Application of computer-aided diagnosis in breast ultrasound interpretation: improvements in diagnostic performance according to reader experience

Choi, Jihye; Kang, Bong Joo; Baek, Ji Eun; Lee, Hyun Sil; Kim, Sung Hun

doi:10.14366/usg.17046

Cited by 73 publications

(82 citation statements)

References 21 publications

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“…S‐Detect was reported to reduce the variability in radiologist interpretations . A higher specificity, accuracy, and positive predictive value were reported for S‐Detect compared to those rates for radiologists, and adding S‐Detect to breast US could improve the specificity, positive predictive value, and accuracy . In addition, S‐Detect could improve the cancer detection rate and have the potential to be a teaching tool for less‐experienced operators, in addition to its role as a possible adjunct tool for breast lesion characterization …”

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confidence: 99%

Computer‐Aided Diagnosis of Solid Breast Lesions With Ultrasound: Factors Associated With False‐negative and False‐positive Results

Zhao

Zhang

et al. 2019

J of Ultrasound Medicine

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consecutive women with 338 solid breast lesions were included. All lesions were examined with US and S-Detect before biopsy or surgical excision. The final US assessments made by radiologists and S-Detect were matched to the pathologic results. Patient and lesion factors in the "true" and "false" S-Detect groups were compared, and multivariate logistic regression analyses were used to identify the factors associated with false S-Detect results.Results-The mean age of the patients AE SD was 42.6 AE 12.9 years (range, 18-77 years). Of the 338 lesions, 209 (61.8%) were benign, and 129 (38.2%) were malignant. Larger lesions, the presence of lesion calcifications detected by B-mode US, and grades of 2 and 3 according to Adler et al (Ultrasound Med Biol 1990; 16:553-559) were significantly associated with false-positive S-Detect results (odds ratio [OR], 1.071; P = .006; OR, 5.851; P = .001; OR, 1.726; P = .009, respectively). Smaller lesions and the absence of calcifications detected by B-mode US in malignant solid breast lesions were significantly associated with false-negative S-Detect results (OR, 1.141; P = .015; OR, 7.434; P = .016). Conclusions-Larger benign lesions, the presence of lesion calcifications, and high degrees of vascularity are likely to show false-positive S-Detect results. Smaller malignant lesions and the absence of calcifications are likely to show false-negative S-Detect results. Abbreviations AUC, area under the curve; BI-RADS, Breast Imaging Reporting and Data

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mentioning

confidence: 99%

Computer‐Aided Diagnosis of Solid Breast Lesions With Ultrasound: Factors Associated With False‐negative and False‐positive Results

Zhao

Zhang

et al. 2019

J of Ultrasound Medicine

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“…The potential use of S-Detect™ to assist doctors in improving diagnostic performance, especially for those who lack experience, has been elucidated in previous studies. Ji-Hye Choi and Eun Cho veri ed that the diagnostic performance of inexperienced readers could be improved with the help of S-Detect™ 26,27 .Mattia Di Segni et al also suggested that S-Detect™ could act as a teaching tool for in-training residents to improve the accuracy of diagnosing breast lesions 19 .…”

Section: Discussionmentioning

confidence: 99%

“…S-Detect™ for Breast is a cutting-edge CAD system that acts as an assistant tool for US imaging diagnosis of breast lesions. The diagnostic e cacy of the CAD software for classifying breast lesions has been validated by several studies [19][20][21] . Furthermore, S-Detect™ has been proven to be of value in increasing the diagnostic performance of the in-training residents 19,22 .BI-RADS 4a lesions posed a potential challenge for breast US.…”

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confidence: 99%

Improving the diagnostic performance of ultrasound in classifying breast lesions: the potential value of S-detect for residents-in-training

Zhao¹,

Xiao²,

Jiang³

et al. 2019

Preprint

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Background To explore the potential value of S-Detect™, a high-end computer-assisted diagnosis (CAD) software system for residents-in-training. Methods Routine breast ultrasound (US) examinations were conducted and assessed by an experienced radiologist. Archived images of the lesions (including grayscale, color Doppler flow and elastography images) were retrospectively assessed by each of five in-training residents who were blinded to the histopathological findings and any other US diagnosis. The diagnostic performances of S-Detect™ and the five residents were measured and compared. Afterwards, category 4a lesions assessed by the residents were downgraded when classified as possibly benign by S-Detect™. The diagnostic performance of the integrated results was compared with the original results of the residents. Results A total of 195 focal breast lesions were consecutively enrolled, including 82 malignant lesions and 113 benign lesions. S-Detect™ presented higher specificity and area under the curve (AUC)than the residents. After combination with S-Detect™ in category 4a lesions, the specificity and AUC of the five residents were significantly improved. The intraclass correlation coefficient (ICC) of the five residents also increased after integration. Conclusions With the help of the CAD software, the specificity, overall diagnostic performances and interobserver agreements of the residents greatly improved. S-Detect™ can be utilized as an assistant tool for residents-in-training in classifying breast lesions.

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“…In this CAD system, the final assessment classification was divided into 'possibly benign' and 'possibly malignant' (4,10). The cutoff for differentiating benign and malignant lesions by the radiologist was set at category 4A and category 4B.…”

Section: Study Population and Cad Systemmentioning

confidence: 99%

“…Many studies have applied CAD systems to breast US to demonstrate the efficiency of CAD systems and to evaluate the usefulness of CAD systems for improving diagnostic accuracy (4)(5)(6)(7)(8)(9)(10)(11). There are two algorithms in breast US-CAD systems for lesion interpretations.…”

Section: Introductionmentioning

confidence: 99%

Breast Ultrasound Computer-Aided Diagnosis: Analysis of Types of Errors

Jeong

Kang

Kim

et al. 2018

J Korean Soc Radiol

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Purpose: The aim of this study was to evaluate the diagnostic performance of breast ultrasound (US) computer-aided diagnosis (CAD) to distinguish between benign and malignant lesions and analyze features of lesions interpreted with errors retrospectively. Materials and Methods: Three hundred and sixteen women with 375 breast lesions were enrolled. We assessed the accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Additionally, we evaluated the causes and patterns of the misinterpretation in the false positive and negative groups. Results: The accuracy, sensitivity, specificity, PPV, and NPV of breast US-CAD were 80.3%, 83.3%, 79.8%, 37.7%, and 97.0%, respectively. There were 8 false negative lesions that were oval in shape and in parallel orientation. There were 66 false positive lesions. The greatest number of errors entailed inappropriate demarcation due to heterogeneous echogenicity, etc. The second exhibited suspicious features with good demarcation and description but were confirmed as benign histologically. The third entailed a benign lesion with suspicious features, such as abscesses. The smallest portion with good demarcations and descriptions indicating benign status exhibited possible malignancy as a final conclusion. Conclusion: Breast US-CAD is expected to be helpful in avoiding unnecessary biopsies due to its high NPV. Therefore, operators need to know the characteristics of lesions prone to misinterpretation. Index terms

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Application of computer-aided diagnosis in breast ultrasound interpretation: improvements in diagnostic performance according to reader experience

Cited by 73 publications

References 21 publications

Computer‐Aided Diagnosis of Solid Breast Lesions With Ultrasound: Factors Associated With False‐negative and False‐positive Results

Computer‐Aided Diagnosis of Solid Breast Lesions With Ultrasound: Factors Associated With False‐negative and False‐positive Results

Improving the diagnostic performance of ultrasound in classifying breast lesions: the potential value of S-detect for residents-in-training

Breast Ultrasound Computer-Aided Diagnosis: Analysis of Types of Errors

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