Comparison of breast density assessment between human eye and automated software on digital and synthetic mammography: Impact on breast cancer risk

Diagnostic accuracy of automated ACR BI-RADS breast density classification using deep convolutional neural networks

OBJECTIVES

High breast density is a well-known risk factor for breast cancer. This study aimed to develop and adapt two (MLO, CC) deep convolutional neural networks (DCNN) for automatic breast density classification on synthetic 2D tomosynthesis reconstructions.

METHODS

In total, 4605 synthetic 2D images (1665 patients, age: 57 ± 37 years) were labeled according to the ACR (American College of Radiology) density (A-D). Two DCNNs with 11 convolutional layers and 3 fully connected layers each, were trained with 70% of the data, whereas 20% was used for validation. The remaining 10% were used as a separate test dataset with 460 images (380 patients). All mammograms in the test dataset were read blinded by two radiologists (reader 1 with two and reader 2 with 11 years of dedicated mammographic experience in breast imaging), and the consensus was formed as the reference standard. The inter- and intra-reader reliabilities were assessed by calculating Cohen's kappa coefficients, and diagnostic accuracy measures of automated classification were evaluated.

RESULTS

The two models for MLO and CC projections had a mean sensitivity of 80.4% (95%-CI 72.2-86.9), a specificity of 89.3% (95%-CI 85.4-92.3), and an accuracy of 89.6% (95%-CI 88.1-90.9) in the differentiation between ACR A/B and ACR C/D. DCNN versus human and inter-reader agreement were both "substantial" (Cohen's kappa: 0.61 versus 0.63).

CONCLUSION

The DCNN allows accurate, standardized, and observer-independent classification of breast density based on the ACR BI-RADS system.

KEY POINTS

• A DCNN performs on par with human experts in breast density assessment for synthetic 2D tomosynthesis reconstructions. • The proposed technique may be useful for accurate, standardized, and observer-independent breast density evaluation of tomosynthesis.

Impact of the 2018 ACR Supplemental Screening Recommendations on MRI Eligibility in Breast Cancer Survivors

PURPOSE

The 2018 ACR recommendations for breast cancer screening in women at higher than average risk include new recommendations for supplemental breast MRI for patients with personal histories of breast cancer (PHBCs) who carry hereditary cancer gene mutations, have dense breast tissue, or were diagnosed before 50 years of age. In comparison, prior guidelines recommended supplemental MRI only for women with PHBCs who carried hereditary cancer gene mutations. The aim of this study was to quantify the increase in the number of patients with breast cancer for whom supplemental breast MRI would now be recommended.

METHODS

Data were extracted from the electronic health records of patients presenting for screening or diagnostic mammography at an urban academic medical center between July 20, 2020, and July 19, 2021. Data extracted included patient-reported PHBC, age at time of breast cancer diagnosis, and hereditary cancer gene mutation carrier status. Descriptive statistics are reported, evaluating the rate of eligibility for supplemental breast MRI in a retrospective population given the new ACR guidelines.

RESULTS

Of the 2,950 patients with self-reported PHBCs who presented for breast cancer screening in the year between July 2020 and July 2021, 1,805 (61%) met the criteria for supplemental breast MRI according to the 2018 guidelines compared with only 3.6% using pre-2018 guidelines.

CONCLUSIONS

Measuring the impact of the 2018 ACR supplemental MRI recommendations using real-world data at a single urban academic medical center demonstrated a 15-fold increase in potential eligibility for supplemental breast MRI in patients with PHBCs.

Artificial Intellgence in the Era of Precision Oncological Imaging

Rapid-paced development and adaptability of artificial intelligence algorithms have secured their almost ubiquitous presence in the field of oncological imaging. Artificial intelligence models have been created for a variety of tasks, including risk stratification, automated detection, and segmentation of lesions, characterization, grading and staging, prediction of prognosis, and treatment response. Soon, artificial intelligence could become an essential part of every step of oncological workup and patient management. Integration of neural networks and deep learning into radiological artificial intelligence algorithms allow for extrapolating imaging features otherwise inaccessible to human operators and pave the way to truly personalized management of oncological patients.Although a significant proportion of currently available artificial intelligence solutions belong to basic and translational cancer imaging research, their progressive transfer to clinical routine is imminent, contributing to the development of a personalized approach in oncology. We thereby review the main applications of artificial intelligence in oncological imaging, describe the example of their successful integration into research and clinical practice, and highlight the challenges and future perspectives that will shape the field of oncological radiology.

Artificial intelligence and machine learning in cancer imaging

An increasing array of tools is being developed using artificial intelligence (AI) and machine learning (ML) for cancer imaging. The development of an optimal tool requires multidisciplinary engagement to ensure that the appropriate use case is met, as well as to undertake robust development and testing prior to its adoption into healthcare systems. This multidisciplinary review highlights key developments in the field. We discuss the challenges and opportunities of AI and ML in cancer imaging; considerations for the development of algorithms into tools that can be widely used and disseminated; and the development of the ecosystem needed to promote growth of AI and ML in cancer imaging.

Impact of artificial intelligence in breast cancer screening with mammography

Objectives

To demonstrate that radiologists, with the help of artificial intelligence (AI), are able to better classify screening mammograms into the correct breast imaging reporting and data system (BI-RADS) category, and as a secondary objective, to explore the impact of AI on cancer detection and mammogram interpretation time.

Methods

A multi-reader, multi-case study with cross-over design, was performed, including 314 mammograms. Twelve radiologists interpreted the examinations in two sessions delayed by a 4 weeks wash-out period with and without AI support. For each breast of each mammogram, they had to mark the most suspicious lesion (if any) and assign it with a forced BI-RADS category and a level of suspicion or “continuous BI-RADS 100”.

Cohen’s kappa correlation coefficient evaluating the inter-observer agreement for BI-RADS category per breast, and the area under the receiver operating characteristic curve (AUC), were used as metrics and analyzed.

Results

On average, the quadratic kappa coefficient increased significantly when using AI for all readers [κ = 0.549, 95% CI (0.528–0.571) without AI and κ = 0.626, 95% CI (0.607–0.6455) with AI]. AUC was significantly improved when using AI (0.74 vs 0.77, p = 0.004). Reading time was not significantly affected for all readers (106 s without AI and vs 102 s with AI; p = 0.754).

Conclusions

When using AI, radiologists were able to better assign mammograms with the correct BI-RADS category without slowing down the interpretation time.

Optimizing risk-based breast cancer screening policies with reinforcement learning

Screening programs must balance the benefit of early detection with the cost of overscreening. Here, we introduce a novel reinforcement learning-based framework for personalized screening, Tempo, and demonstrate its efficacy in the context of breast cancer. We trained our risk-based screening policies on a large screening mammography dataset from Massachusetts General Hospital (MGH; USA) and validated this dataset in held-out patients from MGH and external datasets from Emory University (Emory; USA), Karolinska Institute (Karolinska; Sweden) and Chang Gung Memorial Hospital (CGMH; Taiwan). Across all test sets, we find that the Tempo policy combined with an image-based artificial intelligence (AI) risk model is significantly more efficient than current regimens used in clinical practice in terms of simulated early detection per screen frequency. Moreover, we show that the same Tempo policy can be easily adapted to a wide range of possible screening preferences, allowing clinicians to select their desired trade-off between early detection and screening costs without training new policies. Finally, we demonstrate that Tempo policies based on AI-based risk models outperform Tempo policies based on less accurate clinical risk models. Altogether, our results show that pairing AI-based risk models with agile AI-designed screening policies has the potential to improve screening programs by advancing early detection while reducing overscreening.

Factors associated with mammographic breast density among women in Karachi Pakistan

Background

There are no studies done to evaluate the distribution of mammographic breast density and factors associated with it among Pakistani women.

Methods

Participants included 477 women, who had received either diagnostic or screening mammography at two hospitals in Karachi Pakistan. Mammographic breast density was assessed using the Breast Imaging Reporting and Data System. In person interviews were conducted using a detailed questionnaire, to assess risk factors of interest, and venous blood was collected to measure serum vitamin D level at the end of the interview. To determine the association of potential factors with mammographic breast density, multivariable polytomous logistic regression was used.

Results

High-density mammographic breast density (heterogeneously and dense categories) was high and found in 62.4% of women. There was a significant association of both heterogeneously dense and dense breasts with women of a younger age group < 45 years (OR 2.68, 95% CI 1.60–4.49) and (OR 4.83, 95% CI 2.54–9.16) respectively. Women with heterogeneously dense and dense breasts versus fatty and fibroglandular breasts had a higher history of benign breast disease (OR 1.90, 95% CI 1.14–3.17) and (OR 3.61, 95% CI 1.90–6.86) respectively. There was an inverse relationship between breast density and body mass index. Women with dense breasts and heterogeneously dense breasts had lower body mass index (OR 0.94 95% CI 0.90–0.99) and (OR 0.81, 95% CI 0.76–0.87) respectively. There was no association of mammographic breast density with serum vitamin D levels, diet, and breast cancer.

Conclusions

The findings of a positive association of higher mammographic density with younger age and benign breast disease and a negative association between body mass index and breast density are important findings that need to be considered in developing screening guidelines for the Pakistani population.

Breast nodule classification with two-dimensional ultrasound using Mask-RCNN ensemble aggregation

PURPOSE

The purpose of this study was to create a deep learning algorithm to infer the benign or malignant nature of breast nodules using two-dimensional B-mode ultrasound data initially marked as BI-RADS 3 and 4.

MATERIALS AND METHODS

An ensemble of mask region-based convolutional neural networks (Mask-RCNN) combining nodule segmentation and classification were trained to explicitly localize the nodule and generate a probability of the nodule to be malignant on two-dimensional B-mode ultrasound. These probabilities were aggregated at test time to produce final results. Resulting inferences were assessed using area under the curve (AUC).

RESULTS

A total of 460 ultrasound images of breast nodules classified as BI-RADS 3 or 4 were included. There were 295 benign and 165 malignant breast nodules used for training and validation, and another 137 breast nodules images used for testing. As a part of the challenge, the distribution of benign and malignant breast nodules in the test database remained unknown. The obtained AUC was 0.69 (95% CI: 0.57-0.82) on the training set and 0.67 on the test set.

CONCLUSION

The proposed deep learning solution helps classify benign and malignant breast nodules based solely on two-dimensional ultrasound images initially marked as BIRADS 3 and 4.

Artificial Intelligence in Medical Imaging of the Breast

Artificial intelligence (AI) has invaded our daily lives, and in the last decade, there have been very promising applications of AI in the field of medicine, including medical imaging, in vitro diagnosis, intelligent rehabilitation, and prognosis. Breast cancer is one of the common malignant tumors in women and seriously threatens women’s physical and mental health. Early screening for breast cancer via mammography, ultrasound and magnetic resonance imaging (MRI) can significantly improve the prognosis of patients. AI has shown excellent performance in image recognition tasks and has been widely studied in breast cancer screening. This paper introduces the background of AI and its application in breast medical imaging (mammography, ultrasound and MRI), such as in the identification, segmentation and classification of lesions; breast density assessment; and breast cancer risk assessment. In addition, we also discuss the challenges and future perspectives of the application of AI in medical imaging of the breast.

Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm

BACKGROUND

 Considering radiological examinations not as mere images, but as a source of data, has become the key paradigm in the diagnostic imaging field. This change of perspective is particularly popular in breast imaging. It allows breast radiologists to apply algorithms derived from computer science, to realize innovative clinical applications, and to refine already established methods. In this context, the terminology "imaging biomarker", "radiomics", and "artificial intelligence" are of pivotal importance. These methods promise noninvasive, low-cost (e. g., in comparison to multigene arrays), and workflow-friendly (automated, only one examination, instantaneous results, etc.) delivery of clinically relevant information.

METHODS AND RESULTS

 This paper is designed as a narrative review on the previously mentioned paradigm. The focus is on key concepts in breast imaging and important buzzwords are explained. For all areas of breast imaging, exemplary studies and potential clinical use cases are discussed.

CONCLUSION

 Considering radiological examination as a source of data may optimize patient management by guiding individualized breast cancer diagnosis and oncologic treatment in the age of precision medicine.

KEY POINTS

  · In conventional breast imaging, examinations are interpreted based on patterns perceivable by visual inspection.. · The radiomics paradigm treats breast images as a source of data, containing information beyond what is visible to our eyes.. · This results in radiomic signatures that may be considered as imaging biomarkers, as they provide diagnostic, predictive, and prognostic information.. · Radiomics derived imaging biomarkers may be used to individualize breast cancer treatment in the era of precision medicine.. · The concept and key research of radiomics in the field of breast imaging will be discussed in this narrative review..

CITATION FORMAT

· Dietzel M, Clauser P, Kapetas P et al. Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm. Fortschr Röntgenstr 2021; 193: 898 - 908.