Applications of machine-learning algorithms for prediction of benign and malignant breast lesions using ultrasound radiomics signatures: A multi-center study

Homayoun, Hassan; Chan, Wai Yee; Kuzan, Taha Yusuf; Ling, Leong Wai; Altintoprak, Kübra Murzoglu; Mohammadi, Afshin; Vijayananthan, Anushya; Rahmat, Kartini; Leong, Sook Sam; Mirza-Aghazadeh-Attari, Mohammad; Ejtehadifar, Sajjad; Faeghi, Fariborz; Acharya, U. Rajendra; Ardakani, Ali Abbasian

doi:10.1016/j.bbe.2022.07.004

Cited by 7 publications

(1 citation statement)

References 43 publications

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“…91 The current review extracted and organized the data in tabular form and summarized the application of US images in breast cancer diagnosis (Table 2). 6,8,80,83,90,92–118…”

Section: Introductionmentioning

confidence: 99%

Machine learning and new insights for breast cancer diagnosis

Guo,

Zhang,

Yuan

et al. 2024

J Int Med Res

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Breast cancer (BC) is the most prominent form of cancer among females all over the world. The current methods of BC detection include X-ray mammography, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography and breast thermographic techniques. More recently, machine learning (ML) tools have been increasingly employed in diagnostic medicine for its high efficiency in detection and intervention. The subsequent imaging features and mathematical analyses can then be used to generate ML models, which stratify, differentiate and detect benign and malignant breast lesions. Given its marked advantages, radiomics is a frequently used tool in recent research and clinics. Artificial neural networks and deep learning (DL) are novel forms of ML that evaluate data using computer simulation of the human brain. DL directly processes unstructured information, such as images, sounds and language, and performs precise clinical image stratification, medical record analyses and tumour diagnosis. Herein, this review thoroughly summarizes prior investigations on the application of medical images for the detection and intervention of BC using radiomics, namely DL and ML. The aim was to provide guidance to scientists regarding the use of artificial intelligence and ML in research and the clinic.

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“…91 The current review extracted and organized the data in tabular form and summarized the application of US images in breast cancer diagnosis (Table 2). 6,8,80,83,90,92–118…”

Section: Introductionmentioning

confidence: 99%