A deep learning fusion model with evidence-based confidence level analysis for differentiation of malignant and benign breast tumors using dynamic contrast enhanced MRI

Wu, Yunan; Wu, Jing; Dou, Yuting; Rubert, Nicholas; Wang, Yi; Deng, Jie

doi:10.1016/j.bspc.2021.103319

Cited by 11 publications

(5 citation statements)

References 33 publications

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“… 87 This was consistent with Jiang et al, 88 who evaluated the clinical performance of radiologists in detecting breast cancer at DCE MRI, and found that AUC showed an improvement in accuracy from 0.71 to 0.76 when AI was used. 69 This was reinforced recently by Wu et al, 89 who found that the proposed CNN model based on DCE MRI achieved diagnostic accuracy of 87.7%, precision of 91.2%, sensitivity of 86.1%, and AUC of 91.2%. In addition, it was highlighted that DL can be a favorable tool to increase the proficiency and accessibility of breast MRI.…”

Section: Ai-based Application and Magnetic Resonance Imaging (Mri)mentioning

confidence: 69%

“…In addition, AI and radiomics approach have gained popularity in medical imaging to facilitate disease diagnosis (such as breast lesions). 89 , 90 In an MRI-based radiomics and AI study, entropy of breast lesions found to be a worth parameters to differentiate between malignant and benign breast lesions. 90 , 91 Fusco et al 92 reported consistent findings.…”

Section: Ai-based Application and Magnetic Resonance Imaging (Mri)mentioning

confidence: 99%

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The utilization of artificial intelligence applications to improve breast cancer detection and prognosis

Alsharif

2023

SMJ

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Breast imaging faces challenges with the current increase in medical imaging requests and lesions that breast screening programs can miss. Solutions to improve these challenges are being sought with the recent advancement and adoption of artificial intelligent (AI)-based applications to enhance workflow efficiency as well as patient-healthcare outcomes. rtificial intelligent tools have been proposed and used to analyze different modes of breast imaging, in most of the published studies, mainly for the detection and classification of breast lesions, breast lesion segmentation, breast density evaluation, and breast cancer risk assessment. This article reviews the background of the Conventional Computer-aided Detection system and AI, AI-based applications in breast medical imaging for the identification, segmentation, and categorization of lesions, breast density and cancer risk evaluation. In addition, the challenges, and limitations of AI-based applications in breast imaging are also discussed.

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Section: Ai-based Application and Magnetic Resonance Imaging (Mri)mentioning

confidence: 69%

Section: Ai-based Application and Magnetic Resonance Imaging (Mri)mentioning

confidence: 99%

The utilization of artificial intelligence applications to improve breast cancer detection and prognosis

Alsharif

2023

SMJ

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“…Wu et al [ 34 ] trained a CNN model to analyze and detect lesions from DCE T1-weighted images from 130 patients, 71 of which had malignant lesions and 59 had benign tumors. Fuzzy C-means clustering-based algorithm automatically segmented 3D tumor volumes from DCE images after rectangular region-of-interest were placed by an expert radiologist.…”

Section: Resultsmentioning

confidence: 99%

Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review

et al. 2023

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Deep learning analysis of radiological images has the potential to improve diagnostic accuracy of breast cancer, ultimately leading to better patient outcomes. This paper systematically reviewed the current literature on deep learning detection of breast cancer based on magnetic resonance imaging (MRI). The literature search was performed from 2015 to Dec 31, 2022, using Pubmed. Other database included Semantic Scholar, ACM Digital Library, Google search, Google Scholar, and pre-print depositories (such as Research Square). Articles that were not deep learning (such as texture analysis) were excluded. PRISMA guidelines for reporting were used. We analyzed different deep learning algorithms, methods of analysis, experimental design, MRI image types, types of ground truths, sample sizes, numbers of benign and malignant lesions, and performance in the literature. We discussed lessons learned, challenges to broad deployment in clinical practice and suggested future research directions.

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“…Yunan et al [ 89 ] developed a fusion CNN model based on dynamic contrast-enhanced MRI images to improve breast cancer diagnosis. The proposed network consisted of two branches, a deep branch with a composite grayscale tumor ROI image, and a shallow branch with seven analytical features as input.…”

Section: Deep Learning For Breast Cancermentioning

confidence: 99%

Applying Deep Learning for Breast Cancer Detection in Radiology

Mahoro

Akhloufi

2022

Current Oncology

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Recent advances in deep learning have enhanced medical imaging research. Breast cancer is the most prevalent cancer among women, and many applications have been developed to improve its early detection. The purpose of this review is to examine how various deep learning methods can be applied to breast cancer screening workflows. We summarize deep learning methods, data availability and different screening methods for breast cancer including mammography, thermography, ultrasound and magnetic resonance imaging. In this review, we will explore deep learning in diagnostic breast imaging and describe the literature review. As a conclusion, we discuss some of the limitations and opportunities of integrating artificial intelligence into breast cancer clinical practice.

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A deep learning fusion model with evidence-based confidence level analysis for differentiation of malignant and benign breast tumors using dynamic contrast enhanced MRI

Cited by 11 publications

References 33 publications

The utilization of artificial intelligence applications to improve breast cancer detection and prognosis

The utilization of artificial intelligence applications to improve breast cancer detection and prognosis

Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review

Applying Deep Learning for Breast Cancer Detection in Radiology

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