Dataset Information

Enhancing Performance of Breast Ultrasound in Opportunistic Screening Women by a Deep Learning-Based System: A Multicenter Prospective Study.

ABSTRACT:

Purpose

To validate the feasibility of S-Detect, an ultrasound computer-aided diagnosis (CAD) system using deep learning, in enhancing the diagnostic performance of breast ultrasound (US) for patients with opportunistic screening-detected breast lesions.

Methods

Nine medical centers throughout China participated in this prospective study. Asymptomatic patients with US-detected breast masses were enrolled and received conventional US, S-Detect, and strain elastography subsequently. The final pathological results are referred to as the gold standard for classifying breast mass. The diagnostic performances of the three methods and the combination of S-Detect and elastography were evaluated and compared, including sensitivity, specificity, and area under the receiver operating characteristics (AUC) curve. We also compared the diagnostic performances of S-Detect among different study sites.

Results

A total of 757 patients were enrolled, including 460 benign and 297 malignant cases. S-Detect exhibited significantly higher AUC and specificity than conventional US (AUC, S-Detect 0.83 [0.80-0.85] vs. US 0.74 [0.70-0.77], p < 0.0001; specificity, S-Detect 74.35% [70.10%-78.28%] vs. US 54.13% [51.42%-60.29%], p < 0.0001), with no decrease in sensitivity. In comparison to that of S-Detect alone, the AUC value significantly was enhanced after combining elastography and S-Detect (0.87 [0.84-0.90]), without compromising specificity (73.93% [68.60%-78.78%]). Significant differences in the S-Detect's performance were also observed across different study sites (AUC of S-Detect in Groups 1-4: 0.89 [0.84-0.93], 0.84 [0.77-0.89], 0.85 [0.76-0.92], 0.75 [0.69-0.80]; p [1 vs. 4] < 0.0001, p [2 vs. 4] = 0.0165, p [3 vs. 4] = 0.0157).

Conclusions

Compared with the conventional US, S-Detect presented higher overall accuracy and specificity. After S-Detect and strain elastography were combined, the performance could be further enhanced. The performances of S-Detect also varied among different centers.

SUBMITTER: Zhao C

PROVIDER: S-EPMC8867611 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Enhancing Performance of Breast Ultrasound in Opportunistic Screening Women by a Deep Learning-Based System: A Multicenter Prospective Study.

Zhao Chenyang C Xiao Mengsu M Ma Li L Ye Xinhua X Deng Jing J Cui Ligang L Guo Fajin F Wu Min M Luo Baoming B Chen Qin Q Chen Wu W Guo Jun J Li Qian Q Zhang Qing Q Li Jianchu J Jiang Yuxin Y Zhu Qingli Q

Frontiers in oncology 20220210

<h4>Purpose</h4>To validate the feasibility of S-Detect, an ultrasound computer-aided diagnosis (CAD) system using deep learning, in enhancing the diagnostic performance of breast ultrasound (US) for patients with opportunistic screening-detected breast lesions.<h4>Methods</h4>Nine medical centers throughout China participated in this prospective study. Asymptomatic patients with US-detected breast masses were enrolled and received conventional US, S-Detect, and strain elastography subsequently. ...[more]

PMID: 35223484

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Dataset Information

Enhancing Performance of Breast Ultrasound in Opportunistic Screening Women by a Deep Learning-Based System: A Multicenter Prospective Study.

Purpose

Methods

Results

Conclusions

Publications

Enhancing Performance of Breast Ultrasound in Opportunistic Screening Women by a Deep Learning-Based System: A Multicenter Prospective Study.

Similar Datasets

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