Application of multimodal ultrasonography for differentiating benign and malignant cervical lymphadenopathy

Value of conventional ultrasound and shear‑wave elastography in the assessment of mesenteric lymphadenitis in a paediatric population

The present retrospective study was designed to explore the value of conventional ultrasound (US) and Virtual Touch Tissue Imaging and Quantification (VTIQ) in the assessment of mesenteric lymphadenitis (ML) in a paediatric population. A total of 103 patients with ML and 60 healthy paediatric patients were examined. VTIQ was performed to assess mesenteric lymph node (MLN) stiffness via shear-wave velocity (SWV). Univariate and multivariate logistic regression analyses were conducted to reveal independent variables for the identification of ML. The diagnostic performance of US, and US combined with VTIQ, were compared. All the quantitative VTIQ parameters (including the SWVMean, SWVMax and SWVMin) were significantly greater for MLNs in the control group than for MLNs in the ML group (all P<0.001). The SWV values in the control group were nearly 2-fold greater than that in the ML group. According to the multivariate logistic regression analysis, the longest diameter [odds ratio (OR)=6.042; P=0.046] was revealed to be the strongest independent predictor for ML, followed by the CRP level (OR=2.310; P<0.001) and the SWVMean (OR=0.106; P<0.001). According to the receiver operating characteristic analysis, the area under the curve (AUC) for US combined with VTIQ was 0.890 (95% CI: 0.831-0.949) with a greater sensitivity of 91.26% and a greater specificity of 86.67% than that for US alone (AUC: 0.798; 95% CI: 0.724-0.872; sensitivity: 79.61%; specificity: 80.00%). A significant negative correlation between increased VTIQ parameters and ML was observed. Utilizing VTIQ to assess MLN stiffness offers a non-invasive, convenient, reliable and reproducible approach for identifying mesenteric lymphadenopathy.

Diagnosis of cervical lymphoma using a YOLO-v7-based model with transfer learning

To investigate the ability of an auxiliary diagnostic model based on the YOLO-v7-based model in the classification of cervical lymphadenopathy images and compare its performance against qualitative visual evaluation by experienced radiologists. Three types of lymph nodes were sampled randomly but not uniformly. The dataset was randomly divided into for training, validation, and testing. The model was constructed with PyTorch. It was trained and weighting parameters were tuned on the validation set. Diagnostic performance was compared with that of the radiologists on the testing set. The mAP of the model was 96.4% at the 50% intersection-over-union threshold. The accuracy values of it were 0.962 for benign lymph nodes, 0.982 for lymphomas, and 0.960 for metastatic lymph nodes. The precision values of it were 0.928 for benign lymph nodes, 0.975 for lymphomas, and 0.927 for metastatic lymph nodes. The accuracy values of radiologists were 0.659 for benign lymph nodes, 0.836 for lymphomas, and 0.580 for metastatic lymph nodes. The precision values of radiologists were 0.478 for benign lymph nodes, 0.329 for lymphomas, and 0.596 for metastatic lymph nodes. The model effectively classifies lymphadenopathies from ultrasound images and outperforms qualitative visual evaluation by experienced radiologists in differential diagnosis.

Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging

Due primarily to the excellent soft tissue contrast depictions provided by MRI, the widespread application of head and neck MRI in clinical practice serves to assess various diseases. Artificial intelligence (AI)-based methodologies, particularly deep learning analyses using convolutional neural networks, have recently gained global recognition and have been extensively investigated in clinical research for their applicability across a range of categories within medical imaging, including head and neck MRI. Analytical approaches using AI have shown potential for addressing the clinical limitations associated with head and neck MRI. In this review, we focus primarily on the technical advancements in deep-learning-based methodologies and their clinical utility within the field of head and neck MRI, encompassing aspects such as image acquisition and reconstruction, lesion segmentation, disease classification and diagnosis, and prognostic prediction for patients presenting with head and neck diseases. We then discuss the limitations of current deep-learning-based approaches and offer insights regarding future challenges in this field.

LN-Net: Perfusion Pattern-Guided Deep Learning for Lymph Node Metastasis Diagnosis Based on Contrast-Enhanced Ultrasound Videos

OBJECTIVE

The blood flow in lymph nodes reflects important pathological features. However, most intelligent diagnosis based on contrast-enhanced ultrasound (CEUS) video focuses only on CEUS images, ignoring the process of extracting blood flow information. In the work described here, a parametric imaging method for describing blood perfusion pattern was proposed and a multimodal network (LN-Net) to predict lymph node metastasis was designed.

METHODS

First, the commercially available artificial intelligence object detection model YOLOv5 was improved to detect the lymph node region. Then the correlation and inflection point matching algorithms were combined to calculate the parameters of the perfusion pattern. Finally, the Inception-V3 architecture was used to extract the image features of each modality, with the blood perfusion pattern taken as the guiding factor in fusing the features with CEUS by sub-network weighting.

DISCUSSION

The average precision of the improved YOLOv5s algorithm compared with baseline was improved by 5.8%. LN-Net predicted lymph node metastasis with 84.9% accuracy, 83.7% precision and 80.3% recall. Compared with the model without blood flow feature guidance, accuracy was improved by 2.6%. The intelligent diagnosis method has good clinical interpretability.

CONCLUSION

A static parametric imaging map could describe a dynamic blood flow perfusion pattern, and as a guiding factor, it could improve the classification ability of the model with respect to lymph node metastasis.

Cine MR feature tracking analysis for diagnosing thymic epithelial tumors: a feasibility study

BACKGROUND

To assess the feasibility of the cine MR feature tracking technique for the evaluation of cardiovascular-induced morphological deformation in the diagnosis of thymic epithelial tumors (TETs).

METHODS

Our study population consisted of 43 patients with pathologically proven TETs including 10 low-grade thymomas, 23 high-grade thymomas, and 10 thymic carcinomas. Cine MR images were acquired using a balanced steady-state free precession sequence with short periods of breath-hold in the axial and oblique planes in the slice with the largest lesion cross-sectional area. The tumor margin was manually delineated in the diastolic phase and was automatically tracked for all other cardiac phases. The change rates of the long-to-short diameter ratio (∆LSR) and tumor area (∆area) associated with pulsation were compared between the three pathological groups using the Kruskal-Wallis H test and the Mann-Whitney U test. A receiver-operating characteristic (ROC) curve analysis was performed to assess the ability of each parameter to differentiate thymic carcinomas from thymomas.

RESULTS

∆LSR and ∆area were significantly different among the three groups in the axial plane (p = 0.028 and 0.006, respectively) and in the oblique plane (p = 0.034 and 0.043, respectively). ∆LSR and ∆area values were significantly lower in thymic carcinomas than in thymomas in the axial plane (for both, p = 0.012) and in the oblique plane (p = 0.015 and 0.011, respectively). The area under the ROC curves for ∆LSR and ∆area for the diagnosis of thymic carcinoma ranged from 0.755 to 0.764.

CONCLUSIONS

Evaluation of morphological deformation using cine-MR feature tracking analysis can help diagnose histopathological subtypes of TETs and identify thymic carcinomas preoperatively.

MSMFN: An Ultrasound Based Multi-Step Modality Fusion Network for Identifying the Histologic Subtypes of Metastatic Cervical Lymphadenopathy

Identifying squamous cell carcinoma and adenocarcinoma subtypes of metastatic cervical lymphadenopathy (CLA) is critical for localizing the primary lesion and initiating timely therapy. B-mode ultrasound (BUS), color Doppler flow imaging (CDFI), ultrasound elastography (UE) and dynamic contrast-enhanced ultrasound provide effective tools for identification but synthesis of modality information is a challenge for clinicians. Therefore, based on deep learning, rationally fusing these modalities with clinical information to personalize the classification of metastatic CLA requires new explorations. In this paper, we propose Multi-step Modality Fusion Network (MSMFN) for multi-modal ultrasound fusion to identify histological subtypes of metastatic CLA. MSMFN can mine the unique features of each modality and fuse them in a hierarchical three-step process. Specifically, first, under the guidance of high-level BUS semantic feature maps, information in CDFI and UE is extracted by modality interaction, and the static imaging feature vector is obtained. Then, a self-supervised feature orthogonalization loss is introduced to help learn modality heterogeneity features while maintaining maximal task-consistent category distinguishability of modalities. Finally, six encoded clinical information are utilized to avoid prediction bias and improve prediction ability further. Our three-fold cross-validation experiments demonstrate that our method surpasses clinicians and other multi-modal fusion methods with an accuracy of 80.06%, a true-positive rate of 81.81%, and a true-negative rate of 80.00%. Our network provides a multi-modal ultrasound fusion framework that considers prior clinical knowledge and modality-specific characteristics. Our code will be available at: https://github.com/RichardSunnyMeng/MSMFN.

Evaluating Different Quantitative Shear Wave Parameters of Ultrasound Elastography in the Diagnosis of Lymph Node Malignancies: A Systematic Review and Meta-Analysis

Shear wave elastography (SWE) has shown promise in distinguishing lymph node malignancies. However, the diagnostic accuracies of various SWE parameters that quantify tissue stiffness are yet to be demonstrated. To evaluate the pooled diagnostic accuracy of different SWE parameters for differentiating lymph node malignancies, we conducted a systematic screening of four databases using the PRISMA guidelines. Lymph node biopsy was adopted as the reference standard. E_max (maximum stiffness), E_mean (mean stiffness), E_min (minimum stiffness), and E_sd (standard deviation) SWE parameters were subjected to separate meta-analyses. A sub-group analysis comparing the use of E_max in cervical (including thyroid) and axillary lymph node malignancies was also conducted. Sixteen studies were included in this meta-analysis. E_max and E_sd demonstrated the highest pooled sensitivity (0.78 (95% CI: 0.69-0.87); 0.78 (95% CI: 0.68-0.87)), while E_mean demonstrated the highest pooled specificity (0.93 (95% CI: 0.88-0.98)). From the sub-group analysis, the diagnostic performance did not differ significantly in cervical and axillary LN malignancies. In conclusion, SWE is a promising adjunct imaging technique to conventional ultrasonography in the diagnosis of lymph node malignancy. SWE parameters of E_max and E_sd have been identified as better choices of parameters for screening clinical purposes.

Differential Diagnosis Value of Shear-Wave Elastography for Superficial Enlarged Lymph Nodes

Objectives

To evaluate the diagnostic efficiency and diagnostic threshold of conventional US and shear-wave elastography (SWE) in superficial enlarged lymph nodes (LNs).

Methods

A total of 204 patients with superficial enlarged LNs were enrolled in this retrospective study aged 46.0 ± 15.2 years from March 2020 to March 2021. LNs with a long axis larger than 0.7 cm were considered as superficial enlarged. Before the histological biopsy, LNs that were considered suspicious according to both conventional US and SWE were included, while LNs with no or unclear pathological results, or with no satisfactory SWE images, were excluded. The conventional and 2-D SWE examinations were performed with Aplio i800 and Acuson sequoia equipped with i18LX5 linear-array transducer (5-18 MHz) and 10L4 linear-array transducer (4-10 MHz), respectively. Both E Median and Vs Median parameters were investigated by two senior ultrasound physicians. The pathological results were performed as the gold standard.

Results

Variables including transverse axis size, lymphatic hilum, L/T ratio, echogenicity, and color Doppler pattern were considered significant. The mean E Median value in benign, metastatic LNs, and lymphoma were 28.26 ± 8.87 kPa, 77.46 ± 22.85 kPa, and 50.37 ± 5.41 kPa (p <0.001), while Vs Median values were 3.02 ± 0.50 m/s, 4.87 ± 0.90 m/s, and 4.09 ± 0.22 m/s, respectively (p < 0.001). The diagnostic performance indicated the high sensitivity, specificity, PPV, NPV, and overall accuracy of conventional US combined with SWE. The optimal cutoff values of E Median and Vs Median for predicting malignant LNs were 42.90 kPa and 3.73 m/s, respectively. As AUC value, sensitivity, specificity, accuracy, PPV, and NPV revealed, the indexes of E Median were 0.976, 0.927, 0.975, 0.946, 0.983, and 0.897, respectively, while Vs Median were 0.970, 0.927, 0.963, 0.941, 0.975, and 0.895, respectively (p <0.001). The ROC curves of both E Median (AUC=0.976) Vs Median (AUC=0.970) suggested the remarkable diagnostic efficiency in distinguishing benignity between suspected malignant LNs.

Conclusions

Above results indicated that conventional US together with 2-D SWE could elevate the diagnostic performance. Meanwhile, the parameters of 2-D SWE including E Median and Vs Median could effectively assess malignant LNs, which provide valuable differentiating information in superficial enlarged LNs.

Case Report: Primary Mediastinal Large B-Cell Lymphoma Invasion of Extranodal Thyroid Tissue Mimicking Tuberculosis and Confounded by Similar Ultrasonic Appearance

Background

Primary mediastinal large B-cell lymphoma (PMBCL) is a rare type of diffuse large B-cell lymphoma, which has significant features that overlap with those of Hodgkin’s lymphoma. Ultrasound is a commonly used modality to characterize superficial lymph no5des, and ultrasonic findings are often used to distinguish lymphoma from lymph node tuberculosis in daily clinical practice. Although a common malignancy, lymphoma rarely involves extranodal tissues.

Case Presentation

Here we report the case of a 42-year-old Chinese male patient with PMBCL who was misdiagnosed with tuberculosis because of extranodal invasion. He visited our hospital for a neck mass that he had been noting for 1 week. Ultrasound revealed multiple enlarged lymph nodes on both sides of the neck. The lesions appeared to involve the surrounding soft tissue and thyroid gland, resembling a tuberculous sinus tract formation. Cervical spine computed tomography showed no obvious abnormalities in the cervical cone or bone damage. Contrast-enhanced ultrasound indicated that one of the enlarged lymph nodes in the right neck was rich in blood supply and exhibited centripetal enhancement, with uniform high enhancement at the peak. The patient underwent two ultrasound-guided punctures; the first puncture was performed for an enlarged lymph node in the right neck at Hangzhou Red Cross Hospital. Hodgkin’s lymphoma was suspected based on pathological and immunohistochemical findings, whereas a rare type of diffuse large B-cell lymphoma was suspected at Zhejiang Cancer Hospital.

Conclusions

Lymphoma is often misdiagnosed, causing delayed treatment initiation and affecting patient outcomes as the disease progresses. The present case demonstrates that the ultrasonic appearance of lymphoma may sometimes be confused with that of tuberculosis. Although ultrasound-guided needle biopsy has a high diagnostic accuracy, it may also cause diagnostic deviation because of insufficient sampling volume. Moreover, owing to the enlargement of multiple lymph nodes due to lymphoma or lymph node tuberculosis, puncturing different lymph nodes may provide different results.