Automated classification of focal breast lesions according to S-detect: validation and role as a clinical and teaching tool

Segni, Mattia Di; Soccio, Valeria de; Cantisani, Vito; Bonito, Giacomo; Rubini, Antonello; Segni, Gabriele Di; Lamorte, Sveva; Magri, Valentina; Vito, Corrado De; Migliara, Giuseppe; Bartolotta, Tommaso Vincenzo; Metere, Alessio; Giacomelli, Luciano; Felice, Carlo de; Dambrosio, F

doi:10.1007/s40477-018-0297-2

Cited by 56 publications

(80 citation statements)

References 30 publications

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“…S-detect™ is a recently developed breast cancer CAD system using deep-learning algorithm with big data which providing assistance in morphological analysis by the ACR BI-RADS (4, 5, 9-12). Several studies have reported that S-detect™ could enhance the diagnostic performance of radiologists (5,6,(9)(10)(11)(12)14). In addition, several studies have published that S-detect™ is a useful diagnostic tool that could be clinically used to enhance the specificity, PPV, and accuracy of breast US, regardless of the degree or radiologist's experience (9,12).…”

Section: Discussionmentioning

confidence: 99%

“…Our study aimed to determine the merits of the CAD system in breast US reading using breast phantom. In one previous study, five readers including residents retrospectively reviewed the CAD images and gave their assessments; inter-rater agreement was measured with Cohen's kappa value (11). The conclusion of that study was that S-detect™ is a feasible tool for characterization of breast le-sions; it has a potential as a teaching tool for the less experienced operators.…”

Section: Discussionmentioning

confidence: 99%

“…The conclusion of that study was that S-detect™ is a feasible tool for characterization of breast le-sions; it has a potential as a teaching tool for the less experienced operators. However, in that study, the residents only reviewed and assessed images taken by a radiologist with 32 years of experience in breast imaging (11). In our study, we used breast phantom, not real patients.…”

Section: Discussionmentioning

confidence: 99%

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Reliability Studies of Different Readers Using Computer Aided Diagnosis System in Ultrasound for Determining Identical Lesions in a Breast Phantom

et al. 2019

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Background: The computer-aided diagnosis (CAD) has been applied in multiple studies conducted recently in order to perform ultrasound (US) of the breast. There are several studies that have indicated that CAD is useful for improving the diagnostic performance in less experienced radiologists. However, there is no study on several readers analyzing the same lesions using a breast phantom and rare reliability studies. Objectives: To investigate the reliability of different readers using computer aided diagnosis (CAD) system in US for determining identical lesions in a breast phantom. Patients and Methods: From March 2016 to February 2017, six readers (three senior and three junior residents in the department of radiology) evaluated and analyzed breast phantom including 14 lesions. At the first line study, three senior residents (3rd grade with more than one month of training for breast ultrasound [US]) and three junior residents (1st grade without breast US training) evaluated and analyzed the US and applied CAD for lesions in breast phantom, and they were able to make final decisions by subjective combination. A month later, they conducted the second line study as they did the first line study. We analyzed the inter-and intra-reader reliability and accuracy of US, CAD, and combinations (subjective, conjunctive, and disjunctive). Results: In the total of first and second line studies of six readers, the kappa value of US (0.609) was significantly higher than CAD (0.411). In the subjective combined conclusion, the kappa value was improved by the junior group. In the whole inter-and intrareader analysis, the kappa values of final assessment of the senior and junior group were more variable on CAD than on US, especially in the junior group, and this result was statistically significant. The area under the curve (AUC) of US, CAD, subjective, conjunctive, and disjunctive combination in seniors were all better than those of juniors. In all groups, the AUCs, sensitivities, and specificities were improved on conjunctive combined US with CAD. Conclusion: The combination US with CAD improved the reliability and diagnostic performance. The CAD results of the junior group were variable and inconsistent. Therefore, minimum training and experience for breast US is indispensable for the better use of breast CAD, and, combination US with CAD is useful for all readers.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Reliability Studies of Different Readers Using Computer Aided Diagnosis System in Ultrasound for Determining Identical Lesions in a Breast Phantom

et al. 2019

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“…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.…”

mentioning

confidence: 99%

“…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. And as far as we know, the feasibility of S-Detect™ in improving the diagnostic accuracy of the residents-in-training in BI-RADS 4a lesions has not been investigated by previous studies.…”

mentioning

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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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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Automated classification of focal breast lesions according to S-detect: validation and role as a clinical and teaching tool

Abstract: S-detect is a feasible tool for the characterization of breast lesions; it has a potential as a teaching tool for the less experienced operators.

Cited by 56 publications

References 30 publications

Reliability Studies of Different Readers Using Computer Aided Diagnosis System in Ultrasound for Determining Identical Lesions in a Breast Phantom

Reliability Studies of Different Readers Using Computer Aided Diagnosis System in Ultrasound for Determining Identical Lesions in a Breast Phantom

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

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

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