Objective To evaluate the ability of contrast-enhanced ultrasonography (CEUS)in differentiate reactive lymph node,metastatic lymph node and lymphoma.Methods In a prospective study CEUS was performed in 129 patients with cervical lymph node enlargement.The entire process were recorded and preserved in DICOM format.The results were registered with Sonoliver.The receiver operating characteristic curve (ROC curve)analysis was performed to find the corresponding cutoff values. The selected node was removed surgically and submitted for histology.Results Of all the nodes,26 were reactive nodes,85 were metastases and 1 8 were lymphoma.Enhancement pattern was the most accurate way to characterize lymph nodes.The enhancement pattern of reactive lymph nodes was homogeneous and most of them were enhanced by lymphatic type while the metastatic lymph nodes were inhomogeneously enhanced or weakly enhanced by peripheral type.Lymph node lymphoma usually had no fixed enhancement pattern. Arrive time (AT),rise time (RT),time to peak (TTP),mean transit time (mTT),maximum intensity (IMAX),under the curve (AUC),rising slope(Kup),semi descending slope(Kdown)and perfusion index (PI)were significantly different in the three groups(P <0.05).RT,TTP and mTT of reactive lymph nodes were the shortest,which had significant difference compared with those of metastatic lymph nodes and lymphoma (P < 0.05 ).Kup,Kdown,IMAX%,AUC,PI in the reactive lymph nodes were significantly decreased compared with the metastatic group (P <0.05),but there was no significant difference compared with those in lymphoma (P >0.05 ).When TTP≥ 7.74 s,mTT≥26.54 s,metastatic lymph nodes were considered.When RT≥4.62 s,TTP ≥ 7.74 s,mTT ≥ 28.32 s,reactive lymph nodes were not considered. Conclusions Dynamic contrast-enhanced ultrasound image and enhancement pattern can distinguish neck lymph nodes while the optimal cut-off point time of the time-intensity curve parameters can further contribute to the identification of lymph nodes.

Objective To provide a reference for differential diagnosis of thyroid microcarcinoma classified as TI-RADS 3-4 grade using contrast-enhanced ultrasound.Methods SonoLiver software was used to analyze the process of contrast-enhanced ultrasound of 112 thyroid small nodules classified as TI-RADS 3-4 grade.The dynamic vascular pattern curve(DVPC)and the time-intensity curve(TIC)were automatically acquired for summarization of the types and characteristics of the two curves.The relevant parameters such as the rise time(RT),time to peak(TTP),maximum intensity(IMAX%),mean transit time(mTT),area under curve(AUC)and perfusion index(PI)were acquired and calculated using the software.Receiver operating curve (ROC) analysis was used to achieve the cut-off value of these parameters for differential diagnosis.Results For thyroid microcarcinoma which TI-RADS assessment of grade 3-4,TIC showed as slow rise and slow drop wave or slow rise and fast drop wave,DVPC showed negative waveform or negative-positive bi-directional waveform.ROC analysis demonstrated that IMAX of 96.5%,AUC of 443.08 and PI of 47.64 were the best cut-off values for the differential diagnosis of benign and malignant nodules.When IMAX<96.5%,AUC<443.08,PI<47.64,the nodules were highly likely to be thyroid microcarcinoma.Conclusions The contrast-enhanced ultrasound parameters can provide effective reference for the differential diagnosis of thyroid carcinoma with grade 3-4 in TI-RADS classification.

Psychogenic non-epilepsy seizures (PNES) are defined as paroxysms with clinical symptoms similar to epilepsy without epileptiform discharges. It is often misdiagnosed as epilepsy. Misdiagnosis not only delays the time for patients with PNES to get correct diagnoses and treatments, but also may increase the risk of adverse effects from improper use of antiseizure medications. The diagnostic method of PNES is video electroencephalogram monitoring, but there is no consensus on the optimal duration of monitoring. At the same time, the use of diverse physiological signals, brain imaging, laboratory examination, and the application of machine learning provide new perceptions for the precise identification of PNES and epilepsy. This article reviewed the progress of precise differential diagnosis between PNES and epilepsy.