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Published on: February 16, 2024
Contrast-enhanced harmonic endoscopic ultrasound using time-intensity curve analysis predicts pathological grade of
Saimon Takada1, Hironari Kato2, Yosuke Saragai1
1Department of Gastroenterology and Hepatology, Okayama University Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama, Japan.
Purpose:
Histological grading is important for the treatment algorithm in pancreatic neuroendocrine neoplasms (PNEN). The present study examined the efficacy of contrast-enhanced harmonic endoscopic ultrasound (CH-EUS) and time-intensity curve (TIC) analysis of PNEN diagnosis and grading.
Methods:
TIC analysis was performed in 30 patients using data obtained from CH-EUS, and a histopathological diagnosis was made via EUS-guided fine-needle aspiration or surgical resection. The TIC parameters were analyzed by dividing them into G1/G2 and G3/NEC groups. Then, patients were classified into non-aggressive and aggressive groups and evaluated.
Results:
Twenty-six patients were classified as G1/G2, and four as G3/NEC. From the TIC analysis, five parameters were obtained (I: echo intensity change, II: time for peak enhancement, III: speed of contrast, IV: decrease rate for enhancement, and V: enhancement ratio for node/pancreatic parenchyma). Three of these parameters (I, IV, and V) showed high diagnostic performance. Using the cutoff value obtained from the receiver-operating characteristic (ROC) analysis, the correct diagnostic rates of parameters I, IV, and V were 96.7%, 100%, and 100%, respectively, between G1/G2 and G3/NEC. A total of 21 patients were classified into the non-aggressive group, and nine into the aggressive group. Using the cutoff value obtained from the ROC analysis, the accurate diagnostic rates of I, IV, and V were 86.7%, 86.7%, and 88.5%, respectively, between the non-aggressive and aggressive groups.
Conclusion:
CH-EUS and TIC analysis showed high diagnostic accuracy for grade diagnosis of PNEN. Quantitative perfusion analysis is useful to predict PNEN grade diagnosis preoperatively.
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