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机构地区:[1]北京老年医院放射科,北京100095 [2]北京大学第三医院放射科
出 处:《中华胰腺病杂志》2016年第6期378-382,共5页Chinese Journal of Pancreatology
摘 要:目的探讨MRI鉴别诊断良、恶性胰腺神经内分泌肿瘤(PNETs)的临床价值。方法回顾性分析经病理证实的13例PNETs患者的MRI表现,包括G1~G3级肿瘤的体积、边界、信号及周围脏器受累情况,并计算MRI诊断PNETs的敏感性、特异性及准确性。结果13例患者共计18个肿瘤,良性(G1级)12个,恶性(G2-G3级)6个。66.7%(8/12)G1级肿瘤最长径≤2cm,100%(6/6)G2-G3级肿瘤最长径〉2cm;100%(12/12)G1级肿瘤边界清晰,50%(2/4)G2级肿瘤边界不清晰,100%(2/2)G3级肿瘤边界不清晰;83.3%(10/12)G1级肿瘤内部信号均匀,100%(6/6)G2-G3级肿瘤内部信号不均匀;100%(2/2)G3级肿瘤有脏器受侵、腹膜后淋巴结肿大。MRI鉴别诊断良、恶性PNETs的敏感性、特异性和准确性分别为83.3%、85.7%、84.6%。结论MRI鉴别诊断良、恶性PNETs有较高的敏感性、特异性和准确性,但鉴别诊断部分G1级和G2级肿瘤存在困难。肿瘤体积大小不能独立作为鉴别肿瘤良性和恶性的可靠指标。Objective To determine the clinical value of contrast enhanced MRI in differentiating benign and malignant pancreatic endocrine tumors (PNETs). Methods MRI findings on 13 cases who were pathologically diagnosed as PNETs were retrospectively analyzed, including the tumor volume, border, MRI signal and adjacent organ involvement of Grade 1 - 3 tumors, and the sensitivity, specificity and accuracy of MRI for diagnosing PNETs were calculated. Results A total of 18 tumors were detected, including 12 benign (Grade 1 ) and 6 malignant (Grade 2 - 3) tumors. Eight Grade 1 tumors'(66.7% ) maximal diameters were≤2 cm, while 100% (6/6) Grade 2 - 3 tumors' maximal diameters were ≥2 cm. All of Grade 1 tumors (100%) had clear boundary, while 50% (2/4) Grade 2 and 100% (2/2) Grade 3 tumors had unclear boundary. Ten Grade 1 tumors ( 83.3% ) had intratumoral homogeneous signal, while 100% (6/6) Grade 2 -3 tumors had heterogeneous intratumoral signal. Two Grade 3 tumors (100%) had organ iiffiltration and retroperitoneal lymph node metastasis. The sensitivity, specificity and accuracy of MRI for differentiating benign and malignant PNETs were 83.3% , 85.7% and 84.6%, respectively. Conclusions MRI had higher sensitivity, specificity and accuracy in differentiating benign and malignant PNETs, but it was still difficult to differentiate Grade 1 from Grade 2 tumors by MRI. Tumors size could not be considered to be a reliable indicator for differentiating benign and malignant PNETs.
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