肺神经内分泌肿瘤的CT与18F-FDG PET/CT显像特征的分析

Analysis of CT and 18F-FDG PET/CT imaging features of pulmonary neuroendocrine tumors

  • 摘要:
    目的 探讨肺神经内分泌肿瘤(PNETs)的CT与18F-氟脱氧葡萄糖(FDG)PET/CT显像特征,比较单纯PET/CT与基于PET/CT联合增强CT及高分辨率CT多模态显像的诊断准确率。
    方法 回顾性分析2010年1月至2019年5月于西南医科大学附属医院经组织病理学检查确诊的44例PNETs患者的临床资料和CT、PET/CT影像学资料,其中男性34例、女性10例,年龄14~78(57.3±10.0)岁。将PNETs患者分为类癌组(8例)、大细胞神经内分泌癌(LCNEC)组(15例)和小细胞肺癌(SCLC)组(21例)。分析所有患者的CT与PET/CT表现,观察PNETs各亚型的CT与PET/CT显像特征。采用受试者工作特征(ROC)曲线分析计算最大标准化摄取值(SUVmax)的诊断效能及临界值。以组织病理学检查结果为“金标准”,比较单纯PET/CT与基于PET/CT联合增强CT及高分辨率CT多模态显像的诊断准确率。计量资料的多组间比较采用单因素方差分析,两组间比较采用最小显著差异法;计数资料的比较采用χ2检验和Fisher确切概率法。
    结果 类癌组患者的发病年龄低于LCNEC组与SCLC组患者(46.62±8.09)岁对(61.47±8.03)岁对(58.52±9.39)岁,且差异有统计学意义(F=6.186,P=0.004);在性别、吸烟史等方面与LCNEC组和SCLC组患者的差异均无统计学意义(χ2=1.220、4.539;均P>0.05)。类癌组、LCNEC组、SCLC组患者在肿瘤部位、有无分叶征、阻塞性肺炎或肺不张、纵隔淋巴结转移、肺门淋巴结转移、纵隔及肺门淋巴结同时转移、远处转移、周围血管侵犯等方面的差异均有统计学意义(χ2=6.662~9.877,均P<0.05);而在肿瘤最大径、肿瘤形态、密度、强化程度、有无钙化、毛刺征、有无坏死和囊变、胸腔积液、支气管侵犯和胸膜增厚等方面的差异均无统计学意义(F=0.370,χ2=0.298~8.472;均P>0.05)。LCNEC组和SCLC组的SUVmax显著高于类癌组(13.79±3.06对9.51±2.49对4.52±1.77),且差异有统计学意义(F=32.43,P<0.01);鉴别LCNEC和SCLC的SUVmax临界值为12.25,曲线下面积为0.860(95%CI:0.729~0.991,P<0.01),灵敏度为80.00%,特异度为81.00%。单纯PET/CT与基于PET/CT联合增强CT及高分辨率CT多模态显像诊断PNETs的准确率分别为65.91%(29/44)和87.80%(36/41),且差异有统计学意义(χ2=5.655,P=0.017)。
    结论 PNETs的CT与PET/CT显像具有一定特征性,PET/CT联合增强CT及高分辨率CT多模态显像可提高PNETs的诊断准确率。

     

    Abstract:
    Objective To investigate the CT and 18FDG-fluorodeoxyglucose (FDG) PET/CT imaging features of pulmonary neuroendocrine tumors (PNETs) and to compare the accuracies of PNETs diagnosis based on PET/CT and the multimodal diagnostics of PET/CT combined with enhanced CT and high-resolution CT.
    Methods The clinical, CT, and PET/CT data of 44 patients with PNETs diagnosed via histopathological examination in the Affiliated Hospital of Southwest Medical University from January 2010 to May 2019 were analyzed retrospectively. The patients comprised 34 males and 10 females aged 14–78 (57.3±10.0) years. All patients were divided into the carcinoid group (8 cases), the large-cell neuroendocrine carcinoma group (LCNEC, 15 cases), and the small-cell lung cancer group (SCLC, 21 cases). The CT and PET/CT features of the PNETs were investigated and analyzed. The diagnostic efficacy and critical value of maximum standardized uptake value (SUVmax) were analyzed and calculated by receiver operating characteristic (ROC) curve. By taking the results of histopathological examination as the gold standard, the accuracies of PNET diagnosis based on PET/CT and the multimodal diagnostics of PET/CT combined with enhanced CT and high-resolution CT were compared. Measurement data were compared by using one-way analysis of variance, and the least significant difference method was used to compare the two groups. The qualitative data were compared by applying χ2 test or Fisher's exact probability method.
    Results The age of the carcinoid group was lower than that of the LCNEC and SCLC groups (46.62±8.09 vs. 61.47±8.03 vs. 58.52±9.39), and the difference was statistically significant (F=6.186, P=0.004). However, no significant difference in sex and smoking history (χ2=1.220, 4.539; both P>0.05) was found. Significant differences were discovered in the location, lobulation, obstructive pneumonia or atelectasis, mediastinal lymph node metastasis, hilar lymph node metastasis, simultaneous mediastinal and hilar lymph node metastasis, distant metastasis, and vascular invasion in patients in the carcinoid, LCNEC, and SCLC groups (χ2=6.662–9.877, all P<0.05). However, no significant difference was found in maximum diameter, shape, density, enhancement degree, calcification, spiculation, necrosis and cystic degeneration, pleural effusion, bronchial invasion, pleural thickening (F=0.370, χ2=0.298–8.472, all P>0.05). The SUVmax of the LCNEC and SCLC groups was significantly higher than that of the carcinoid group (13.79±3.06 vs. 9.51±2.49 vs. 4.52±1.77), and the difference was statistically significant (F=32.43, P<0.01). For differentiating LCNEC from SCLC, the cutoff value of SUVmax was 12.25, the area under curve was 0.860 (95%CI: 0.729−0.991, P<0.01), the sensitivity was 80.00%, and the specificity was 81.00%. The accuracy of PNETs diagnosis based on PET/CT was 65.91%(29/44) and that of the multimodal diagnostics of PET/CT combined with enhanced CT and high-resolution CT was 87.80%(36/41). The difference was statistically significant (χ2=5.655, P=0.017).
    Conclusions The CT and PET/CT manifestations of PNETs have certain characteristics, and the multimodal diagnostics of PET/CT combined with enhanced CT and high-resolution CT can improve the accuracy of diagnosing PNETs.

     

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