Generalisable deep learning method for mammographic density prediction across imaging techniques and self-reported race.

BACKGROUND: Breast density is an important risk factor for breast cancer complemented by a higher risk of cancers being missed during screening of dense breasts due to reduced sensitivity of mammography. Automated, deep learning-based prediction of breast density could provide subject-specific risk assessment and flag difficult cases during screening. However, there is a lack of evidence for generalisability across imaging techniques and, importantly, across race. METHODS: This study used a large, racially diverse dataset with 69,697 mammographic studies comprising 451,642 individual images from 23,057 female participants. A deep learning model was developed for four-class BI-RADS density prediction. A comprehensive performance evaluation assessed the generalisability across two imaging techniques, full-field digital mammography (FFDM) and two-dimensional synthetic (2DS) mammography. A detailed subgroup performance and bias analysis assessed the generalisability across participants' race. RESULTS: Here we show that a model trained on FFDM-only achieves a 4-class BI-RADS classification accuracy of 80.5% (79.7-81.4) on FFDM and 79.4% (78.5-80.2) on unseen 2DS data. When trained on both FFDM and 2DS images, the performance increases to 82.3% (81.4-83.0) and 82.3% (81.3-83.1). Racial subgroup analysis shows unbiased performance across Black, White, and Asian participants, despite a separate analysis confirming that race can be predicted from the images with a high accuracy of 86.7% (86.0-87.4). CONCLUSIONS: Deep learning-based breast density prediction generalises across imaging techniques and race. No substantial disparities are found for any subgroup, including races that were never seen during model development, suggesting that density predictions are unbiased.

Women with dense breasts have a higher risk of breast cancer. For dense breasts, it is also more difficult to spot cancer in mammograms, which are the X-ray images commonly used for breast cancer screening. Thus, knowing about an individual's breast density provides important information to doctors and screening participants. This study investigated whether an artificial intelligence algorithm (AI) can be used to accurately determine the breast density by analysing mammograms. The study tested whether such an algorithm performs equally well across different imaging devices, and importantly, across individuals from different self-reported race groups. A large, racially diverse dataset was used to evaluate the algorithm's performance. The results show that there were no substantial differences in the accuracy for any of the groups, providing important assurances that AI can be used safely and ethically for automated prediction of breast density.

Prospective implementation of AI-assisted screen reading to improve early detection of breast cancer.

Artificial intelligence (AI) has the potential to improve breast cancer screening; however, prospective evidence of the safe implementation of AI into real clinical practice is limited. A commercially available AI system was implemented as an additional reader to standard double reading to flag cases for further arbitration review among screened women. Performance was assessed prospectively in three phases: a single-center pilot rollout, a wider multicenter pilot rollout and a full live rollout. The results showed that, compared to double reading, implementing the AI-assisted additional-reader process could achieve 0.7-1.6 additional cancer detection per 1,000 cases, with 0.16-0.30% additional recalls, 0-0.23% unnecessary recalls and a 0.1-1.9% increase in positive predictive value (PPV) after 7-11% additional human reads of AI-flagged cases (equating to 4-6% additional overall reading workload). The majority of cancerous cases detected by the AI-assisted additional-reader process were invasive (83.3%) and small-sized (≤10 mm, 47.0%). This evaluation suggests that using AI as an additional reader can improve the early detection of breast cancer with relevant prognostic features, with minimal to no unnecessary recalls. Although the AI-assisted additional-reader workflow requires additional reads, the higher PPV suggests that it can increase screening effectiveness.

Automatic correction of performance drift under acquisition shift in medical image classification.

Image-based prediction models for disease detection are sensitive to changes in data acquisition such as the replacement of scanner hardware or updates to the image processing software. The resulting differences in image characteristics may lead to drifts in clinically relevant performance metrics which could cause harm in clinical decision making, even for models that generalise in terms of area under the receiver-operating characteristic curve. We propose Unsupervised Prediction Alignment, a generic automatic recalibration method that requires no ground truth annotations and only limited amounts of unlabelled example images from the shifted data distribution. We illustrate the effectiveness of the proposed method to detect and correct performance drift in mammography-based breast cancer screening and on publicly available histopathology data. We show that the proposed method can preserve the expected performance in terms of sensitivity/specificity under various realistic scenarios of image acquisition shift, thus offering an important safeguard for clinical deployment.

Comparing Prognostic Factors of Cancers Identified by Artificial Intelligence (AI) and Human Readers in Breast Cancer Screening.

Invasiveness status, histological grade, lymph node stage, and tumour size are important prognostic factors for breast cancer survival. This evaluation aims to compare these features for cancers detected by AI and human readers using digital mammography. Women diagnosed with breast cancer between 2009 and 2019 from three UK double-reading sites were included in this retrospective cohort evaluation. Differences in prognostic features of cancers detected by AI and the first human reader (R1) were assessed using chi-square tests, with significance at p < 0.05. From 1718 screen-detected cancers (SDCs) and 293 interval cancers (ICs), AI flagged 85.9% and 31.7%, respectively. R1 detected 90.8% of SDCs and 7.2% of ICs. Of the screen-detected cancers detected by the AI, 82.5% had an invasive component, compared to 81.1% for R1 (p-0.374). For the ICs, this was 91.5% and 93.8% for AI and R1, respectively (p = 0.829). For the invasive tumours, no differences were found for histological grade, tumour size, or lymph node stage. The AI detected more ICs. In summary, no differences in prognostic factors were found comparing SDC and ICs identified by AI or human readers. These findings support a potential role for AI in the double-reading workflow.

Multi-vendor evaluation of artificial intelligence as an independent reader for double reading in breast cancer screening on 275,900 mammograms.

BACKGROUND: Double reading (DR) in screening mammography increases cancer detection and lowers recall rates, but has sustainability challenges due to workforce shortages. Artificial intelligence (AI) as an independent reader (IR) in DR may provide a cost-effective solution with the potential to improve screening performance. Evidence for AI to generalise across different patient populations, screening programmes and equipment vendors, however, is still lacking. METHODS: This retrospective study simulated DR with AI as an IR, using data representative of real-world deployments (275,900 cases, 177,882 participants) from four mammography equipment vendors, seven screening sites, and two countries. Non-inferiority and superiority were assessed for relevant screening metrics. RESULTS: DR with AI, compared with human DR, showed at least non-inferior recall rate, cancer detection rate, sensitivity, specificity and positive predictive value (PPV) for each mammography vendor and site, and superior recall rate, specificity, and PPV for some. The simulation indicates that using AI would have increased arbitration rate (3.3% to 12.3%), but could have reduced human workload by 30.0% to 44.8%. CONCLUSIONS: AI has potential as an IR in the DR workflow across different screening programmes, mammography equipment and geographies, substantially reducing human reader workload while maintaining or improving standard of care. TRIAL REGISTRATION: ISRCTN18056078 (20/03/2019; retrospectively registered).

Artificial Intelligence as Supporting Reader in Breast Screening: A Novel Workflow to Preserve Quality and Reduce Workload.

OBJECTIVE: To evaluate the effectiveness of a new strategy for using artificial intelligence (AI) as supporting reader for the detection of breast cancer in mammography-based double reading screening practice. METHODS: Large-scale multi-site, multi-vendor data were used to retrospectively evaluate a new paradigm of AI-supported reading. Here, the AI served as the second reader only if it agrees with the recall/no-recall decision of the first human reader. Otherwise, a second human reader made an assessment followed by the standard clinical workflow. The data included 280 594 cases from 180 542 female participants screened for breast cancer at seven screening sites in two countries and using equipment from four hardware vendors. The statistical analysis included non-inferiority and superiority testing of cancer screening performance and evaluation of the reduction in workload, measured as arbitration rate and number of cases requiring second human reading. RESULTS: Artificial intelligence as a supporting reader was found to be superior or noninferior on all screening metrics compared with human double reading while reducing the number of cases requiring second human reading by up to 87% (245 395/280 594). Compared with AI as an independent reader, the number of cases referred to arbitration was reduced from 13% (35 199/280 594) to 2% (5056/280 594). CONCLUSION: The simulation indicates that the proposed workflow retains screening performance of human double reading while substantially reducing the workload. Further research should study the impact on the second human reader because they would only assess cases in which the AI prediction and first human reader disagree.

Perceived Realism of High-Resolution Generative Adversarial Network-derived Synthetic Mammograms.

PURPOSE: To explore whether generative adversarial networks (GANs) can enable synthesis of realistic medical images that are indiscernible from real images, even by domain experts. MATERIALS AND METHODS: In this retrospective study, progressive growing GANs were used to synthesize mammograms at a resolution of 1280 × 1024 pixels by using images from 90 000 patients (average age, 56 years ± 9) collected between 2009 and 2019. To evaluate the results, a method to assess distributional alignment for ultra-high-dimensional pixel distributions was used, which was based on moment plots. This method was able to reveal potential sources of misalignment. A total of 117 volunteer participants (55 radiologists and 62 nonradiologists) took part in a study to assess the realism of synthetic images from GANs. RESULTS: A quantitative evaluation of distributional alignment shows 60%-78% mutual-information score between the real and synthetic image distributions, and 80%-91% overlap in their support, which are strong indications against mode collapse. It also reveals shape misalignment as the main difference between the two distributions. Obvious artifacts were found by an untrained observer in 13.6% and 6.4% of the synthetic mediolateral oblique and craniocaudal images, respectively. A reader study demonstrated that real and synthetic images are perceptually inseparable by the majority of participants, even by trained breast radiologists. Only one out of the 117 participants was able to reliably distinguish real from synthetic images, and this study discusses the cues they used to do so. CONCLUSION: On the basis of these findings, it appears possible to generate realistic synthetic full-field digital mammograms by using a progressive GAN architecture up to a resolution of 1280 × 1024 pixels.Supplemental material is available for this article.© RSNA, 2020.