Characteristic Analysis of the Time-density Curve of Volume Shuttle CT Perfusion of Benign Parotid Tumors
-
摘要: 目的:探讨容积穿梭CT灌注时间-密度曲线特征结合形态学参数对腮腺良性肿瘤鉴别诊断的价值。方法:收集我院拟诊腮腺肿瘤患者100例,术前均行容积穿梭CT灌注成像检查,术后经手术病理证实。将所得影像数据传送至工作站及pacs系统,观察分析不同病理类型腮腺肿瘤的形态学参数(位置、大小、密度、边界、颈部淋巴结、增强扫描强化幅值)及时间-密度曲线(TDC)类型,计数资料比较采用χ2检验,计量资料符合正态分布采用两独立样本t检验,不符合正态分布的采用秩和检验,检验水准α=0.05。结果:腮腺良性肿瘤(多行性腺瘤、腺淋巴瘤、基底细胞瘤及肌上皮瘤)间的形态学参数无统计学差异;时间-密度曲线类型差异有统计学差异。结论:CT灌注时间-密度曲线特征结合形态学参数对腮腺良性肿瘤的鉴别诊断有重要价值,为患者术式的选择有重要指导意义。Abstract: Objective: We intend to explore the value of volume shuttle CT perfusion time-density curve characteristics combined with morphological parameters when applied in the differentiation diagnosis of parotid benign tumors. Methods: We performed volume shuttle CT perfusion imaging on 100 collected patients with parotid tumor before operation and got confirmed by the pathology after operation. The image data acquired was sent to the workstation and PACS system. We observed and analyzed the morphological parameters (location, size, density, boundary, neck lymph node, heightened amplitude of enhanced scan) and types of time-density curve, then adopted χ2 test to deal with enumeration data comparison. The enumeration data matched with normal distribution was dealt with two independent samples t test, the rest data was dealt with rank sum test, test level α=0.05. Results: There was no statistical difference in the morphological parameters of benign parotid tumors (pleomorphic adenoma, adenolymphoma, basal cell adenoma; and myoepithelioma), but we found statistical difference in time-density curves. Conclusion: CT perfusion time-density curve characteristics combined with morphological parameters are of great value in the differentiation diagnosis of parotid benign tumors, and have important guiding significance for the selection of surgical procedures.
-
Keywords:
- tomography /
- X-ray computer /
- parotid neoplasms /
- volume shuttle perfusion
-
-
[1] LONN S, AHLBOM A, CHRISTENSENH C, et al. Mobile phone use and risk of parotid gland tumor[J]. American Journal of Epidemiology, 2006, 164(7):637-643.
[2] CHEUNG S H, KWAN W Y W, TSUI K P, et al. Partial parotidectomy under local anesthesia for benign parotid tumors:An experience of 50 cases[J]. American Journal of Otolaryngology, 2018, 39(3):286-289.
[3] 李雨奇, 刘挨师, 王杰, 等. 容积穿梭CT灌注成像在肾上腺肿瘤诊断中的初步研究[J]. 实用放射学杂志, 2015, 31(2):272-276. LI Y Q, LIU A S, WANG J, et al. Preliminary study of volume shuttle CT perfusion in diagnosis of adrenal tumors[J]. Journal of Practical Radiology, 2015, 31(2):272-276. (in Chinese).
[4] 杨振兴, 刘挨师, 郝粉娥. CT灌注成像在腮腺肿瘤诊断中的应用进展[J]. 中国中西医结合影像学杂志, 2015, 13(3):340-343. [5] 程琦, 许实成, 房文亮, 等. CT灌注对腮腺肿块定性诊断的临床价值评价[J]. 中国医学影像学杂志, 2011, 19(10):721-725. CHEN Q, XU S C, FANG W L, et al. Evaluation of CT perfusion in the diagnosis of parotid gland mass[J]. Chinese Journal of Medical Imaging, 2011, 19(10):721-725. (in Chinese).
[6] 潘桂海, 罗泽斌, 夏俊, 等. 腮腺混合瘤CT灌注成像及MRI动态增强的特征表现[J]. 广东医科大学学报, 2018, 36(1):79-82. PAN G H, LUO Z B, XIA J, et al. Characteristics of CT perfusion imaging and MRI dynamic enhancement in parotid mixed tumor[J]. Journal of Guangdong Medical University, 2018, 36(1):79-82. (in Chinese).
[7] NIAZI M, MOHAMMADZADEH M, AGHAZADEH K, et al. Perfusion computed tomography scan imaging in differentiation of benign from malignant parotid lesions[J]. International Archives of Otorhinolaryngology, 2020, 24(2):e160-e169.
[8] 帕力丹木·吾买尔, 阿里木江·阿卜杜凯尤木, 沈君. 腮腺常见良性肿瘤CT特征分析[J]. CT理论与应用研究, 2019, 28(6):739-745. DOI:10.15953/j.1004-4140.2019.28.06.12. WUMAIER P, ABUDUKAIYOUMU A, SHEN J. CT feature analysis of common benign tumors of parotid gland[J]. CT Theory and Applications, 2019, 28(6):739-745. DOI:10.15953/j.1004-4140. 2019.28.06.12. (in Chinese).
[9] 温晓玲, 沈江, 伍东升, 等. 腮腺少见肿瘤的增强CT表现及其病理基础[J]. 华西口腔医学杂志,2015, 33(4):414-418. WEN X L, SHEN J, WU D S, et al. Rare parotid gland tumors:Enhanced computed tomography and pathological correlation[J]. West China Journal of Stomatology, 2015, 33(4):414-418. (in Chinese).
[10] 吕慧欣, 王卓然, 高愉淇, 等. 3724例唾液腺肿瘤的临床病理分析[J]. 中华口腔医学杂志, 2019, 54(1):10-12. LV H X, WANG Z R, GAO Y Q, et al. Clinical pathologic analysis on 3724 cases of salivary gland tumors[J]. Chinese Journal of Stomatology, 2019, 54(1):10-12. (in Chinese).
[11] 任思桐, 李小虎, 刘斌, 等. CT平扫图像纹理分析鉴别腮腺多形性腺瘤与恶性肿瘤的初步研究[J]. CT理论与应用研究, 2019, 28(6):685-691. DOI:10.15953/j.1004-4140.2019.28.06.06. REN S T, LI X H, LIU B, et al. Preliminary study on differentiating pleomorphic adenoma and malignant tumors of the parotid gland by texture analysis of non-enhanced CT images[J]. CT Theory and Applications, 2019, 28(6):685-691. DOI:10.15953/j.1004-4140.2019.28.06.06. (in Chinese).
[12] GREEN M A, HUTCHINS G D. Positron emission tomography (PET) assessment of renal perfusion[J]. Seminars in Nephrology, 2011, 31(3):291-299.
[13] 袁道英, 李巧凤, 许凯, 等. 细针针吸细胞学在腮腺肿瘤性疾病诊断中的评价[J]. 中国耳鼻咽喉头颈外科, 2016, 23(12):702-704. YUAN D Y, LI Q F, XU K, et al. Evaluation of diagnostic accuracy of preoperative fine needle aspiration cytology in salivary gland lesions[J]. Chinese Archives of Otolaryngology-Head and Neck Surgery, 2016, 23(12):702-704. (in Chinese).
[14] ASH L, TEKNOS T N, GANDHI D, et al. Head and neck squamous cell carcinoma:CT perfusion can help noninvasively predict intratumoral microvessel density[J]. Radiology, 2009, 251(2):422-428.
[15] 文萌萌, 张勇, 程敬亮, 等. 基于增强T1WI的直方图分析在腮腺常见肿瘤中的鉴别诊断价值[J]. 临床放射学杂志, 2020, 39(5):895-899. WEN M M, ZHANG Y, CHENG J L, et al. The value of histogram analysis based on enhanced T1WI among common parotid tumors[J]. Journal of Clinical Radiology, 2020, 39(5):895-899. (in Chinese).
[16] 雷晓雯, 程敬亮, 冉云彩. ADC全域直方图分析对腮腺多形性腺瘤和腺淋巴瘤的鉴别诊断价值[J]. 放射学实践, 2020, 35(8):1005-1008. LEI X W, CHENG J L, RAN Y C. The differential diagnostic value of whole lesion ADC histogram analysis in parotid pleomorphic adenoma and adenolymphoma[J]. Radiol Practice, 2020, 35(8):1005-1008. (in Chinese).
[17] LAM P D, KURIBAYASHI A, IMAIZUMI A, et al. Differentiating benign and malignant salivary gland tumors:Diagnostic criteria and the accuracy of dynamic contrast-enhanced MRI with high temporal resolution[J]. The British Journal of Radiology, 2015, 88(1049):8-12. (in Chinese).
[18] 高鑫, 程敬亮, 文宝红, 等. T2WI全域直方图分析鉴别诊断腮腺多形性腺瘤与腺淋巴瘤[J]. 中国医学影像技术, 2018, 34(12):1796-1800. GAO X, CHENG J L, WEN B H, et al. Whole-lesion histogram analysis of T2WI in identification of parotid gland pleomorphic adenoma from adenolymphoma[J]. Chinese Journal of Medical Imaging Technology, 2018, 34(12):1796-1800. (in Chinese).
[19] 李泉江. 能谱CT对腮腺常见良性肿瘤的鉴别诊断价值[D]. 重庆:重庆医科大学, 2020. [20] 朱丹, 苏潇, 赵倩倩, 等. 腮腺基底细胞腺瘤的双期增强CT特点[J]. 中国CT和MRI杂志, 2016, 14(4):4-7 , 26. ZHU D, SU X, ZHAO Q Q, et al. Dual-phase CT enhancement features of basal cell adenoma in parotid gland[J]. Chinese Journal of CT and MRI, 2016, 14(4):4-7, 26. (in Chinese).
[21] 张玉穗, 刘嵘, 肖润, 等. 腮腺肿瘤CT影像与病理对照分析[J]. 影像研究与医学应用, 2018, 2(21):55-57. [22] 杨振兴, 杨晓光, 郝粉娥, 等. 腮腺肌上皮瘤的CT影像分析[J]. 医学影像学杂志, 2019, 29(11):1840-1842 , 1860. YANG Z X, YANG X G, HAO F E, et al. CT imaging analysis of parotid myoepithelloma[J]. Journal of Medical Imaging, 2019, 29(11):1840-1842, 1860. (in Chinese).
[23] PATEL M N, RAJPURA H K K, SOLANKL R N, et al. Role of multiphase CT in differentiating pleomorphic adenoma from other parotid neoplasms[J]. Journal of Evidence Based Medicine and Healthcare, 2018, 5(39):2769-2771.
-
期刊类型引用(10)
1. 刘动,林沛元,李伟科,黄胜,马保松. 跨孔CT岩溶识别方法准确性的统计学评价. 岩土力学. 2024(03): 822-834+926 . 百度学术
2. Dao-han Liu,Lei Wang,Lei Liu,Jun-jie Xu,Jian-qiang Wu,Pan Liu. Application of geophysical methods in fine detection of urban concealed karst: A case study of Wuhan City, China. China Geology. 2024(03): 517-532 . 必应学术
3. 王瑞芳. 综合勘探方法在湖底岩溶区隧道的应用. 重庆建筑. 2023(01): 61-64 . 百度学术
4. 彭军,李期佳,高建华,王鹏,熊友亮. 电磁波CT技术在城市隧道岩溶勘查工程中的应用. CT理论与应用研究. 2023(04): 471-479 . 本站查看
5. 郑军. 井间电磁波CT不同初始模型成像效果对比. 工程地球物理学报. 2022(06): 868-876 . 百度学术
6. 刘剑. 电磁波CT在既有铁路路基塌陷精细探测中的应用. 工程地球物理学报. 2021(05): 555-560 . 百度学术
7. 王薇,邓小虎,金聪,周红伟,林松. 电磁波CT揭露重大工程岩溶发育特征——以某地铁岩溶勘察为例. 科学技术与工程. 2020(34): 13977-13982 . 百度学术
8. 罗安华. 高密度电法在地下暗河探测中的应用——以石林诗玛溶洞为例. 科学技术与工程. 2019(27): 81-87 . 百度学术
9. 黄生根,刘东军,胡永健. 电磁波CT技术探测溶洞的模拟分析与应用研究. 岩土力学. 2018(S1): 544-550 . 百度学术
10. 罗日春. 电磁波CT技术在岩溶勘探中的应用与研究. 勘察科学技术. 2018(S1): 107-110 . 百度学术
其他类型引用(1)
计量
- 文章访问数:
- HTML全文浏览量:
- PDF下载量:
- 被引次数: 11