personalized screening strategies. Moreover, the integration of artificial intelligence and radiomics techniques has the potential to enhance the performance of risk-adapted screening. This review article summarizes the current evidence and challenges in breast cancer screening and highlights potential future perspectives for various imaging techniques in a risk-adapted breast cancer screening approach. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: Stage 5.

Optimizing the Pairs of Radiologists That Double Read Screening Mammograms

Background Despite variation in performance characteristics among radiologists, the pairing of radiologists for the double reading of screening mammograms is performed randomly. It is unknown how to optimize pairing to improve screening performance. Purpose To investigate whether radiologist performance characteristics can be used to determine the optimal set of pairs of radiologists to double read screening mammograms for improved accuracy. Materials and Methods This retrospective study was performed with reading outcomes from breast cancer screening programs in Sweden (2008-2015), England (2012-2014), and Norway (2004-2018). Cancer detection rates (CDRs) and abnormal interpretation rates (AIRs) were calculated, with AIR defined as either reader flagging an examination as abnormal. Individual readers were divided into performance categories based on their high and low CDR and AIR. The performance of individuals determined the classification of pairs. Random pair performance, for which any type of pair was equally represented, was compared with the performance of specific pairing strategies, which consisted of pairs of readers who were either opposite or similar in AIR and/or CDR. Results Based on a minimum number of examinations per reader and per pair, the final study sample consisted of 3 592 414 examinations (Sweden, n = 965 263; England, n = 837 048; Norway, n = 1 790 103). The overall AIRs and CDRs for all specific pairing strategies (Sweden AIR range, 45.5-56.9 per 1000 examinations and CDR range, 3.1-3.6 per 1000; England AIR range, 68.2-70.5 per 1000 and CDR range, 8.9-9.4 per 1000; Norway AIR range, 81.6-88.1 per 1000 and CDR range, 6.1-6.8 per 1000) were not significantly different from the random pairing strategy (Sweden AIR, 54.1 per 1000 examinations and CDR, 3.3 per 1000; England AIR, 69.3 per 1000 and CDR, 9.1 per 1000; Norway AIR, 84.1 per 1000 and CDR, 6.3 per 1000). Conclusion Pairing a set of readers based on different pairing strategies did not show a significant difference in screening performance when compared with random pairing. © RSNA, 2023.

Multi-modal artificial intelligence for the combination of automated 3D breast ultrasound and mammograms in a population of women with predominantly dense breasts

OBJECTIVES

To assess the stand-alone and combined performance of artificial intelligence (AI) detection systems for digital mammography (DM) and automated 3D breast ultrasound (ABUS) in detecting breast cancer in women with dense breasts.

METHODS

430 paired cases of DM and ABUS examinations from a Asian population with dense breasts were retrospectively collected. All cases were analyzed by two AI systems, one for DM exams and one for ABUS exams. A selected subset (n = 152) was read by four radiologists. The performance of AI systems was based on analysis of the area under the receiver operating characteristic curve (AUC). The maximum Youden's index and its associated sensitivity and specificity were also reported for each AI systems. Detection performance of human readers in the subcohort of the reader study was measured in terms of sensitivity and specificity.

RESULTS

The performance of the AI systems in a multi-modal setting was significantly better when the weights of AI-DM and AI-ABUS were 0.25 and 0.75, respectively, than each system individually in a single-modal setting (AUC-AI-Multimodal = 0.865; AUC-AI-DM = 0.832, p = 0.026; AUC-AI-ABUS = 0.841, p = 0.041). The maximum Youden's index for AI-Multimodal was 0.707 (sensitivity = 79.4%, specificity = 91.2%). In the subcohort that underwent human reading, the panel of four readers achieved a sensitivity of 93.2% and specificity of 32.7%. AI-multimodal achieves superior or equal sensitivity as single human readers at the same specificity operating points on the ROC curve.

CONCLUSION

Multimodal (ABUS + DM) AI systems for detecting breast cancer in women with dense breasts are a potential solution for breast screening in radiologist-scarce regions.

Comparison and risk factors analysis of multiple breast cancer screening methods in the evaluation of breast non-mass-like lesions

OBJECTIVE

To compare multiple breast cancer screening methods for evaluating breast non-mass-like lesions (NMLs), and investigate new best screening method for breast non-mass-like lesions and the value of the lexicon of ACR BI-RADS in NML evaluation.

METHODS

This retrospective study examined 253 patients aged 24-68 years who were diagnosed with breast NMLs and described the lexicon of ACR BI-RADS from April 2017 to December 2019. All lesions were evaluated by HHUS, MG, and ABUS to determine BI-RADS category, and underwent pathological examination within six months or at least 2 years of follow-up. The sensitivity, specificity, accuracy, positive predictive values (PPV), and negative predictive values (NPV) of MG, HHUS and ABUS in the prediction of malignancy were compared. Independent risk factors for malignancy were assessed using non-conditional logistic regression.

RESULTS

HHUS, MG and ABUS findings significantly differed between benign and malignant breast NML, including internal echo, hyperechoic spot, peripheral blood flow, internal blood flow, catheter change, peripheral change, coronal features of ABUS, and structural distortion, asymmetry, and calcification in MG. ABUS is superior to MG and HHUS in sensitivity, specificity, PPV, NPV, as well as in evaluating the necessity of biopsy and accuracy in identifying malignancy. MG was superior to HHUS in specificity, PPV, and accuracy in evaluating the need for biopsy.

CONCLUSIONS

ABUS was superior to HHUS and MG in evaluating the need for biopsy in breast NMLs. Compared to each other, HHUS and MG had their own relative advantages. Internal blood flow, calcification, and coronal plane feature was independent risk factors in NMLs Management, and different screening methods had their own advantages in NML management. The lexicon of ACR BI-RADS could be used not only in the evaluation of mass lesions, but also in the evaluation of NML.

Detecting Abnormal Axillary Lymph Nodes on Mammograms Using a Deep Convolutional Neural Network

The purpose of this study was to determine the feasibility of a deep convolutional neural network (dCNN) to accurately detect abnormal axillary lymph nodes on mammograms. In this retrospective study, 107 mammographic images in mediolateral oblique projection from 74 patients were labeled to three classes: (1) “breast tissue”, (2) “benign lymph nodes”, and (3) “suspicious lymph nodes”. Following data preprocessing, a dCNN model was trained and validated with 5385 images. Subsequently, the trained dCNN was tested on a “real-world” dataset and the performance compared to human readers. For visualization, colored probability maps of the classification were calculated using a sliding window approach. The accuracy was 98% for the training and 99% for the validation set. Confusion matrices of the “real-world” dataset for the three classes with radiological reports as ground truth yielded an accuracy of 98.51% for breast tissue, 98.63% for benign lymph nodes, and 95.96% for suspicious lymph nodes. Intraclass correlation of the dCNN and the readers was excellent (0.98), and Kappa values were nearly perfect (0.93–0.97). The colormaps successfully detected abnormal lymph nodes with excellent image quality. In this proof-of-principle study in a small patient cohort from a single institution, we found that deep convolutional networks can be trained with high accuracy and reliability to detect abnormal axillary lymph nodes on mammograms.

Interval Cancer Detection Using a Neural Network and Breast Density in Women with Negative Screening Mammograms

Background Inclusion of mammographic breast density (BD) in breast cancer risk models improves accuracy, but accuracy remains modest. Interval cancer (IC) risk prediction may be improved by combining assessments of BD and an artificial intelligence (AI) cancer detection system. Purpose To evaluate the performance of a neural network (NN)-based model that combines the assessments of BD and an AI system in the prediction of risk of developing IC among women with negative screening mammography results. Materials and Methods This retrospective nested case-control study performed with screening examinations included women who developed IC and women with normal follow-up findings (from January 2011 to January 2015). An AI cancer detection system analyzed all studies yielding a score of 1-10, representing increasing likelihood of malignancy. BD was automatically computed using publicly available software. An NN model was trained by combining the AI score and BD using 10-fold cross-validation. Bootstrap analysis was used to calculate the area under the receiver operating characteristic curve (AUC), sensitivity at 90% specificity, and 95% CIs of the AI, BD, and NN models. Results A total of 2222 women with IC and 4661 women in the control group were included (mean age, 61 years; age range, 49-76 years). AUC of the NN model was 0.79 (95% CI: 0.77,0.81), which was higher than AUC of the AI cancer detection system or BD alone (AUC, 0.73 [95% CI: 0.71, 0.76] and 0.69 [95% CI: 0.67, 0.71], respectively; P < .001 for both). At 90% specificity, the NN model had a sensitivity of 50.9% (339 of 666 women; 95% CI: 45.2, 56.3) for prediction of IC, which was higher than that of the AI system (37.5%; 250 of 666 women; 95% CI: 33.0, 43.7; P < .001) or BD percentage alone (22.4%; 149 of 666 women; 95% CI: 17.9, 28.5; P < .001). Conclusion The combined assessment of an artificial intelligence detection system and breast density measurements enabled identification of a larger proportion of women who would develop interval cancer compared with either method alone. Published under a CC BY 4.0 license.

Mammographically occult breast cancers detected with AI-based diagnosis supporting software: clinical and histopathologic characteristics

Background

To demonstrate the value of an artificial intelligence (AI) software in the detection of mammographically occult breast cancers and to determine the clinicopathologic patterns of the cancers additionally detected using the AI software.

Methods

By retrospectively reviewing our institutional database (January 2017–September 2019), we identified women with mammographically occult breast cancers and analyzed their mammography with an AI software that provided a malignancy score (range 0–100; > 10 considered as positive). The hot spots in the AI report were compared with the US and MRI findings to determine if the cancers were correctly marked by the AI software. The clinicopathologic characteristics of the AI-detected cancers were analyzed and compared with those of undetected cancers.

Results

Among the 1890 breast cancers, 6.8% (128/1890) were mammographically occult, among which 38.3% (49/128) had positive results in the AI analysis. Of them, 81.6% (40/49) were correctly marked by the AI software and determined as “AI-detected cancers.” As such, 31.3% (40/128) of mammographically occult breast cancers could be identified by the AI software. Of the AI-detected cancers, 97.5% were found in heterogeneously or extremely dense breasts, 52.5% were asymptomatic, 86.5% were invasive, and 29.7% had axillary lymph node metastasis. Compared with undetected cancers, the AI-detected cancers were more likely to be found in younger patients (p < 0.001), undergo neoadjuvant chemotherapy as well as mastectomy rather than breast-conserving operation (both p < 0.001), and accompany axillary lymph node metastasis (p = 0.003).

Conclusions

AI conferred an added value in the detection of mammographically occult breast cancers.

Development and validation of a deep learning model for detection of breast cancers in mammography from multi-institutional datasets

Objectives

The objective of this study was to develop and validate a state-of-the-art, deep learning (DL)-based model for detecting breast cancers on mammography.

Methods

Mammograms in a hospital development dataset, a hospital test dataset, and a clinic test dataset were retrospectively collected from January 2006 through December 2017 in Osaka City University Hospital and Medcity21 Clinic. The hospital development dataset and a publicly available digital database for screening mammography (DDSM) dataset were used to train and to validate the RetinaNet, one type of DL-based model, with five-fold cross-validation. The model’s sensitivity and mean false positive indications per image (mFPI) and partial area under the curve (AUC) with 1.0 mFPI for both test datasets were externally assessed with the test datasets.

Results

The hospital development dataset, hospital test dataset, clinic test dataset, and DDSM development dataset included a total of 3179 images (1448 malignant images), 491 images (225 malignant images), 2821 images (37 malignant images), and 1457 malignant images, respectively. The proposed model detected all cancers with a 0.45–0.47 mFPI and had partial AUCs of 0.93 in both test datasets.

Conclusions

The DL-based model developed for this study was able to detect all breast cancers with a very low mFPI. Our DL-based model achieved the highest performance to date, which might lead to improved diagnosis for breast cancer.

True and Missed Interval Cancer in Organized Mammographic Screening: A Retrospective Review Study of Diagnostic and Prior Screening Mammograms

RATIONALE AND OBJECTIVES

To explore radiological aspects of interval breast cancer in a population-based screening program.

MATERIALS AND METHODS

We performed a consensus-based informed review of mammograms from diagnosis and prior screening from women diagnosed with interval cancer 2004-2016 in BreastScreen Norway. Cases were classified as true (no findings on prior screening mammograms), occult (no findings at screening or diagnosis), minimal signs (minor/non-specific findings) and missed (obvious findings). We analyzed mammographic findings, density, time since prior screening, and histopathological characteristics between the classification groups.

RESULTS

The study included 1010 interval cancer cases. Mean age at diagnosis was 61 years (SD = 6), mean time between screening and diagnosis 14 months (SD = 7). A total of 48% (479/1010) were classified as true or occult, 28% (285/1010) as minimal signs and 24% (246/1010) as missed. We observed no differences in mammographic density between the groups, except from a higher percentage of dense breasts in women with occult cancer. Among cancers classified as missed, about 1/3 were masses and 1/3 asymmetries at prior screening. True interval cancers were diagnosed later in the screening interval than the other classification categories. No differences in histopathological characteristics were observed between true, minimal signs and missed cases.

CONCLUSION

In an informed review, 24% of the interval cancers were classified as missed based on visibility and mammographic findings on prior screening mammograms. Three out of four true interval cancers were diagnosed in the second year of the screening interval. We observed no statistical differences in histopathological characteristics between true and missed interval cancers.

Re-evaluation of high-risk breast mammography lesions by target ultrasound and ABUS of breast non-mass-like lesions

OBJECTIVE

The purpose of this study was to reevaluate the high-risk breast non-mass-like lesions (NMLs) in mammography (MG) by target ultrasound (US) and Automated breast ultrasonography (ABUS), and to analyze the correlation between different imaging findings and the factors influencing the classification of lesions.

METHODS

A total of 161 patients with 166 breast lesions were recruited in this retrospectively study. All cases were diagnosed as BI-RADS 4 or 5 by MG and as NML on ultrasound. While all NMLs underwent mammography, target US and ABUS before breast surgery or biopsy in the consistent position of breast. The imaging and pathological features of all cases were collected. All lesions were classified according to the lexion of ACR BI-RADS®.

RESULTS

There were significant differences between benign and malignant breast NML in all the features of target US and ABUS. US, especially ABUS, was superior to MG in determining the malignant breast NML. There was a significant difference in the detection rate of calcification between MG and Target US (P < 0.001), and there was a significant difference in the detection rate of structural distortion between ABUS and MG (P < 0.001).

CONCLUSIONS

Target US, especially ABUS, can significantly improve the sensitivity, specificity and accuracy of the diagnosis of high-risk NMLs in MG. The features of Target US and ABUS such as blood supply, hyperechogenicity, ductal changes, peripheral changes and coronal features could be employed to predict benign and malignant lesions. The coronal features of ABUS were more sensitive than those of Target HHUS in showing structural abnormalities. Target US was less effective than MG in local micro-calcification.

Impact of Full-Field Digital Mammography Versus Film-Screen Mammography in Population Screening: A Meta-Analysis

BACKGROUND

Breast screening programs replaced film mammography with digital mammography, and the effects of this practice shift in population screening on health outcomes can be measured through examination of cancer detection and interval cancer rates.

METHODS

A systematic review and random effects meta-analysis were undertaken. Seven databases were searched for publications that compared film with digital mammography within the same population of asymptomatic women and reported cancer detection and/or interval cancer rates.

RESULTS

The analysis included 24 studies with 16 583 743 screening examinations (10 968 843 film and 5 614 900 digital). The pooled difference in the cancer detection rate showed an increase of 0.51 per 1000 screens (95% confidence interval [CI] = 0.19 to 0.83), greater relative increase for ductal carcinoma in situ (25.2%, 95% CI = 17.4% to 33.5%) than invasive (4%, 95% CI = -3% to 13%), and a recall rate increase of 6.95 (95% CI = 3.47 to 10.42) per 1000 screens after the transition from film to digital mammography. Seven studies (80.8% of screens) reported interval cancers: the pooled difference showed no change in the interval cancer rate with -0.02 per 1000 screens (95% CI = -0.06 to 0.03). Restricting analysis to studies at low risk of bias resulted in findings consistent with the overall pooled results for all outcomes.

CONCLUSIONS

The increase in cancer detection following the practice shift to digital mammography did not translate into a reduction in the interval cancer rate. Recall rates were increased. These results suggest the transition from film to digital mammography did not result in health benefits for screened women. This analysis reinforces the need to carefully evaluate effects of future changes in technology, such as tomosynthesis, to ensure new technology leads to improved health outcomes and beyond technical gains.

Survival among women diagnosed with screen-detected or interval breast cancer classified as true, minimal signs, or missed through an informed radiological review

OBJECTIVES

"True" breast cancers, defined as not being visible on prior screening mammograms, are expected to be more aggressive than "missed" cancers, which are visible in retrospect. However, the evidence to support this hypothesis is limited. We compared the risk of death from any cause for women with true, minimal signs, and missed invasive screen-detected (SDC) and interval breast cancers (IC).

METHODS

This nation-wide study included 1022 SDC and 788 IC diagnosed through BreastScreen Norway during 2005-2016. Cancers were classified as true, minimal signs, or missed by five breast radiologists in a consensus-based informed review of prior screening and diagnostic images. We used multivariable Cox regression to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the risk of death from any cause associated with true, minimal signs, and missed breast cancers, adjusting for age at diagnosis, histopathologic tumour diameter and grade, and subtype. Separate models were created for SDC and IC.

RESULTS

Among SDC, 463 (44%) were classified as true and 242 (23%) as missed; among IC, 325 (39%) were classified as true and 235 (32%) missed. Missed SDC were associated with a similar risk of death as true SDC (HR = 1.20, 95% CI (0.49, 2.46)). Similar results were observed for missed versus true IC (HR = 1.31, 95% CI (0.77, 2.23)).

CONCLUSIONS

We did not observe a statistical difference in the risk of death for women diagnosed with true or missed SDC or IC; however, the number of cases reviewed and follow-up time limited the precision of our estimates.

KEY POINTS

• An informed radiological review classified screen-detected and interval cancers as true, minimal signs, or missed based on prior screening and diagnostic mammograms. • It has been hypothesised that true cancers, not visible on the prior screening examination, may be more aggressive than missed cancers. • We did not observe a statistical difference in the risk of death from any cause for women with missed versus true screen-detected or interval breast cancers.

Which type of cancer is detected in breast screening programs? Review of the literature with focus on the most frequent histological features

Breast cancer is the most frequent type of cancer affecting female patients. The introduction of breast cancer screening programs led to a substantial reduction of mortality from breast cancer. Nevertheless, doubts are being raised on the real efficacy of breast screening programs. The aim of the present paper is to review the main pathological type of cancers detected in breast cancer screening programs. Specifically, attention will be given to: in situ carcinoma, invasive carcinoma histotypes and interval cancer.

Retrospective radiological review and classification of interval breast cancers within population-based breast screening programmes for the purposes of open disclosure: A systematic review

INTRODUCTION

Interval breast cancers occur following a negative breast screening mammogram and before the next scheduled appointment within screening programmes. Radiological review classifies them as cancers that develop between screens, cancers with no obvious malignant abnormalities on prior screens or cancers not detected at screening. This study aimed to systematically review published literature on the occurrence of open disclosure following interval cancer radiological reviews by breast screening programmes internationally in a retrospective setting and examine methodologies used for radiological reviews for the purposes of disclosure.

METHODS

A search for relevant articles published (January 2000 - May 2019) was conducted according to PICO and PRISMA guidelines. The databases Pubmed, Scopus, Google Scholar, Cinahl, Web of Science, Embase, Science Direct and Global Health were searched. Relevant studies were reviewed if they had completed a retrospective review and classification of interval breast cancers.

RESULTS

Of 46 relevant articles included, no study was identified that conducted a retrospective review purposely for open disclosure. Retrospective reviews were conducted for audit/quality assurance, and research including for radiologist education and learning. Variation in methodology was found across review type (non-blinded/semi-informed approach), number of reviewers and classification categories. The proportion of false negative cancers classified among the studies ranged from 4 to 40 %.

DISCUSSION

Variation among radiological review practices were observed, which likely impacts classification results. To ensure standardised classification of interval breast cancers are employed for the purposes of open disclosure in screening settings, reproducible and consistent methodology is required.

Are artificial intelligence systems useful in breast cancer screening programs?

Population-based breast cancer screening programs are efficacious in reducing the mortality due to breast cancer. These programs use mammography to screen the women who are invited to participate. Digital mammography makes it possible to develop computer-assisted diagnosis (CAD) systems that promise to reduce the workload of radiologists participating in screening programs. However, various studies have shown that CAD results in a high rate of false positive diagnoses. Systems based on artificial intelligence are being more widely implemented, and studies have shown that these systems have better diagnostic performance than traditional CAD systems. This article explains the fundamentals of artificial intelligence systems and an overview of possible applications of these systems within the framework of breast cancer screening programs.

Digital Mammography as a Screening Tool in Korea

국가암검진사업에서 매년 400만 명 이상의 여성이 유방촬영술을 이용한 유방암 검진을 받고 있다. 2000년 디지털 유방촬영술의 도입 이후, 선행 연구들에 의하면 디지털 유방촬영술은 치밀유방을 가진 여성에서 제한적으로 기존의 필름 방식 또는 computed radiography (이하 CR)보다 높은 진단 정확도를 보고하였다. 최근 국가암검진사업에서 수행된 자료를 분석한 결과에 따르면 디지털 유방촬영술의 진단 정확도가 필름 또는 CR 방식에 비해서 치밀유방을 가진 여성뿐만 아니라 모든 연령대의 여성에서 검진 횟수와 상관없이 보다 정확하였다. 우리나라는 OECD 국가 중에서도 높은 유방촬영기기 보급률에도 불구하고 현재 디지털 유방촬영기기의 보급은 전체 유방촬영기기 중, 35% 정도 수준으로 더디기만 하다. 디지털 유방촬영기기로의 신속한 전환을 위하여 수가제도의 개선, 유방 영상 판독 교육 지원 등 관련법과 제도의 정비가 필요할 것이다. 아울러 국가암검진사업에서 보다 많은 여성이 디지털 유방촬영기기를 이용한 유방암 검진을 받을 수 있도록 장비 보급의 지역 간 격차 해소를 위해 노력해야 할 것이다.

Computational Radiology in Breast Cancer Screening and Diagnosis Using Artificial Intelligence

Breast cancer screening has been shown to significantly reduce mortality in women. The increased utilization of screening examinations has led to growing demands for rapid and accurate diagnostic reporting. In modern breast imaging centers, full-field digital mammography (FFDM) has replaced traditional analog mammography, and this has opened new opportunities for developing computational frameworks to automate detection and diagnosis. Artificial intelligence (AI), and its subdomain of deep learning, is showing promising results and improvements on diagnostic accuracy, compared to previous computer-based methods, known as computer-aided detection and diagnosis.

In this commentary, we review the current status of computational radiology, with a focus on deep neural networks used in breast cancer screening and diagnosis. Recent studies are developing a new generation of computer-aided detection and diagnosis systems, as well as leveraging AI-driven tools to efficiently interpret digital mammograms, and breast tomosynthesis imaging. The use of AI in computational radiology necessitates transparency and rigorous testing. However, the overall impact of AI to radiology workflows will potentially yield more efficient and standardized processes as well as improve the level of care to patients with high diagnostic accuracy.

A deep learning-based automated diagnostic system for classifying mammographic lesions

BACKGROUND

Screening mammography has led to reduced breast cancer-specific mortality and is recommended worldwide. However, the resultant doctors' workload of reading mammographic scans needs to be addressed. Although computer-aided detection (CAD) systems have been developed to support readers, the findings are conflicting regarding whether traditional CAD systems improve reading performance. Rapid progress in the artificial intelligence (AI) field has led to the advent of newer CAD systems using deep learning-based algorithms which have the potential to reach human performance levels. Those systems, however, have been developed using mammography images mainly from women in western countries. Because Asian women characteristically have higher-density breasts, it is uncertain whether those AI systems can apply to Japanese women. In this study, we will construct a deep learning-based CAD system trained using mammography images from a large number of Japanese women with high quality reading.

METHODS

We will collect digital mammography images taken for screening or diagnostic purposes at multiple institutions in Japan. A total of 15,000 images, consisting of 5000 images with breast cancer and 10,000 images with benign lesions, will be collected. At least 1000 images of normal breasts will also be collected for use as reference data. With these data, we will construct a deep learning-based AI system to detect breast cancer on mammograms. The primary endpoint will be the sensitivity and specificity of the AI system with the test image set.

DISCUSSION

When the ability of AI reading is shown to be on a par with that of human reading, images of normal breasts or benign lesions that do not have to be read by a human can be selected by AI beforehand. Our AI might work well in Asian women who have similar breast density, size, and shape to those of Japanese women.

TRIAL REGISTRATION

UMIN, trial number UMIN000039009. Registered 26 December 2019, https://www.umin.ac.jp/ctr/.

Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists

BACKGROUND

Artificial intelligence (AI) systems performing at radiologist-like levels in the evaluation of digital mammography (DM) would improve breast cancer screening accuracy and efficiency. We aimed to compare the stand-alone performance of an AI system to that of radiologists in detecting breast cancer in DM.

METHODS

Nine multi-reader, multi-case study datasets previously used for different research purposes in seven countries were collected. Each dataset consisted of DM exams acquired with systems from four different vendors, multiple radiologists' assessments per exam, and ground truth verified by histopathological analysis or follow-up, yielding a total of 2652 exams (653 malignant) and interpretations by 101 radiologists (28 296 independent interpretations). An AI system analyzed these exams yielding a level of suspicion of cancer present between 1 and 10. The detection performance between the radiologists and the AI system was compared using a noninferiority null hypothesis at a margin of 0.05.

RESULTS

The performance of the AI system was statistically noninferior to that of the average of the 101 radiologists. The AI system had a 0.840 (95% confidence interval [CI] = 0.820 to 0.860) area under the ROC curve and the average of the radiologists was 0.814 (95% CI = 0.787 to 0.841) (difference 95% CI = -0.003 to 0.055). The AI system had an AUC higher than 61.4% of the radiologists.

CONCLUSIONS

The evaluated AI system achieved a cancer detection accuracy comparable to an average breast radiologist in this retrospective setting. Although promising, the performance and impact of such a system in a screening setting needs further investigation.

Comparison of Mortality Among Participants of Women's Health Initiative Trials With Screening-Detected Breast Cancers vs Interval Breast Cancers

IMPORTANCE

Interval breast cancers (IBCs) are cancers that emerge after a mammogram with negative results but before the patient's next scheduled screening. Interval breast cancer has a worse prognosis than cancers detected by screening; however, it is unknown whether the length of the interscreening period is associated with prognostic features and mortality.

OBJECTIVE

To compare the prognostic features and mortality rate of women with IBCs diagnosed within 1 year or between 1 and 2.5 years of a mammogram with negative results with the prognostic features and mortality rate of women with breast cancers detected by screening.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study used mammography data, tumor characteristics, and patient demographic data from the Women's Health Initiative study, which recruited participants from 1993 to 1998 and followed up with participants for a median of 19 years. The present study sample for these analyses included women aged 50 to 79 years who participated in the Women's Health Initiative study and includes data collected through March 31, 2018. There were 5455 incidents of breast cancer; only 3019 women compliant with screening were retained in analyses. Statistical analysis was performed from October 25, 2018, to November 24, 2019. Breast cancers detected by screening and IBCs were defined based on mammogram history, date of last mammogram, type of visit, and results of examination. Interval breast cancers were subdivided into those occurring within 1 year or between 1 and 2.5 years after the last protocol-mandated mammogram with negative results.

MAIN OUTCOMES AND MEASURES

The primary outcome of this study was breast cancer-specific mortality for each case of breast cancer detected by screening and IBCs detected within 1 year or between 1 and 2.5 years from a mammogram with negative results. Secondary outcomes included prognostic and tumor characteristics for each group. Comparisons between groups were made using the t test, the χ2 test, and Fine-Gray multivariable cumulative incidence regression analyses.

RESULTS

Among the 3019 participants in this analysis, all were women with a mean (SD) age of 63.1 (6.8) years at enrollment and 68.5 (7.1) years at diagnosis. A total of 1050 cases of IBC were identified, with 324 (30.9%) diagnosed within 1 year from a mammogram with negative results and 726 (69.1%) diagnosed between 1 and 2.5 years after last mammogram with negative results. The remaining 1969 cases were breast cancers detected by screening. Interval breast cancers diagnosed within 1 year from a mammogram with negative results had significantly more lobular histologic characteristics (13.0% vs. 8.1%), a larger tumor size (1.97 cm vs 1.43 cm), a higher clinical stage (28.4% vs 17.3% regional and 3.7% vs 0.6% distant), and more lymph node involvement (27.1% vs 17.0%) than cancers detected by screening. Unadjusted breast cancer-specific mortality hazard ratios were significantly higher for IBCs diagnosed within 1 year from a mammogram with negative results compared with breast cancers detected by screening (hazard ratio, 1.92; 95% CI, 1.39-2.65). Higher breast cancer-specific mortality remained statistically significant for IBCs diagnosed within 1 year after adjusting for trial group, molecular subtype, waist to hip ratio, histologic characteristics, and either tumor size (hazard ratio, 1.46; 95% CI, 1.03-2.08) or lymph node involvement (hazard ratio, 1.44; 95% CI, 1.03-2.01). However, significance was lost when tumor size and lymph node involvement were both included in the model (hazard ratio, 1.34; 95% CI, 0.96-1.88). Interval breast cancers diagnosed between 1 and 2.5 years from a mammogram with negative results were not different from breast cancers detected by screening based on prognostic factors or mortality.

CONCLUSIONS AND RELEVANCE

Women with IBCs diagnosed within 1 year of negative mammogram results overall were associated with worse survival than women with breast cancers detected by screening. These differences in survival may be due to a uniquely aggressive biology among IBC cases.

Interval breast cancer is associated with other types of tumors

Breast cancer (BC) patients diagnosed between two screenings (interval cancers) are more likely than screen-detected patients to carry rare deleterious mutations in cancer genes potentially leading to increased risk for other non-breast cancer (non-BC) tumors. In this study, we include 14,846 women diagnosed with BC of which 1,772 are interval and 13,074 screen-detected. Compared to women with screen-detected cancers, interval breast cancer patients are more likely to have a non-BC tumor before (Odds ratio (OR): 1.43 [1.19–1.70], P = 9.4 x 10⁻⁵) and after (OR: 1.28 [1.14–1.44], P = 4.70 x 10⁻⁵) breast cancer diagnosis, are more likely to report a family history of non-BC tumors and have a lower genetic risk score based on common variants for non-BC tumors. In conclusion, interval breast cancer is associated with other tumors and common cancer variants are unlikely to be responsible for this association. These findings could have implications for future screening and prevention programs.

Interval cancer patients are more likely to carry rare gene mutations than screen-detected breast cancer patients. Here, the authors report that interval cancer patients are more likely cancer survivors and are at a greater risk of developing other non-breast tumors.

Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives

Although computer-aided diagnosis (CAD) is widely used in mammography, conventional CAD programs that use prompts to indicate potential cancers on the mammograms have not led to an improvement in diagnostic accuracy. Because of the advances in machine learning, especially with use of deep (multilayered) convolutional neural networks, artificial intelligence has undergone a transformation that has improved the quality of the predictions of the models. Recently, such deep learning algorithms have been applied to mammography and digital breast tomosynthesis (DBT). In this review, the authors explain how deep learning works in the context of mammography and DBT and define the important technical challenges. Subsequently, they discuss the current status and future perspectives of artificial intelligence-based clinical applications for mammography, DBT, and radiomics. Available algorithms are advanced and approach the performance of radiologists-especially for cancer detection and risk prediction at mammography. However, clinical validation is largely lacking, and it is not clear how the power of deep learning should be used to optimize practice. Further development of deep learning models is necessary for DBT, and this requires collection of larger databases. It is expected that deep learning will eventually have an important role in DBT, including the generation of synthetic images.

Comparison of Digital and Screen-Film Mammography for Breast-Cancer Screening: A Systematic Review and Meta-Analysis

Purpose

Digital mammography (DM) has replaced screen-film mammography (SFM). However, findings of comparisons between the performance indicators of DM and SFM for breast-cancer screening have been inconsistent. Moreover, the summarized results from studies comparing the performance of screening mammography according to device type vary over time. Therefore, this study aimed to compare the performance of DM and SFM using recently published data.

Methods

The MEDLINE, Embase, and Cochrane Library databases were searched for paired studies, cohorts, and randomized controlled trials published through 2018 that compared the performance of DM and SFM. All studies comparing the diagnostic accuracy of DM and SFM in asymptomatic, average-risk women aged 40 years and older were included. Two reviewers independently assessed the study quality and extracted the data.

Results

Thirteen studies were included in the meta-analysis. The pooled sensitivity (DM, 0.76 [95% confidence interval {CI}, 0.70–0.81]; SFM, 0.76 [95% CI, 0.70–0.81]), specificity (DM, 0.96 [95% CI, 0.94–0.97]; SFM, 0.97 [95% CI, 0.94–0.98]), and area under the receiver-operating characteristic curve (DM, 0.94 [95% CI, 0.92–0.96]; SFM, 0.92 [95% CI, 0.89–0.94]) were similar for both DM and SFM. The pooled screening performance indicators reinforced superior accuracy of full-field DM, which is a more advanced type of mammography, than SFM. The advantage of DM appeared greater among women aged 50 years or older. There was high heterogeneity among studies in the pooled sensitivity, specificity, and overall diagnostic accuracy estimates. Stratifying by study design (prospective or retrospective) and removing studies with a 2-year or greater follow-up period resulted in homogeneous overall diagnostic accuracy estimates.

Conclusion

The breast-cancer screening performance of DM is similar to that of SFM. The diagnostic performance of DM depends on the study design, and, in terms of performance, full-field DM is superior to SFM, unlike computed radiography systems.

Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System

Purpose To compare breast cancer detection performance of radiologists reading mammographic examinations unaided versus supported by an artificial intelligence (AI) system. Materials and Methods An enriched retrospective, fully crossed, multireader, multicase, HIPAA-compliant study was performed. Screening digital mammographic examinations from 240 women (median age, 62 years; range, 39-89 years) performed between 2013 and 2017 were included. The 240 examinations (100 showing cancers, 40 leading to false-positive recalls, 100 normal) were interpreted by 14 Mammography Quality Standards Act-qualified radiologists, once with and once without AI support. The readers provided a Breast Imaging Reporting and Data System score and probability of malignancy. AI support provided radiologists with interactive decision support (clicking on a breast region yields a local cancer likelihood score), traditional lesion markers for computer-detected abnormalities, and an examination-based cancer likelihood score. The area under the receiver operating characteristic curve (AUC), specificity and sensitivity, and reading time were compared between conditions by using mixed-models analysis dof variance and generalized linear models for multiple repeated measurements. Results On average, the AUC was higher with AI support than with unaided reading (0.89 vs 0.87, respectively; P = .002). Sensitivity increased with AI support (86% [86 of 100] vs 83% [83 of 100]; P = .046), whereas specificity trended toward improvement (79% [111 of 140]) vs 77% [108 of 140]; P = .06). Reading time per case was similar (unaided, 146 seconds; supported by AI, 149 seconds; P = .15). The AUC with the AI system alone was similar to the average AUC of the radiologists (0.89 vs 0.87). Conclusion Radiologists improved their cancer detection at mammography when using an artificial intelligence system for support, without requiring additional reading time. Published under a CC BY 4.0 license. See also the editorial by Bahl in this issue.

Tumour characteristics of bilateral screen-detected cancers and bilateral interval cancers in women participating at biennial screening mammography

BACKGROUND

Unilateral interval breast cancers show less favourable prognostic features than unilateral screen-detected cancers, but data on tumour characteristics of bilateral interval cancers in a systematically screened population are sparse. Therefore, we compared tumour characteristics of bilateral interval cancers with those of bilateral screen-detected cancers.

METHODS

We included all 468,720 screening mammograms of women who underwent biennial screening mammography in the South of the Netherlands between January 2005 and January 2015. We collected breast imaging reports, biopsy results and surgical reports of all recalled women and of all women who presented with interval breast cancer. In women with synchronous bilateral breast cancer, the tumour with the highest tumour stage was defined as the index cancer. For comparison of data between both groups Fisher exact test and Chi-square test were used.

RESULTS

Synchronous bilateral cancer was diagnosed in 2.2% of screen-detected cancers (64/2947) and in 3.2% of interval cancers (24/753) (P = 0.1). Index tumours of bilateral screen-detected cancers and interval cancers showed similar characteristics, except for a larger proportion of T-stage 2 or worse (T2+) cancers among interval cancers (16/24 (66.7%) versus 23/58 (39.7%) (P = 0.03). Index cancers, compared to contralateral cancers, were less frequently stage T1 in both bilateral screen-detected cancers and bilateral interval cancers (35/64 (60.3%) versus 40/64 (88.9%) (P = 0.001) and 8/24 (33.3%) versus 18/24 (85.7%) (P < 0.001), respectively). In bilateral screen-detected cancers, contralateral cancers were more often stage 1a-c (P < 0.001) compared to index cancers. In bilateral index cancers, index cancers were more often of the lobular subtype (P < 0.001).

CONCLUSION

Index cancers of bilateral screen-detected cancers and bilateral interval cancers show significant differences in tumour size, whereas nodal status, receptor status and final surgical treatment are comparable. In bilateral screen-detected cancer, index cancers had a significantly higher tumour stage. In bilateral screen-detected cancer, index cancers were more often the ductal invasive subtype compared to contralateral cancers.

Organized screening detects breast cancer at earlier stage regardless of molecular phenotype

PURPOSE

Mortality reduction attributable to organized breast screening is modest. Screening may be less effective at detecting more aggressive cancers at an earlier stage. This study was conducted to determine the relative efficacy of screening mammography to detect cancers at an earlier stage by molecular phenotype.

METHODS

We identified 2882 women with primary invasive breast cancer diagnosed between January 1, 2008 and December 31, 2012 and who had a mammogram through the Ontario Breast Screening Program in the 28 months before diagnosis. Five tumor phenotypes were defined by expression of estrogen (ER) and progesterone (PR) receptors and HER2/neu oncogene. We conducted univariable and multivariable analyses to describe the predictors of detection as an interval cancer. Additional analyses identified predictors of detection at stages II, III, or IV compared with stage I, by phenotype. Analyses were adjusted for the effects of age, grade, and breast density.

RESULTS

ER negative and HER2 positive tumors were over-represented among interval cancers, and triple negative cancers were more likely than ER +/HER2 - cancers to be detected as interval cancers OR 2.5 (95% CI 2.0-3.2, p < 0.0001). Method of detection (interval vs. screen) and molecular phenotype were independently associated with stage at diagnosis (p < 0.0001), but there was no interaction between method of detection and phenotype (p = 0.44).

CONCLUSION

In a screened population, triple negative and HER2 + breast cancers are diagnosed at a higher stage but this appears to be due to higher growth rates of these tumors rather than a relative inability of screening to detect them.

The frequency of missed breast cancers in women participating in a high-risk MRI screening program

Purpose

To evaluate the frequency of missed cancers on breast MRI in women participating in a high-risk screening program.

Methods

Patient files from women who participated in an increased risk mammography and MRI screening program (2003–2014) were coupled to the Dutch National Cancer Registry. For each cancer detected, we determined whether an MRI scan was available (0–24 months before cancer detection), which was reported to be negative. These negative MRI scans were in consensus re-evaluated by two dedicated breast radiologists, with knowledge of the cancer location. Cancers were scored as invisible, minimal sign, or visible. Additionally, BI-RADS scores, background parenchymal enhancement, and image quality (IQ; perfect, sufficient, bad) were determined. Results were stratified by detection mode (mammography, MRI, interval cancers, or cancers in prophylactic mastectomies) and patient characteristics (presence of BRCA mutation, age, menopausal state).

Results

Negative prior MRI scans were available for 131 breast cancers. Overall 31% of cancers were visible at the initially negative MRI scan and 34% of cancers showed a minimal sign. The presence of a BRCA mutation strongly reduced the likelihood of visible findings in the last negative MRI (19 vs. 46%, P < 0.001). Less than perfect IQ increased the likelihood of visible findings and minimal signs in the negative MRI (P = 0.021).

Conclusion

This study shows that almost one-third of cancers detected in a high-risk screening program are already visible at the last negative MRI scan, and even more in women without BRCA mutations. Regular auditing and double reading for breast MRI screening is warranted.

Incidence and tumour characteristics of bilateral and unilateral interval breast cancers at screening mammography

BACKGROUND

Detected by screening mammography, bilateral breast cancer has a different pathological profile compared to unilateral breast cancer. We investigated the incidence of bilateral interval breast cancers and compared their characteristics with those of unilateral interval breast cancers.

METHODS

We included all 468,720 screening mammograms of women who underwent biennial screening mammography in the South of the Netherlands between January 2005 and January 2015. We collected breast imaging reports, biopsy results and surgical reports of all referred women and of all women who presented with interval breast cancer. The tumour with the highest tumour stage (index cancer) was used for comparison with unilateral interval cancers.

RESULTS

A total of 753 interval cancers were detected, of which 24 (3.2%) were bilateral. Among the invasive interval cancers, bilateral cancers more frequently showed a lobular histology than unilateral cancers (37.5% (9/24) vs. 16.1% (111/691), P = .01). There is a trend towards a larger proportion of bilateral than unilateral interval cancers graded 1 (45.8% (11/24) vs. 27.8% (192/691), P = .08). There were no other statistically significant differences in tumour characteristics. Also, the proportion of interval cancers showing significant mammographic abnormalities at the latest screen was comparable for unilateral and bilateral interval cancers (23.0% vs. 25.0%, P = .9).

DISCUSSION

Bilateral interval cancers comprise a small proportion of all interval cancers. Except of a higher proportion of invasive lobular cancers and a more favourable histological grade of invasive cancers, tumour characteristics are comparable for bilateral and unilateral interval breast cancers.

The importance of early detection of calcifications associated with breast cancer in screening

PURPOSE

The aim of this study was to assess how often women with undetected calcifications in prior screening mammograms are subsequently diagnosed with invasive cancer.

METHODS

From a screening cohort of 63,895 women, exams were collected from 59,690 women without any abnormalities, 744 women with a screen-detected cancer and a prior negative exam, 781 women with a false positive exam based on calcifications, and 413 women with an interval cancer. A radiologist identified cancer-related calcifications, selected by a computer-aided detection system, on mammograms taken prior to screen-detected or interval cancer diagnoses. Using this ground truth and the pathology reports, the sensitivity for calcification detection and the proportion of lesions with visible calcifications that developed into invasive cancer were determined.

RESULTS

The screening sensitivity for calcifications was 45.5%, at a specificity of 99.5%. A total of 68.4% (n = 177) of cancer-related calcifications that could have been detected earlier were associated with invasive cancer when diagnosed.

CONCLUSIONS

Screening sensitivity for detection of malignant calcifications is low. Improving the detection of these early signs of cancer is important, because the majority of lesions with detectable calcifications that are not recalled immediately but detected as interval cancer or in the next screening round are invasive at the time of diagnosis.

The epidemiology, radiology and biological characteristics of interval breast cancers in population mammography screening

An interval breast cancer is a cancer that emerges following a negative mammographic screen. This overview describes the epidemiology, and the radiological and biological characteristics of interval breast cancers in population mammography screening. Notwithstanding possible differences in ascertainment of interval breast cancers, there was broad variability in reported interval breast cancer rates (range 7.0 to 49.3 per 10,000 screens) reflecting heterogeneity in underlying breast cancer rates, screening rounds (initial or repeat screens), and the length and phase of the inter-screening interval. The majority of studies (based on biennial screening) reported interval breast cancer rates in the range of 8.4 to 21.1 per 10,000 screens spanning the two-year interval with the larger proportion occurring in the second year. Despite methodological limitations inherent in radiological surveillance (retrospective mammographic review) of interval breast cancers, this form of surveillance consistently reveals that the majority of interval cancers represent either true interval or occult cancers that were not visible on the index mammographic screen; approximately 20–25% of interval breast cancers are classified as having been missed (false-negatives). The biological characteristics of interval breast cancers show that they have relatively worse tumour prognostic characteristics and biomarker profile, and also survival outcomes, than screen-detected breast cancers; however, they have similar characteristics and prognosis as breast cancers occurring in non-screened women. There was limited evidence on the effect on interval breast cancer frequency and outcomes following transition from film to digital mammography screening.

Quantification of masking risk in screening mammography with volumetric breast density maps

Purpose

Fibroglandular tissue may mask breast cancers, thereby reducing the sensitivity of mammography. Here, we investigate methods for identification of women at high risk of a masked tumor, who could benefit from additional imaging.

Methods

The last negative screening mammograms of 111 women with interval cancer (IC) within 12 months after the examination and 1110 selected normal screening exams from women without cancer were used. From the mammograms, volumetric breast density maps were computed, which provide the dense tissue thickness for each pixel location. With these maps, three measurements were derived: (1) percent dense volume (PDV), (2) percent area where dense tissue thickness exceeds 1 cm (PDA), and (3) dense tissue masking model (DTMM). Breast density was scored by a breast radiologist using BI-RADS. Women with heterogeneously and extremely dense breasts were considered at high masking risk. For each masking measure, mammograms were divided into a high- and low-risk category such that the same proportion of the controls is at high masking risk as with BI-RADS.

Results

Of the women with IC, 66.1, 71.9, 69.2, and 63.0% were categorized to be at high masking risk with PDV, PDA, DTMM, and BI-RADS, respectively, against 38.5% of the controls. The proportion of IC at high masking risk is statistically significantly different between BI-RADS and PDA (p-value 0.022). Differences between BI-RADS and PDV, or BI-RADS and DTMM, are not statistically significant.

Conclusion

Measures based on density maps, and in particular PDA, are promising tools to identify women at high risk for a masked cancer.

Breast cancers detected in only one of two arms of a tomosynthesis (3D-mammography) population screening trial (STORM-2)

The prospective 'screening with tomosynthesis or standard mammography-2 (STORM-2)' trial compared mammography screen-reading strategies and showed that each of integrated 2D/3D-mammography or 2Dsynthetic/3D-mammography detected significantly more breast cancers than 2D-mammography alone. This short report describes 13 (from 90) cancers detected in only one of two parallel double-reading arms implemented in STORM-2. Amongst this subset of cases, the majority was invasive cancer ≤16 mm, mostly depicted as irregular masses or distortions. Furthermore, most were detected at 3D-mammography only and predominantly by one reader from double-reading pairs, highlighting that 3D-mammography may enable detection of cancers that are challenging to perceive at routine screening.