[18 F-FDG PET/CT Manifestations of Massive Type Active Pulmonary Tuberculosis and Its Differentiation From Lung Cancer]

Nan Fang Yi Ke Da Xue Xue Bao. 2020 Jan 30;40(1):49-55. doi: 10.12122/j.issn.1673-4254.2020.01.08.
[Article in Chinese]

Abstract

Objective: To investigate 18F-FDG PET/CT manifestations of massive type active tuberculosis and lung cancer and the differential diagnosis of the two diseases based on 18F-FDG PET/CT findings.

Methods: We retrospectively collected the data from 74 patients with active tuberculosis and 64 patients with lung cancer, whose lesions presented as solid masses on CT. The demographic and clinical data of the patients, 18F-FDG PET characteristics including SUVmax, 18F-FDG uptake (higher than mediastinal blood pool or not), radioactive defect within the lesion, and the CT findings including the lesion size, signs of cavity, vacuoles, lobulation, smooth border, and mediastinal/lung window ratio (M/L ratio) of the lesions were analyzed. Univariate and multivariate analyses were used to compare the variables between the two groups, and a logistic regression model was established for differentiation of the two diseases. The diagnostic efficiency was evaluated by area under the receiver-operating characteristic (ROC) curve analysis.

Results: No significant differences were found in the quantitative index (SUVmax >2.5 or not) or in the qualitative index (uptake of lesion higher than mediastinal blood pool or not) in PET between massive type active tuberculosis and lung cancer (P>0.05). Univariate analysis revealed that SUVmax, 18F-FDG uptake of the lesion, age, lesion size, signs of cavity, or M/L ratio were not significantly different (P>0.05), but gender, signs of radioactive defect, vacuoles, smooth border and lobulation were significantly different (P < 0.05) between the two diseases. Multivariate analysis showed that gender, signs of radioactive defect, smooth border and lobulation of the lesion were independent factors for discrimination of the two diseases (P < 0.05). A risk prediction model for active tuberculosis was established based on logistic regression analysis: P=1/(1+e-x), X=-0.530+1.978×gender+3.343×radioactive defect +2.846×smooth border-2.116×lobulation. For diagnosis of active tuberculosis, the sensitivity, specificity, accuracy, positive predictive value and negative predictive value of this model were 78.4%, 92.2%, 84.8%, 92.1%, and 78.7%, respectively.

Conclusions: The combined analysis of gender, signs of radioactive defect, smooth border and lobulation of the lesions is useful for discriminating massive type active tuberculosis from lung cancer in the majority of the patients, whereas 18F-FDG uptake alone has only limited value for a differential diagnosis.

目的: 分析肿块型活动性肺结核与肺癌18F-FDG PET/CT影像学特点,探索综合分析PET和CT的多种影像改变鉴别两者的可能性。

方法: 纳入在CT上呈现为肿块型的实性病灶的活动性肺结核74例和肺癌64例患者,分析患者的年龄、性别以及病灶的18F-FDG PET代谢信息(包括SUVmax、病灶代谢是否高于纵隔血池、病灶中心是否存在放射性缺损)和CT信息(包括病灶大小、空洞、空泡征、分叶征、边缘是否光滑和纵隔/肺窗比值)等参数。通过单因素和多因素分析比较两种疾病的参数,建立鉴别两者的回归公式,通过ROC曲线分析其鉴别诊断效能。

结果: 肿块型活动性肺结核与肺癌病灶在PET定量指标(SUVmax是否高于2.5)及定性指标(病灶代谢是否高于纵隔血池)方面差异无统计学意义(P>0.05)。单因素分析显示,虽然肿块型活动性结核与肺癌在SUVmax、代谢是否明显高于纵隔血池、患者年龄、病灶大小、空洞以及CT纵隔/肺窗比值等方面差异无统计学意义(P>0.05),但在患者性别、病灶内有无放射性缺损、空泡征、边缘是否光滑和分叶征等方面的差异有统计学意义(P<0.05)。多因素分析显示性别、放射性缺损、边缘光滑和分叶征对鉴别两种疾病有价值(P<0.05)。采用Logistic回归模型将上述4个因子纳入分析而得活动性结核风险预测模型:P=1/(1+e-x),X=-0.530+1.978×性别+3.343×放射性缺损+2.846×边缘光滑-2.116×分叶征。该模型用于诊断肿块型活动性肺结核的灵敏度为78.4%,特异度为92.2%,准确率为84.8%,阳性预测值为92.1%,阴性预测值为78.7%。

结论: 18F-FDG PET/CT显像仅根据病灶的代谢高低难以将肿块型活动性肺结核与肺癌进行鉴别,综合分析患者性别、病灶是否存在放射性缺损以及病灶边缘信息有助于将大多数活动性结核病灶与肺癌鉴别。

Keywords: F-18; emission computer; fluorodeoxyglucose; lung cancer; tomography; tomography, X-ray computed; tuberculosis.

MeSH terms

  • Aged
  • Diagnosis, Differential
  • Female
  • Fluorodeoxyglucose F18
  • Humans
  • Lung Neoplasms / diagnostic imaging*
  • Male
  • Middle Aged
  • Positron Emission Tomography Computed Tomography*
  • Radiopharmaceuticals
  • Retrospective Studies
  • Tuberculosis, Pulmonary / diagnostic imaging*

Substances

  • Radiopharmaceuticals
  • Fluorodeoxyglucose F18

Grant support

广东省医学科研基金(A2016590)