Artificial Intelligence for Breast Ultrasound: An Adjunct Tool to Reduce Excessive Lesion Biopsy

AbstractUltrasound is an important imaging modality for the detection and characterization of breast cancer. Though consistently shown to detect mammographically occult cancers, especially in women with dense breasts, breast ultrasound has been noted to have high false-positive rates. In this work, we present an artificial intelligence (AI) system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images. To develop and validate this system, we curated a dataset consisting of 288,767 ultrasound exams from 143,203 patients examined at NYU Langone Health, between 2012 and 2019. On a test set consisting of 44,755 exams, the AI system achieved an area under the receiver operating characteristic curve (AUROC) of 0.976. In a reader study, the AI system achieved a higher AUROC than the average of ten board-certified breast radiologists (AUROC: 0.962 AI, 0.924±0.02 radiologists). With the help of the AI, radiologists decreased their false positive rates by 37.4% and reduced the number of requested biopsies by 27.8%, while maintaining the same level of sensitivity. To confirm its generalizability, we evaluated our system on an independent external test dataset where it achieved an AUROC of 0.911. This highlights the potential of AI in improving the accuracy, consistency, and efficiency of breast ultrasound diagnosis worldwide.

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Diagnostic Performance of An Artificial Intelligence System in Breast Ultrasound

Journal of Ultrasound in Medicine ◽

10.1002/jum.15684 ◽

2021 ◽

Author(s):

Avice M. O'Connell ◽

Tommaso V. Bartolotta ◽

Alessia Orlando ◽

Sin‐Ho Jung ◽

Jihye Baek ◽

...

Keyword(s):

Artificial Intelligence ◽

Diagnostic Performance ◽

Breast Ultrasound ◽

Artificial Intelligence System ◽

Intelligence System

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Artificial intelligence in breast ultrasound

World Journal of Radiology ◽

10.4329/wjr.v11.i2.19 ◽

2019 ◽

Vol 11 (2) ◽

pp. 19-26 ◽

Cited By ~ 14

Author(s):

Ge-Ge Wu ◽

Li-Qiang Zhou ◽

Jian-Wei Xu ◽

Jia-Yu Wang ◽

Qi Wei ◽

...

Keyword(s):

Artificial Intelligence ◽

Breast Ultrasound

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Artificial Intelligence for Breast Ultrasound: Will It Impact Radiologists’ Accuracy?

Journal of Breast Imaging ◽

10.1093/jbi/wbab022 ◽

2021 ◽

Author(s):

Manisha Bahl

Keyword(s):

Artificial Intelligence ◽

Breast Ultrasound ◽

It Impact

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Can artificial intelligence replace ultrasound as a complementary tool to mammogram for the diagnosis of the breast cancer?

British Journal of Radiology ◽

10.1259/bjr.20210820 ◽

2021 ◽

Author(s):

Sahar Mansour ◽

Rasha Kamal ◽

Lamiaa Hashem ◽

Basma ElKalaawy

Keyword(s):

Breast Cancer ◽

Artificial Intelligence ◽

Breast Imaging ◽

Breast Ultrasound ◽

Qualitative Assessment ◽

Time Interval ◽

Breast Lesions ◽

Digital Mammogram ◽

Combined Evaluation ◽

The Impact

Objectives: to study the impact of artificial intelligence (AI) on the performance of mammogram with regard to the classification of the detected breast lesions in correlation to ultrasound aided mammograms. Methods: Ethics committee approval was obtained in this prospective analysis. The study included 2000 mammograms. The mammograms were interpreted by the radiologists and breast ultrasound was performed for all cases. The Breast Imaging Reporting and Data System (BI-RADS) score was applied regarding the combined evaluation of the mammogram and the ultrasound modalities. Each breast side-was individually assessed with the aid of AI scanning in the form of targeted heat-map and then, a probability of malignancy (abnormality scoring percentage) was obtained. Operative and the histopathology data were the standard of reference. Results: Normal assigned cases (BI-RADS 1) with no lesions were excluded from the statistical evaluation. The study included 538 benign and 642 malignant breast lesions (n = 1180, 59%). BI-RADS categories for the breast lesions with regard to the combined evaluation of the digital mammogram and ultrasound were assigned BI-RADS 2 (Benign) in 385 lesions with AI median value of the abnormality scoring percentage of 10, (n = 385/1180, 32.6%), and BI-RADS 5 (malignant) in 471, that had showed median percentage AI value of 88 (n = 471/1180, 39.9%). AI abnormality scoring of 59% yielded a sensitivity of 96.8% and specificity of 90.1% in the discrimination of the breast lesions detected on the included mammograms. Conclusions: AI could be considered as an optional primary reliable complementary tool to the digital mammogram for the evaluation of the breast lesions. The color hue and the abnormality scoring percentage presented a credible method for the detection and discrimination of breast cancer of near accuracy to the breast ultrasound. So consequently, AI- mammogram combination could be used as a one setting method to discriminate between cases that require further imaging or biopsy from those that need only time interval follows up. Advances in knowledge: Recently, the indulgence of AI in the work up of breast cancer was concerned. AI noted as a screening strategy for the detection of breast cancer. In the current work, the performance of AI was studied with regard to the diagnosis not just the detection of breast cancer in the mammographic-detected breast lesions. The evaluation was concerned with AI as a possible complementary reading tool to mammogram and included the qualitative assessment of the color hue and the quantitative integration of the abnormality scoring percentage.

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