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

Boulch, Marie Le; Bekhouche, Asma; Kermarrec, E.; Milon, Audrey; Wahab, Cendos Abdel; Zilberman, Sonia; Chabbert-Buffet, Nathalie; Thomassin‐Naggara, Isabelle

doi:10.1016/j.diii.2020.07.004

Cited by 18 publications

(9 citation statements)

References 50 publications

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“… 25 It can be successfully assessed by AI algorithms with an excellent agreement and high intraclass correlation coefficient between the AI software and expert readers. 26 A hybrid DL model evaluated by Yala et al included mammographic breast density, age, weight, height, menarche age, menopausal status, detailed family history of breast and ovarian cancer, breast cancer gene (BRCA) mutation status, history of atypical hyperplasia, and history of lobular carcinoma in situ. The IBIS/TC and hybrid DL models showed an area under the curve (AUC) of 0.62 (95% confidence interval [CI]: 0.57-0.66), and 0.70 (95% CI: 0.66-0.75), respectively.…”

Section: Risk Stratificationmentioning

confidence: 99%

Artificial Intelligence in the Era of Precision Oncological Imaging

Cellina

Cè

Khenkina

et al. 2022

Technol Cancer Res Treat

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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.

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Section: Risk Stratificationmentioning

confidence: 99%

Artificial Intelligence in the Era of Precision Oncological Imaging

Cellina

Cè

Khenkina

et al. 2022

Technol Cancer Res Treat

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“…Several guidelines already recommend supplemental imaging or chemo-prevention based on risk assessments [29,31,32] and recent results from the DENSE trial [33] have shown that a breast density based screening strategy could significantly reduce interval cancers compared to current screening. Our work is most closely related to the MyPeBS trial [34], which prospectively compares a personalized screening followup strategy based either Tyrer-Cuzick [8] or MammoRisk [35] risk assessments to current national recommendations. These studies point to substantial clinical interest in risk-based screening, however, current methods for devising screening policies rely on categorizing patients into a few coarse categories (e.g low and high risk), limiting personalization.…”

Section: Discussionmentioning

confidence: 99%

Optimizing risk-based breast cancer screening policies with reinforcement learning

Yala

Mikhael

Lehman

et al. 2022

Nat Med

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Screening programs must balance the benefits of early detection against the costs of over screening. Achieving this goal relies on two complementary technologies: (1) the ability to assess patient risk, (2) the ability to develop personalized screening programs given that risk. While methodologies for assessing patient risk have significantly improved with new advances in deep learning applied to imaging and genetics, our ability to personalize screening policies still lags behind. 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 them on held-out patients from MGH, and on external datasets from Emory USA, Karolinska Sweden and Chang Gung Memorial Hospital (CGMH) Taiwan. Across all test sets, we found that a Tempo policy combined with an image-based AI risk model was significantly more efficient than current regimes used in clinical practice in terms of simulated early detection per screen frequency. Moreover, we showed that the same Tempo policy can be easily adapted to a wide range of possible screening preferences, allowing clinicians to select their desired early detection to screening cost trade-off without training a new policy. Finally, we demonstrated Tempo policies based on AI-based risk models out performed 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, advancing early detection while reducing over-screening.

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“…Still, some misclassification of MBD could have affected our results since we used visual classification using BI RADS by two experienced radiologists. However, a study reported very good agreement between automatic assessment software of breast density based on artificial intelligence (AI) and visual assessment by a senior and a junior radiologist [ 51 ]. The strengths of the study are a good quality of data collection by medical doctors, and comprehensive assessment of all factors associated with MBD.…”

Section: Discussionmentioning

confidence: 99%

Factors associated with mammographic breast density among women in Karachi Pakistan

et al. 2021

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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.

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

Cited by 18 publications

References 50 publications

Artificial Intelligence in the Era of Precision Oncological Imaging

Artificial Intelligence in the Era of Precision Oncological Imaging

Optimizing risk-based breast cancer screening policies with reinforcement learning

Factors associated with mammographic breast density among women in Karachi Pakistan

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