Toward robust mammography-based models for breast cancer risk

Yala, Adam; Mikhael, Peter G.; Strand, Fredrik; Lin, Gigin; Smith, Kevin; Wan, Yung‐Liang; Lamb, Leslie; Hughes, Kevin S.; Lehman, Constance; Barzilay, Regina

doi:10.1126/scitranslmed.aba4373

Cited by 126 publications

(149 citation statements)

References 67 publications

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“…32 We evaluated our results in a diverse, community-based cohort, using a rigorous design and methods to evaluate AI under both pragmatic and optimal conditions. Our observed discrimination was consistent with prior publications for the Mirai AI algorithm 13 and the BCSC clinical risk model. 6,7,33 Our results are not an endorsement of any one AI algorithm, but a demonstration of the inherent predictive power in mammography-based deep learning using a sample of 5 AI algorithms.…”

Section: Discussionsupporting

confidence: 92%

“…This incremental improvement was also noted in other studies combining mammography AI and clinical risk. 12,13 The combined model also decreased overall differences in discrimination between AI algorithms. Larger gains in improvement may be derived by combining clinical risk and mammography AI with single nucleotide polymorphism polygenic risk scores, 12 which we will evaluate in the future.…”

Section: Discussionmentioning

confidence: 96%

“…11 A few studies have evaluated future risk performance for AI algorithms explicitly trained for this task; these studies suggest substantial improvements over clinical risk models alone. 12, 13 It is unknown whether AI trained for detection at shorter time horizons—the majority of mammography AI algorithms—can also predict longer-term risk.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Mammography Artificial Intelligence Algorithms for 5-year Breast Cancer Risk Prediction

Arasu

Habel

Achacoso

et al. 2022

Preprint

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PURPOSE: To examine the ability of 5 artificial intelligence (AI)-based computer vision algorithms, most trained to detect visible breast cancer on mammograms, to predict future risk relative to the Breast Cancer Surveillance Consortium clinical risk prediction model (BCSC v2). PATIENTS AND METHODS: In this case-cohort study, women who had a screening mammogram in 2016 at Kaiser Permanente Northern California with no evidence of cancer on final imaging assessment were followed through September 2021. Women with prior breast cancer or a known highly penetrant gene mutation were excluded. From the 329,814 total eligible women, a random subcohort of 13,881 women (4.2%) were selected, of whom 197 had incident cancer. All 4,475 additional incident cancers were also included. Continuous AI-predicted scores were generated from the index 2016 mammogram. Risk estimates were generated with the Kaplan-Meier method and time-varying area under the curve [AUC(t)]. RESULTS: For incident cancers at 0-1 year (interval cancer risk), BCSC demonstrated an AUC(t) of 0.62 (95% CI, 0.58-0.66), and the AI algorithms had AUC(t)s ranging from 0.66-0.71, all significantly higher than BCSC (P < .05). For incident cancers at 1 to 5 years (5-year future cancer risk), BCSC demonstrated an AUC(t) of 0.61 (95% CI, 0.60-0.62), and the AI algorithms had AUC(t)s ranging from 0.63 to 0.67, all significantly higher than BCSC. Combined BCSC and AI models demonstrated AUC(t)s for interval cancer risk of 0.67-0.73 and for 5-year future cancer risk of 0.66-0.68. CONCLUSION: The AI mammography algorithms we evaluated had significantly higher discrimination than the BCSC clinical risk model for interval and 5-year future cancer risk. Combined AI and BCSC models had slightly higher discrimination than AI alone.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

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Comparison of Mammography Artificial Intelligence Algorithms for 5-year Breast Cancer Risk Prediction

Arasu

Habel

Achacoso

et al. 2022

Preprint

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“…In addition, computer vision methods have been applied to mammography to identify breast cancer risk using deep learning (DL) with artificial neural network models (10)(11)(12). Previous studies using DL models achieved areas under the receiver operating characteristic curve (AUCs) ranging from 0.65 to 0.81 for the differentiation between women with and women without cancer (12)(13)(14). However, these studies did not consider how the cancer was detected (eg, regular mammographic screening or other means).…”

Section: Imaging Examinationsmentioning

confidence: 99%

Deep Learning Predicts Interval and Screening-detected Cancer from Screening Mammograms: A Case-Case-Control Study in 6369 Women

Zhu¹,

Wolfgruber²,

Leong³

et al. 2021

Radiology

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R andomized controlled trials have shown that screening mammography reduces breast cancer mortality by r educing the incidence of advanced cancer (1,2). However, mammography has reduced sensitivity in the detection of breast cancers in breasts with radiologically dense and complex tissue (3,4). These cancers discovered within 12 months after normal screening mammograms are defined as interval cancers, and the reduction of mammographic sensitivity from breast density is commonly called masking. Roughly 13% of breast cancers diagnosed in the United States are interval cancers (3). Interval cancers usually have more aggressive tumor biology and are typically discovered at an advanced stage (3-6). It is therefore important to identify women who have a high risk for interval breast cancer and provide additional prevention strategies such as supplemental screenings (3).Previous studies have shown breast density is both a risk factor for breast cancer and a masking factor of interval breast cancer. One study of more than 547 women found that the cancer detection rate with mammography was 80% in women with predominantly fatty breasts and 30% in women with extremely dense breasts (7). A larger study of more than 240 000 women found that combined relative risks of incident breast cancer showed positive correlation with the percentage breast density, reporting relative risks

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“…Artificial intelligence-driven improvements in image analysis are expected to improve the precision of breast imaging to distinguish benign lesions from malignant lesions. 69 Novel molecular diagnostic tests are emerging in TNBC and HER2 + breast cancers to refine prognosis beyond the clinical stage, the same way that gene expression profiling assays did in hormone receptor + disease. The extent of lymphocytic infiltration is showing clinically meaningful prognostic risk stratification in TNBC, 70 and a combination of HER2 expression, PIK3CA mutation, and molecular subtype may identify HER2 + breast cancers with excellent prognosis.…”

Section: What Is Next?mentioning

confidence: 99%

The Way of the Future: Personalizing Treatment Plans Through Technology

Liefaard

Lips

Wesseling

et al. 2021

American Society of Clinical Oncology Educational Book

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Advances in tissue analysis methods, image analysis, high-throughput molecular profiling, and computational tools increasingly allow us to capture and quantify patient-to patient variations that impact cancer risk, prognosis, and treatment response. Statistical models that integrate patient-specific information from multiple sources (e.g., family history, demographics, germline variants, imaging features) can provide individualized cancer risk predictions that can guide screening and prevention strategies. The precision, quality, and standardization of diagnostic imaging are improving through computer-aided solutions, and multigene prognostic and predictive tests improved predictions of prognosis and treatment response in various cancer types. A common theme across many of these advances is that individually moderately informative variables are combined into more accurate multivariable prediction models. Advances in machine learning and the availability of large data sets fuel rapid progress in this field. Molecular dissection of the cancer genome has become a reality in the clinic, and molecular target profiling is now routinely used to select patients for various targeted therapies. These technology-driven increasingly more precise and quantitative estimates of benefit versus risk from a given intervention empower patients and physicians to tailor treatment strategies that match patient values and expectations.

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Toward robust mammography-based models for breast cancer risk

Cited by 126 publications

References 67 publications

Comparison of Mammography Artificial Intelligence Algorithms for 5-year Breast Cancer Risk Prediction

Comparison of Mammography Artificial Intelligence Algorithms for 5-year Breast Cancer Risk Prediction

Deep Learning Predicts Interval and Screening-detected Cancer from Screening Mammograms: A Case-Case-Control Study in 6369 Women

The Way of the Future: Personalizing Treatment Plans Through Technology

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