Dynamic Changes of Convolutional Neural Network-based Mammographic Breast Cancer Risk Score Among Women Undergoing Chemoprevention Treatment

Manley, Haley; Mutasa, Simukayi; Chang, Peter D.; Desperito, Elise; Crew, Katherine D.; Ha, Richard

doi:10.1016/j.clbc.2020.11.007

Cited by 9 publications

(11 citation statements)

References 12 publications

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“…Data-driven techniques, notably DL and CNNs, are commonly used in AI. CNNs, a subset of DL, handle image-based tasks such as detection, segmentation, and classification, and are capable of predicting breast cancer risk from mammograms alone or in conjunction with additional factors ( 43 – 46 ).…”

Section: Resultsmentioning

confidence: 99%

Breast cancer risk prediction using machine learning: a systematic review

Hussain,

Ali,

Naseem

et al. 2024

Front. Oncol.

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BackgroundBreast cancer is the leading cause of cancer-related fatalities among women worldwide. Conventional screening and risk prediction models primarily rely on demographic and patient clinical history to devise policies and estimate likelihood. However, recent advancements in artificial intelligence (AI) techniques, particularly deep learning (DL), have shown promise in the development of personalized risk models. These models leverage individual patient information obtained from medical imaging and associated reports. In this systematic review, we thoroughly investigated the existing literature on the application of DL to digital mammography, radiomics, genomics, and clinical information for breast cancer risk assessment. We critically analyzed these studies and discussed their findings, highlighting the promising prospects of DL techniques for breast cancer risk prediction. Additionally, we explored ongoing research initiatives and potential future applications of AI-driven approaches to further improve breast cancer risk prediction, thereby facilitating more effective screening and personalized risk management strategies.Objective and methodsThis study presents a comprehensive overview of imaging and non-imaging features used in breast cancer risk prediction using traditional and AI models. The features reviewed in this study included imaging, radiomics, genomics, and clinical features. Furthermore, this survey systematically presented DL methods developed for breast cancer risk prediction, aiming to be useful for both beginners and advanced-level researchers.ResultsA total of 600 articles were identified, 20 of which met the set criteria and were selected. Parallel benchmarking of DL models, along with natural language processing (NLP) applied to imaging and non-imaging features, could allow clinicians and researchers to gain greater awareness as they consider the clinical deployment or development of new models. This review provides a comprehensive guide for understanding the current status of breast cancer risk assessment using AI.ConclusionThis study offers investigators a different perspective on the use of AI for breast cancer risk prediction, incorporating numerous imaging and non-imaging features.

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Section: Resultsmentioning

confidence: 99%

Breast cancer risk prediction using machine learning: a systematic review

Hussain,

Ali,

Naseem

et al. 2024

Front. Oncol.

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“…Several studies showed novel applications of DL breast cancer risk models beyond screening. In 2021, Manley et al designed a DL model to score risk [ 19 ]. Changes in risk score in women who underwent risk-reducing chemoprevention treatment such as Tamoxifen or Aromatase Inhibitors was evaluated.…”

Section: Study Selectionmentioning

confidence: 99%

“…The CNN, a DL architecture utilizing layers of convolution operations, is the most popular architecture for image-based models. These convolutional layers serve as image feature extractors, and when paired with fully connected neuron layers can be used for object detection, segmentation, and classification, to name a few [ 16 , 17 , 18 , 19 ]. CNNs can be applied to a patient’s mammography study with or without existing risk factors to calculate a patient specific breast cancer risk, an example architecture is depicted in Figure 1 below.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approaches with Digital Mammography for Evaluating Breast Cancer Risk, a Narrative Review

et al. 2023

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Breast cancer remains the leading cause of cancer-related deaths in women worldwide. Current screening regimens and clinical breast cancer risk assessment models use risk factors such as demographics and patient history to guide policy and assess risk. Applications of artificial intelligence methods (AI) such as deep learning (DL) and convolutional neural networks (CNNs) to evaluate individual patient information and imaging showed promise as personalized risk models. We reviewed the current literature for studies related to deep learning and convolutional neural networks with digital mammography for assessing breast cancer risk. We discussed the literature and examined the ongoing and future applications of deep learning techniques in breast cancer risk modeling.

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“…Going one step further, Manley et al 46 demonstrated that their DL breast cancer risk score tool is modifiable, and that chemoprevention can decrease risk. DL tools have also been developed to automate the assessment of mammographic breast density, and have been clinically implemented at both academic and clinical radiology centers.…”

Section: Mammography and Digital Breast Tomosynthesismentioning

confidence: 99%

Deep learning in breast imaging

Bhowmik

Eskreis‐Winkler

2022

BJR|Open

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Millions of breast imaging exams are performed each year in an effort to reduce morbidity and mortality rates of breast cancer. Breast imaging exams are mainly performed for cancer screening, diagnostic workup of suspicious findings, evaluating extent of disease in recently diagnosed breast cancer patients, and determining treatment response. Yet, the interpretation of breast imaging can be subjective, tedious, time-consuming, and prone to human error. Retrospective and small reader studies suggest that deep learning (DL) has great potential to perform medical imaging tasks at or above human-level performance, and may be used to automate aspects of the breast cancer screening process, improve cancer detection rates, decrease unnecessary callbacks and biopsies, optimize patient risk assessment, and open up new possibilities for disease prognostication. Prospective trials are urgently needed to validate these proposed tools, and to evaluate whether they are ready for real-world clinical use. Additionally, new regulatory frameworks are needed to address the unique ethical, medicolegal, and quality control issues that DL algorithms present. In this article, we review the basics of DL, describe recent DL breast imaging applications including cancer detection and risk prediction, and finally conclude by discussing the challenges and future directions of artificial intelligence-based systems in the field of breast cancer.

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Dynamic Changes of Convolutional Neural Network-based Mammographic Breast Cancer Risk Score Among Women Undergoing Chemoprevention Treatment

Cited by 9 publications

References 12 publications

Breast cancer risk prediction using machine learning: a systematic review

Breast cancer risk prediction using machine learning: a systematic review

Deep Learning Approaches with Digital Mammography for Evaluating Breast Cancer Risk, a Narrative Review

Deep learning in breast imaging

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