Top 100 Most-Cited Publications on Breast Cancer and Machine Learning Research: A Bibliometric Analysis

Background: Rapid advancement in computing technology and digital information leads to the possible use of machine learning on breast cancer. Objective: This study aimed to evaluate the research output of the top 100 publications and further identify a research theme of breast cancer and machine-learning studies. Methods: Databases of Scopus and Web of Science were used to extract the top 100 publications. These publications were filtered based on the total citation of each paper. Additionally, a bibliometric analysis was applied to the top 100 publications. Results: The top 100 publications were published between 1993 and 2019. The most productive author was Giger ML, and the top two institutions were the University of Chicago and the National University of Singapore. The most active countries were the USA, Germany and China. Ten clusters were identified as both basic and specialised themes of breast cancer and machine learning. Conclusion: Various countries demonstrated comparable interest in breast cancer and machine-learning research. A few Asian countries, such as China, India and Singapore, were listed in the top 10 countries based on the total citation. Additionally, the use of deep learning and breast imaging data was trending in the past 10 years in the field of breast cancer and machine-learning research.

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Color Doppler Ultrasound Improves Machine Learning Diagnosis of Breast Cancer

Diagnostics ◽

10.3390/diagnostics10090631 ◽

2020 ◽

Vol 10 (9) ◽

pp. 631

Author(s):

Afaf F. Moustafa ◽

Theodore W. Cary ◽

Laith R. Sultan ◽

Susan M. Schultz ◽

Emily F. Conant ◽

...

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Breast Imaging ◽

Color Doppler ◽

Color Doppler Ultrasound ◽

Ensemble Classifier ◽

Diagnostic Model ◽

Ultrasound Images ◽

Breast Masses ◽

Roc Area

Color Doppler is used in the clinic for visually assessing the vascularity of breast masses on ultrasound, to aid in determining the likelihood of malignancy. In this study, quantitative color Doppler radiomics features were algorithmically extracted from breast sonograms for machine learning, producing a diagnostic model for breast cancer with higher performance than models based on grayscale and clinical category from the Breast Imaging Reporting and Data System for ultrasound (BI-RADSUS). Ultrasound images of 159 solid masses were analyzed. Algorithms extracted nine grayscale features and two color Doppler features. These features, along with patient age and BI-RADSUS category, were used to train an AdaBoost ensemble classifier. Though training on computer-extracted grayscale features and color Doppler features each significantly increased performance over that of models trained on clinical features, as measured by the area under the receiver operating characteristic (ROC) curve, training on both color Doppler and grayscale further increased the ROC area, from 0.925 ± 0.022 to 0.958 ± 0.013. Pruning low-confidence cases at 20% improved this to 0.986 ± 0.007 with 100% sensitivity, whereas 64% of the cases had to be pruned to reach this performance without color Doppler. Fewer borderline diagnoses and higher ROC performance were both achieved for diagnostic models of breast cancer on ultrasound by machine learning on color Doppler features.

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A five-year (2015 to 2019) analysis of studies focused on breast cancer prediction using machine learning: A systematic review and bibliometric analysis

Journal of Public Health Research ◽

10.4081/jphr.2020.1772 ◽

2020 ◽

Vol 9 (1) ◽

Author(s):

Zakia Salod ◽

Yashik Singh

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Systematic Review ◽

Bibliometric Analysis ◽

The United States ◽

Blood Analysis ◽

Google Scholar ◽

Breast Cancer Dataset ◽

Cancer Dataset ◽

Data Source

The objective 1 of this study was to investigate trends in breast cancer (BC) prediction using machine learning (ML) publications by analysing country, first author, journal, institutional collaborations and co-occurrence of author keywords. The objective 2 was to provide a review of studies on BC prediction using ML and a blood analysis dataset (Breast Cancer Coimbra Dataset [BCCD]), the objective 3 was to provide a brief review of studies based on BC prediction using ML and patients’ fine needle aspirate cytology data (Wisconsin Breast Cancer Dataset [WBCD]). The design of this study was as follows: for objective 1: bibliometric analysis, data source PubMed (2015-2019); for objective 2: systematic review, data source: Google and Google Scholar (2018-2019); for objective 3: systematic review, data source: Google Scholar (2016-2019). The results showed that the United States of America (USA) produced the highest number of publications (n=803). In total, 2419 first authors contributed towards the publications. Breast Cancer Research and Treatment was the highest ranked journal. Institutional collaborations mainly occurred within the USA. The use of ML for BC screening and detection was the most researched topic. A total of 19 distinct papers were included for objectives 2 and 3. The findings from these studies were never presented to clinicians for validations. In conclusion, the use of ML for BC screening and detection is promising.

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Feasibility of Portable Microwave Imaging Device for Breast Cancer Detection

Diagnostics ◽

10.3390/diagnostics12010027 ◽

2021 ◽

Vol 12 (1) ◽

pp. 27

Author(s):

Mio Adachi ◽

Tsuyoshi Nakagawa ◽

Tomoyuki Fujioka ◽

Mio Mori ◽

Kazunori Kubota ◽

...

Keyword(s):

Breast Cancer ◽

Breast Imaging ◽

Ductal Carcinoma ◽

Microwave Imaging ◽

Breast Cancers ◽

Radio Waves ◽

Imaging Data ◽

Imaging Device ◽

Dense Breast Tissue ◽

Microwave Radar

Purpose: Microwave radar-based breast imaging technology utilizes the principle of radar, in which radio waves reflect at the interface between target and normal tissues, which have different permittivities. This study aims to investigate the feasibility and safety of a portable microwave breast imaging device in clinical practice. Materials and methods: We retrospectively collected the imaging data of ten breast cancers in nine women (median age: 66.0 years; range: 37–78 years) who had undergone microwave imaging examination before surgery. All were Japanese and the tumor sizes were from 4 to 10 cm. Using a five-point scale (1 = very poor; 2 = poor; 3 = fair; 4 = good; and 5 = excellent), a radiologist specialized in breast imaging evaluated the ability of microwave imaging to detect breast cancer and delineate its location and size in comparison with conventional mammography and the pathological findings. Results: Microwave imaging detected 10/10 pathologically proven breast cancers, including non-invasive ductal carcinoma in situ (DCIS) and micro-invasive carcinoma, whereas mammography failed to detect 2/10 breast cancers due to dense breast tissue. In the five-point evaluation, median score of location and size were 4.5 and 4.0, respectively. Conclusion: The results of the evaluation suggest that the microwave imaging device is a safe examination that can be used repeatedly and has the potential to be useful in detecting breast cancer.

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