Clinical Utility of Contrast-enhanced Ultrasound for the Diagnosis of Lymphadenopathy

Clinical diagnostic model for predicting indolent or aggressive lymphoma based on clinical information and ultrasound features of superficial lymph nodes

PURPOSE

The aim of this study was to develop a diagnostic model for predicting indolent lymphoma or aggressive lymphoma using clinical information and ultrasound characteristics of superficial lymph nodes.

METHOD

Patients with confirmed pathological lymphoma subtypes who had undergone ultrasound and contrast-enhanced ultrasound examinations were enrolled. Clinical and ultrasound imaging features were retrospectively analysed and compared to the pathological results, which were considered the gold standard for diagnosis. Two diagnostic models were developed: a clinical model (Model-C) using clinical data only, and a combined model (Model-US) integrating ultrasound features into the clinical model. The efficacy of these models in differentiating between indolent and aggressive lymphoma was compared.

RESULTS

In total, 236 consecutive patients were enrolled, including 78 patients with indolent lymphomas and 158 patients with aggressive lymphomas. Receiver operating characteristic (ROC) curve analysis revealed that the areas under the curves of Model-C and Model-US were 0.78 (95 % confidence interval: 0.72-0.84) and 0.87 (95 % confidence interval: 0.82-0.92), respectively (p < 0.001). Model-US was further evaluated for calibration and is presented as a nomogram.

CONCLUSIONS

The diagnostic model incorporated clinical and ultrasound characteristics and offered a noninvasive method for assessing lymphoma with good discrimination and calibration.

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.

Ultrasonography of Superficial Soft-Tissue Masses: Society of Radiologists in Ultrasound Consensus Conference Statement

The Society of Radiologists in Ultrasound convened a panel of specialists from radiology, orthopedic surgery, and pathology to arrive at a consensus regarding the management of superficial soft-tissue masses imaged with US. The recommendations in this statement are based on analysis of current literature and common practice strategies. This statement reviews and illustrates the US features of common superficial soft-tissue lesions that may manifest as a soft-tissue mass and suggests guidelines for subsequent management.

The Value of Contrast-Enhanced Ultrasound Liver Imaging Reporting and Data System in the Diagnosis of Hepatocellular Carcinoma:: A Meta-Analysis

OBJECTIVES

The aim of this study is to systematically evaluate the diagnostic ability of the contrast-enhanced ultrasound (CEUS) liver imaging reporting and data system (LI-RADS) in hepatocellular carcinoma (HCC).

METHODS

We searched relevant studies from PubMed, Medline, and Embase database. After literature search, duplicate removal, and data extraction, we calculated and analyzed the pooled sensitivity, pooled specificity, pooled odds ratios of diagnostic, pooled likelihood ratio (LR) of positive and negative, and area under the curve (AUC), accuracy, and F1 score to evaluate the diagnostic value of CEUS LI-RADS for HCC.

RESULTS

Thirteen studies and 6491 patients were included in this analysis. The pooled sensitivity and pooled specificity were 0.72 (95% confidence interval [CI], 0.70-0.73) and 0.92 (95% CI, 0.91-0.93), respectively. The positive LR was 8.02 (95% CI, 4.93-13.06) and the negative LR was 0.31 (95% CI, 0.27-0.37). The pooled diagnostic odds ratio was 27.91 (95% CI, 15.39-50.63). The overall AUC was 0.8406 and the accuracy was 0.77.

CONCLUSIONS

CEUS LI-RADS is an effective and promising method to diagnose HCC.

Qualitative and Quantitative Diagnosis in Head and Neck Cancer

The diagnosis is the art of determining the nature of a disease, and an accurate diagnosis is the true cornerstone on which rational treatment should be built. Within the workflow in the management of head and neck tumours, there are different types of diagnosis. The purpose of this work is to point out the differences and the aims of the different types of diagnoses and to highlight their importance in the management of patients with head and neck tumours. Qualitative diagnosis is performed by a pathologist and is essential in determining the management and can provide guidance on prognosis. The evolution of immunohistochemistry and molecular biology techniques has made it possible to obtain more precise diagnoses and to identify prognostic markers and precision factors. Quantitative diagnosis is made by the radiologist and consists of identifying a mass lesion and the estimation of the tumour volume and extent using imaging techniques, such as CT, MRI, and PET. The distinction between the two types of diagnosis is clear, as the methodology is different. The accurate establishment of both diagnoses plays an essential role in treatment planning. Getting the right diagnosis is a key aspect of health care, and it provides an explanation of a patient’s health problem and informs subsequent decision. Deep learning and radiomics approaches hold promise for improving diagnosis.