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

Siddique, Maham; Liu, Michael Z.; Duong, Phuong; Jambawalikar, Sachin; Ha, Richard

doi:10.3390/tomography9030091

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…Similarly, investigators ( 20 ) have focused on the use of DL techniques, specifically mammography, for breast density assessment and risk analysis. Recently, the authors ( 21 ) conducted a narrative review focusing specifically on convolutional neural network (CNN) applications in digital mammography. Furthermore, the authors examined the patterns in scale, structure, risk elements, and medical factors that could potentially affect the effectiveness of CNNs in evaluating the risk of breast cancer.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

Breast cancer risk prediction using machine learning: a systematic review

Hussain,

Ali,

Naseem

et al. 2024

Front. Oncol.

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A review of cancer data fusion methods based on deep learning

Zhao,

Li,

Zhou

et al. 2024

Information Fusion

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Nuclear localization of heparanase 2 (Hpa2) attenuates breast carcinoma growth and metastasis

Hilwi,

Shulman,

Naroditsky

et al. 2024

Cell Death Dis

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Unlike the intense research effort devoted to exploring the significance of heparanase in cancer, very little attention was given to Hpa2, a close homolog of heparanase. Here, we explored the role of Hpa2 in breast cancer. Unexpectedly, we found that patients endowed with high levels of Hpa2 exhibited a higher incidence of tumor metastasis and survived less than patients with low levels of Hpa2. Immunohistochemical examination revealed that in normal breast tissue, Hpa2 localizes primarily in the cell nucleus. In striking contrast, in breast carcinoma, Hpa2 expression is not only decreased but also loses its nuclear localization and appears diffuse in the cell cytoplasm. Importantly, breast cancer patients in which nuclear localization of Hpa2 is retained exhibited reduced lymph-node metastasis, suggesting that nuclear localization of Hpa2 plays a protective role in breast cancer progression. To examine this possibility, we engineered a gene construct that directs Hpa2 to the cell nucleus (Hpa2-Nuc). Notably, overexpression of Hpa2 in breast carcinoma cells resulted in bigger tumors, whereas targeting Hpa2 to the cell nucleus attenuated tumor growth and tumor metastasis. RNAseq analysis was performed to reveal differentially expressed genes (DEG) in Hpa2-Nuc tumors vs. control. The analysis revealed, among others, decreased expression of genes associated with the hallmark of Kras, beta-catenin, and TNF-alpha (via NFkB) signaling. Our results imply that nuclear localization of Hpa2 prominently regulates gene transcription, resulting in attenuation of breast tumorigenesis. Thus, nuclear Hpa2 may be used as a predictive parameter in personalized medicine for breast cancer patients.

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

Cited by 3 publications

References 32 publications

Breast cancer risk prediction using machine learning: a systematic review

Breast cancer risk prediction using machine learning: a systematic review

A review of cancer data fusion methods based on deep learning

Nuclear localization of heparanase 2 (Hpa2) attenuates breast carcinoma growth and metastasis

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