Three-dimensional ultrasound fusion imaging in precise needle placement for thermal ablation of hepatocellular carcinoma.

PURPOSE: To investigate the value of three-dimensional ultrasound fusion imaging (3DUS FI) technique for guiding needle placement in hepatocellular carcinoma (HCC) thermal ablation. METHODS: A total of 57 patients with 60 HCCs with 3DUS FI-guided thermal ablation were retrospectively included in the study. 3DUS volume data of liver were acquired preoperatively by freehand scanning with the tumor and predetermined 5 mm ablative margin automatically segmented. Plan of needle placement was made through a predetermined simulated ablation zone to ensure a 5 mm ablative margin with the coverage rate toward tumor and ablative margin. With real-time ultrasound and 3DUS fusion imaging, ablation needles were placed according to the plan. After ablation, the ablative margin was immediately evaluated by contrast-enhanced ultrasound and 3DUS fusion imaging. The rate of adequate ablative margin, complete response (CR), local tumor progression (LTP), disease-free survival (DFS), and overall survival (OS) was evaluated. RESULTS: According to postoperative contrast-enhanced CT or MR imaging, the complete response rate was 100% (60/60), and 83% of tumors (30/36) achieved adequate ablative margin (>5 mm) three-dimensionally. During the follow-up period of 6.0-42.6 months, LTP occurred in 5 lesions, with 1- and 2-year LTP rates being 7.0% and 9.4%. The 1- and 2-year DFS rates were 76.1% and 65.6%, and 1- and 2-year OS rates were 98.1% and 94.0%. No major complications or ablation-related deaths were observed in any patients. CONCLUSIONS: Three-dimensional ultrasound fusion imaging technique may improve the needle placement of thermal ablation for HCC and reduce the rate of LTP.

3D fusion is superior to 2D point-to-point contrast-enhanced US to evaluate the ablative margin after RFA for hepatocellular carcinoma.

PURPOSE: To compare the efficiency of three-dimensional (3D) and two-dimensional (2D) contrast-enhanced ultrasound (CEUS)-derived techniques in evaluating the ablative margin (AM) after radiofrequency ablation (RFA) for hepatocellular carcinoma (HCC). METHODS: In total, 98 patients with 98 HCCs were enrolled. The 2D CEUS point-to-point imaging (2D CEUS-PI) was conducted by comparing the pre- and post-RFA 2D CEUS images manually, and the 3D CEUS fusion imaging (3D CEUS-FI) was conducted by fusing the pre- and post-RFA 3D CEUS images automatically. These two techniques were compared in distinguishing an adequate AM ≥ 5 mm. Risk factors for local tumor progression (LTP) after RFA were analyzed by the Kaplan-Meier method with log-rank test. RESULTS: The mean registration time of 3D CEUS-FI and 2D CEUS-PI was 5.0 and 9.3 min, respectively (p < 0.0001). The kappa coefficient was 0.680 for agreement between 2D CEUS-PI and 3D CEUS-FI in the evaluation of AM (p < 0.0001). Tumors with AM < 5 mm by 2D CEUS-PI were all identified as AM < 5 mm by 3D CEUS-FI. Nonetheless, 16 (26%) tumors identified as AM ≥ 5 mm by 2D CEUS-PI were re-classified as AM < 5 mm by 3D CEUS-FI. During a median follow-up time of 31.2 months (range, 3.2-66.0 months), LTP was identified in 8 tumors. The estimated 1-/2-/3-year cumulative incidence of LTP was 4.4%, 8.1%, and 10.3%, respectively. Higher estimated cumulative incidence of LTP was identified in tumors with AM < 5 mm by 2D CEUS-PI (at 3-year, 27.2% vs 0%; p < 0.001), and by 3D CEUS-FI (at 3-year, 20.7% vs 0%; p = 0.004). CONCLUSION: 3D CEUS-FI excelled in the evaluation of AM when compared with 2D CEUS-PI. With equivalent efficacy in the prediction of LTP, 3D CEUS-FI was superior to 2D CEUS-PI for its automatic and time-saving procedure. CLINICAL RELEVANCE STATEMENT: 3D CEUS fusion imaging may serve as an effective tool in evaluating ablative margin and predicting local tumor progression after RFA in HCC. KEY POINTS: • Both 2D and 3D CEUS-derived techniques could evaluate ablative margin (AM) after RFA for hepatocellular carcinoma. • 3D CEUS fusion imaging was more precise in the evaluation of AM compared to 2D CEUS point-to-point imaging, with advantages of its automatic and time-saving procedure. • An inadequate AM < 5 mm evaluated by CEUS-derived techniques was the only risk factor of LTP after RFA for hepatocellular carcinoma (p < 0.001 for 2D CEUS point-to-point imaging, and p = 0.004 for 3D CEUS fusion imaging).

Costunolide covalently targets and inhibits CaMKII phosphorylation to reduce ischemia-associated brain damage.

BACKGROUND: Chronic cerebral hypoperfusion (CCH) is a leading cause of disability and mortality worldwide. Restoring cerebral blood flow (CBF) through vasodilatation is particularly important in the treatment of CCH. Costunolide (Cos) is a natural sesquiterpenoid compound with vasodilatory effect, but its mechanism is unclear. PURPOSE: This study aimed to investigate the vasodilatory mechanism of Cos and provide a new therapeutic regimen for treating CCH. METHODS: The therapeutic effect of Cos on CCH was assessed in a rat model of permanent common carotid artery occlusion. The direct target protein for improving CBF was identified by drug affinity responsive target stability combined with quantitative differential proteomics analysis. The molecular mechanism of Cos acting on its target protein was analyzed by multidisciplinary approaches. The signalling involved was assessed using site-directed pharmacological intervention. RESULTS: Cos has a significant therapeutic effect on ischemic brain injury by restoring CBF. Multifunctional calcium/calmodulin-dependent protein kinase II (CaMKII) was identified as a direct target of the natural small molecule Cos with a therapeutic effect on CCH. Mechanistic studies revealed that the α,ß-unsaturated-γ-lactone ring of Cos covalently binds to the Cys116 residue of CaMKII. It then inhibits the phosphorylation of CaMKII and reduces the calcium concentration in vascular smooth muscle cells, thus playing a role in vasodilation and increasing CBF. Notably, this covalent binding between Cos and CaMKII can exert a long-term vasodilator activity. CONCLUSION: We reported for the first time that Cos reduced ischemia-associated brain damage by covalently binding to the Cys116 residue of CaMKII, inhibiting CaMKII phosphorylation, and exerting long-term vasodilatory activity. This study not only found a new covalent inhibitor against the phosphorylation of CaMKII but also suggested that pharmacologically targeting CaMKII is a promising therapeutic strategy for CCH.

Ultrasound-based deep learning in the establishment of a breast lesion risk stratification system: a multicenter study.

OBJECTIVES: To establish a breast lesion risk stratification system using ultrasound images to predict breast malignancy and assess Breast Imaging Reporting and Data System (BI-RADS) categories simultaneously. METHODS: This multicenter study prospectively collected a dataset of ultrasound images for 5012 patients at thirty-two hospitals from December 2018 to December 2020. A deep learning (DL) model was developed to conduct binary categorization (benign and malignant) and BI-RADS categories (2, 3, 4a, 4b, 4c, and 5) simultaneously. The training set of 4212 patients and the internal test set of 416 patients were from thirty hospitals. The remaining two hospitals with 384 patients were used as an external test set. Three experienced radiologists performed a reader study on 324 patients randomly selected from the test sets. We compared the performance of the DL model with that of three radiologists and the consensus of the three radiologists. RESULTS: In the external test set, the DL model achieved areas under the receiver operating characteristic curve (AUCs) of 0.980 and 0.945 for the binary categorization and six-way categorizations, respectively. In the reader study set, the DL BI-RADS categories achieved a similar AUC (0.901 vs. 0.933, p = 0.0632), sensitivity (90.98% vs. 95.90%, p = 0.1094), and accuracy (83.33% vs. 79.01%, p = 0.0541), but higher specificity (78.71% vs. 68.81%, p = 0.0012) than those of the consensus of the three radiologists. CONCLUSIONS: The DL model performed well in distinguishing benign from malignant breast lesions and yielded outcomes similar to experienced radiologists. This indicates the potential applicability of the DL model in clinical diagnosis. KEY POINTS: • The DL model can achieve binary categorization for benign and malignant breast lesions and six-way BI-RADS categorizations for categories 2, 3, 4a, 4b, 4c, and 5, simultaneously. • The DL model showed acceptable agreement with radiologists for the classification of breast lesions. • The DL model performed well in distinguishing benign from malignant breast lesions and had promise in helping reduce unnecessary biopsies of BI-RADS 4a lesions.

Influence factors on registration of three-dimensional contrast-enhanced ultrasound fusion imaging in evaluating the ablative margin - A phantom study.

BACKGROUND: Registration of three-dimensional contrast-enhanced ultrasound fusion imaging (3DCEUS-FI) is time-consuming to obtain high success rate. OBJECTIVE: To investigate the influence factors on registration success rate of 3DCEUS-FI. METHODS: Water tank phantoms were made to obtain mimicked pre- and post- radiofrequency ablation three-dimensional contrast-enhanced ultrasound (3DCEUS) and CT images. Orthogonal trials were designed according to factors including size, depth, enhancement level of mimicked tumor, diameter and number of mimicked adjacent vessels. Mimicked pre- and post-RFA 3DCEUS images of 72 trials were fused to assess ablative margin (AM) by two radiologists. With CT images as standard, 3DCEUS-FI accuracy was considered as the consistency of AM evaluation. The inter-observer agreement and the influence factors on registration success rates were analyzed. RESULTS: The intraclass correlation coefficient (ICC) for the consistency of AM evaluation between CT and 3DCEUS-FI in x-axis, y-axis or z-axis was 0.840∼0.948 (P < 0.001). The ICC for inter-observer agreement was 0.840∼0.948 (P < 0.001). The success rates of registration within mimicked vessels with diameter of 2 mm were significantly lower than those with diameter of 3 mm and 4 mm. CONCLUSIONS: The mimicked AM measured by 3DCEUS-FI had high accuracy and inter-observer agreement. Diameter of the mimicked adjacent vessels was significantly related to success rate of registration.

Deep learning based on ultrasound images assists breast lesion diagnosis in China: a multicenter diagnostic study.

BACKGROUND: Studies on deep learning (DL)-based models in breast ultrasound (US) remain at the early stage due to a lack of large datasets for training and independent test sets for verification. We aimed to develop a DL model for differentiating benign from malignant breast lesions on US using a large multicenter dataset and explore the model's ability to assist the radiologists. METHODS: A total of 14,043 US images from 5012 women were prospectively collected from 32 hospitals. To develop the DL model, the patients from 30 hospitals were randomly divided into a training cohort (n = 4149) and an internal test cohort (n = 466). The remaining 2 hospitals (n = 397) were used as the external test cohorts (ETC). We compared the model with the prospective Breast Imaging Reporting and Data System assessment and five radiologists. We also explored the model's ability to assist the radiologists using two different methods. RESULTS: The model demonstrated excellent diagnostic performance with the ETC, with a high area under the receiver operating characteristic curve (AUC, 0.913), sensitivity (88.84%), specificity (83.77%), and accuracy (86.40%). In the comparison set, the AUC was similar to that of the expert (p = 0.5629) and one experienced radiologist (p = 0.2112) and significantly higher than that of three inexperienced radiologists (p < 0.01). After model assistance, the accuracies and specificities of the radiologists were substantially improved without loss in sensitivities. CONCLUSIONS: The DL model yielded satisfactory predictions in distinguishing benign from malignant breast lesions. The model showed the potential value in improving the diagnosis of breast lesions by radiologists.

Development of a Fully Automated Graf Standard Plane and Angle Evaluation Method for Infant Hip Ultrasound Scans.

BACKGROUND: Graf's method is currently the most commonly used ultrasound-based technique for the diagnosis of developmental dysplasia of the hip (DDH). However, the efficiency and accuracy of diagnosis are highly affected by the sonographers' qualification and the time and effort expended, which has a significant intra- and inter-observer variability. METHODS: Aiming to minimize the manual intervention in the diagnosis process, we developed a deep learning-based computer-aided framework for the DDH diagnosis, which can perform fully automated standard plane detection and angle measurement for Graf type I and type II hips. The proposed framework is composed of three modules: an anatomical structure detection module, a standard plane scoring module, and an angle measurement module. This framework can be applied to two common clinical scenarios. The first is the static mode, measurement and classification are performed directly based on the given standard plane. The second is the dynamic mode, where a standard plane from ultrasound video is first determined, and measurement and classification are then completed. To the best of our knowledge, our proposed framework is the first CAD method that can automatically perform the entire measurement process of Graf's method. RESULTS: In our experiments, 1051 US images and 289 US videos of Graf type I and type II hips were used to evaluate the performance of the proposed framework. In static mode, the mean absolute error of α, ß angles are 1.71° and 2.40°, and the classification accuracy is 94.71%. In dynamic mode, the mean absolute error of α, ß angles are 1.97° and 2.53°, the classification accuracy is 89.51%, and the running speed is 31 fps. CONCLUSIONS: Experimental results demonstrate that our fully automated framework can accurately perform standard plane detection and angle measurement of an infant's hip at a fast speed, showing great potential for clinical application.

Esophageal Carcinosarcoma: Analysis of Clinical Features and Prognosis of 24 Cases and a Literature Review.

OBJECTIVE: Esophageal carcinosarcoma (ECS) is a rare malignant tumor that accounts for only 0.5%-2.8% of all esophageal malignancies. As most current studies are case reports, the relationship between clinical features and prognosis remains controversial. METHODS: We investigated the clinical features and prognosis of 24 patients with ECS in a single center from 2006 to 2018. There were 18 male and 6 female patients aged 52-82 years with a median age of 62.5 years. In addition, we included 9 studies on ECS from PubMed and a literature review. RESULTS: The median follow-up time of the 24 patients was 70.5 (range, 10-156)months. The 3-year and 5-year survival rates were 83.3% and 70.8%, respectively. Among the 24 patients, none of the 10 (41.7%) stage T1 cancer patients had lymph node metastasis; however, lymph node metastasis was noted in 8 (57.1%) stage T2-4 cancer patients. The literature review revealed that 211 patients had a 5-year survival rate of 11.8%-68.2%, and 54.5%-95.8% study participants had early stage ECS. Although the information provided in the literature review is limited, it appears to be a characteristic of the early stage of the disease and predicts better prognosis when ECS is diagnosed, which is similar to the result of the current study. CONCLUSION: Our results indicate that ECS has a favorable prognosis, even among patients with early stage ECS who undergo radical esophagectomy with lymph node dissection. Because of the low incidence of ECS, further studies with more cases need to investigate this rare malignancy.

Application of Deep Learning in Quantitative Analysis of 2-Dimensional Ultrasound Imaging of Nonalcoholic Fatty Liver Disease.

OBJECTIVES: To verify the value of deep learning in diagnosing nonalcoholic fatty liver disease (NAFLD) by comparing 3 image-processing techniques. METHODS: A total of 240 participants were recruited and divided into 4 groups (normal, mild, moderate, and severe NAFLD groups), according to the definition and the ultrasound scoring system for NAFLD. Two-dimensional hepatic imaging was analyzed by the envelope signal, grayscale signal, and deep-learning index obtained by 3 image-processing techniques. The values of the 3 methods ranged from 0 to 65,535, 0 to 255, and 0 to 4, respectively. We compared the values among the 4 groups, draw receiver operating characteristic curves, and compared the area under the curve (AUC) values to identify the best image-processing technique. RESULTS: The envelope signal value, grayscale value, and deep-learning index had a significant difference between groups and increased with the severity of NAFLD (P < .05). The 3 methods showed good ability (AUC > 0.7) to identify NAFLD. Meanwhile, the deep-learning index showed the superior diagnostic ability in distinguishing moderate and severe NAFLD (AUC = 0.958). CONCLUSIONS: The envelope signal and grayscale values were vital parameters in the diagnosis of NAFLD. Furthermore, deep learning had the best sensitivity and specificity in assessing the severity of NAFLD.

Identification of Chemical Markers for the Discrimination of Radix Angelica sinensis Grown in Geoherb and Non-Geoherb Regions Using UHPLC-QTOF-MS/MS Based Metabolomics.

This research aimed to discover chemical markers for discriminating radix Angelica sinensis (RAS) from different regions and to explore the differences of RAS in the content of four active compounds and anti-inflammatory activities on lipopolysacchride (LPS)-induced RAW264.7 cells and calcium antagonists on the HEK 293T cells of RAS. Nine compounds were selected as characteristic chemical markers by ultra-high-performance liquid chromatography-quadrupole/time-of-flight mass spectrometry (UHPLC-QTOF-MS/MS), based on metabolomics, in order to rapidly discriminate RAS from geoherb and non-geoherb regions. The contents of senkyunolide I and butylidenephthalide in geoherb samples were higher than those in non-geoherb samples, but the contents of ferulic acid and levistolide A were lower in the geoherb samples. Furthermore, the geoherbs showed better nitric oxide (NO) inhibitory and calcium antagonistic activities than the non-geoherbs. These results demonstrate the diversity in quality of RAS between geoherbs and non-geoherbs.

3-D Contrast-Enhanced Ultrasound Fusion Imaging: A New Technique to Evaluate the Ablative Margin of Radiofrequency Ablation for Hepatocellular Carcinoma.

To investigate the feasibility of assessing the ablative margin (AM) of radiofrequency ablation (RFA) for hepatocellular carcinoma (HCC) with 3-D contrast-enhanced ultrasound fusion imaging (3-DCEUS-FI), pre- and post-RFA 3-DCEUS images of 84 patients with HCC were fused for two radiologists to independently assess the AMs. The success rate, duration and influencing factors for registration; inter-observer agreement for AM classification; and local tumor progression (LTP) rate were evaluated. The success rate of the automatic registration (AR), which was completed within 4-12 s, was 57.1% (48/84). The duration and success rate of the interactive registration (IR) were 4.2 ± 1.8 min and 91.7% (77/84) for radiologist A and 4.8 ± 2.1 min and 91.7% (77/84) for radiologist B, respectively. The multivariate analysis demonstrated that the pre-RFA image quality, number of vessels (≥3 mm) and presence of acoustic shadow were independent factors for AR (p < 0.05), while the number of vessels was an independent factor for IR (p = 0.001). The agreement between observers was excellent (kappa = 0.914). LTP rate was significantly higher for AMs <5 mm than for AMs ≥5 mm (p = 0.024). Quantitatively evaluating the AM immediately after RFA for HCC with 3-DCEUS-FI was feasible.

Local structure orientation descriptor based on intra-image similarity for multimodal registration of liver ultrasound and MR images.

PURPOSE: Ultrasound (US)-magnetic resonance (MR) fusion imaging is a profitable tool for image-guided abdominal diagnosis and biopsy. However, the automatic registration of liver US and MR images remains a challenging task. An effective local structure orientation descriptor (LSOD) for use in registering multimodal images is proposed in this study. METHODS: LSOD utilizes a normalized similarity distance vector of intra-image patch pairs to extract intensity change orientations from intensity value changes in a local area. The multimodal similarity measure is then derived using the LSOD vector difference. Experiments were performed on simulated US and liver 2D US-3D MR images from a phantom, two healthy volunteers, and seven patients. RESULTS: Using the LSOD-based method, the root-mean-square target registration errors (RMS-TREs) were 1.76±1.90mm/2.03±0.84mm in phantom/clinical experiments. All of the results outperformed those obtained using modality independent neighborhood descriptor (MIND)- and linear correlation of linear combination (LC(2))-based methods (phantom/clinical: 5.23±3.35mm/4.32±3.63mm and 9.79±5.03mm/6.29±3.85mm, respectively). The registration cover range for all subjects of the LSOD-based method was 9.16mm, which was larger than those of the MIND- and LC(2)-based methods (5.06 and 5.12mm, respectively). CONCLUSIONS: The results demonstrated that the LSOD-based registration method could robustly register 2D US and 3D MR images of different liver sections with acceptable accuracy for clinical requirements. This approach is useful for the practical clinical application of the US-MR fusion imaging technique.