MA Yuwan, SONG MinJie, DING Chongyang. CT and positron emission tomography/computed tomography using (18)F-fluorodeoxyglucose findings in 21 patients with pulmonary mucosa-associated lymphoidtissue lymphoma[J]. Journal of Clinical Medicine in Practice, 2022, 26(14): 18-21, 25. DOI: 10.7619/jcmp.20220377
Citation: MA Yuwan, SONG MinJie, DING Chongyang. CT and positron emission tomography/computed tomography using (18)F-fluorodeoxyglucose findings in 21 patients with pulmonary mucosa-associated lymphoidtissue lymphoma[J]. Journal of Clinical Medicine in Practice, 2022, 26(14): 18-21, 25. DOI: 10.7619/jcmp.20220377

CT and positron emission tomography/computed tomography using (18)F-fluorodeoxyglucose findings in 21 patients with pulmonary mucosa-associated lymphoidtissue lymphoma

More Information
  • Received Date: February 07, 2022
  • Available Online: July 13, 2022
  • Objective 

    To investigate imaging manifestations of CT and positron emission tomography/computed tomography (PET)-CT using (18)F-fluorodeoxyglucose (18F-FDG) in pulmonary mucosa-associated lymphoid tissue (MALT) lymphoma.

    Methods 

    CT and PET-CT imaging findings of 21 patients with pulmonary MALT lymphoma confirmed by pathological results were retrospectively analyzed, and the correlation between affinity of 18F-FDG and imaging findings was analyzed by Logistic regression analysis.

    Results 

    A total of 60 pulmonary lesions were found in 21 patients, including 22 consolidation-like lesions, 28 nodular or mass-like lesions, and 10 ground-glass lesions. A total of 12 cases were single lesions and 9 cases were multiple lesions. Among the patients with multiple lesions, 7 cases were bilateral and 2 cases were unilateral lesions. 18F-FDG PET-CT showed that 45 of the 60 lesions were hypermetabolic lesions and 15 were hypometabolic lesions. Logistic regression analysis showed that 18F-FDG affinity was correlated with morphology and diameter of lesions (P<0.01).

    Conclusion 

    The imaging manifestations of pulmonary MALT lymphoma are diverse, including nodular or mass type, pneumonic type and ground glass type. Most of them have 18F-FDG affinity that is related to tumor diameter and morphology.

  • [1]
    JHAVERI K, DIMAS D J, VAKIL A, et al. Primary pulmonary involvement in mucosa-associated lymphoid tissue lymphoma[J]. Cureus, 2019, 11(7): e5110.
    [2]
    高珂梦, 丁重阳, 孙晋, 等. 原发性肺黏膜相关淋巴组织淋巴瘤的18F-FDG PET/CT影像学表现[J]. 国际放射医学核医学杂志, 2019, 43(2): 140-144. doi: 10.3760/cma.j.issn.1673-4114.2019.02.008
    [3]
    徐芬. 以胃肠道症状为首发表现的非霍奇金淋巴瘤3例误诊分析[J]. 实用临床医药杂志, 2008, 12(13): 86-87. doi: 10.3969/j.issn.1672-2353.2008.12.044
    [4]
    HARRIS N L, JAFFE E S, DIEBOLD J, et al. The World Health Organization classification of hematological malignancies report of the Clinical Advisory Committee Meeting, Airlie House, Virginia, November 1997[J]. Mod Pathol, 2000, 13(2): 193-207. doi: 10.1038/modpathol.3880035
    [5]
    MIYAZAKI S, HACHIYA K, NARA Y, et al. Pulmonary mucosa-associated lymphoid tissue lymphoma mimicking lung cancer[J]. Clin Case Rep, 2019, 7(8): 1615-1616. doi: 10.1002/ccr3.2276
    [6]
    NAKAMURA D, KOBAYASHI N, MIYAZAWA M, et al. Pulmonary metastasis with coexisting pulmonary mucosa-associated lymphoid tissue (MALT) lymphoma 20 years after endometrioid adenocarcinoma surgery: a case report[J]. Thorac Cancer, 2021, 12(3): 402-406. doi: 10.1111/1759-7714.13776
    [7]
    陈来荣, 俞同福. 肺黏膜相关淋巴组织淋巴瘤的CT表现[J]. 医学影像学杂志, 2016, 26(1): 130-132. https://www.cnki.com.cn/Article/CJFDTOTAL-XYXZ201601044.htm
    [8]
    WANG L, YE G, LIU Z, et al. Clinical characteristics, diagnosis, treatment, and prognostic factors of pulmonary mucosa-associated lymphoid tissue-derived lymphoma[J]. Cancer Med, 2019, 8(18): 7660-7668. doi: 10.1002/cam4.2683
    [9]
    BI W L, ZHAO S, WU C C, et al. Pulmonary mucosa-associated lymphoid tissue lymphoma: CT findings and pathological basis[J]. J Surg Oncol, 2021, 123(5): 1336-1344. doi: 10.1002/jso.26403
    [10]
    陈利军, 韩月东, 张明. 肺黏膜相关淋巴组织淋巴瘤的CT表现[J]. 肿瘤影像学, 2021, 30(3): 191-194. https://www.cnki.com.cn/Article/CJFDTOTAL-YXYX202103009.htm
    [11]
    YAMASAKI M, TAKENAKA T, MATSUMOTO N, et al. Primary pulmonary collision tumor comprising squamous cell carcinoma and mucosa-associated lymphoid tissue lymphoma[J]. Lung Cancer Amsterdam Neth, 2019, 129: 107-109. doi: 10.1016/j.lungcan.2018.12.019
    [12]
    朱小云, 单飞, 邢伟, 等. 肺黏膜相关淋巴组织淋巴瘤的CT表现[J]. 临床放射学杂志, 2014, 33(3): 456-459. https://www.cnki.com.cn/Article/CJFDTOTAL-LCFS201403041.htm
    [13]
    CHEN Y N, CHEN A P, JIANG H L, et al. HRCT in primary pulmonary lymphoma: can CT imaging phenotypes differentiate histological subtypes between mucosa-associated lymphoid tissue (MALT) lymphoma and non-MALT lymphoma[J]. J Thorac Dis, 2018, 10(11): 6040-6049. doi: 10.21037/jtd.2018.10.63
    [14]
    ZHAO J, WANG H Q. Correlation between 18F-FDG PET/CT semiquantitative parameters and Ki-67 expression in pulmonary mucosa-associated lymphoid tissue lymphoma[J]. J Med Imaging Radiat Oncol, 2021, 65(2): 188-194. doi: 10.1111/1754-9485.13146
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