Going beyond a First Reader: A Machine Learning Methodology for Optimizing Cost and Performance in Breast Ultrasound Diagnosis

Venkatesh, Santosh S.; Levenback, Benjamin J.; Sultan, Laith R.; Bouzghar, Ghizlane; Sehgal, Chandra M.

doi:10.1016/j.ultrasmedbio.2015.07.020

Cited by 31 publications

(24 citation statements)

References 29 publications

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“…According to the literatures, the use of morphological features and texture features is not limited to the diagnosis of benign and malignant diseases, and these features also help classify malignant tumour subtypes [13,15,[17][18][19]. and malignant lesions, which is consistent with the literature [11].…”

Section: Discussionsupporting

confidence: 81%

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: Discussionsupporting

confidence: 81%

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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“…AI exhibits high accuracy in the diagnosis of breast lesions [15,16]. AI significantly improves the diagnostic accuracy of doctors and improves the consistency among observers [7].…”

Section: Discussionmentioning

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

BMC Cancer

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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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“…Continued enhancement of image analysis algorithms has decreased false positive rates and unnecessary biopsies, particularly in breast cancer diagnostics [7] , [18] . These tools complement a trend towards task-shifting in cancer care, enabling alternate sectors of the healthcare workforce such as primary care providers, nurses, or community health workers to share the workload of over-burdened specialists [19] , for example: Computer-based algorithms combine a human image reading with a machine learning methodology as a second reader to augment early detection strategies for breast cancer [20] . Elastography techniques draw on radiofrequency data of ultrasound readings to calculate tissue elasticity and determine stage status for cervical and prostate cancers with minimal additional equipment or training [21] .…”

Section: Imaging Technologies For the Detection And Diagnosis Of Cancmentioning

confidence: 99%

“…Computer-based algorithms combine a human image reading with a machine learning methodology as a second reader to augment early detection strategies for breast cancer [20] .…”

Section: Imaging Technologies For the Detection And Diagnosis Of Cancmentioning

confidence: 99%

The Role of Affordable, Point-of-Care Technologies for Cancer Care in Low- and Middle-Income Countries: A Review and Commentary

Haney

Tandon

Divi

et al. 2017

IEEE J. Transl. Eng. Health Med.

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As the burden of non-communicable diseases such as cancer continues to rise in low- and middle-income countries (LMICs), it is essential to identify and invest in promising solutions for cancer control and treatment. Point-of-care technologies (POCTs) have played critical roles in curbing infectious disease epidemics in both high- and low-income settings, and their successes can serve as a model for transforming cancer care in LMICs, where access to traditional clinical resources is often limited. The versatility, cost-effectiveness, and simplicity of POCTs warrant attention for their potential to revolutionize cancer detection, diagnosis, and treatment. This paper reviews the landscape of affordable POCTs for cancer care in LMICs with a focus on imaging tools, in vitro diagnostics, and treatment technologies and aspires to encourage innovation and further investment in this space.

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Going beyond a First Reader: A Machine Learning Methodology for Optimizing Cost and Performance in Breast Ultrasound Diagnosis

Cited by 31 publications

References 29 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

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

The Role of Affordable, Point-of-Care Technologies for Cancer Care in Low- and Middle-Income Countries: A Review and Commentary

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