Masking risk predictors in screening mammography

Mainprize, James G.; Alonzo-Proulx, Olivier; Alshafeiy, Taghreed; Patrie, James T.; Harvey, Jennifer A.; Yaffe, Martin J.

doi:10.1117/12.2318074

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…13 Mainprize et al quantified a detectability index and other features that help to quantify mammographic masking and interval risk. 10,14 These studies have shown that analysis of detectability in mammograms can help to identify risk of interval cancer and could be studied and examined further. Our goal was to extend upon these studies by creating and analyzing metrics of detectability.…”

Section: Introductionmentioning

confidence: 99%

Derived mammographic masking measures based on simulated lesions predict the risk of interval cancer after controlling for known risk factors: a case‐case analysis

Hinton

Mahmoudzadeh

et al. 2019

Medical Physics

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Purpose Women with radiographically dense or texturally complex breasts are at increased risk for interval cancer, defined as cancers diagnosed after a normal screening examination. The purpose of this study was to create masking measures and apply them to identify interval risk in a population of women who experienced either screen‐detected or interval cancers after controlling for breast density. Methods We examined full‐field digital screening mammograms acquired from 2006 to 2015. Examinations associated with 182 interval cancers were matched to 173 screen‐detected cancers on age, race, exam date and time since last imaging examination. Local Image Quality Factor (IQF) values were calculated and used to create IQF maps that represented mammographic masking. We used various statistics to define global masking measures of these maps. Association of these masking measures with interval cancer vs screen‐detected cancer was estimated using conditional logistic regression in a univariate and adjusted model for Breast Imaging‐Reporting and Data System (BI‐RADS) density. Receiver operator curves were calculated in each case to compare specificity vs sensitivity, and area under those curves were generated. Proportion of screen‐detected cancer was estimated for stratifications of IQF features. Results Several masking features showed significant association with interval compared to screen‐detected cancers after adjusting for BI‐RADS density (up to P = 2.52E‐6), and the 10th percentile of the IQF value (P = 1.72E‐3) showed the strongest improvement in the area under the receiver operator curve, increasing from 0.65 using only BI‐RADS density to 0.69. The highest masking group had a 32% proportion of screen‐detected cancers while the low masking group had a 69% proportion. Conclusions We conclude that computer vision methods using model observers may improve quantifying the probability of breast cancer detection beyond using breast density alone.

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Section: Introductionmentioning

confidence: 99%

Derived mammographic masking measures based on simulated lesions predict the risk of interval cancer after controlling for known risk factors: a case‐case analysis

Hinton

Mahmoudzadeh

et al. 2019

Medical Physics

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Identifying Women With Mammographically- Occult Breast Cancer Leveraging GAN-Simulated Mammograms

Lee

Nishikawa

2022

IEEE Trans. Med. Imaging

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Our objective is to show the feasibility of using simulated mammograms to detect mammographically-occult (MO) cancer in women with dense breasts and a normal screening mammogram who could be triaged for additional screening with magnetic resonance imaging (MRI) or ultrasound. We developed a Conditional Generative Adversarial Network (CGAN) to simulate a mammogram with normal appearance using the opposite mammogram as the condition. We used a Convolutional Neural Network (CNN) trained on Radon Cumulative Distribution Transform (RCDT) processed mammograms to detect MO cancer. For training CGAN, we used screening mammograms of 1366 women. For MO cancer detection, we used screening mammograms of 333 women (97 MO cancer) with dense breasts. We simulated the right mammogram for normal controls and the cancer side for MO cancer cases. We created two RCDT images, one from a real mammogram pair and another from a realsimulated mammogram pair. We finetuned a VGG16 on resulting RCDT images to classify the women with MO cancer. We compared the classification performance of the CNN trained on fused RCDT images, CNNFused to that of trained only on real RCDT images, CNNReal, and to that of trained only on simulated RCDT images, CNNSimulated. The test AUC for CNNFused was 0.77 with a 95% confidence interval (95CI) of [0.71, 0.83], which was statistically better (p-value < 0.02) than the CNNReal AUC of 0.70 with a 95CI of [0.64, 0.77] and CNNSimulated AUC of 0.68 with a 95CI of [0.62, 0.75]. It showed that CGAN simulated mammograms can help MO cancer detection.

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Clinical Significance of Combined Density and Deep-Learning-Based Texture Analysis for Stratifying the Risk of Short-Term and Long-Term Breast Cancer in Screening

Vilmun,

Napolitano,

Lauritzen

et al. 2024

Diagnostics

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Assessing a woman’s risk of breast cancer is important for personalized screening. Mammographic density is a strong risk factor for breast cancer, but parenchymal texture patterns offer additional information which cannot be captured by density. We aimed to combine BI-RADS density score 4th Edition and a deep-learning-based texture score to stratify women in screening and compare rates among the combinations. This retrospective study cohort study included 216,564 women from a Danish populations-based screening program. Baseline mammograms were evaluated using BI-RADS density scores (1–4) and a deep-learning texture risk model, with scores categorized into four quartiles (1–4). The incidence rate ratio (IRR) for screen-detected, interval, and long-term cancer were adjusted for age, year of screening and screening clinic. Compared with subgroup B1-T1, the highest IRR for screen-detected cancer were within the T4 category (3.44 (95% CI: 2.43–4.82)−4.57 (95% CI: 3.66–5.76)). IRR for interval cancer was highest in the BI-RADS 4 category (95% CI: 5.36 (1.77–13.45)−16.94 (95% CI: 9.93–30.15)). IRR for long-term cancer increased both with increasing BI-RADS and increasing texture reaching 5.15 (4.31–6.16) for the combination of B4-T4 compared with B1-T1. Deep-learning-based texture analysis combined with BI-RADS density categories can reveal subgroups with increased rates beyond what density alone can ascertain, suggesting the potential of combining texture and density to improve risk stratification in breast cancer screening.

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Masking risk predictors in screening mammography

Cited by 3 publications

References 8 publications

Derived mammographic masking measures based on simulated lesions predict the risk of interval cancer after controlling for known risk factors: a case‐case analysis

Derived mammographic masking measures based on simulated lesions predict the risk of interval cancer after controlling for known risk factors: a case‐case analysis

Identifying Women With Mammographically- Occult Breast Cancer Leveraging GAN-Simulated Mammograms

Clinical Significance of Combined Density and Deep-Learning-Based Texture Analysis for Stratifying the Risk of Short-Term and Long-Term Breast Cancer in Screening

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