Toomatari Seyed Babak Moosavi, Mohammadi Afshin, Sepehrvand Nariman, Toomatari Seyed Ehsan Moosavi, Ghasemi-Rad Mohammad, Shamspour Saber Zafar, Rezayi Seyfollah, Toubaei Mohammadreza, Sarabi Zahra Karimi
Department of Surgery, Zanjan University of Medical Sciences, Zanjan, Iran.
Department of Radiology, Urmia University of Medical Sciences, Urmia, Iran.
Pol J Radiol. 2019 Dec 6;84:e517-e521. doi: 10.5114/pjr.2019.91208. eCollection 2019.
Only five percent of thyroid nodules are malignant. It is important to find reliable and at the same time non-invasive methods to identify high-risk nodules. The aim of this study was to determine the diagnostic validity of a morphologic feature-oriented approach of ultrasound study for the identification of malignant thyroid nodules.
Seventy-one thyroid nodules in 71 consecutive patients were evaluated with both ultrasonography (US) and US-assisted fine needle aspiration biopsy (FNAB). Thyroid grey-scale and power Doppler US were performed, and a Windows-based software was designed to process power Doppler US (PDUS) images that were recorded directly by the US device. We provided a histogram graph of coloured pixels and calculated the Malignancy Index to identify the probability of malignancy for each thyroid nodule.
Thirty-six nodules (50.7%) were determined to be malignant in FNAB. Area under the receiver operating curve was 0.91 (95% CI: 0.85-0.98) for PDUS-based malignancy index in differentiating malignant thyroid nodules from benign ones. The best cut-off point for malignancy index was determined to be 0.092, with a sensitivity of 86.1% and specificity of 80% in identifying malignant nodules.
This PDUS-driven malignancy index using a contour-finding algorithm approach could accurately and reliably differentiate malignant and benign thyroid nodules. As a pre-FNAB assessment, the malignancy index may be able to reduce the number of patients with nodular thyroid disease undergoing this invasive procedure.
仅5%的甲状腺结节是恶性的。找到可靠且同时非侵入性的方法来识别高危结节很重要。本研究的目的是确定一种以形态学特征为导向的超声研究方法对识别恶性甲状腺结节的诊断有效性。
对71例连续患者中的71个甲状腺结节进行了超声检查（US）和超声引导下细针穿刺活检（FNAB）评估。进行了甲状腺灰阶和能量多普勒超声检查，并设计了基于Windows的软件来处理由超声设备直接记录的能量多普勒超声（PDUS）图像。我们提供了彩色像素的直方图，并计算了恶性指数以确定每个甲状腺结节的恶性概率。
FNAB确定36个结节（50.7%）为恶性。基于PDUS的恶性指数在区分恶性甲状腺结节与良性结节时，受试者操作曲线下面积为0.91（95%CI：0.85 - 0.98）。恶性指数的最佳截断点确定为0.092，在识别恶性结节时灵敏度为86.1%，特异性为80%。
这种使用轮廓查找算法的基于PDUS的恶性指数能够准确可靠地区分恶性和良性甲状腺结节。作为FNAB前的评估，恶性指数可能能够减少接受这种侵入性手术的结节性甲状腺疾病患者数量。
