Introduction

Contrast-enhanced harmonic endoscopic ultrasound (CEH-EUS) can demonstrate microvascularization of the tumors. However, most images acquired from CEH-EUS are interpreted subjectively without quantification by time-intensity curves (TICs) analysis. The role of quantitative CEH-EUS through time-intensity curve (TIC) has not been explored in differential diagnosis of gallbladder(GB) polypoid lesions.

Aims & Methods

Aim: In this study, we used a handcrafted software to quantify TIC curve of CEH-EUS images to differentiate malignant gallbladder polypoid lesions from benign ones.
Method: We analyzed the videos of CEH-EUS for GB polypoid lesions from the patients in National Taiwan University Hospital. The convex-type echoendoscope (GF-UCT260; Olympus, Tokyo, Japan) equipped with ultrasound (US) systems (EU-ME2) was used for the procedures. The second generation of US contrast agent with Sonazoid (GE Healthcare, Oslo, Norway), which contains perfluorobutane microbubbles with a median diameter of 2–3 μm, was administered bolus and intravenously with a dosage of 0.015 ml encapsulated gas per kilogram body weight. The videos were recorded in AVI formats with 30 frames/second.
The algorithm for getting TICs includes the following steps:
(1) Circling the region of interest(ROI) in the B-mode
(2) Performing speckle tracking of the ROI in the B-mode
(3) Simultaneously obtaining the intensity of ROI (the mean pixel values in the ROI) in the corresponding contrast-enhanced harmonic image
(4) Outputting TICs after subtracting the baseline intensity (for eliminating the different baseline intensities caused by other settings).
(5) Outputting histogram of pixel values and excel file containing all pixel values from the frame with the highest mean pixel value automatically.
(6) Calculating the standard variance of (5) to represent the heterogeneity inside the ROI.
The speckling tracking composes of searching and matching processes. We used full search for the searching process and normalization cross-correlation for the matching process. By using the hardware of CPU with Intel i7-8750H and GPU with Nvidia GTX 1050 Ti, the computation time was 0.7 ms/frame. Based on this algorithm, we designed a graphical user interface of this model.

Results

A total of 30 eligible videos were enrolled for analysis. Fourteen lesions were pathologically proven of malignancies (including 11 adenocarcinomas, two neuroendocrine tumors, one B-cell lymphoma) and the other 16 lesions were benign cases. Among 16 benign lesions, 6 were chronic cholecystitis, 4 were adenomyoma, 2 were intracholecystic papillary neoplasm, one was xanthogranulomatous cholecystitis, one was cholesterol polyps, one was hyperplastic polyp and one was Mycobacterium tuberculosis. The lesion size ranged from 8.1 to 90mm in malignant group and from 4.4 to 29mm in benign group. The mean peak intensity was 46.36 dB (SD=31.77) in malignant group and 55.94 dB (SD=44.59) in benign group respectively (p=.509). The mean time to peak was 30.14 s (SD=10.00) in malignant group and 20.50 s (SD=6.70) in benign group respectively (p=.004).

Conclusion

This preliminary report of using TIC of CEH-EUS images for differential diagnosis of GB polypoid lesions showed significant difference in time to peak between malignant group and benign group. Further large scale of studies are needed for comfirming the application of this novel technique.

Disclosure

Conflict of Interest