scholarly journals Evaluation of the effect of computer aided diagnosis system on breast ultrasound for inexperienced radiologists in describing and determining breast lesions

2019 ◽  
Vol 21 (3) ◽  
pp. 239
Author(s):  
Jeongmin Lee ◽  
Sanghee Kim ◽  
Bong Joo Kang ◽  
Sung Hun Kim ◽  
Ga Eun Park
Keyword(s):  
Breast Imaging ◽  
Diagnostic Performance ◽  
Computer Aided Diagnosis ◽  
Breast Ultrasound ◽  
Breast Lesions ◽  
Cad System ◽  
Computer Aided ◽  
Kappa Value ◽  
Inexperienced Reader ◽  
Aided Diagnosis

Aim: To investigate the effect of a computer-aided diagnosis (CAD) system on breast ultrasound (US) for inexperienced radiologists in describing and determining breast lesions.Materials and methods: Between October 2015 to January 2017, 500 suspicious or probable benign lesions in 413 patients were reviewed. Five experienced readers retrospectively reviewed for each of 100 lesions according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon and category, with CAD system (S-detectTM). The readers then made final decisions by combining CAD results to their US results. Using the nested experiment design, five inexperienced readers were asked to select the appropriate BI-RADS lexicons, categories, CAD results, and combination results for each of the 100 lesions, retrospectively. Diagnostic performance of experienced and inexperienced radiologists and CAD were assessed. For each case, agreements in the lexicons and categories were analyzed among the experienced reader, inexperienced reader and CAD.Results: Indicators of the diagnostic performance for breast malignancy of the experienced group (AUC=0.83, 95%CI [0.80, 0.86]) were similar or higher than those of CAD (AUC = 0.79, 95%CI[0.74, 0.83], p=0.101), except for specificity. Conversely, indicators of diagnostic performance of inexperienced group (AUC=0.65, 95%CI[0.58, 0.71]) did not differ from or were lower than those of CAD(AUC=0.73, 95%CI[0.67, 0.78], p=0.013). Also, the diagnostic performance of the inexperienced group after combination with the CAD result was significantly improved (0.71, 95% CI [0.65, 0.77], p=0.001), whereas that of the experienced group did not change after combination with the CAD result, except for specificity and positive predictive value (PPV). Kappa values for the agreement of the categorization between CAD and each radiologist group were increased after applying the CAD result to their result of general US. Especially, the increase of the Kappa value was higher in the inexperienced group than in the experienced group. Also, for all the lexicons, the Kappa values between the experienced group and CAD were higher than those between the inexperienced group and CAD.Conclusion: By using the CAD system for classification of breast lesions, diagnostic performance of the inexperienced radiologists for malignancy was significantly improved, and better agreement was observed in lexicons between the experienced group and CAD than between the inexperienced group and CAD. CAD may be beneficial and educational for the inexperienced group.

Clinical Application of Computer-Aided Diagnosis According to S-Detect in Category 4 Breast Lesions: Prospective Single-Center Study (Preprint)

2020 ◽  
Author(s):  
Pengfei Sun ◽  
Chen Chen ◽  
Weiqi Wang ◽  
Lei Liang ◽  
Dan Luo ◽  
...  
Keyword(s):  
Clinical Management ◽  
Diagnostic Performance ◽  
Breast Biopsy ◽  
Computer Aided Diagnosis ◽  
Breast Lesions ◽  
Breast Masses ◽  
Computer Aided ◽  
Conventional Ultrasound ◽  
Us Findings ◽  
Aided Diagnosis

BACKGROUND Computer-aided diagnosis (CAD) is a useful tool that can provide a reference for the differential diagnosis of benign and malignant breast lesion. Previous studies have demonstrated that CAD can improve the diagnostic performance. However, conventional ultrasound (US) combined with CAD were used to adjust the classification of category 4 lesions has been few assessed. OBJECTIVE The objective of our study was to evaluate the diagnosis performance of conventional ultrasound combined with a CAD system S-Detect in the category of BI-RADS 4 breast lesions. METHODS Between December 2018 and May 2020, we enrolled patients in this study who received conventional ultrasound and S-Detect before US-guided biopsy or surgical excision. The diagnostic performance was compared between US findings only and the combined use of US findings with S-Detect, which were correlated with pathology results. RESULTS A total of 98 patients (mean age 51.06 ±16.25 years, range 22-81) with 110 breast masses (mean size1.97±1.38cm, range0.6-8.5) were included in this study. Of the 110 breast masses, 64/110 (58.18%) were benign, 46/110 (41.82%) were malignant. Compared with conventional ultrasound, a significant increase in specificity (0% to 53.12%, P<.001), accuracy (41.81% to70.19%, P<.001) were noted, with no statistically significant decrease on sensitivity(100% to 95.65% ,P=.48). According to S-Detect-guided US BI-RADS re-classification, 30 out of 110 (27.27%) breast lesions underwent a correct change in clinical management, 74of 110 (67.27%) breast lesions underwent no change and 6 of 110 (5.45%) breast lesions underwent an incorrect change in clinical management. The biopsy rate decreased from 100% to 67.27 % (P<.001).Benign masses among subcategory 4a had higher rates of possibly benign assessment on S-Detect for the US only (60% to 0%, P<.001). CONCLUSIONS S-Detect can be used as an additional diagnostic tool to improve the specificity and accuracy in clinical practice. S-Detect have the potential to be used in downgrading benign masses misclassified as BI-RADS category 4 on US by radiologist, and may reduce unnecessary breast biopsy. CLINICALTRIAL none

scholarly journals Computer-Aided Diagnosis Systems in Diagnosing Malignant Thyroid Nodules on Ultrasonography: A Systematic Review and Meta-Analysis

2019 ◽  
Vol 9 (4) ◽  
pp. 186-193
Author(s):  
Lei Xu ◽  
Junling Gao ◽  
Quan Wang ◽  
Jichao Yin ◽  
Pengfei Yu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Deep Learning ◽  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
Computer Aided Diagnosis ◽  
Cad Systems ◽  
Cad System ◽  
Computer Aided ◽  
Aided Diagnosis

Background: Computer-aided diagnosis (CAD) systems are being applied to the ultrasonographic diagnosis of malignant thyroid nodules, but it remains controversial whether the systems add any accuracy for radiologists. Objective: To determine the accuracy of CAD systems in diagnosing malignant thyroid nodules. Methods: PubMed, EMBASE, and the Cochrane Library were searched for studies on the diagnostic performance of CAD systems. The diagnostic performance was assessed by pooled sensitivity and specificity, and their accuracy was compared with that of radiologists. The present systematic review was registered in PROSPERO (CRD42019134460). Results: Nineteen studies with 4,781 thyroid nodules were included. Both the classic machine learning- and the deep learning-based CAD system had good performance in diagnosing malignant thyroid nodules (classic machine learning: sensitivity 0.86 [95% CI 0.79–0.92], specificity 0.85 [95% CI 0.77–0.91], diagnostic odds ratio (DOR) 37.41 [95% CI 24.91–56.20]; deep learning: sensitivity 0.89 [95% CI 0.81–0.93], specificity 0.84 [95% CI 0.75–0.90], DOR 40.87 [95% CI 18.13–92.13]). The diagnostic performance of the deep learning-based CAD system was comparable to that of the radiologists (sensitivity 0.87 [95% CI 0.78–0.93] vs. 0.87 [95% CI 0.85–0.89], specificity 0.85 [95% CI 0.76–0.91] vs. 0.87 [95% CI 0.81–0.91], DOR 40.12 [95% CI 15.58–103.33] vs. DOR 44.88 [95% CI 30.71–65.57]). Conclusions: The CAD systems demonstrated good performance in diagnosing malignant thyroid nodules. However, experienced radiologists may still have an advantage over CAD systems during real-time diagnosis.

Evaluation of the Accuracy of a Computer-aided Diagnosis (CAD) System in Breast Ultrasound according to the Radiologist’s Experience

2012 ◽  
Vol 19 (3) ◽  
pp. 311-319 ◽  
Author(s):  
Marie-Laure Chabi ◽  
Isabelle Borget ◽  
Rosario Ardiles ◽  
Ghassen Aboud ◽  
Samia Boussouar ◽  
...  
Keyword(s):  
Computer Aided Diagnosis ◽  
Breast Ultrasound ◽  
Cad System ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals A Beneficial Role of Computer-aided Diagnosis System for Less Experienced Physicians in the Diagnosis of Thyroid Nodule on Ultrasound

2021 ◽  
Author(s):  
Sunyoung Kang ◽  
Eunjung Lee ◽  
Chae Won Chung ◽  
Han Na Jang ◽  
Joon Ho Moon ◽  
...  
Keyword(s):  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
Needle Aspiration ◽  
Computer Aided Diagnosis ◽  
Ultrasound Images ◽  
Cad System ◽  
Computer Aided ◽  
Before And After ◽  
Needle Aspiration Cytology ◽  
Aided Diagnosis

Abstract Ultrasonography is the primary diagnostic tool for thyroid nodules, while the accuracy is highly operator-dependent. The aim of this study was to investigate whether ultrasonography with computer-aided diagnosis (CAD) has assisting roles to physicians in the diagnosis of thyroid nodules. 451 thyroid nodules (³ 1 cm) evaluated by fine-needle aspiration cytology following surgery were included. 300 (66.5%) of them were diagnosed as malignancy. Thirteen physicians who had 0 months (E0, n=8), 1 year (E1, n=2), or more than 5 years (E5, n=3) of experience in ultrasonography reviewed the prepared ultrasound images of thyroid nodules before and after CAD assistance. The diagnostic performance of CAD was comparable to that of the E5 group, and better than those of the E0 and E1 groups. The AUC of the CAD for conventional PTC was higher than that for FTC and follicular variant PTC (0.925 vs. 0.499), independent of tumor size. CAD assistance significantly improved diagnostic performance in E0 group, but not in the E1 and E5 groups. In conclusion, the CAD system showed good performance in the diagnosis of conventional PTC. CAD assistance improved the diagnostic performance of physicians with less experience in ultrasonography, especially in the diagnosis of conventional PTC.

scholarly journals Diagnostic performance evaluation of different TI-RADS using ultrasound computer-aided diagnosis of thyroid nodules: An experience with adjusted settings

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245617
Author(s):  
Nonhlanhla Chambara ◽  
Shirley Y. W. Liu ◽  
Xina Lo ◽  
Michael Ying
Keyword(s):  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
Computer Aided Diagnosis ◽  
Ultrasound Images ◽  
American Thyroid Association ◽  
Korean Society ◽  
Cad System ◽  
Computer Aided ◽  
Aided Diagnosis ◽  
Default Setting

Background Thyroid cancer diagnosis has evolved to include computer-aided diagnosis (CAD) approaches to overcome the limitations of human ultrasound feature assessment. This study aimed to evaluate the diagnostic performance of a CAD system in thyroid nodule differentiation using varied settings. Methods Ultrasound images of 205 thyroid nodules from 198 patients were analysed in this retrospective study. AmCAD-UT software was used at default settings and 3 adjusted settings to diagnose the nodules. Six risk-stratification systems in the software were used to classify the thyroid nodules: The American Thyroid Association (ATA), American College of Radiology Thyroid Imaging, Reporting, and Data System (ACR-TIRADS), British Thyroid Association (BTA), European Union (EU-TIRADS), Kwak (2011) and the Korean Society of Thyroid Radiology (KSThR). The diagnostic performance of CAD was determined relative to the histopathology and/or cytology diagnosis of each nodule. Results At the default setting, EU-TIRADS yielded the highest sensitivity, 82.6% and lowest specificity, 42.1% while the ATA-TIRADS yielded the highest specificity, 66.4%. Kwak had the highest AUROC (0.74) which was comparable to that of ACR, ATA, and KSThR TIRADS (0.72, 0.73, and 0.70 respectively). At a hyperechoic foci setting of 3.5 with other settings at median values; ATA had the best-balanced sensitivity, specificity and good AUROC (70.4%; 67.3% and 0.71 respectively). Conclusion The default setting achieved the best diagnostic performance with all TIRADS and was best for maximizing the sensitivity of EU-TIRADS. Adjusting the settings by only reducing the sensitivity to echogenic foci may be most helpful for improving specificity with minimal change in sensitivity.

scholarly journals Performance of Computer-Aided Diagnosis in Ultrasonography for Detection of Breast Lesions Less and More Than 2 cm: Prospective Comparative Study

10.2196/16334 ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. e16334 ◽  
Author(s):  
Liang Yongping ◽  
Ping Zhou ◽  
Zhang Juan ◽  
Zhao Yongfeng ◽  
Wengang Liu ◽  
...  
Keyword(s):  
Breast Imaging ◽  
Characteristic Curve ◽  
Computer Aided Diagnosis ◽  
Breast Lesions ◽  
Paired Data ◽  
Clinical Trial Registry ◽  
Experienced Reader ◽  
Experienced Radiologist ◽  
Computer Aided ◽  
Aided Diagnosis

Background Computer-aided diagnosis (CAD) is used as an aid tool by radiologists on breast lesion diagnosis in ultrasonography. Previous studies demonstrated that CAD can improve the diagnosis performance of radiologists. However, the optimal use of CAD on breast lesions according to size (below or above 2 cm) has not been assessed. Objective The aim of this study was to compare the performance of different radiologists using CAD to detect breast tumors less and more than 2 cm in size. Methods We prospectively enrolled 261 consecutive patients (mean age 43 years; age range 17-70 years), including 398 lesions (148 lesions>2 cm, 79 malignant and 69 benign; 250 lesions≤2 cm, 71 malignant and 179 benign) with breast mass as the prominent symptom. One novice radiologist with 1 year of ultrasonography experience and one experienced radiologist with 5 years of ultrasonography experience were each assigned to read the ultrasonography images without CAD, and then again at a second reading while applying the CAD S-Detect. We then compared the diagnostic performance of the readers in the two readings (without and combined with CAD) with breast imaging. The McNemar test for paired data was used for statistical analysis. Results For the novice reader, the area under the receiver operating characteristic curve (AUC) improved from 0.74 (95% CI 0.67-0.82) from the without-CAD mode to 0.88 (95% CI 0.83-0.93; P<.001) at the combined-CAD mode in lesions≤2 cm. For the experienced reader, the AUC improved from 0.84 (95% CI 0.77-0.90) to 0.90 (95% CI 0.86-0.94; P=.002). In lesions>2 cm, the AUC moderately decreased from 0.81 to 0.80 (novice reader) and from 0.90 to 0.82 (experienced reader). The sensitivity of the novice and experienced reader in lesions≤2 cm improved from 61.97% and 73.23% at the without-CAD mode to 90.14% and 97.18% (both P<.001) at the combined-CAD mode, respectively. Conclusions S-Detect is a feasible diagnostic tool that can improve the sensitivity for both novice and experienced readers, while also improving the negative predictive value and AUC for lesions≤2 cm, demonstrating important application value in the clinical diagnosis of breast cancer. Trial Registration Chinese Clinical Trial Registry ChiCTR1800019649; http://www.chictr.org.cn/showprojen.aspx?proj=33094

scholarly journals Breast elastography: diagnostic performance of computer-aided diagnosis software and interobserver agreement

2020 ◽  
Vol 53 (1) ◽  
pp. 27-33 ◽  
Author(s):  
Eduardo F. C. Fleury ◽  
Karem Marcomini
Keyword(s):  
Breast Imaging ◽  
Interobserver Agreement ◽  
Visual Assessment ◽  
Computer Aided Diagnosis ◽  
Ultrasound Elastography ◽  
Breast Masses ◽  
Cutoff Value ◽  
Cad System ◽  
Computer Aided ◽  
Aided Diagnosis

Abstract Objective: To determine the best cutoff value for classifying breast masses by ultrasound elastography, using dedicated software for strain elastography, and to determine the level of interobserver agreement. Materials and Methods: We enrolled 83 patients with 83 breast masses identified on ultrasound and referred for biopsy. After B-mode ultrasound examination, the lesions were manually segmented by three radiologists with varying degrees of experience in breast imaging, designated reader 1 (R1, with 15 years), reader 2 (R2, with 2 years), and reader 3 (R3, with 8 years). Elastography was performed automatically on the best image with computer-aided diagnosis (CAD) software. Cutoff values of 70%, 75%, 80%, and 90% of hard areas were applied for determining the performance of the CAD software. The best cutoff value for the most experienced radiologists was then compared with the visual assessment. Interobserver agreement for the best cutoff value was determined, as were the interclass correlation coefficient and concordance among the radiologists for the areas segmented. Results: The best cutoff value of the proportion of hard area within a breast mass, for experienced radiologists, was found to be 75%. At a cutoff value of 75%, the interobserver agreement was excellent between R1 and R2, as well as between R1 and R3, and good between R2 and R3. The interclass concordance coefficient among the three radiologists was 0.950. When assessing the segmented areas by size, we found that the level of agreement was higher among the more experienced radiologists. Conclusion: The best cutoff value for a quantitative CAD system to classify breast masses was 75%.

scholarly journals A beneficial role of computer-aided diagnosis system for less experienced physicians in the diagnosis of thyroid nodule on ultrasound

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sunyoung Kang ◽  
Eunjung Lee ◽  
Chae Won Chung ◽  
Han Na Jang ◽  
Joon Ho Moon ◽  
...  
Keyword(s):  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
Needle Aspiration ◽  
Computer Aided Diagnosis ◽  
Cad System ◽  
The Us ◽  
Computer Aided ◽  
Needle Aspiration Cytology ◽  
Aided Diagnosis

AbstractUltrasonography (US) is the primary diagnostic tool for thyroid nodules, while the accuracy is operator-dependent. It is widely used not only by radiologists but also by physicians with different levels of experience. The aim of this study was to investigate whether US with computer-aided diagnosis (CAD) has assisting roles to physicians in the diagnosis of thyroid nodules. 451 thyroid nodules evaluated by fine-needle aspiration cytology following surgery were included. 300 (66.5%) of them were diagnosed as malignancy. Physicians with US experience less than 1 year (inexperienced, n = 10), or more than 5 years (experienced, n = 3) reviewed the US images of thyroid nodules with or without CAD assistance. The diagnostic performance of CAD was comparable to that of the experienced group, and better than those of the inexperienced group. The AUC of the CAD for conventional PTC was higher than that for FTC and follicular variant PTC (0.925 vs. 0.499), independent of tumor size. CAD assistance significantly improved diagnostic performance in the inexperienced group, but not in the experienced groups. In conclusion, the CAD system showed good performance in the diagnosis of conventional PTC. CAD assistance improved the diagnostic performance of less experienced physicians in US, especially in diagnosis of conventional PTC.

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