Ultrasound and clinicopathological characteristics of breast cancer for predicting axillary lymph node metastasis

Bai, Xiaofang; Wang, Yunyue; Song, Ruxi; Li, Shangan; Song, Yan; Wang, Huan; Tong, Xiaoning; Wei, Wei; Ruan, Litao; Zhao, Qiaoling

doi:10.3233/ch-231777

Cited by 1 publication

(1 citation statement)

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“…Ultrasound is a broadly available imaging technique with minimal side effects, and it offers a wide range of applications, including the detection and assessment of lymph nodes. Indications for the sonographic assessment of lymph nodes include, for example, the detection of cancer spread, re-staging after oncological treatment or, as a complement to clinical examination, the differential diagnosis of unclear lymph node enlargement [1,2,3,4,5,6].…”

Section: Introductionmentioning

confidence: 99%

Smart scanning: automatic detection of superficially located lymph nodes using ultrasound – initial results

Rink,

Künzel,

Stroszczynski

et al. 2024

Rofo

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Over the last few years, there has been an increasing focus on integrating artificial intelligence (AI) into existing imaging systems. This also applies to ultrasound. There are already applications for thyroid and breast lesions that enable AI-assisted sonography directly on the device. However, this is not yet the case for lymph nodes.The aim was to test whether already established programs for AI-assisted sonography of breast lesions and thyroid nodules are also suitable for identifying and measuring superficial lymph nodes. For this purpose, the two programs were used as a supplement to routine ultrasound examinations of superficial lymph nodes. The accuracy of detection by AI was then evaluated using a previously defined score. If available, a comparison was made with cross-sectional imaging.The programs that were used are able to adequately detect lymph nodes in the majority of cases (78.6%). Problems were caused in particular by a high proportion of echo-rich fat, blurred differentiation from the surrounding tissues and the occurrence of lymph node conglomerates. The available cross-sectional images did not contradict the classification of the lesion as a lymph node in any case.In the majority of cases, the tested programs are already able to detect and measure superficial lymph nodes. Further improvement can be expected through specific training of the software. Further developments and studies are required to assess risk of malignancy.

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