Classification of breast cancer in ultrasound imaging using a generic deep learning analysis software: a pilot study

Becker, Anton S.; Mueller, Michael; Stoffel, Elina; Marcon, Magda; Ghafoor, Soleen; Boss, Andreas

doi:10.1259/bjr.20170576

Cited by 96 publications

(104 citation statements)

References 28 publications

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“…The development of arti cial intelligence (AI) provides a new method for breast BI-RADS classi cation [2]. AI analyses the morphological and texture features of breast lesions, identi es the ultrasonic features of lesions, and overcomes the shortcomings of human visual observation [3,4,5]. The present study analysed the morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI and compared the results of the AI analysis to the ultrasonic characteristics of BI-RADS 4A benign and malignant lesions to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions.…”

Section: Introductionmentioning

confidence: 99%

Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A

Niu

Huang

et al. 2020

Preprint

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Background: The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS categorisation. We analysed the ultrasonic morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and these ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions.Methods: A total of 206 lesions of BI-RADS 4A examined using ultrasonography were analysed retrospectively, including 174 benign lesions and 32 malignant lesions. All of the lesions were contoured manually, and the ultrasonic morphological and texture features of the lesions, such as circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, margin lobulation, energy, entropy, grey mean, internal calcification and angle between the long axis of the lesion and skin, were calculated using grey level gradient co-occurrence matrix analysis. Differences between benign and malignant lesions of BI-RADS 4A were analysed.Results: Significant differences in margin lobulation, entropy, internal calcification and ALS were noted between the benign group and malignant group (P=0.013, 0.045, 0.045, and 0.002, respectively). The malignant group had more margin lobulations and lower entropy compared with the benign group, and the benign group had more internal calcifications and a greater angle between the long axis of the lesion and skin compared with the malignant group. No significant differences in circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, energy, and grey mean were noted between benign and malignant lesions.Conclusions: Compared with the naked eye, AI can reveal more subtle differences between benign and malignant BI-RADS 4A lesions. These results remind us carefully observation of the margin and the internal echo is of great significance. With the help of morphological and texture information provided by AI, doctors can make a more accurate judgment on such atypical benign and malignant lesions.

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Section: Introductionmentioning

confidence: 99%

Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A

Niu

Huang

et al. 2020

Preprint

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“…Most studies provided limited information on the methods used to assemble the source imaging datasets and the extent that these were verified in terms of a reference standard, with many studies simply citing the source image dataset [10,12,13,19,20,[22][23][24]26,27,30]. However, several studies described an appropriate reference standard that included histopathology with either clinical follow-up or cancer registry matching to ascertain outcomes [9,14,16,18,21,28,31]. Studies proposed to develop and/or evaluate AI models or techniques for breast cancer detection [9,11,18,21,22,27,28,26], or for diagnosis (classification) or interpretation of mammographic examinations [13,14,15,16,20,[23][24][25]30], or dealt with advancing computer-aided detection (CAD) systems through new AI models [10,12,17,19,29]; and one study investigated AI for discrimination between benign and cancerous lesions jointly with cancer risk prediction [31].…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence (AI) for the early detection of breast cancer: a scoping review to assess AI’s potential in breast screening practice

Houssami

Kirkpatrick-Jones

Noguchi

et al. 2019

Expert Review of Medical Devices

122

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Introduction: Various factors are driving interest in the application of artificial intelligence (AI) for breast cancer (BC) detection, but it is unclear whether the evidence warrants large-scale use in population-based screening. Areas covered: We performed a scoping review, a structured evidence synthesis describing a broad research field, to summarize knowledge on AI evaluated for BC detection and to assess AI's readiness for adoption in BC screening. Studies were predominantly small retrospective studies based on highly selected image datasets that contained a high proportion of cancers (median BC proportion in datasets 26.5%), and used heterogeneous techniques to develop AI models; the range of estimated AUC (area under ROC curve) for AI models was 69.2-97.8% (median AUC 88.2%). We identified various methodologic limitations including use of non-representative imaging data for model training, limited validation in external datasets, potential bias in training data, and few comparative data for AI versus radiologists' interpretation of mammography screening. Expert opinion: Although contemporary AI models have reported generally good accuracy for BC detection, methodological concerns, and evidence gaps exist that limit translation into clinical BC screening settings. These should be addressed in parallel to advancing AI techniques to render AI transferable to large-scale population-based screening.

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“…Alternatively, a DL model can also be trained “end to end” for classification without distinct intermediary steps. Becker et al used DL to classify breast cancer on US imaging. This approach does not need finer annotation as in the previous case and could work with coarser image level labels.…”

Section: Deep‐learning Models’ Output For Pocusmentioning

confidence: 99%

“…This approach does not need finer annotation as in the previous case and could work with coarser image level labels. There are several examples of DL being used for frame labeling in US images …”

Section: Deep‐learning Models’ Output For Pocusmentioning

confidence: 99%

Enhanced Point‐of‐Care Ultrasound Applications by Integrating Automated Feature‐Learning Systems Using Deep Learning

Shokoohi

LeSaux

Roohani

et al. 2018

J of Ultrasound Medicine

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Recent applications of artificial intelligence (AI) and deep learning (DL) in health care include enhanced diagnostic imaging modalities to support clinical decisions and improve patients' outcomes. Focused on using automated DLbased systems to improve point-of-care ultrasound (POCUS), we look at DL-based automation as a key field in expanding and improving POCUS applications in various clinical settings. A promising additional value would be the ability to automate training model selections for teaching POCUS to medical trainees and novice sonologists. The diversity of POCUS applications and ultrasound equipment, each requiring specialized AI models and domain expertise, limits the use of DL as a generic solution. In this article, we highlight the most advanced potential applications of AI in POCUS tailored to high-yield models in automated image interpretations, with the premise of improving the accuracy and efficacy of POCUS scans.

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Classification of breast cancer in ultrasound imaging using a generic deep learning analysis software: a pilot study

Cited by 96 publications

References 28 publications

Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A

Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A

Artificial Intelligence (AI) for the early detection of breast cancer: a scoping review to assess AI’s potential in breast screening practice

Enhanced Point‐of‐Care Ultrasound Applications by Integrating Automated Feature‐Learning Systems Using Deep Learning

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