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

Wu, Jiayi; Zhao, Zizhuo; Zhang, Wen‐yue; Ming, Liang; Ou, Bing; Yang, Haiyan; Luo, Baoming

doi:10.1002/jum.15020

Cited by 29 publications

(31 citation statements)

References 29 publications

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“…Although the CAD methods were not exactly the same as in previous studies [17,18], high sensitivity balanced with specificity is a remarkable superiority of the quadri-planes method. Instead, the proportion of the benign lesions in our study was lower (Table 1), and the mean size of the lesions was larger; also, all the patients had breast masses as their prominent symptom, which may lead to differences between the results as in the study by Wu et al [22]. According to the BI-RADS criteria [23], we subdivided category 4 into categories 4A, 4B, and 4C, and the threshold was set to category 4A in grayscale ultrasound; consequently, the specificity was high and the sensitivity was relatively low.…”

Section: Principal Findingscontrasting

confidence: 76%

Evaluation of the Quadri-Planes Method in Computer-Aided Diagnosis of Breast Lesions by Ultrasonography: Prospective Single-Center Study

et al. 2020

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Background Computer-aided diagnosis (CAD) is a tool that can help radiologists diagnose breast lesions by ultrasonography. Previous studies have demonstrated that CAD can help reduce the incidence of missed diagnoses by radiologists. However, the optimal method to apply CAD to breast lesions using diagnostic planes has not been assessed. Objective The aim of this study was to compare the performance of radiologists with different levels of experience when using CAD with the quadri-planes method to detect breast tumors. Methods From November 2018 to October 2019, we enrolled patients in the study who had a breast mass as their most prominent symptom. We assigned 2 ultrasound radiologists (with 1 and 5 years of experience, respectively) to read breast ultrasonography images without CAD and then to perform a second reading while applying CAD with the quadri-planes method. We then compared the diagnostic performance of the readers for the 2 readings (without and with CAD). The McNemar test for paired data was used for statistical analysis. Results A total of 331 patients were included in this study (mean age 43.88 years, range 17-70, SD 12.10), including 512 lesions (mean diameter 1.85 centimeters, SD 1.19; range 0.26-9.5); 200/512 (39.1%) were malignant, and 312/512 (60.9%) were benign. For CAD, the area under the receiver operating characteristic curve (AUC) improved significantly from 0.76 (95% CI 0.71-0.79) with the cross-planes method to 0.84 (95% CI 0.80-0.88; P<.001) with the quadri-planes method. For the novice reader, the AUC significantly improved from 0.73 (95% CI 0.69-0.78) for the without-CAD mode to 0.83 (95% CI 0.80-0.87; P<.001) for the combined-CAD mode with the quadri-planes method. For the experienced reader, the AUC improved from 0.85 (95% CI 0.81-0.88) to 0.87 (95% CI 0.84-0.91; P=.15). The kappa indicating consistency between the experienced reader and the novice reader for the combined-CAD mode was 0.63. For the novice reader, the sensitivity significantly improved from 60.0% for the without-CAD mode to 79.0% for the combined-CAD mode (P=.004). The specificity, negative predictive value, positive predictive value, and accuracy improved from 84.9% to 87.8% (P=.53), 76.8% to 86.7% (P=.07), 71.9% to 80.6% (P=.13), and 75.2% to 84.4% (P=.12), respectively. For the experienced reader, the sensitivity improved significantly from 76.0% for the without-CAD mode to 87.0% for the combined-CAD mode (P=.045). The NPV and accuracy moderately improved from 85.8% and 86.3% to 91.0% (P=.27) and 87.0% (P=.84), respectively. The specificity and positive predictive value decreased from 87.4% to 81.3% (P=.25) and from 87.2% to 93.0% (P=.16), respectively. Conclusions S-Detect is a feasible diagnostic tool that can improve the sensitivity, accuracy, and AUC of the quadri-planes method for both novice and experienced readers while also improving the specificity for the novice reader. It demonstrates important application value in the clinical diagnosis of breast cancer. Trial Registration ChiCTR.org.cn 1800019649; http://www.chictr.org.cn/showproj.aspx?proj=33094

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Section: Principal Findingscontrasting

confidence: 76%

Evaluation of the Quadri-Planes Method in Computer-Aided Diagnosis of Breast Lesions by Ultrasonography: Prospective Single-Center Study

et al. 2020

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“…All the 10 studies (English = 5, Chinese = 5) were diagnostic cohort and prospective studies [10,13,16,17,19,[22][23][24][25][26]. A total of 2054 breast masses (diameters range from 0.2 cm to 1.32 cm) from 1777 patients (age ranged from 14 to 83 years) were identified and 720 (35%) masses of them were malignant.…”

Section: Study Characteristicsmentioning

confidence: 99%

“…The pub-lication dates of the literature were from 2016 to 2019. Nine articles in the analysis evaluated breast masses in Asia [10,13,16,17,[22][23][24][25][26], while only 1 article was concerned with Africa [19]. All US examinations were performed with RS80A (Samsung Medison Co., Ltd., Seoul, Korea) which equipped S-Detect technique in US system [10,13,16,17,19,[22][23][24][25][26].…”

Section: Study Characteristicsmentioning

confidence: 99%

“…Nine articles in the analysis evaluated breast masses in Asia [10,13,16,17,[22][23][24][25][26], while only 1 article was concerned with Africa [19]. All US examinations were performed with RS80A (Samsung Medison Co., Ltd., Seoul, Korea) which equipped S-Detect technique in US system [10,13,16,17,19,[22][23][24][25][26]. Three articles used the fourth edition BI-RADS [13,17,24], while 6 articles used the fifth edition to diagnosis if the lesion was benign or malignant [10,16,19,22,23,26], and one article did not specify which version it used.…”

Section: Study Characteristicsmentioning

confidence: 99%

“…All US examinations were performed with RS80A (Samsung Medison Co., Ltd., Seoul, Korea) which equipped S-Detect technique in US system [10,13,16,17,19,[22][23][24][25][26]. Three articles used the fourth edition BI-RADS [13,17,24], while 6 articles used the fifth edition to diagnosis if the lesion was benign or malignant [10,16,19,22,23,26], and one article did not specify which version it used. All studies were based on histology biopsy as the gold standard [10,13,16,17,19,[22][23][24][25][26].…”

Section: Study Characteristicsmentioning

confidence: 99%

See 2 more Smart Citations

The value of S-Detect for the differential diagnosis of breast masses on ultrasound: a systematic review and pooled meta-analysis

Li¹,

Sang²,

Yu³

et al. 2020

Med Ultrason

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Aim: To evaluate the value of S-Detect (a computer aided diagnosis system using deep learning) in breast ultrasound (US) for discriminating benign and malignant breast masses.Material and methods: A literature search was performed and relevant studies using S-Detect for the differential diagnosis of breast masses were selected. The quality of included studies was assessed using a Quality Assessment of Diagnostic Accuracy Studies (QUADAS) questionnaire. Two review authors independently searched the articles and assessed the eligibility of the reports.Results: A total of ten studies were included in the meta-analysis. The pooled estimates of sensitivity and specificity were 0.82 (95%CI: 0.77-0.87) and 0.86 (95%CI: 0.76-0.92), respectively. In addition, the diagnostic odds ratios, positive likelihood ratio and negative likelihood ratio were 28 (95%CI: 16- 49), 5.7 (95%CI: 3.4-9.5), and 0.21 (95%CI: 0.16-0.27), respectively. Area under the curve was 0.89 (95%CI: 0.86-0.92). No significant publication bias was observed.Conclusions: S-Detect exhibited a favourable diagnostic value in assisting physicians discriminating benign and malignant breast masses and it can be considered as a useful complement for conventional US.

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Diagnostic Performance of an Artificial Intelligence System in Breast Ultrasound

O’Connell

Bartolotta

Orlando

et al. 2021

J of Ultrasound Medicine

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Objectives We study the performance of an artificial intelligence (AI) program designed to assist radiologists in the diagnosis of breast cancer, relative to measures obtained from conventional readings by radiologists. Methods A total of 10 radiologists read a curated, anonymized group of 299 breast ultrasound images that contained at least one suspicious lesion and for which a final diagnosis was independently determined. Separately, the AI program was initialized by a lead radiologist and the computed results compared against those of the radiologists. Results The AI program's diagnoses of breast lesions had concordance with the 10 radiologists' readings across a number of BI‐RADS descriptors. The sensitivity, specificity, and accuracy of the AI program's diagnosis of benign versus malignant was above 0.8, in agreement with the highest performing radiologists and commensurate with recent studies. Conclusion The trained AI program can contribute to accuracy of breast cancer diagnoses with ultrasound.

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

Cited by 29 publications

References 29 publications

Evaluation of the Quadri-Planes Method in Computer-Aided Diagnosis of Breast Lesions by Ultrasonography: Prospective Single-Center Study

Evaluation of the Quadri-Planes Method in Computer-Aided Diagnosis of Breast Lesions by Ultrasonography: Prospective Single-Center Study

The value of S-Detect for the differential diagnosis of breast masses on ultrasound: a systematic review and pooled meta-analysis

Diagnostic Performance of an Artificial Intelligence System in Breast Ultrasound

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