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.

ObjectiveTo improve the accuracy of clinical diagnosis by analyzing different contrast‐enhanced ultrasound (CEUS) imagines of specific subtypes of uterine leiomyomas.MethodsA total of 147 female patients received preoperative CEUS examination. The scanning plane of the biggest tumors for CEUS was found by common B‐mode ultrasonographic scanning on pelvic cavity, then 1.5 mL SonoVue were injected into the median cubital vein. According to the CEUS images, the lesion enhanced time, enhanced level and enhanced morphology were recorded. The time‐intensity curve was acquired and analyzed, meanwhile, the relevant parameters were calculated, including rise time (RT), peak intensity (PI), time to peak (TTP) and mean transit time (MTT).ResultsIn cellular uterine leiomyoma group, the percentage of high enhancement, early enhancement was higher, equal enhancement and synchronic enhancement were lower than those in the common uterine leiomyomas group. In hysteromyoma with hyaline degeneration group, the percentage of high enhancement, early enhancement was lower, while low enhancement and delayed enhancement were higher than those in the common uterine leiomyomas group. The ratio of PI in cellular uterine leiomyoma group was the highest, but the ratios of RT, TTP and MTT were the lowest of the three benign groups. The ratio of PI in hysteromyoma with hyaline degeneration group was the lowest, while the ratios of RT and TTP was the highest among the three benign groups.ConclusionDifferent pathological types of uterine leiomyomas have their own signal performance on CEUS. CEUS can be used to infer their pathological types and help differential diagnosis.

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.

Background: It is challenging to differentiate between phyllodes tumors (PTs) and fibroadenomas (FAs).Artificial intelligence (AI) can provide quantitative information regarding the morphology and textural features of lesions. This study attempted to use AI to evaluate the ultrasonic images of PTs and FAs and to explore the diagnostic performance of AI features in the differential diagnosis of PTs and FAs.Methods: A total of 40 PTs and 290 FAs <5 cm in maximum diameter found in female patients were retrospectively analyzed. All tumors were segmented by doctors, and the features of the lesions were collated, including circularity, height-to-width ratio, margin spicules, margin coarseness (MC), margin indistinctness, margin lobulation (ML), internal calcification, angle between the long axis of the lesion and skin, energy, grey entropy, and grey mean. The differences between PTs and FAs were analyzed, and the diagnostic performance of AI features in the differential diagnosis of PTs and FAs was evaluated.Results: Statistically significant differences (P<0.05) were found in the height-to-width ratio, ML, energy, and grey entropy between the PTs and FAs. Receiver operating characteristic (ROC) curve analysis of single features showed that the area under the curve [(AUC) 0.759] of grey entropy was the largest among the four features with statistically significant differences, and the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 0.925, 0.459, 0.978, and 0.190, respectively. When considering the combinations of the features, the combination of height-to-width ratio, margin indistinctness, ML, energy, grey entropy, and internal calcification was the most optimal of the combinations of features with an AUC of 0.868, and a sensitivity, specificity, PPV, and NPV of 0.734, 0.900, 0.982, and 0.316, respectively.Conclusions: Quantitative analysis of AI can identify subtle differences in the morphology and textural features between small PTs and FAs. Comprehensive consideration of multiple features is important for the differential diagnosis of PTs and FAs.

Background: To diagnose gastroparesis, it is necessary to assess gastric emptying accurately. This study aims to investigate the role of three-dimensional ultrasonography (3-D US) on the measurement of gastric volume to evaluate gastric accommodation in healthy patients.Methods: In this study, 21 volunteers, 46 patients with diabetic gastroparesis (DG), and 22 patients with postsurgical gastroparesis (PSG) underwent 3-D US after oral administration of 250 mL gastrointestinal contrast at 2, 30, 60, and 90 min. The volume of the contrast agent in the stomach was then calculated using the virtual organ computer-aided analysis (VOCAL) (Virtual Organ Computer-aided AnaLysis, General Electric Medical Systems, Kretztechnik, Zipf, Austria).Results: In the DG group, the gastric residue volumes at postprandial 60 and 90 min were significantly higher than those in the healthy group (P<0.05), and the areas under the receiver operating characteristic (ROC) curve of these parameters were 0.830 and 0.957, respectively. There were significant differences between the PSG and healthy groups at 60 and 90 min; however, the AUC of gastric residue at 90 min (0.955) was higher than the AUC at 60 min (0.697).Conclusions: Therefore, this study showed that the 3-D US is a powerful tool for assessing gastric emptying and provides a new strategy for diagnosing gastroparesis.