&lt;p&gt;Feasibility of computer-assisted diagnosis for breast ultrasound: the results of the diagnostic performance of S-detect from a single center in China&lt;/p&gt;

Zhao, Chenyang; Xiao, Mengsu; Jiang, Yuxin; Li, He; Wang, Ming; Wang, Hongyan; Sun, Qiang; Zhu, Qingli

doi:10.2147/cmar.s190966

Cited by 30 publications

(47 citation statements)

References 23 publications

(29 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Second, in the aspect of intralesional calcification, we only studied the value of the number of calcification in the differential diagnosis of BI-RADS 4A benign and malignant lesions, but the significance of the size and shape of calcification in the differential diagnosis was not clear. Finally, this study was based on manually contoured images for quantitative analyses of ROI, which was different from other studies that focused on lesions that are automatically contoured by AI [26]. The present study did not evaluate the automatic identification efficiency for BI-RADS 4A lesions of our AI diagnosis system, and these aspects will be studied in the future.…”

Section: Discussionmentioning

confidence: 90%

Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A

Niu

Huang

et al. 2020

BMC Cancer

View full text Add to dashboard Cite

Background The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS categorisation. We analysed the ultrasonic morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and these ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions. Methods A total of 206 lesions of BI-RADS 4A examined using ultrasonography were analysed retrospectively, including 174 benign lesions and 32 malignant lesions. All of the lesions were contoured manually, and the ultrasonic morphological and texture features of the lesions, such as circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, margin lobulation, energy, entropy, grey mean, internal calcification and angle between the long axis of the lesion and skin, were calculated using grey level gradient co-occurrence matrix analysis. Differences between benign and malignant lesions of BI-RADS 4A were analysed. Results Significant differences in margin lobulation, entropy, internal calcification and ALS were noted between the benign group and malignant group (P = 0.013, 0.045, 0.045, and 0.002, respectively). The malignant group had more margin lobulations and lower entropy compared with the benign group, and the benign group had more internal calcifications and a greater angle between the long axis of the lesion and skin compared with the malignant group. No significant differences in circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, energy, and grey mean were noted between benign and malignant lesions. Conclusions Compared with the naked eye, AI can reveal more subtle differences between benign and malignant BI-RADS 4A lesions. These results remind us carefully observation of the margin and the internal echo is of great significance. With the help of morphological and texture information provided by AI, doctors can make a more accurate judgment on such atypical benign and malignant lesions.

show abstract

Section: Discussionmentioning

confidence: 90%

Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A

Niu

Huang

et al. 2020

BMC Cancer

View full text Add to dashboard Cite

show abstract

“…Second, although there were signi cant differences between the benign group and malignant group in regard to margin lobules, entropy, internal calci cation and ALS, a cut-off value for differential diagnosis between these two groups was not obtained. This study was based on manually contoured images for the performance of quantitative analyses of ROI, which is different from other studies that focus on lesions that are automatically contoured by AI [20].The present study did not evaluate the automatic identi cation e ciency for BI-RADS 4A lesions of our AI diagnosis system, and these aspects will be studied in the future.…”

Section: Discussionmentioning

confidence: 90%

Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A

Niu

Huang

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS categorisation. We analysed the ultrasonic morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and these ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions.Methods: A total of 206 lesions of BI-RADS 4A examined using ultrasonography were analysed retrospectively, including 174 benign lesions and 32 malignant lesions. All of the lesions were contoured manually, and the ultrasonic morphological and texture features of the lesions, such as circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, margin lobulation, energy, entropy, grey mean, internal calcification and angle between the long axis of the lesion and skin, were calculated using grey level gradient co-occurrence matrix analysis. Differences between benign and malignant lesions of BI-RADS 4A were analysed.Results: Significant differences in margin lobulation, entropy, internal calcification and ALS were noted between the benign group and malignant group (P=0.013, 0.045, 0.045, and 0.002, respectively). The malignant group had more margin lobulations and lower entropy compared with the benign group, and the benign group had more internal calcifications and a greater angle between the long axis of the lesion and skin compared with the malignant group. No significant differences in circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, energy, and grey mean were noted between benign and malignant lesions.Conclusions: Compared with the naked eye, AI can reveal more subtle differences between benign and malignant BI-RADS 4A lesions. These results remind us carefully observation of the margin and the internal echo is of great significance. With the help of morphological and texture information provided by AI, doctors can make a more accurate judgment on such atypical benign and malignant lesions.

show abstract

“…This system has good performance in diagnosing benign and malignant breast lesions and especially in improving the specificity of ultrasound (8). Our early study showed that the deep learning-based CAD had the same diagnostic accuracy as experienced radiologists, and the specificity of the CAD was higher than that of the radiologists, which helped to reduce the number of unnecessary biopsies (9). Our recent study also showed that the deep learning-based CAD had a better performance in the breast benign lesions than the radiologists, especially in fibroadenomas and adenosis (10).…”

Section: Introductionmentioning

confidence: 93%

“…The deep learning-based CAD system used in our study (Samsung corporation, Seoul Korea) is a newly developed CAD system for breast ultrasound based on deep learning of raw ultrasound signals through a convolutional neural network. After extensive learning and training on a large number of databases, the deep learning-based CAD system could extract high-order statistics and optimize the balance of input and output data through multiple hidden layers to provide an accurate diagnosis (9). The original unprocessed ultrasound signals were collected as the raw data and information for the deep learning-based CAD system to analyze through a complex hierarchical framework.…”

Section: The Role Of Deep Learning-based Cad System In the Breast Lesmentioning

confidence: 99%

Diagnostic Value of Breast Lesions Between Deep Learning-Based Computer-Aided Diagnosis System and Experienced Radiologists: Comparison the Performance Between Symptomatic and Asymptomatic Patients

Xiao

Zhao

et al. 2020

Front. Oncol.

Self Cite

View full text Add to dashboard Cite

The purpose of this study was to compare the diagnostic performance of breast lesions between deep learning-based computer-aided diagnosis (deep learning-based CAD) system and experienced radiologists and to compare the performance between symptomatic and asymptomatic patients. Methods: From January to December 2018, a total of 451 breast lesions in 389 consecutive patients were examined (mean age 46.86 ± 13.03 years, range 19-84 years) by both ultrasound and deep learning-based CAD system, all of which were biopsied, and the pathological results were obtained. The lesions were diagnosed by two experienced radiologists according to the fifth edition Breast Imaging Reporting and Data System (BI-RADS). The final deep learning-based CAD assessments were dichotomized as possibly benign or possibly malignant. The diagnostic performances of the radiologists and deep learning-based CAD were calculated and compared for asymptomatic patients and symptomatic patients. Results: There were 206 asymptomatic screening patients with 235 lesions (mean age 45.06 ± 10.90 years, range 21-73 years) and 183 symptomatic patients with 216 lesions (mean age 50.03 ± 14.97 years, range 19-84 years). The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy and area under the receiver operating characteristic curve (AUC) of the deep learning-based CAD in asymptomatic patients were 93.8, 83.9, 75.0, 96.3, 87.2, and 0.89%, respectively. In asymptomatic patients, the specificity (83.9 vs. 66.5%, p < 0.001), PPV (75.0 vs. 59.4%, p = 0.013), accuracy (87.2 vs. 76.2%, p = 0.002) and AUC (0.89 to 0.81, p = 0.0013) of CAD were all significantly higher than those of the experienced radiologists. The sensitivity (93.8 vs. 80.0%), specificity (83.9 vs. 61.8%,), accuracy (87.2 vs. 73.6%) and AUC (0.89 vs. 0.71) of CAD were all higher for asymptomatic patients than for symptomatic patients. If the BI-RADS 4a lesions diagnosed by the radiologists in asymptomatic patients were Xiao et al. CAD in Breast Lesions downgraded to BI-RADS 3 according to the CAD, then 54.8% (23/42) of the lesions would avoid biopsy without missing the malignancy. Conclusion: The deep learning-based CAD system had better performance in asymptomatic patients than in symptomatic patients and could be a promising complementary tool to ultrasound for increasing diagnostic specificity and avoiding unnecessary biopsies in asymptomatic screening patients.

show abstract

<p>Feasibility of computer-assisted diagnosis for breast ultrasound: the results of the diagnostic performance of S-detect from a single center in China</p>

Cited by 30 publications

References 23 publications

Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A

Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A

Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A

Diagnostic Value of Breast Lesions Between Deep Learning-Based Computer-Aided Diagnosis System and Experienced Radiologists: Comparison the Performance Between Symptomatic and Asymptomatic Patients

Contact Info

Product

Resources

About