Enhancing Breast Cancer Diagnosis: A Nomogram Model Integrating AI Ultrasound and Clinical Factors.

PURPOSE: A novel nomogram incorporating artificial intelligence (AI) and clinical features for enhanced ultrasound prediction of benign and malignant breast masses. MATERIALS AND METHODS: This study analyzed 340 breast masses identified through ultrasound in 308 patients. The masses were divided into training (n = 260) and validation (n = 80) groups. The AI-based analysis employed the Samsung Ultrasound AI system (S-detect). Univariate and multivariate analyses were conducted to construct nomograms using logistic regression. The AI-Nomogram was based solely on AI results, while the ClinAI- Nomogram incorporated additional clinical factors. Both nomograms underwent internal validation with 1000 bootstrap resamples and external validation using the independent validation group. Performance was evaluated by analyzing the area under the receiver operating characteristic (ROC) curve (AUC) and calibration curves. RESULTS: The ClinAI-Nomogram, which incorporates patient age, AI-based mass size, and AI-based diagnosis, outperformed an existing AI-Nomogram in differentiating benign from malignant breast masses. The ClinAI-Nomogram surpassed the AI-Nomogram in predicting malignancy with significantly higher AUC scores in both training (0.873, 95% CI: 0.830-0.917 vs. 0.792, 95% CI: 0.748-0.836; p = 0.016) and validation phases (0.847, 95% CI: 0.763-0.932 vs. 0.770, 95% CI: 0.709-0.833; p < 0.001). Calibration curves further revealed excellent agreement between the ClinAI-Nomogram's predicted probabilities and actual observed risks of malignancy. CONCLUSION: The ClinAI- Nomogram, combining AI alongside clinical data, significantly enhanced the differentiation of benign and malignant breast masses in clinical AI-facilitated ultrasound examinations.

Diagnostic efficiency among Eu-/C-/ACR-TIRADS and S-Detect for thyroid nodules: a systematic review and network meta-analysis.

Background: The performance in evaluating thyroid nodules on ultrasound varies across different risk stratification systems, leading to inconsistency and uncertainty regarding diagnostic sensitivity, specificity, and accuracy. Objective: Comparing diagnostic performance of detecting thyroid cancer among distinct ultrasound risk stratification systems proposed in the last five years. Evidence acquisition: Systematic search was conducted on PubMed, EMBASE, and Web of Science databases to find relevant research up to December 8, 2022, whose study contents contained elucidation of diagnostic performance of any one of the above ultrasound risk stratification systems (European Thyroid Imaging Reporting and Data System[Eu-TIRADS]; American College of Radiology TIRADS [ACR TIRADS]; Chinese version of TIRADS [C-TIRADS]; Computer-aided diagnosis system based on deep learning [S-Detect]). Based on golden diagnostic standard in histopathology and cytology, single meta-analysis was performed to obtain the optimal cut-off value for each system, and then network meta-analysis was conducted on the best risk stratification category in each system. Evidence synthesis: This network meta-analysis included 88 studies with a total of 59,304 nodules. The most accurate risk category thresholds were TR5 for Eu-TIRADS, TR5 for ACR TIRADS, TR4b and above for C-TIRADS, and possible malignancy for S-Detect. At the best thresholds, sensitivity of these systems ranged from 68% to 82% and specificity ranged from 71% to 81%. It identified the highest sensitivity for C-TIRADS TR4b and the highest specificity for ACR TIRADS TR5. However, sensitivity for ACR TIRADS TR5 was the lowest. The diagnostic odds ratio (DOR) and area under curve (AUC) were ranked first in C-TIRADS. Conclusion: Among four ultrasound risk stratification options, this systemic review preliminarily proved that C-TIRADS possessed favorable diagnostic performance for thyroid nodules. Systematic review registration: https://www.crd.york.ac.uk/prospero, CRD42022382818.

Application value of S-Detect combined with contrast-enhanced ultrasound in the Breast Imaging Reporting and Data System 4 breast lesions / 中华超声影像学杂志

Objective:To assess the value of S-Detect and contrast-enhanced ultrasound (CEUS) in the differential diagnosis of Breast Imaging Reporting and Data System(BI-RADS) 4 breast lesions.Methods:A total of 104 breast lesions in 100 patients diagnosed as BI-RADS category 4 by conventional ultrasound were prospectively enrolled, and all of them were received S-Detect and CEUS examination at the same time. Taking pathology as the gold standard, ROC curve was constructed to compare the diagnostic efficacy of conventional ultrasound, S-Detect, CEUS and their combination.Results:Among the 104 BI-RADS category 4 breast lesions, 63 were benign and 41 were malignant. The sensitivities of conventional ultrasound, S-Detect, CEUS and S-Detect combined with CEUS were 73.17%, 87.80%, 87.80%, 90.24%; the specificities were 57.14%, 60.32%, 68.25%, 77.78%; the positive predictive values were 52.63%, 59.02%, 64.29% and 72.55%; the negative predictive values were 76.60%, 88.37%, 89.59% and 92.45%; the accuracies were 63.46%, 71.15%, 75.96% and 82.69%; and the areas under the ROC curve (AUC) were 0.652, 0.741, 0.780 and 0.840. The sensitivity, specificity, positive predictive value, negative predictive value and accuracy of S-Detect and CEUS diagnosis were improved compared with conventional ultrasound. The AUC of combined diagnosis was higher than that of S-Detect, CEUS alone, and the differences were statistically significant(all P<0.05). The AUC of CEUS was higher than that of conventional ultrasound, and the difference was statistically significant ( P<0.05). There were no significant differences in AUC between any two of other groups (all P>0.05). Conclusions:The combined application of S-Detect and CEUS could achieve complementary advantages, which is of great significance for the differential diagnosis of benign and malignant in BI-RADS 4 breast lesions.

Evaluating breast ultrasound S-detect image analysis for small focal breast lesions.

Background: S-Detect is a computer-assisted, artificial intelligence-based system of image analysis that has been integrated into the software of ultrasound (US) equipment and has the capacity to independently differentiate between benign and malignant focal breast lesions. Since the revision and upgrade in both the breast imaging-reporting and data system (BI-RADS) US lexicon and the S-Detect software in 2013, evidence that supports improved accuracy and specificity of radiologists' assessment of breast lesions has accumulated. However, such assessment using S-Detect technology to distinguish malignant from breast lesions with a diameter no greater than 2 cm requires further investigation. Methods: The US images of focal breast lesions from 295 patients in our hospital from January 2019 to June 2022 were collected. The BI-RADS data were evaluated by the embedded program and as manually modified prior to the determination of a pathological diagnosis. The receiver operator characteristic (ROC) curves were constructed to compare the diagnostic accuracy between the assessments of the conventional US images, the S-Detect classification, and the combination of the two. Results: There were 326 lesions identified in 295 patients, of which pathological confirmation demonstrated that 239 were benign and 87 were malignant. The sensitivity, specificity, and accuracy of the conventional imaging group were 75.86%, 93.31%, and 88.65%. The sensitivity, specificity, and accuracy of the S-Detect classification group were 87.36%, 88.28%, and 88.04%, respectively. The assessment of the amended combination of S-Detect with US image analysis (Co-Detect group) was improved with a sensitivity, specificity, and accuracy of 90.80%, 94.56%, and 93.56%, respectively. The diagnostic accuracy of the conventional US group, the S-Detect group, and the Co-Detect group using area under curves was 0.85, 0.88 and 0.93, respectively. The Co-Detect group had a better diagnostic efficiency compared with the conventional US group (Z = 3.882, p = 0.0001) and the S-Detect group (Z = 3.861, p = 0.0001). There was no significant difference in distinguishing benign from malignant small breast lesions when comparing conventional US and S-Detect techniques. Conclusions: The addition of S-Detect technology to conventional US imaging provided a novel and feasible method to differentiate benign from malignant small breast nodules.

Application of S-detect combined with virtual touch imaging quantification in ultrasound for diagnosis of breast mass. / S-detectè”åˆå£°è§¦è¯Šç»„ç»‡æˆåƒå®šé‡åœ¨ä¹³è ºè‚¿å—è¶ å£°è¯Šæ–­ä¸­çš„åº”ç”¨.

OBJECTIVES: Ultrasound is a safe and timely diagnosis method commonly used for breast lesion, however it depends on the operator to a certain degree. As an emerging technology, artificial intelligence can be combined with ultrasound in depth to improve the intelligence and precision of ultrasound diagnosis and avoid diagnostic errors caused by subjectivity of radiologists. This paper aims to investigate the value of artificial intelligence S-detect system combined with virtual touch imaging quantification (VTIQ) technique in the differential diagnosis of benign and malignant breast masses by conventional ultrasound (CUS). respectively, and AUCs for them were 0.74, 0.86, 0.79, and 0.94, respectively. The diagnostic accuracy of CUS+S-detect was higher than that of CUS (P<0.05). The diagnostic accuracy of CUS+S-detect was higher than that of CUS (P<0.05). The diagnostic specificity of CUS+VTIQ was higher than that of CUS (P<0.05). The diagnostic accuracy and AUC of CUS+S-detect+VTIQ were higher than those of S-detect or VTIQ applied to CUS alone (P<0.05). The sensitivities of CUS for senior radiologists, CUS for junior radiologists, CUS+S-detect+VTIQ for senior radiologists, and CUS+S-detect+VTIQ for junior radiologists were 60.00%, 80.00%, 72.73%, and 90.00%, respectively, when diagnosing benign and malignant breast masses in 50 randomly selected cases. The specificities for them was 66.67%, 76.67%, 80.00%, and 81.25%, respectively. The accuracies for them was 64.00%, 78.00%, 80.00%, and 88.00%, respectively. The AUCs for them were 0.63, 0.78, 0.88, and 0.80, respectively. Compared with the CUS for junior radiologists, the CUS+S-detect+VTIQ for junior radiologists had higher sensitivity, specificity, and accuracy (all P<0.05). The consistency of the combined application of S-detect and VTIQ for diagnosing breast masses at 2 different times was good among junior radiologists (Kappa=0.800). METHODS: CUS, S-detects, and VTIQ were used to differentially diagnose benign and malignant breast masses in 108 cases, and the final pathological results were referred to as the gold standard for classifying breast masses. The diagnostic efficacy were evaluated and compared, among the 3 methods and among S-detect applied to CUS (CUS+S-detect), VTIQ applied to CUS (CUS+VTIQ), and S-detect combined with VTIQ applied to CUS (CUS+S-detect+VTIQ). Fifty cases were acquired randomly from the collected breast masses, and 2 radiologists with different years of experience (2 and 8 years) used S-detect combined with VTIQ for the ultrasonic differential diagnosis of benign and malignant breast masses. RESULTS: The differences in sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC) of the 3 diagnostic methods of CUS, S-detect, and VTIQ were not statistically significant (all P>0.05). The sensitivities of CUS, CUS+Sdetect, CUS+VTIQ, and CUS+S-detect+VTIQ were 78.57%, 92.86%, 69.05%, and 95.24%, respectively, the specificities for them were 69.70%, 78.79%, 87.88%, and 92.42%, respectively, the accuracies for them were 73.15%, 84.26%, 80.56%, and 93.52%. CONCLUSIONS: S-detect combined with VTIQ when applied to CUS can overcome the shortcomings of separate applications and complement each other, especially for junior radiologists, and can more effectively improve the diagnostic efficacy of ultrasound for benign and malignant breast masses.

[Value of ultrasonic S-Detect technique in diagnosis of breast masses].

OBJECTIVE: To evaluate the value of ultrasound S-Detect in the diagnosis of breast masses. METHODS: A total of 85 breast masses in 62 female patients were diagnosed by S-Detect technique and conventional ultrasound. The diagnostic efficacy of conventional ultrasound and S-Detect technique was analyzed and compared with postoperative pathological results as the gold standard. RESULTS: When operated by junior physicians, the diagnostic efficacy of conventional ultrasound was significantly lower than that of S-Detect technique (P < 0.05), but this difference was not observed in moderately experienced and senior physicians (P>0.05). S-Detect technique was positively correlated with the diagnostic results of senior physicians (r=0.97). Using S-Detect technique, the diagnostic efficacy did not differ significantly between the long axis section and its vertical section (P>0.05). Routine ultrasound showed a better diagnostic efficacy than S-Detect for breast masses with a diameter below 20 mm (P < 0.05), but for larger breast masses, its diagnostic efficacy was significantly lower than that of SDetect (P < 0.05). CONCLUSION: S-Detect can be used in differential diagnosis of benign and malignant breast masses, and its diagnostic efficiency can be comparable with that of BI-RADS classification for moderately experienced and senior physicians, but its diagnostic efficacy can be low for breast masses less than 20 mm in diameter.

The added value of S-detect in the diagnostic accuracy of breast masses by senior and junior radiologist groups: a systematic review and meta-analysis.

Background: S-detect is an emerging computer-aided diagnosis (CAD) technique that provides a reference for radiologists to identify breast cancer. Some studies have shown that US (ultrasound) + S-detect can improve the diagnostic accuracy of junior radiologists more than senior radiologists, but the results are inconsistent in various studies. Therefore, this meta-analysis aimed to assess the value of S-detect combined with the US outcomes from senior and junior radiologists for the diagnosis of breast cancer. Methods: We searched the PubMed, Cochrane Library, Embase, Web of Science, and Wanfang databases, China Biology Medicine disc, China National Knowledge Infrastructure (CNKI), and VIP database for trials on the diagnostic accuracy of US + S-detect for the diagnosis of breast masses. The search time frame was from the date of establishment of the database to August 20, 2022. Two researchers independently screened the literature, extracted the information, and evaluated the quality of the included literature using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) scale. StataSE 15.1 software was utilized to assess pooled metrics, including sensitivity, specificity, and the area under the curve (AUC). Results: A total of 19 articles with 3,349 patients and 3,895 breast masses were included in this meta-analysis. Of these, seventeen articles evaluated the diagnostic performance of senior radiologists' US + S-detect for breast cancer, while twelve articles reported junior radiologists' diagnostic performance. The risk of bias was primarily attributed to patient selection, flow and timing. In the senior radiologist group, the pooled sensitivity and specificity of US + S-detect were 0.93 [95% confidence interval (CI): 0.89-0.95] and 0.86 (95% CI: 0.80-0.90), respectively, with an AUC of 0.96. As for the junior radiologist group, the pooled sensitivity and specificity of US + S-detect were 0.89 (95% CI: 0.83-0.93) and 0.79 (95% CI: 0.72-0.84), respectively, and the AUC was 0.91. Conclusions: The results of this meta-analysis showed that the pooled sensitivity and the AUC of both the senior and junior radiologist groups were high, with good diagnostic efficacy and high clinical application. However, the results of this study are highly heterogeneous and need to be validated by collecting more high-quality studies and accumulating a larger sample size.

Combination of S-Detect and strain elastography in the conventional ultrasonic diagnosis of breast tumors / 中国综合临床

Objective:To explore the value of s-detect combined with elastography in the differential diagnosis of benign and malignant breast tumors.Methods:The ultrasound diagnosis data of 136 patients with breast mass examined by ultrasound in the First Affiliated Hospital of Shantou University Medical College from April 2018 to January 2020 were retrospectively analyzed. The breast lesions diagnosed as BI-RADS 3 or above were analyzed by conventional ultrasound, strain elastic imaging strain ratio (SR) and S-Detect Computer-aided Diagnosis (CAD) technology successively and were used for cross-sectional study. The corresponding benign and malignant judgment results were obtained, and the efficacy of individual diagnosis and combined diagnosis were compared and analyzed.Results:Conventional ultrasound, SR, S-Detect alone and conventional ultrasound+SR, conventional ultrasound+S-detect, conventional ultrasound + S-detect +SR combined diagnosis of breast tumors, the area under the receiver operating curve (AREA under the receiver operating curve) Characteristic curve, AUC) were 0.776, 0.839, 0.802, 0.861, 0.832 and 0.870, respectively. SR, S-Detect, conventional ultrasound +SR, conventional ultrasound + S-detect and conventional ultrasound +SR+ S-detect were compared with conventional ultrasound group, Z values were 1.49, 0.70, 2.76, 2.52, 2.96, respectively, and P values were 0.137, 0.484, 0.006, 0.012 and 0.003, respectively. The difference was statistically significant. The accuracy of conventional ultrasound +S-Detect+SR was the highest (84.1%), compared with pathological results, its Kappa value was 0.687, showing the strongest consistency. Conclusion:S-detect combined with strain elastography assisted by conventional ultrasound can significantly improve the diagnostic efficiency of benign and malignant breast tumors.

Value of ultrasonic S-Detect technique in diagnosis of breast masses / 南方医科大学学报

OBJECTIVE@#To evaluate the value of ultrasound S-Detect in the diagnosis of breast masses.@*METHODS@#A total of 85 breast masses in 62 female patients were diagnosed by S-Detect technique and conventional ultrasound. The diagnostic efficacy of conventional ultrasound and S-Detect technique was analyzed and compared with postoperative pathological results as the gold standard.@*RESULTS@#When operated by junior physicians, the diagnostic efficacy of conventional ultrasound was significantly lower than that of S-Detect technique (P < 0.05), but this difference was not observed in moderately experienced and senior physicians (P>0.05). S-Detect technique was positively correlated with the diagnostic results of senior physicians (r=0.97). Using S-Detect technique, the diagnostic efficacy did not differ significantly between the long axis section and its vertical section (P>0.05). Routine ultrasound showed a better diagnostic efficacy than S-Detect for breast masses with a diameter below 20 mm (P < 0.05), but for larger breast masses, its diagnostic efficacy was significantly lower than that of SDetect (P < 0.05).@*CONCLUSION@#S-Detect can be used in differential diagnosis of benign and malignant breast masses, and its diagnostic efficiency can be comparable with that of BI-RADS classification for moderately experienced and senior physicians, but its diagnostic efficacy can be low for breast masses less than 20 mm in diameter.

Differential diagnosis of breast cancer assisted by S-Detect artificial intelligence system.

Objective Traditional breast ultrasound relies too much on the operation skills of diagnostic doctors, and the repeatability in different doctors was low. This study aimed to evaluate the assistant diagnostic value of S-Detect artificial intelligence (AI) system in differentiating benign from malignant breast masses. Methods The ultrasound images of 40 patients who underwent ultrasound examination in our hospital were collected. The conventional ultrasound images, elastic images, and S-Detect mode of breast lesions were analyzed. The breast imaging reporting and data system recommended by the American Society of Radiology (BI-RADS) classification for each breast mass was evaluated both by the doctor and AI. The receiver operator characteristics (ROC) curves were drawn to compare the diagnostic efficiency. Result Among the 40 lesions, 16 were benign, and 24 were malignant. The S-Detect AI system had a high diagnostic efficiency for malignant mass, with sensitivity, specificity, and accuracy of 95.8%, 93.8%, and 89.6%. The accuracy of AI was higher than the elastic image and then than the conventional gray-scale image. With the assistance of the S-Detect AI system, the accuracy of BI-RADS classification was improved significantly. Conclusion The S-Detect AI system will enhance breast cancer diagnostic accuracy and improve ultrasound examination quality.

Artificial intelligence for breast ultrasound: An adjunct tool to reduce excessive lesion biopsy.

PURPOSE: To determine whether adding an artificial intelligence (AI) system to breast ultrasound (US) can reduce unnecessary biopsies. METHODS: Conventional US and AI analyses were prospectively performed on 173 suspicious breast lesions before US-guided core needle biopsy or vacuum-assisted excision. Conventional US images were retrospectively reviewed according to the BI-RADS 2013 lexicon and categories. Two downgrading stratifications based on AI assessments were manually used to downgrade the BI-RADS category 4A lesions to category 3. Stratification A was used to downgrade if the assessments of both orthogonal sections of a lesion from AI were possibly benign. Stratification B was used to downgrade if the assessment of any of the orthogonal sections was possibly benign. The effects of AI-based diagnosis on lesions to reduce unnecessary biopsy were analyzed using histopathological results as reference standards. RESULTS: Forty-three lesions diagnosed as BI-RADS category 4A by conventional US received AI-based hypothetical downgrading. While downgrading with stratification A, 14 biopsies were correctly avoided. The biopsy rate for BI-RADS category 4A lesions decreased from 100 % to 67.4 % (P <  0.001). While downgrading with stratification B, 27 biopsies could be avoided with two malignancies missed, and the biopsy rate would decrease to 37.2 % (P <  0.05, compared with conventional US and stratification A). CONCLUSION: Adding an AI system to breast US could reduce unnecessary lesion biopsies. Downgrading stratification A was recommended for its lower misdiagnosis rate.

S-Detect Software vs. EU-TIRADS Classification: A Dual-Center Validation of Diagnostic Performance in Differentiation of Thyroid Nodules.

Computer-aided diagnosis (CAD) and other risk stratification systems may improve ultrasound image interpretation. This prospective study aimed to compare the diagnostic performance of CAD and the European Thyroid Imaging Reporting and Data System (EU-TIRADS) classification applied by physicians with S-Detect 2 software CAD based on Korean Thyroid Imaging Reporting and Data System (K-TIRADS) and combinations of both methods (MODELs 1 to 5). In all, 133 nodules from 88 patients referred to thyroidectomy with available histopathology or with unambiguous results of cytology were included. The S-Detect system, EU-TIRADS, and mixed MODELs 1-5 for the diagnosis of thyroid cancer showed a sensitivity of 89.4%, 90.9%, 84.9%, 95.5%, 93.9%, 78.9% and 93.9%; a specificity of 80.6%, 61.2%, 88.1%, 53.7%, 73.1%, 89.6% and 80.6%; a positive predictive value of 81.9%, 69.8%, 87.5%, 67%, 77.5%, 88.1% and 82.7%; a negative predictive value of 88.5%, 87.2%, 85.5%, 92.3%, 92.5%, 81.1% and 93.1%; and an accuracy of 85%, 75.9%, 86.5%, 74.4%, 83.5%, 84.2%, and 87.2%, respectively. Comparison showed superiority of the similar MODELs 1 and 5 over other mixed models as well as EU-TIRADS and S-Detect used alone (p-value < 0.05). S-Detect software is characterized with high sensitivity and good specificity, whereas EU-TIRADS has high sensitivity, but rather low specificity. The best diagnostic performance in malignant thyroid nodule (TN) risk stratification was obtained for the combined model of S-Detect ("possibly malignant" nodule) and simultaneously obtaining 4 or 5 points (MODEL 1) or exactly 5 points (MODEL 5) on the EU-TIRADS scale.

The assistant diagnostic value of S‐Detect technique in identification of breast cancer / 中华超声影像学杂志

Objective To evaluate the assistant diagnostic value of S‐Detect artificial intelligence system in differential diagnosis of benign and malignant breast tumors . Methods Clinical data and ultrasound images of 201 patients undergoing breast ultrasound examination in Tongji Hospital from M arch 2018 to M ay 2018 were acquired . Two‐dimensional grayscale and color Doppler ultrasound images ,S‐Detect mode images and elastographic images of 220 breast lesions were analyzed . T he BI‐RADS categories of each lesion were divided into two groups :experienced group and random group .And according to w hether to refer to S‐Detect diagnostic results ,the BI‐RADS categories in experienced group were divided into A 1 group and P1 group .In additional ,the highest and lowest categories of the same tumor in random group were A 2 group ,and the diagnostic results of A 2 group combining with S‐Detect system were belonged to P2 group . T he ROC curves were plotted and the area under the curve ,sensitivity ,specificity or the accuracy of the different groups were compared . Agreements of diagnostic results between different groups were analyzed by Kappa test . Results Out of 220 breast lesions ,181 lesions were benign and 39 lesions were malignant . The S‐Detect artificial intelligence system had a relatively high diagnostic efficiency ,and the sensitivity , specificity and accuracy of S‐Detect classification were 92 .3% ,90 .6% ,90 .9% , respectively . With its assistance ,the specificity and accuracy in the experienced group had an increasing trend ( A 1 group :86 .7% , 88 .6% ; P1 group :91 .2% ,92 .3% ) ,and the diagnostic accuracy in random group was significantly improved ( A2 group :63 .6% －85 .5% ; P2 group :93 .2% －94 .1% ) . Both S‐Detect system and elasticity score helped to improve the efficacy of ultrasound physicians in differential diagnosis of benign and malignant breast lesions . But there were differences in diagnostic performance and assistant diagnostic ability between the two techniques . Conclusions S‐Detect technique contributes to the augment of diagnostic accuracy of ultrasound doctors in identifying breast cancer , improves the quality of random breast ultrasound examinations ,and reduces missed diagnosis and misdiagnosis of breast examinations .

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

PURPOSE: To assess the diagnostic performance and the potential as a teaching tool of S-detect in the assessment of focal breast lesions. METHODS: 61 patients (age 21-84 years) with benign breast lesions in follow-up or candidate to pathological sampling or with suspicious lesions candidate to biopsy were enrolled. The study was based on a prospective and on a retrospective phase. In the prospective phase, after completion of baseline US by an experienced breast radiologist and S-detect assessment, 5 operators with different experience and dedication to breast radiology performed elastographic exams. In the retrospective phase, the 5 operators performed a retrospective assessment and categorized lesions with BI-RADS 2013 lexicon. Integration of S-detect to in-training operators evaluations was performed by giving priority to S-detect analysis in case of disagreement. 2 × 2 contingency tables and ROC analysis were used to assess the diagnostic performances; inter-rater agreement was measured with Cohen's k; Bonferroni's test was used to compare performances. A significance threshold of p = 0.05 was adopted. RESULTS: All operators showed sensitivity > 90% and varying specificity (50-75%); S-detect showed sensitivity > 90 and 70.8% specificity, with inter-rater agreement ranging from moderate to good. Lower specificities were improved by the addition of S-detect. The addition of elastography did not lead to any improvement of the diagnostic performance. CONCLUSIONS: 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.

The primary application of S-Detect technique in the diagnosis of thyroid tumors / 中华超声影像学杂志

Objective To evaluate the value of S-Detect technology of ultrasonography in the diagnosis of thyroid tumors. Methods Ninety-three thyroid tumors in 93 patients were enrolled in the group.A varied image features of the thyroid masses in gray-scale ultrasonography were analyzed by S-Detect technology and experienced doctor separately.The results were compared and the diagnostic ability were also compared between the two methods. Results There were 44 malignant tumors and 49 benign tumors in these thyroid nodules.The sensitivity of S-Detect technology in the diagnosis of thyroid malignant tumors was higher,up to 88.7%. In the five image features of the thyroid tumors in gray-scale ultrasonography,the result of composition of the mass obtained by S-Detect was the most consistent with that of the doctors and Kappa value was 0.89.Conclusions S-Detect is a kind of computer-aided diagnosis system,which is suitable for the ultrasound beginners in the diagnosis of thyroid tumors.

Application of S-Detect classification system in diagnosis of breast benign and malignant mass by ultrasound / 中华超声影像学杂志

Objective To investigate the value of S-Detect classification in differential diagnosis of breast mass . Methods The data of forty-seven patients with breast mass lesions ( n=61) from our hospital during January to December in 2016 were retrospectively analyzed . Both the man-made BI-RADS classification ( identified by three different specialist physicians with 2 ,5 and 7 years of experience , respectively) and computer S-Detect classification were performed . The sensitivity ,specificity ,accuracy , positive predictive value and negative predictive value of the man-made BI-RADS classification and S-Detect classification of the benign or malignant diagnosis of breast lumps were calculated . The ROC curve was further plotted ,and the area under the curve ( AUC) of each group was compared ,respectively . Results Sixty-one breast mass lesions were confirmed 36 benign lesions and 25 malignant lesions by pathological biopsy . The sensitivity ,specificity and accuracy of man-made BI-RADS classification were as follows:2-year experience physicians 69 .4％ ,72 .0％ and 70 .5％ ;5-year experience physicians:64 .0％ ,92 .0％ and 75 .4％ ;7-year experience physicians:69 .4％ , 92 .0％ and 78 .7％ . The diagnostic sensitivity , specificity , and accuracy of S-Detect classification were 80 .6％ ,96 .0％ and 86 .9％ . The specificity ,accuracy and positive predictive value of S-Detect classification were significantly higher than those of 2-year experience physicians by BI-RADS classification ( P <0 .05) . The area under the ROC curve of each group was 0 .729 ,0 .786 and 0 .801 for 2 , 5 and 7-year experience physicians , respectively , and 0 .917 for S-Detect classification . Conclusions Compared with the man-made BI-RADS classification ,S-Detect classification has advantages in diagnosis of the benign or malignant of breast mass and is helpful to improve the accuracy of diagnosis , especially for junior physicians .