Diagnostic performance of combined use of automated breast volume scanning &amp; hand-held ultrasound for breast lesions

Li, Xiaofeng; Liu, Jialin; Zhou, Yang; Wu, Jialing; Li, Peng; Liang, Xinyu; Duan, Haonan; Wu, Xueqing; Hou, Xiukun

doi:10.4103/ijmr.ijmr_836_19

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…The radiologist scans the whole breast with handheld ultrasound and gives BI-RADS category. However, since handheld ultrasound depends on the operators and experience, different radiologists have different opinions on the interpretation of BI-RADS characteristics, resulting in a high inter-observer variability, poor repeatability and low work efficiency (4)(5)(6).…”

Section: Introductionmentioning

confidence: 99%

Prospective assessment of breast lesions AI classification model based on ultrasound dynamic videos and ACR BI-RADS characteristics

Qiu,

Zhuang,

et al. 2023

Front. Oncol.

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IntroductionAI-assisted ultrasound diagnosis is considered a fast and accurate new method that can reduce the subjective and experience-dependent nature of handheld ultrasound. In order to meet clinical diagnostic needs better, we first proposed a breast lesions AI classification model based on ultrasound dynamic videos and ACR BI-RADS characteristics (hereafter, Auto BI-RADS). In this study, we prospectively verify its performance.MethodsIn this study, the model development was based on retrospective data including 480 ultrasound dynamic videos equivalent to 18122 static images of pathologically proven breast lesions from 420 patients. A total of 292 breast lesions ultrasound dynamic videos from the internal and external hospital were prospectively tested by Auto BI-RADS. The performance of Auto BI-RADS was compared with both experienced and junior radiologists using the DeLong method, Kappa test, and McNemar test.ResultsThe Auto BI-RADS achieved an accuracy, sensitivity, and specificity of 0.87, 0.93, and 0.81, respectively. The consistency of the BI-RADS category between Auto BI-RADS and the experienced group (Kappa:0.82) was higher than that of the juniors (Kappa:0.60). The consistency rates between Auto BI-RADS and the experienced group were higher than those between Auto BI-RADS and the junior group for shape (93% vs. 80%; P = .01), orientation (90% vs. 84%; P = .02), margin (84% vs. 71%; P = .01), echo pattern (69% vs. 56%; P = .001) and posterior features (76% vs. 71%; P = .0046), While the difference of calcification was not significantly different.DiscussionIn this study, we aimed to prospectively verify a novel AI tool based on ultrasound dynamic videos and ACR BI-RADS characteristics. The prospective assessment suggested that the AI tool not only meets the clinical needs better but also reaches the diagnostic efficiency of experienced radiologists.

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Section: Introductionmentioning

confidence: 99%

Prospective assessment of breast lesions AI classification model based on ultrasound dynamic videos and ACR BI-RADS characteristics

Qiu,

Zhuang,

et al. 2023

Front. Oncol.

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A systematic review and meta-analysis comparing the diagnostic capability of automated breast ultrasound and contrast-enhanced ultrasound in breast cancer

Zhang,

Hu,

Meng

et al. 2024

Front. Oncol.

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ObjectiveTo compare the diagnostic performance of automated breast ultrasound (ABUS) and contrast-enhanced ultrasound (CEUS) in breast cancer.MethodsPublished studies were collected by systematically searching the databases PubMed, Embase, Cochrane Library and Web of Science. The sensitivities, specificities, likelihood ratios and diagnostic odds ratio (DOR) were confirmed. The symmetric receiver operator characteristic curve (SROC) was used to assess the threshold of ABUS and CEUS. Fagan’s nomogram was drawn. Meta-regression and subgroup analyses were applied to search for sources of heterogeneity among the included studies.ResultsA total of 16 studies were included, comprising 4115 participants. The combined sensitivity of ABUS was 0.88 [95% CI (0.73–0.95)], specificity was 0.93 [95% CI (0.82–0.97)], area under the SROC curve (AUC) was 0.96 [95% CI (0.94–0.96)] and DOR was 89. The combined sensitivity of CEUS was 0.88 [95% CI (0.84–0.91)], specificity was 0.76 [95% CI (0.66–0.84)], AUC was 0.89 [95% CI (0.86–0.92)] and DOR was 24. The Deeks’ funnel plot showed no existing publication bias. The prospective design, partial verification bias and blinding contributed to the heterogeneity in specificity, while no sources contributed to the heterogeneity in sensitivity. The post-test probability of ABUS in BC was 75%, and the post-test probability of CEUS in breast cancer was 48%.ConclusionCompared with CEUS, ABUS showed higher specificity and DOR for detecting breast cancer. ABUS is expected to further improve the accuracy of BC diagnosis.

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Diagnostic performance of combined use of automated breast volume scanning & hand-held ultrasound for breast lesions

Cited by 2 publications

References 22 publications

Prospective assessment of breast lesions AI classification model based on ultrasound dynamic videos and ACR BI-RADS characteristics

Prospective assessment of breast lesions AI classification model based on ultrasound dynamic videos and ACR BI-RADS characteristics

A systematic review and meta-analysis comparing the diagnostic capability of automated breast ultrasound and contrast-enhanced ultrasound in breast cancer

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