Accuracy of ultrasound-guided fine-needle aspiration for small cervical lymph nodes: A retrospective review of 505 cases.

Purpose: The overall diagnostic value of fine-needle aspiration (FNA) is not as excellent as that of core needle biopsy (CNB). Limited research has investigated small cervical lymph nodes inaccessible to ultrasound-guided CNB due to technical challenges associated with their small size. Therefore, this study aimed to evaluate the accuracy of ultrasound-guided FNA in determining the etiology of small cervical lymph nodes. Methods: A retrospective analysis was conducted on patients who underwent FNA between May 2018 and May 2021 at our hospital. Cytological, histopathological, and clinical follow-up data were analyzed. The diagnostic yield of FNA was assessed based on sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy calculations. Results: This study included 505 patients, each with a small cervical lymph node under evaluation (total number of lymph nodes: 505). The average maximal diameter of the lymph nodes was 14.6 ± 6.2 mm. According to the Sydney system, the cytology results were as follows: Category I in 26 lymph nodes (5.1 %); Category II in 269 (53.3 %); Category III in 35 (6.9 %); Category IV in 17 (3.4 %); and Category V in 158 (31.3 %). We identified 212 malignant cases (203 metastases and 9 lymphomas) and 293 benign lymph nodes. FNA achieved high sensitivity (88.8 %), specificity (99.6 %), PPV (99.4 %), NPV (91.8 %), and overall accuracy (94.8 %) in determining the etiology of small cervical lymph nodes. Conclusion: FNA cytology is suitable for small lesions inaccessible by CNB and provides a diagnostic basis for implementing clinically appropriate treatment measures.

Longitudinal ultrasound-based AI model predicts axillary lymph node response to neoadjuvant chemotherapy in breast cancer: a multicenter study.

OBJECTIVES: Developing a deep learning radiomics model from longitudinal breast ultrasound and sonographer's axillary ultrasound diagnosis for predicting axillary lymph node (ALN) response to neoadjuvant chemotherapy (NAC) in breast cancer. METHODS: Breast cancer patients undergoing NAC followed by surgery were recruited from three centers between November 2016 and December 2022. We collected ultrasound images for extracting tumor-derived radiomics and deep learning features, selecting quantitative features through various methods. Two machine learning models based on random forest were developed using pre-NAC and post-NAC features. A support vector machine integrated these data into a fusion model, evaluated via the area under the curve (AUC), decision curve analysis, and calibration curves. We compared the fusion model's performance against sonographer's diagnosis from pre-NAC and post-NAC axillary ultrasonography, referencing histological outcomes from sentinel lymph node biopsy or axillary lymph node dissection. RESULTS: In the validation cohort, the fusion model outperformed both pre-NAC (AUC: 0.899 vs. 0.786, p < 0.001) and post-NAC models (AUC: 0.899 vs. 0.853, p = 0.014), as well as the sonographer's diagnosis of ALN status on pre-NAC and post-NAC axillary ultrasonography (AUC: 0.899 vs. 0.719, p < 0.001). Decision curve analysis revealed patient benefits from the fusion model across threshold probabilities from 0.02 to 0.98. The model also enhanced sonographer's diagnostic ability, increasing accuracy from 71.9% to 79.2%. CONCLUSION: The deep learning radiomics model accurately predicted the ALN response to NAC in breast cancer. Furthermore, the model will assist sonographers to improve their diagnostic ability on ALN status before surgery. CLINICAL RELEVANCE STATEMENT: Our AI model based on pre- and post-neoadjuvant chemotherapy ultrasound can accurately predict axillary lymph node metastasis and assist sonographer's axillary diagnosis. KEY POINTS: Axillary lymph node metastasis status affects the choice of surgical treatment, and currently relies on subjective ultrasound. Our AI model outperformed sonographer's visual diagnosis on axillary ultrasound. Our deep learning radiomics model can improve sonographers' diagnosis and might assist in surgical decision-making.

Regulation of CTLs/Tregs via Highly Stable and Ultrasound-Responsive Cerasomal Nano-Modulators for Enhanced Colorectal Cancer Immunotherapy.

Immunotherapy is showing good potential for colorectal cancer therapy, however, low responsive rates and severe immune-related drug side effects still hamper its therapeutic effectiveness. Herein, a highly stable cerasomal nano-modulator (DMC@P-Cs) with ultrasound (US)-controlled drug delivery capability for selective sonodynamic-immunotherapy is fabricated. DMC@P-Cs' lipid bilayer is self-assembled from cerasome-forming lipid (CFL), pyrophaeophorbid conjugated lipid (PL), and phospholipids containing unsaturated chemical bonds (DOPC), resulting in US-responsive lipid shell. Demethylcantharidin (DMC) as an immunotherapy adjuvant is loaded in the hydrophilic core of DMC@P-Cs. With US irradiation, reactive oxygen species (ROS) can be effectively generated from DMC@P-Cs, which can not only kill tumor cells for inducing immunogenic cell death (ICD), but also oxidize unsaturated phospholipids-DOPC to change the permeability of the lipid bilayers and facilitate controlled release of DMC, thus resulting in down-regulation of regulatory T cells (Tregs) and amplification of anti-tumor immune responses. After intravenous injection, DMC@P-Cs can efficiently accumulate at the tumor site, and local US treatment resulted in 94.73% tumor inhibition rate. In addition, there is no detectable systemic toxicity. Therefore, this study provides a highly stable and US-controllable smart delivery system to achieve synergistical sonodynamic-immunotherapy for enhanced colorectal cancer therapy.

Predicting three or more metastatic nodes using contrast-enhanced lymphatic US findings in early breast cancer.

OBJECTIVES: To develop and validate a nomogram for predicting ≥ 3 metastatic axillary lymph nodes (ALNs) in early breast cancer with no palpable axillary adenopathy by clinicopathologic data, contrast-enhanced (CE) lymphatic ultrasound (US), and grayscale findings of sentinel lymph nodes (SLNs). MATERIALS AND METHODS: Women with T1-2N0 invasive breast cancer were consecutively recruited for the CE lymphatic US. Patients from Center 1 were grouped into development and internal validation cohorts at a ratio of 2:1. The external validation cohort was constructed from Center 2. The clinicopathologic data and US findings of SLNs were analyzed. A nomogram was developed to predict women with ≥ 3 metastatic ALNs. Nomogram performance was assessed with the area under the receiver operating characteristic curve (AUC) and calibration curve analysis. RESULTS: One hundred seventy-nine from Center 1 were considered the development cohorts. The remaining 90 participants from Center 1 were internal cohorts and 197 participants from Center 2 were external validation cohorts. The US findings of no enhancement (odds ratio (OR), 15.3; p = 0.01), diffuse (OR, 19.1; p = 0.01) or focal eccentric (OR, 27.7; p = 0.003) cortical thickening, and absent hilum (OR, 169.7; p < 0.001) were independently associated with ≥ 3 metastatic ALNs. Compared to grayscale US or CE lymphatic US alone, the nomogram showed the highest AUC of 0.88 (0.85, 0.91). The nomogram showed a calibration slope of 1.0 (p = 0.80-0.81; Brier = 0.066-0.067) in validation cohorts in predicting ≥ 3 metastatic ALNs. CONCLUSION: Patients likely to have ≥ 3 metastatic ALNs were identified by combining the lymphatic and grayscale US findings of SLNs. Our nomogram could aid in multidisciplinary treatment decision-making. TRIAL REGISTRATION: This trial is registered on www.chictr.org.cn : ChiCTR2000031231. Registered March 25, 2020. CRITICAL RELEVANCE STATEMENT: A nomogram combining lymphatic CEUS and grayscale US findings of SLNs could identify early breast cancer patients with low or high axillary tumor burden preoperatively, which is more applicable to the Z0011 era. Our nomogram could be useful in aiding multidisciplinary treatment decision-making for patients with early breast cancer. KEY POINTS: • CEUS can help identify and diagnose SLN in early breast cancer preoperatively. • Combining lymphatic and grayscale US findings can predict axillary tumor burden. • The nomogram showed a high diagnostic value in validation cohorts.

Development of a Novel Contrast-Enhanced Ultrasound-Based Nomogram for Superficial Lymphadenopathy Differentiation: Postvascular Phase Value.

OBJECTIVE: The aim of this study was to develop and prospectively validate a prediction model for superficial lymphadenopathy differentiation using Sonazoid contrast-enhanced ultrasound (CEUS) combined with ultrasound (US) and clinical data. METHODS: The training cohort comprised 260 retrospectively enrolled patients with 260 pathological lymph nodes imaged between January and December 2020. Two clinical US-CEUS models were created using multivariable logistic regression analysis and compared using receiver operating characteristic curve analysis: Model 1 included clinical and US characteristics; Model 2 included all confirmed predictors, including CEUS characteristics. Feature contributions were evaluated using the SHapley Additive exPlanations (SHAP) algorithm. Data from 172 patients were prospectively collected between January and May 2021 for model validation. RESULTS: Age, tumor history, long-axis diameter of lymph node, blood flow distribution, echogenic hilus, and the mean postvascular phase intensity (MPI) were identified as independent predictors for malignant lymphadenopathy. The area under the curve (AUC), sensitivity, specificity, and accuracy of MPI alone was 0.858 (95% confidence interval [CI], 0.817-0.891), 86.47%, 74.55%, and 81.2%, respectively. Model 2 had an AUC of 0.919 (95% CI, 0.879-0.949) and good calibration in training and validation cohorts. The incorporation of MPI significantly enhanced diagnostic capability (p < 0.0001 and p = 0.002 for training and validation cohorts, respectively). Decision curve analysis indicated Model 2 as the superior diagnostic tool. SHAP analysis highlighted MPI as the most pivotal feature in the diagnostic process. CONCLUSION: The employment of our straightforward prediction model has the potential to enhance clinical decision-making and mitigate the need for unwarranted biopsies.

Application of computer-aided diagnosis to predict malignancy in BI-RADS 3 breast lesions.

Purpose: To evaluate the ability of computer-aided diagnosis (CAD) system (S-Detect) to identify malignancy in ultrasound (US) -detected BI-RADS 3 breast lesions. Materials and methods: 148 patients with 148 breast lesions categorized as BI-RADS 3 were included in the study between January 2021 and September 2022. The malignancy rate, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC) were calculated. Results: In this study, 143 breast lesions were found to be benign, and 5 breast lesions were malignant (malignancy rate, 3.4 %, 95 % confidence interval (CI): 0.5-6.3). The malignancy rate rose significantly to 18.2 % (4/22, 95 % CI: 2.1-34.3) in the high-risk group with a "possibly malignant" CAD result (p = 0.017). With a "possibly benign" CAD result, the malignancy rate decreased to 0.8 % (1/126, 95 % CI: 0-2.2) in the low-risk group (p = 0.297). The AUC, sensitivity, specificity, accuracy, PPV, and NPV of the CAD system in BI-RADS 3 breast lesions were 0.837 (95 % CI: 77.7-89.6), 80.0 % (95 % CI: 73.6-86.4), 87.4 % (95 % CI: 82.0-92.7), 87.2 % (95 % CI: 81.8-92.6), 18.2 % (95 % CI: 2.1-34.3) and 99.2 % (95 % CI: 97.8-100.0), respectively. Conclusions: CAD system (S-Detect) enables radiologists to distinguish a high-risk group and a low-risk group among US-detected BI-RADS 3 breast lesions, so that patients in the low-risk group can receive follow-up without anxiety, while those in the high-risk group with a significantly increased malignancy rate should actively receive biopsy to avoid delayed diagnosis of breast cancer.

Nanomaterials augmented bioeffects of ultrasound in cancer immunotherapy.

Immunotherapy as a milestone in cancer treatment has made great strides in the past decade, but it is still limited by low immune response rates and immune-related adverse events. Utilizing bioeffects of ultrasound to enhance tumor immunotherapy has attracted more and more attention, including sonothermal, sonomechanical, sonodynamic and sonopiezoelectric immunotherapy. Moreover, the emergence of nanomaterials has further improved the efficacy of ultrasound mediated immunotherapy. However, most of the summaries in this field are about a single aspect of the biological effects of ultrasound, which is not comprehensive and complete currently. This review proposes the recent progress of nanomaterials augmented bioeffects of ultrasound in cancer immunotherapy. The concept of immunotherapy and the application of bioeffects of ultrasound in cancer immunotherapy are initially introduced. Then, according to different bioeffects of ultrasound, the representative paradigms of nanomaterial augmented sono-immunotherapy are described, and their mechanisms are discussed. Finally, the challenges and application prospects of nanomaterial augmented ultrasound mediated cancer immunotherapy are discussed in depth, hoping to pave the way for cancer immunotherapy and promote the clinical translation of ultrasound mediated cancer immunotherapy through the reasonable combination of nanomaterials augmented ultrasonic bioeffects.

Sono-anatomy of the middle cervical sympathetic ganglion verified with pathology.

Objectives: Cervical discomfort and other symptoms may be attributable to the middle cervical sympathetic ganglion. The aim of this study was to explore the sonographic features of this ganglion in anatomical specimens and cadavers and evaluate the feasibility of its visualization using high-resolution ultrasonography. Methods: We examined three cervical sympathetic-ganglion specimens and two fresh cadavers using high-resolution ultrasound to explore the sonographic features of this ganglion. Basic imaging characteristics examined included the shape, echo intensity, and location of the ganglion. Core-needle biopsy was performed to examine the suspected middle cervical sympathetic ganglion in the two fresh cadavers and verify the accuracy of the sonographic identification via pathological examination. Results: The middle cervical sympathetic ganglion appeared on high-resolution ultrasonography as an oval-shaped hypoechoic structure, with at least one continuous hypoechoic line connected to each ending in the anatomical specimens and fresh cadavers, and it was distinctly different from the adjacent lymph nodes. Discussion: Based on an adequate understanding of both its location and sonographic features, the direct visualization of the middle cervical sympathetic ganglion using high-resolution ultrasonography is feasible.

Differential diagnosis of cervical lymphadenopathy: Integration of postvascular phase of contrast-enhanced ultrasound and predictive nomogram model.

BACKGROUND: Distinguishing benign from malignant cervical lymph nodes is critical yet challenging. This study evaluates the postvascular phase of contrast-enhanced ultrasound (CEUS) and develops a user-friendly nomogram integrating demographic, conventional ultrasound, and CEUS features for accurate differentiation. METHODS: We retrospectively analyzed 395 cervical lymph nodes from 395 patients between January 2020 and December 2022. The cohort was divided into training and validation sets using stratified random sampling. A predictive model, based on demographic, ultrasound, and CEUS features, was created and internally validated. RESULTS: The training set included 280 patients (130 benign, 150 malignant nodes) and the validation set 115 patients (46 benign, 69 malignant). Relative hypoenhancement in the postvascular phase emerged as a promising indicator for MLN, with sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 96.7 %,52.3 %, 70.0 %, 93.2 %, and 76.1 %, respectively in the training set and 95.7 %, 52.2 %, 75.0 %, 88.9 %, and 74.8 % in the validation set. Age over 50 years, history of malignancy, short-axis diameter greater than 1.00 cm, focal hyperechogenicity, ill-defined borders, and centripetal perfusion were also identified as independent MLN indicators. The nomogram prediction model showed outstanding accuracy, with an area under the curve (AUC) of 0.922 (95 % CI: 0.892-0.953) in the training set and 0.914 (95 % CI: 0.864-0.963) in the validation set. CONCLUSION: Relative hypoenhancement in the postvascular phase of CEUS, combined with demographics and ultrasound features, is effective for identifying MLNs. The developed prediction model, with a user-friendly nomogram, can facilitate clinical decision-making.

Chinese Ultrasound Doctors Association Guideline on Operational Standards for 2-D Shear Wave Elastography Examination of Musculoskeletal Tissues.

The Ultrasound Physician Branch of the Chinese Medical Doctor Association sought to develop evidence-based recommendations on the operational standards for 2-D shear wave elastography examination of musculoskeletal tissues. A consensus panel of 22 Chinese musculoskeletal ultrasound experts reviewed current scientific evidence and proposed a set of 12 recommendations for 13 key issues, including instruments, operating methods, influencing factors and image interpretation. A final consensus was reached through discussion and voting. On the basis of research evidence and expert opinions, the strength of recommendation for each proposition was assessed using a visual analog scale, while further emphasizing the best available evidence during the question-and-answer session. These expert consensus guidelines encourage facilitation of the standardization of clinical practices for collecting and reporting shear wave elastography data.

Development and Clinical Application of Artificial Intelligence Assistant System for Rotator Cuff Ultrasound Scanning.

OBJECTIVE: We developed an intelligent assistance system for shoulder ultrasound imaging, incorporating deep-learning algorithms to facilitate standard plane recognition and automatic tissue segmentation of the rotator cuff and its surrounding structures. We evaluated the system's performance using a dedicated data set of rotator cuff ultrasound images to assess its feasibility in clinical practice. METHODS: To fulfill the system's primary functions, we designed a standard plane recognition module based on the ResNet50 network and an automatic tissue segmentation module using the Mask R-CNN model. The modules were trained on carefully curated data sets. The standard plane recognition module automatically identifies a specific standard plane based on the ultrasound image characteristics. The automatic tissue segmentation module effectively delineates and segments anatomical structures within the identified standard plane. RESULTS: With the use of 59,265 shoulder joint ultrasound images, the standard plane recognition model achieved an impressive recognition accuracy of 94.9% in the test set, with an average precision rate of 96.4%, recall rate of 95.4% and F1 score of 95.9%. The automatic tissue segmentation model, tested on 1886 images, exhibited a commendable average intersection over union value of 96.2%, indicating robustness and accuracy. The model achieved mean intersection over union values exceeding 90.0% for all standard planes, indicating its effectiveness in precisely delineating the anatomical structures. CONCLUSION: Our shoulder joint musculoskeletal intelligence system swiftly and accurately identifies standard planes and performs automatic tissue segmentation.

Deep learning models of ultrasonography significantly improved the differential diagnosis performance for superficial soft-tissue masses: a retrospective multicenter study.

BACKGROUND: Most of superficial soft-tissue masses are benign tumors, and very few are malignant tumors. However, persistent growth, of both benign and malignant tumors, can be painful and even life-threatening. It is necessary to improve the differential diagnosis performance for superficial soft-tissue masses by using deep learning models. This study aimed to propose a new ultrasonic deep learning model (DLM) system for the differential diagnosis of superficial soft-tissue masses. METHODS: Between January 2015 and December 2022, data for 1615 patients with superficial soft-tissue masses were retrospectively collected. Two experienced radiologists (radiologists 1 and 2 with 8 and 30 years' experience, respectively) analyzed the ultrasound images of each superficial soft-tissue mass and made a diagnosis of malignant mass or one of the five most common benign masses. After referring to the DLM results, they re-evaluated the diagnoses. The diagnostic performance and concerns of the radiologists were analyzed before and after referring to the results of the DLM results. RESULTS: In the validation cohort, DLM-1 was trained to distinguish between benign and malignant masses, with an AUC of 0.992 (95% CI: 0.980, 1.0) and an ACC of 0.987 (95% CI: 0.968, 1.0). DLM-2 was trained to classify the five most common benign masses (lipomyoma, hemangioma, neurinoma, epidermal cyst, and calcifying epithelioma) with AUCs of 0.986, 0.993, 0.944, 0.973, and 0.903, respectively. In addition, under the condition of the DLM-assisted diagnosis, the radiologists greatly improved their accuracy of differential diagnosis between benign and malignant tumors. CONCLUSIONS: The proposed DLM system has high clinical application value in the differential diagnosis of superficial soft-tissue masses.

Clinical Application of Computer-Aided Diagnosis System in Breast Ultrasound: A Prospective Multicenter Study.

OBJECTIVES: Ultrasound tends to present very high sensitivity but relatively low specificity and positive predictive value (PPV), which would result in unnecessary breast biopsies. The purpose of this study is to analyze the diagnostic performance of computer-aided diagnosis (CAD) (S-Detect) system in differentiating breast lesions and reducing unnecessary biopsies in non-university hospitals in less-developed regions of China. METHODS: The study was a prospective multicenter study from 8 hospitals. The ultrasound images, and cine, CAD analysis, and BI-RADS were recorded. The accuracy, sensitivity, specificity, PPV, negative predictive value (NPV), and area under the curve (AUC) were analyzed and compared between CAD and radiologists. The Youden Index (YI) was used to determine optimal cut-off for the number of planes to downgrade. RESULTS: A total of 491 breast lesions were included in the study. Less-experienced radiologists combined CAD was superior to less-experienced radiologists alone in AUC (0.878 vs 0.712, p < 0.001), and specificity (81.3% vs 44.6%, p < 0.001). There was no statistical difference in AUC (0.891 vs 0.878, p = 0.346), and specificity (82.3% vs 81.3%, p = 0.791) between experienced radiologists and less-experienced radiologists combined CAD. With CAD assistance, the biopsy rate of less-experienced radiologists was significantly decreased (100.0% vs 25.6%, p < 0.001), and malignant rate of biopsy was significantly increased (15.0% vs 43.9%, p < 0.001). CONCLUSIONS: CAD system can be an effective auxiliary tool in differentiating breast lesions and reducing unnecessary biopsies for radiologists from non-university hospitals in less-developed regions of China.

The value of shear wave elastography in diagnosis and assessment of systemic sclerosis.

Objective: The aim was to determine the efficacy of shear wave elastography (SWE) in assessing skin stiffness and aiding in the diagnosis of patients with systemic sclerosis (SSc). Methods: A total of 66 patients with SSc, 100 healthy individuals and 27 patients with SSc-like disorders were included. SWE was performed at 17 modified Rodnan skin score (mRSS) measurement sites. The correlation between SWE and clinical profiles was assessed, and the diagnostic value of SSc was explored. Results: The SWE values at all 17 mRSS sites were significantly higher in SSc than in the healthy group [54.95 (45.95, 66.55) vs 41.10 (39.18, 45.45) m/s, P < 0.001]. For clinically uninvolved sites (mRSS = 0) of patients with SSc, 11 of 17 sites showed significantly higher SWE values compared with healthy controls. SWE was positively correlated with total mRSS (r = 0.783, P < 0.001), the European Scleroderma Study Group disease activity index (r = 0.707, P < 0.001) and histological collagen deposition (r = 0.749, P = 0.013). SWE effectively distinguished patients with SSc from patients with SSc-like disorders (area under the curve, AUC = 0.819). Use of SWE-detected skin sclerosis showed a significantly higher sensitivity compared with 1980 ACR criteria [0.818 (95% CI 0.709, 0.893) vs 0.727 (95% CI 0.610, 0.820), P = 0.031]. Conclusion: SWE correlates well with disease activity and collagen deposition in the skin, provides greater reliability than mRSS and aids in the diagnosis of SSc. SWE could be considered as a convenient and reliable quantitative tool for assessing skin sclerosis and disease progression in SSc.

The performance of the 2022 ACR/EULAR classification criteria for Takayasu's arteritis as compared to the 1990 ACR classification criteria in a Chinese population.

In this study, we studied the performance of the 2022 American College of Rheumatology (ACR)/ European Alliance of Associations for Rheumatology (EULAR) classification criteria for Takayasu's arteritis (TAK) as compared to the 1990 ACR classification criteria in a Chinese population. The sensitivity, specificity, positive predictive value, negative predictive value, accuracy and the area under the receiver operating characteristics curve (AUC) of the above two criteria were compared. The sensitivity (92.6%), positive predictive value (95.6%), negative predictive value (94.6%), accuracy (95.0%) and AUC (0.981) of the 2022 criteria were superior to those of the 1990 criteria (45.7%, 91.5%, 70.5%, 75.0% and 0.874, respectively), and the difference of AUC was statistically significant (Z = 5.362, P < 0.001). In addition, we included new imaging modalities in the 1990 criteria, whose sensitivity, positive predictive value, negative predictive value, accuracy and AUC were significantly improved, but still lower than those of the 2022 criteria, the difference in AUC was also statistically significant (Z = 2.023, P = 0.043). The 2022 criteria for TAK exhibited superior performance compared with the 1990 criteria and may be more appropriate for the Chinese population. Incorporating additional imaging modalities could enhance the classification performance of the 1990 criteria even further.

Evaluation of facial artery course variations, diameters, and depth by Doppler ultrasonography.

BACKGROUND: The facial artery (FA) is the main blood vessel supplying blood to the face. It is essential to understand the anatomy of FA around the nasolabial fold (NLF). This study aimed to provide the detailed anatomy and relative positioning of FA to help avoid unexpected complications in plastic surgery. METHODS: FA was observed from the inferior border of the mandible to the end of its terminal branch in 66 hemifaces of 33 patients with Doppler ultrasonography. The evaluation parameters were: (1) location, (2) diameter, (3) FA-skin depth, (4) relationship between the NLF and FA, (5) distance between the FA and significant surgical landmarks, and (6) the running layer. The FA course is classified based on the terminal branch. RESULTS: The most common FA course was Type 1, which had an angular branch as the final branch (59.1%). The most common FA-NLF relationship was that the FA was situated inferior to the NLF (50.0%). The mean FA diameter was 1.56 ± 0.36 mm at the mandibular origin, 1.40 ± 0.37 mm at the cheilion, and 1.32 ± 0.34 mm at the nasal ala. The FA diameter on the right hemiface was thicker than that on the left hemiface (p < 0.05). CONCLUSION: The FA mainly terminates in the angular branch, running in the medial NLF and in dermis and subcutaneous tissue, with a blood supply advantage in the right hemisphere. We suppose that a deep injection into periosteum around the NLF may be safer than an injection into the superficial musculoaponeurotic system (SMAS) layer.

Deep Learning-Based Computer-Aided Diagnosis for Breast Lesion Classification on Ultrasound: A Prospective Multicenter Study of Radiologists Without Breast Ultrasound Expertise.

BACKGROUND. Computer-aided diagnosis (CAD) systems for breast ultrasound interpretation have been primarily evaluated at tertiary and/or urban medical centers by radiologists with breast ultrasound expertise. OBJECTIVE. The purpose of this study was to evaluate the usefulness of deep learning-based CAD software on the diagnostic performance of radiologists without breast ultrasound expertise at secondary or rural hospitals in the differentiation of benign and malignant breast lesions measuring up to 2.0 cm on ultrasound. METHODS. This prospective study included patients scheduled to undergo biopsy or surgical resection at any of eight participating secondary or rural hospitals in China of a breast lesion classified as BI-RADS category 3-5 on prior breast ultrasound from November 2021 to September 2022. Patients underwent an additional investigational breast ultrasound, performed and interpreted by a radiologist without breast ultrasound expertise (hybrid body/breast radiologists, either who lacked breast imaging subspecialty training or for whom the number of breast ultrasounds performed annually accounted for less than 10% of all ultrasounds performed annually by the radiologist), who assigned a BI-RADS category. CAD results were used to upgrade reader-assigned BI-RADS category 3 lesions to category 4A and to downgrade reader-assigned BI-RADS category 4A lesions to category 3. Histologic results of biopsy or resection served as the reference standard. RESULTS. The study included 313 patients (mean age, 47.0 ± 14.0 years) with 313 breast lesions (102 malignant, 211 benign). Of BI-RADS category 3 lesions, 6.0% (6/100) were upgraded by CAD to category 4A, of which 16.7% (1/6) were malignant. Of category 4A lesions, 79.1% (87/110) were downgraded by CAD to category 3, of which 4.6% (4/87) were malignant. Diagnostic performance was significantly better after application of CAD, in comparison with before application of CAD, in terms of accuracy (86.6% vs 62.6%, p < .001), specificity (82.9% vs 46.0%, p < .001), and PPV (72.7% vs 46.5%, p < .001) but not significantly different in terms of sensitivity (94.1% vs 97.1%, p = .38) or NPV (96.7% vs 97.0%, p > .99). CONCLUSION. CAD significantly improved radiologists' diagnostic performance, showing particular potential to reduce the frequency of benign breast biopsies. CLINICAL IMPACT. The findings indicate the ability of CAD to improve patient care in settings with incomplete access to breast imaging expertise.

Head-to-head comparison of perfluorobutane contrast-enhanced US and multiparametric MRI for breast cancer: a prospective, multicenter study.

BACKGROUND: Multiparametric magnetic resonance imaging (MP-MRI) has high sensitivity for diagnosing breast cancers but cannot always be used as a routine diagnostic tool. The present study aimed to evaluate whether the diagnostic performance of perfluorobutane (PFB) contrast-enhanced ultrasound (CEUS) is similar to that of MP-MRI in breast cancer and whether combining the two methods would enhance diagnostic efficiency. PATIENTS AND METHODS: This was a head-to-head, prospective, multicenter study. Patients with breast lesions diagnosed by US as Breast Imaging Reporting and Data System (BI-RADS) categories 3, 4, and 5 underwent both PFB-CEUS and MP-MRI scans. On-site operators and three reviewers categorized the BI-RADS of all lesions on two images. Logistic-bootstrap 1000-sample analysis and cross-validation were used to construct PFB-CEUS, MP-MRI, and hybrid (PFB-CEUS + MP-MRI) models to distinguish breast lesions. RESULTS: In total, 179 women with 186 breast lesions were evaluated from 17 centers in China. The area under the receiver operating characteristic curve (AUC) for the PFB-CEUS model to diagnose breast cancer (0.89; 95% confidence interval [CI] 0.74, 0.97) was similar to that of the MP-MRI model (0.89; 95% CI 0.73, 0.97) (P = 0.85). The AUC of the hybrid model (0.92, 95% CI 0.77, 0.98) did not show a statistical advantage over the PFB-CEUS and MP-MRI models (P = 0.29 and 0.40, respectively). However, 90.3% false-positive and 66.7% false-negative results of PFB-CEUS radiologists and 90.5% false-positive and 42.8% false-negative results of MP-MRI radiologists could be corrected by the hybrid model. Three dynamic nomograms of PFB-CEUS, MP-MRI and hybrid models to diagnose breast cancer are freely available online. CONCLUSIONS: PFB-CEUS can be used in the differential diagnosis of breast cancer with comparable performance to MP-MRI and with less time consumption. Using PFB-CEUS and MP-MRI as joint diagnostics could further strengthen the diagnostic ability. Trial registration Clinicaltrials.gov; NCT04657328. Registered 26 September 2020. IRB number 2020-300 was approved in Chinese PLA General Hospital. Every patient signed a written informed consent form in each center.

High-Resolution Ultrasound Imaging of Axillary Nerve and Relevant Injury.

OBJECTIVE: To evaluate the feasibility of axillary nerve (AN) visualization in healthy volunteers and the diagnostic value of AN injury via high-resolution ultrasonography (HRUS). METHODS: AN was examined by HRUS on both sides of 48 healthy volunteers and oriented the transducer according to three anatomical landmarks: quadrilateral space, anterior to subscapular muscle, and posterior to axillary artery. The maximum short-axis diameter (SD) and cross-sectional area (CSA) of AN were measured at different levels, and AN visibility was graded by using a five-point scale. The patients suspected of having AN injury were assessed by HRUS, and the HRUS features of AN injury were observed. RESULTS: AN can be visualized on both sides in all volunteers. There was no significant difference in SD and CSA of AN at the three levels between the left and right sides or in SD between males and females. However, the CSA of males at different levels was slightly larger than those of females (P < .05). In most volunteers, AN visibility at different levels was excellent or good, and AN was best displayed anterior to subscapular muscle. Rank correlation analysis revealed that the degree of AN visibility had correlation with height, weight, and BMI. A total of 15 patients diagnosed with AN injury, 12 patients showed diffuse swelling or focal thickening in AN, and 3 patients showed AN discontinuity. CONCLUSION: HRUS is able to reliably visualize AN, and it could be considered as the first choice for diagnosing AN injury.

Ultrasound Assessment of Hip Subspine Bone Morphology Soft-tissue Correlates with Clinical Diagnosis of Impingement.

PURPOSE: To determine the ultrasound imaging manifestations associated with subspine impingement (SSI), including the osseous and soft-tissue injuries adjacent to anterior inferior iliac spine (AIIS) and to investigate the diagnostic value of ultrasound for SSI. METHODS: We retrospectively evaluated patients who attended the sports medicine department of our hospital and underwent arthroscopic treatment for femoroacetabular impingement (FAI) between September 2019 and October 2020, with preoperative hip joint ultrasound and computed tomography (CT) examination within 1 month before surgery. All of the FAI patients were divided into the SSI group and non-SSI group, according to the clinical and intraoperative findings. The preoperative ultrasound and CT findings were assessed. The sensitivity, specificity, and positive predictive value (PPV) of some indicators were calculated and compared. Multivariable logistic regression and receiver operating characteristic curve (ROC) were also used. RESULTS: A total of 71 hips were included, with a mean age of 35.4 ± 10.4 years, 56.3% were women. Of these, 40 hips had clinically confirmed SSI. The bone morphology type III, heterogeneous hypoecho in anterosuperior joint capsule and the direct head of rectus femoris (dRF) tendon adjacent to AIIS on the Standard Section of the dRF in ultrasound were associated with SSI. Among them, the heterogeneous hypoecho in the anterosuperior joint capsule had the best diagnostic value for the SSI (85.0% sensitivity, 58.1% specificity, AUC = 0.681). The AUC of the ultrasound composite indicators was 0.750. The AUC and PPV of CT low-lying AIIS for the SSI diagnosis was 0.733 and 71.7%, which could be improved when CT was combined with the ultrasound composite indicators with AUC = 0.831 and PPV = 85.7%. CONCLUSIONS: Bone morphology abnormalities and soft-tissue injuries adjacent to the AIIS through sonographic evaluation were associated with SSI. Ultrasound could be used as a feasible method to predict SSI. The diagnostic value for SSI could be improved when ultrasound is combined with CT. LEVEL OF EVIDENCE: Level IV, case series.