Deep learning-based breast region segmentation in raw and processed digital mammograms:generalization across views and vendors

Purpose: We developed a segmentation method suited for both raw (for processing) and processed (for presentation) digital mammograms (DMs) that is designed to generalize across images acquired with systems from different vendors and across the two standard screening views. Approach: A U-Net was trained to segment mammograms into background, breast, and pectoral muscle. Eight different datasets, including two previously published public sets and six sets of DMs from as many different vendors, were used, totaling 322 screen film mammograms (SFMs) and 4251 DMs (2821 raw/processed pairs and 1430 only processed) from 1077 different women. Three experiments were done: first training on all SFM and processed images, second also including all raw images in training, and finally testing vendor generalization by leaving one dataset out at a time. Results: The model trained on SFM and processed mammograms achieved a good overall performance regardless of projection and vendor, with a mean (±std. dev.) dice score of 0.96 0.06 for all datasets combined. When raw images were included in training, the mean (±std. dev.) dice score for the raw images was 0.95 0.05 and for the processed images was 0.96 0.04. Testing on a dataset with processed DMs from a vendor that was excluded from training resulted in a difference in mean dice varying between −0.23 to þ0.02 from that of the fully trained model. Conclusions: The proposed segmentation method yields accurate overall segmentation results for both raw and processed mammograms independent of view and vendor. The code and model weights are made available.</p

The dilemma of recalling well-circumscribed masses in a screening population: A narrative literature review and exploration of Dutch screening practice

Background: In Dutch breast cancer screening, solitary, new or growing well-circumscribed masses should be recalled for further assessment. This results in cancers detected but also in false positive recalls, especially at initial screening. The aim of this study was to determine characteristics of well-circumscribed masses at mammography and identify potential methods to improve the recall strategy. Methods: A systematic literature search was performed using PubMed. In addition, follow-up data were retrieved on all 8860 recalled women in a Dutch screening region from 2014 to 2019. Results: Based on 15 articles identified in the literature search, we found that probably benign well-circumscribed masses that were kept under surveillance had a positive predictive value (PPV) of 0–2%. New or enlarging solitary well-circumscribed masses had a PPV of 10–12%. In general the detected carcinomas had a favorable prognosis. In our exploration of screening practice, 25% of recalls (2133/8860) were triggered by a well-circumscribed mass. Those recalls had a PPV of 2.0% for initial and 10.6% for subsequent screening. Most detected carcinomas had a favorable prognosis as well. Conclusion: To recognize malignancies presenting as well-circumscribed masses, identifying solitary, new or growing lesions is key. This information is missing at initial screening since prior examinations are not available, leading to a low PPV. Access to prior clinical examinations may therefore improve this PPV. In addition, given the generally favorable prognosis of screen-detected malignant well-circumscribed masses, one may opt to recall these lesions at subsequent screening, if grown, rather than at initial screening

Impact of the COVID-19 pandemic on breast cancer incidence and tumor stage in the Netherlands and Norway: A population-based study

Background: Comparing the impact of the COVID-19 pandemic on the incidence of newly diagnosed breast tumors and their tumor stage between the Netherlands and Norway will help us understand the effect of differences in governmental and social reactions towards the pandemic. Methods: Women newly diagnosed with breast cancer in 2017–2021 were selected from the Netherlands Cancer Registry and the Cancer Registry of Norway. The crude breast cancer incidence rate (tumors per 100,000 women) during the first (March-September 2020), second (October 2020-April 2021), and Delta COVID-19 wave (May-December 2021) was compared with the incidence rate in the corresponding periods in 2017, 2018, and 2019. Incidence rates were stratified by age group, method of detection, and clinical tumor stage. Results: During the first wave breast cancer incidence declined to a larger extent in the Netherlands than in Norway (27.7% vs. 17.2% decrease, respectively). In both countries, incidence decreased in women eligible for screening. In the Netherlands, incidence also decreased in women not eligible for screening. During the second wave an increase in the incidence of stage IV tumors in women aged 50–69 years was seen in the Netherlands. During the Delta wave an increase in overall incidence and incidence of stage I tumors was seen in Norway. Conclusion: Alterations in breast cancer incidence and tumor stage seem related to a combined effect of the suspension of the screening program, health care avoidance due to the severity of the pandemic, and other unknown factors

Estimating deep learning model uncertainty of breast lesion classification to guide reading strategy in breast cancer screening

Estimating model uncertainty of artificial intelligence (AI)-based breast cancer detection algorithms could help guide the reading strategy in breast cancer screening. For example, the recall decision can be made solely by AI when it exhibits high certainty, while cases where the certainty is low should be read by radiologists. This study aims to evaluate two metrics to predict model uncertainty of a lesion characterization network: 1) the variance of a set of outputs generated with stochastic layer depth, and 2) the entropy of the average output. To test these approaches, 367 mammography exams with cancer (333 screen-detected, and 34 interval) and 367 cancer-negative exams from the Dutch Breast Cancer Screening Program were included. Using a commercial lesion detection algorithm operating at high sensitivity, 6,477 suspicious regions were included (14.1% labeled malignant). By varying the uncertainty threshold, the predictions were classified as certain or uncertain by a specified proportion. Radiologists double reading had a sensitivity of 90.9% (95% CI 89.0% – 92.7%) and a specificity of 93.8% (95% CI 93.2% – 96.2%) for all regions. At equal specificity, the network had a sensitivity of 92.1% (95% CI 89.9% – 94.0%) for all regions. The sensitivity of the network was higher for regions with low uncertainty for both approaches; for the top 50% most certain regions the sensitivity was 96.9% (95% CI 94.7% – 98.4%) and 97.1% (95% CI 94.9% – 98.8%) at equal specificity to radiologists. In conclusion, AI-based lesion classification uncertainty of breast regions can be estimated by applying stochastic layer depth during prediction.</p

Polygenetische risicopredictie van veelvoorkomende ziekten: Van epidemiologie naar klinische toepassing

Sinds in 2001 de eerste kaart van het volledige genoom werd gepubliceerd, is de kennis over onze genetische code exponentieel toegenomen. Naast hoogrisicogenen voor monogenetische ziekten, zoals de ziekte van Huntington en cystische fibrose, zijn er voor veelvoorkomende ziekten als borstkanker en hart- en vaatziekten middels genoombrede associatiestudies vele genetische varianten geïdentificeerd die elk een klein risicoverhogend effect hebben. Op basis van deze ‘single nucleotide polymorphisms’ (SNP’s) kan een polygenetische risicoscore (PRS) worden berekend, waarmee steeds accurater een inschatting van het individuele ziekterisico kan worden gemaakt. De resultaten van epidemiologische studies waarin een PRS wordt gebruikt om iemands totale genetische risico op bepaalde ziekten te voorspellen, zijn veelbelovend. In de toekomst is de PRS mogelijk een waardevolle aanvulling op traditionele monogenetische tests, maar het is van belang dat de voorspellende waarde van de genetische risicoprofielen verder toeneemt en dat duidelijker wordt hoe de clinicus zo’n genetisch risicoprofiel – in combinatie met traditionele risicofactoren – moet interpreteren

Professionals’ views on the justification for esophageal adenocarcinoma screening: A systematic literature search and qualitative analysis

Screening for early esophageal adenocarcinoma (EAC), including screening for its precursor Barrett's esophagus (BE), has the potential to reduce EAC-related mortality and morbidity. This literature review aimed to explore professionals’ views on the justification for EAC screening. A systematic search of Ovid Medline, EMBASE, and PsycInfo, from January 1, 2000 to September 22, 2022, identified 5 original studies and 63 expert opinion articles reporting professionals’ perspectives on EAC screening. Included articles were qualitatively analyzed using the framework method, which was deductively led by modernized screening principles. The analyses showed that many professionals are optimistic about technological advancements in BE detection and treatment. However, views on whether the societal burden of EAC merits screening were contradictory. In addition, knowledge of the long-term benefits and risks of EAC screening is still considered insufficient. There is no consensus on who to screen, how often to screen, which screening test to use, and how to manage non-dysplastic BE. Professionals further point out the need to develop technology that facilitates automated test sample processing and public education strategies that avoid causing disproportionately high cancer worry and social stigma. In conclusion, modernized screening principles are currently insufficiently fulfilled to justify widespread screening for EAC. Results from future clinical screening trials and risk prediction modeling studies may shift professionals’ thoughts regarding justification for EAC screening

A Bivariate Binormal Model for Modelling Double Reading of Screening Mammograms

Double reading of screening mammograms, a feature of many breast cancer screening programs, is impacted by interactions between the two image readers. In this work, we describe how the bivariate binormal (BVBN) model, originally developed for statistical analysis of reader studies, can be used to analyze double reading of screening mammograms. The model posits two bivariate normal distributions that describe the distribution of latent decision variables of the two readers for cancer and non-cancer cases. The BVBN allows for the estimation of correlation coefficients between the decision variables of two readers, independent of performance and the threshold for recall. We contend that these correlation coefficients are a useful way to characterize interactions between readers because they characterize associations at the level of the perceptual response in a way that is consistent with Signal Detection Theory. We describe the BVBN model and show how parameters can be estimated from count data under an assumed multinomial distribution. The analysis presented focuses on two aspects of the BVBN model. For implementation using binary data, an equal-variance assumption on latent decision variables is required. Otherwise, the model is over-parameterized. We characterize and discuss the consequence of this assumption. We also show how disagreement rates, an alternative measure of reader interactions, suffer from base-rate effects making them more difficult to interpret than the correlation coefficients of the BVBN model.</p

Ordering mammograms for improved mammography screening performance

We aim to investigate if ordering mammograms based on texture features promotes visual adaptation, allowing observers to more correctly and/or rapidly detect abnormalities in screening mammograms, thereby improving performance. A fully-crossed, multi-reader multi-case evaluation with 150 screening mammograms (1:1, positive:negative) and 10 screening radiologists was performed to test three different orders of mammograms. The mammograms were either randomly ordered, ordered by Volpara density (low to high), or ordered by a self-supervised learning (SSL) encoding. Level of suspicion (0–100) scores and recall decisions were given per examination by each radiologist. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were compared between ordering conditions using the open-access iMRMC software. Median reading times were compared with the Wilcoxon signed rank test. The radiologist-averaged AUC was higher when interpreting screening mammograms from low to high density than when interpreting mammograms in a random order (0.924 vs 0.936, P=0.013). The radiologist-averaged specificity for the mammograms ordered by density tended to increase (87.3% vs 91.2%, P=0.047) at similar sensitivities (79.9% vs 80.4%, P=0.846) with reduced reading time (29.3 seconds vs 25.1 seconds, P&lt;0.001). For the SSL order no significant difference in screening performance (AUC: 0.924 vs 0.914, P=0.381) and reading time (both 29.3 seconds, P=0.221) with the random order was found. In conclusion, this study suggests that ordering screening mammograms from low to high density enables radiologists to improve their screening performance. Studies within a screening setting are needed to confirm these findings.</p