The diagnostic performance of ultrasound computer-aided diagnosis system for distinguishing breast masses: a prospective multicenter study

Wei, Qi; Yan, Yichao; Wu, Ge-Ge; Ye, Xi-Rong; Jiang, Fan; Liu, Jie; Wang, Gang; Wang, Yi; Song, Juan; Pan, Zhi-Ping; Hu, Jinhua; Jin, Chao-Ying; Wang, Xiang; Dietrich, Christoph F.; Cui, Xin‐Wu

doi:10.1007/s00330-021-08452-1

Cited by 17 publications

(17 citation statements)

References 24 publications

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“…The diagnostic performance of less‐experienced radiologists with the addition of S‐Detect reached the level of an experienced radiologist from a university level III hospital, the AUC value, specificity and PPV of whom were 0.891, 82.3%, and 75.0%, respectively. In addition, our study showed that S‐Detect could not only significantly reduce the biopsy rate, but also improve the malignant rate of biopsy in BI‐RADS 4A lesions by less‐experienced radiologists, which were consistent with the results of the previous study [8]. It should be noted that in this study, 6 breast malignancies were misdiagnosed by S‐Detect.…”

Section: Discussionsupporting

confidence: 91%

“…US is widely used for breast screening and diagnosis because of its convenience and low cost. However, US tends to present very high sensitivity but relatively low specificity and PPV, which would result in unnecessary breast biopsies [7,8]. Previous studies have proven excellent diagnostic performance of CAD system (S-Detect) in differentiating breast lesions by increasing the AUC value (0.777-0.906), specificity (69.4-87.7%) and PPV (69.6-80.8%) [7][8][9].…”

Section: Discussionmentioning

confidence: 99%

“…However, US tends to present very high sensitivity but relatively low specificity and PPV, which would result in unnecessary breast biopsies [7,8]. Previous studies have proven excellent diagnostic performance of CAD system (S-Detect) in differentiating breast lesions by increasing the AUC value (0.777-0.906), specificity (69.4-87.7%) and PPV (69.6-80.8%) [7][8][9]. Similar to these previous studies, the AUC value, specificity and PPV of this study increased from 0.712, 44.6%, 49.7% to 0.878, 81.3%, 73.7%, respectively, after only the BI-RADS 4A lesions by less- experienced radiologists from non-university hospitals were downgraded to category 3.…”

Section: Discussionmentioning

confidence: 99%

“…S-Detect has been used to assist in the diagnosis of breast and thyroid masses [2]. Several previous studies have validated the value of S-Detect in increasing the specificity, positive predictive value (PPV), and reducing unnecessary biopsies of breast lesions [2,[7][8][9]. The ultrasonic diagnostic level in university level III hospitals located in highly developed regions of China is higher than that in non-university hospitals in less-developed regions of China, where there is a shortage of breast imaging specialists [7,10].…”

Section: Introductionmentioning

confidence: 99%

“…However, there are currently no guidelines for selecting planes of breast lesions for S-Detect analysis. A single or two orthogonal representative planes of the lesion were selected for S-Detect analysis in previous studies [7][8][9]11].…”

Section: Introductionmentioning

confidence: 99%

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Clinical Application of Computer‐Aided Diagnosis System in Breast Ultrasound: A Prospective Multicenter Study

He,

Chen,

Bai

et al. 2023

World j. surg.

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Objectives Ultrasound tends to present very high sensitivity but relatively low specificity and positive predictive value (PPV), which would result in unnecessary breast biopsies. The purpose of this study is to analyze the diagnostic performance of computer-aided diagnosis (CAD) (S-Detect) system in differentiating breast lesions and reducing unnecessary biopsies in non-university hospitals in less-developed regions of China. Methods The study was a prospective multicenter study from 8 hospitals. The ultrasound images, and cine, CAD analysis, and BI-RADS were recorded. The accuracy, sensitivity, specificity, PPV, negative predictive value (NPV), and area under the curve (AUC) were analyzed and compared between CAD and radiologists. The Youden Index (YI) was used to determine optimal cut-off for the number of planes to downgrade. Results A total of 491 breast lesions were included in the study. Less-experienced radiologists combined CAD was superior to less-experienced radiologists alone in AUC (0.878 vs 0.712, p \ 0.001), and specificity (81.3% vs 44.6%, p \ 0.001). There was no statistical difference in AUC (0.891 vs 0.878, p = 0.346), and specificity (82.3% vs 81.3%, p = 0.791) between experienced radiologists and less-experienced radiologists combined CAD. With CAD assistance, the biopsy rate of less-experienced radiologists was significantly decreased (100.0% vs 25.6%, p \ 0.001), and malignant rate of biopsy was significantly increased (15.0% vs 43.9%, p \ 0.001). Conclusions CAD system can be an effective auxiliary tool in differentiating breast lesions and reducing unnecessary biopsies for radiologists from non-university hospitals in less-developed regions of China.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Clinical Application of Computer‐Aided Diagnosis System in Breast Ultrasound: A Prospective Multicenter Study

He,

Chen,

Bai

et al. 2023

World j. surg.

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BUS‐BRA: A breast ultrasound dataset for assessing computer‐aided diagnosis systems

Gómez‐Flores,

Gregorio‐Calas,

Coelho de Albuquerque Pereira

2023

Medical Physics

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PurposeComputer‐aided diagnosis (CAD) systems on breast ultrasound (BUS) aim to increase the efficiency and effectiveness of breast screening, helping specialists to detect and classify breast lesions. CAD system development requires a set of annotated images, including lesion segmentation, biopsy results to specify benign and malignant cases, and BI‐RADS categories to indicate the likelihood of malignancy. Besides, standardized partitions of training, validation, and test sets promote reproducibility and fair comparisons between different approaches. Thus, we present a publicly available BUS dataset whose novelty is the substantial increment of cases with the above‐mentioned annotations and the inclusion of standardized partitions to objectively assess and compare CAD systems.Acquisition and Validation MethodsThe BUS dataset comprises 1875 anonymized images from 1064 female patients acquired via four ultrasound scanners during systematic studies at the National Institute of Cancer (Rio de Janeiro, Brazil). The dataset includes biopsy‐proven tumors divided into 722 benign and 342 malignant cases. Besides, a senior ultrasonographer performed a BI‐RADS assessment in categories 2 to 5. Additionally, the ultrasonographer manually outlined the breast lesions to obtain ground truth segmentations. Furthermore, 5‐ and 10‐fold cross‐validation partitions are provided to standardize the training and test sets to evaluate and reproduce CAD systems. Finally, to validate the utility of the BUS dataset, an evaluation framework is implemented to assess the performance of deep neural networks for segmenting and classifying breast lesions.Data Format and Usage NotesThe BUS dataset is publicly available for academic and research purposes through an open‐access repository under the name BUS‐BRA: A Breast Ultrasound Dataset for Assessing CAD Systems. BUS images and reference segmentations are saved in Portable Network Graphic (PNG) format files, and the dataset information is stored in separate Comma‐Separated Value (CSV) files.Potential ApplicationsThe BUS‐BRA dataset can be used to develop and assess artificial intelligence‐based lesion detection and segmentation methods, and the classification of BUS images into pathological classes and BI‐RADS categories. Other potential applications include developing image processing methods like despeckle filtering and contrast enhancement methods to improve image quality and feature engineering for image description.

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Applications of Artificial Intelligence in Ultrasound Medicine

Xu,

Shen,

Zhou

et al. 2024

Artificial Intelligence in Medical Imaging in China

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The diagnostic performance of ultrasound computer-aided diagnosis system for distinguishing breast masses: a prospective multicenter study

Cited by 17 publications

References 24 publications

Clinical Application of Computer‐Aided Diagnosis System in Breast Ultrasound: A Prospective Multicenter Study

Clinical Application of Computer‐Aided Diagnosis System in Breast Ultrasound: A Prospective Multicenter Study

BUS‐BRA: A breast ultrasound dataset for assessing computer‐aided diagnosis systems

Applications of Artificial Intelligence in Ultrasound Medicine

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