ObjectiveTo investigate the feasibility of a CAD system S-detect on a database from a single Chinese medical center.Materials and methodsAn experienced radiologist performed breast US examinations and made assessments of 266 consecutive breast lesions in 227 patients. S-detect classified the lesions automatically in a dichotomous form. An in-training resident who was blind to both the US diagnostic results and histological results reviewed the images afterward. The final histological results were considered as the diagnostic gold standard. The diagnostic performances and interrater agreements were analyzed.ResultsA total of 266 focal breast lesions (161 benign lesions and 105 malignant lesions) were assessed in this study. S-detect had a lower sensitivity (87.07%) and a higher specificity (72.27%) compared with the experienced radiologist (sensitivity 98.1% and specificity 65.43%). The sensitivity and specificity of S-detect were better than that of the resident (sensitivity 82.86% and specificity 68.94%). The AUC value of S-detect (0.807) showed no significant difference with the experienced radiologist (0.817) and was higher than that of the resident (0.758). S-detect had moderate agreement with the experienced radiologist.ConclusionIn this single-center study, a high level of diagnostic performance of S-detect on 266 breast lesions of Chinese women was observed. S-detect had almost equal diagnostic capacity with an experienced radiologist and performed better than a novice reader. S-detect was also distinguished for its high specificity. These results supported the feasibility of S-detect in aiding the diagnosis of breast lesions on an independent database.

ObjectiveThe aim of the study is to explore the potential value of S-Detect for residents-in-training, a computer-assisted diagnosis system based on deep learning (DL) algorithm.MethodsThe study was designed as a cross-sectional study. Routine breast ultrasound examinations were conducted by an experienced radiologist. The ultrasonic images of the lesions were retrospectively assessed by five residents-in-training according to the Breast Imaging Report and Data System (BI-RADS) lexicon, and a dichotomic classification of the lesions was provided by S-Detect. The diagnostic performances of S-Detect and the five residents were measured and compared using the pathological results as the gold standard. The category 4a lesions assessed by the residents were downgraded to possibly benign as classified by S-Detect. The diagnostic performance of the integrated results was compared with the original results of the residents.ParticipantsA total of 195 focal breast lesions were consecutively enrolled, including 82 malignant lesions and 113 benign lesions.ResultsS-Detect presented higher specificity (77.88%) and area under the curve (AUC) (0.82) than the residents (specificity: 19.47%–48.67%, AUC: 0.62–0.74). A total of 24, 31, 38, 32 and 42 identified as BI-RADS 4a lesions by residents 1, 2, 3, 4 and 5 were downgraded to possibly benign lesions by S-Detect, respectively. Among these downgraded lesions, 24, 28, 35, 30 and 40 lesions were proven to be pathologically benign, respectively. After combining the residents' results with the results of the software in category 4a lesions, the specificity and AUC of the five residents significantly improved (specificity: 46.02%–76.11%, AUC: 0.71–0.85, p<0.001). The intraclass correlation coefficient of the five residents also increased after integration (from 0.480 to 0.643).ConclusionsWith the help of the DL software, the specificity, overall diagnostic performance and interobserver agreement of the residents greatly improved. The software can be used as adjunctive tool for residents-in-training, downgrading 4a lesions to possibly benign and reducing unnecessary biopsies.

Objective:Contrast-enhanced ultrasound (CEUS) is a well-established imaging modality which has been put into clinical use in recent years with the development of second-generation contrast agent and imaging devices, and its applications in the assessment of inflammatory arthritis, such as rheumatoid arthritis, psoriatic arthritis, and ankylosing spondylitis, have provoked abundant discussion and researches among radiologists and rheumatologists. To summarize the achievements of clinical studies on CEUS in the application of arthritis, and to keep up with the latest progresses of the imaging technique, we reviewed the literature in recent years, hoping to establish the role of CEUS in joint diseases.Data Sources:PubMed and EMBASE.Study Selection:We searched the database with the conditions “contrast-enhanced ultrasound AND arthritis” with the time limitation of recent 10 years. Clinical studies applying CEUS in inflammatory arthritis and review articles about development of CEUS in joint diseases in English were selected.Results:As it is proved by most studies in recent years, by delineating microvasculature within the inflamed joints, CEUS can indicate early arthritis with high sensitivity and specificity. Moreover, the imaging of CEUS has been proved to be consistent with histopathological changes of inflammatory arthritis. Quantitative analysis of CEUS permits further evaluation of disease activity. CEUS also plays a significant role in the therapeutic monitoring of the disease, which has been backed up by a number of studies.Conclusions:CEUS may be a new choice for the rheumatologists to evaluate inflammatory arthritis, because of its low price, ability to provide dynamic pictures, and high sensitivity to angiogenesis. It can also be applied in disease classification and therapeutic monitoring. More studies about CEUS need to be done to set up the diagnostic standards.