Krak Nanda C, van der Hoeven Jacobus J M, Hoekstra Otto S, Twisk Jos W R, van der Wall Elsken, Lammertsma Adriaan A
Clinical PET Centre, VU University Medical Centre, Amsterdam, The Netherlands.
Eur J Nucl Med Mol Imaging. 2003 May;30(5):674-81. doi: 10.1007/s00259-003-1127-z. Epub 2003 Mar 15.
Over the years several analytical methods have been proposed for the measurement of glucose metabolism using fluorine-18 fluorodeoxyglucose ([(18)F]FDG) and positron emission tomography (PET). The purpose of this study was to evaluate which of these (often simplified) methods could potentially be used for clinical response monitoring studies in breast cancer. Prior to chemotherapy, dynamic [(18)F]FDG scans were performed in 20 women with locally advanced ( n=10) or metastasised ( n=10) breast cancer. Additional PET scans were acquired after 8 days ( n=8), and after one, three and six courses of chemotherapy ( n=18, 10 and 6, respectively). Non-linear regression (NLR) with the standard two tissue compartment model was used as the gold standard for measurement of [(18)F]FDG uptake and was compared with the following methods: Patlak graphical analysis, simplified kinetic method (SKM), SUV-based net influx constant ("Sadato" method), standard uptake value [normalised for weight, lean body mass (LBM) and body surface area (BSA), with and without corrections for glucose (g)], tumour to non-tumour ratio (TNT), 6P model and total lesion evaluation (TLE). Correlation coefficients between each analytical method and NLR were calculated using multilevel analysis. In addition, for the most promising methods (Patlak, SKM, SUV(LBMg) and SUV(BSAg)) it was explored whether correlation with NLR changed with different time points after the start of therapy. Three methods showed excellent correlation ( r>0.95) with NLR for the baseline scan: Patlak10-60 and Patlak10-45 ( r=0.98 and 0.97, respectively), SKM40-60 ( r=0.96) and SUV(LBMg) ( r=0.96). Good correlation was found between NLR and SUV-based net influx constant, TLE and SUV(BSAg) (0.90< r<0.95). The 6P model and TNT had the lowest correlation ( r<or=0.84). SUV was least accurate in predicting changes in [(18)F]FDG uptake over time during therapy. For all methods, correlation with NLR was significantly lower for bone metastases than for other (primary or metastatic) tumour lesions ( P<0.05). In conclusion, three methods with different degrees of complexity appear to be promising alternatives to NLR for measuring glucose metabolism in breast cancer: Patlak, SKM and SUV (normalised for LBM and with a correction for plasma glucose).
多年来，已经提出了几种使用氟 - 18氟脱氧葡萄糖（[(18)F]FDG）和正电子发射断层扫描（PET）来测量葡萄糖代谢的分析方法。本研究的目的是评估这些（通常较为简化的）方法中哪些可能用于乳腺癌的临床反应监测研究。在化疗前，对20名局部晚期（n = 10）或转移性（n = 10）乳腺癌女性进行了动态[(18)F]FDG扫描。在8天后（n = 8）以及化疗1个疗程、3个疗程和6个疗程后（分别为n = 18、10和6）进行了额外的PET扫描。使用标准双组织隔室模型的非线性回归（NLR）作为测量[(18)F]FDG摄取的金标准，并与以下方法进行比较：Patlak图形分析、简化动力学方法（SKM）、基于SUV的净流入常数（“佐田”方法）、标准摄取值[根据体重、瘦体重（LBM）和体表面积（BSA）进行归一化，有或无葡萄糖（g）校正]、肿瘤与非肿瘤比值（TNT）、6P模型和总病变评估（TLE）。使用多水平分析计算每种分析方法与NLR之间的相关系数。此外，对于最有前景的方法（Patlak、SKM、SUV(LBMg)和SUV(BSAg)），探讨了与NLR的相关性在治疗开始后的不同时间点是否发生变化。三种方法在基线扫描时与NLR显示出极好的相关性（r > 0.95）：Patlak10 - 60和Patlak10 - 45（分别为r = 0.98和0.97）、SKM40 - 60（r = 0.96）和SUV(LBMg)（r = 0.96）。发现NLR与基于SUV的净流入常数、TLE和SUV(BSAg)之间具有良好的相关性（0.90 < r < 0.95）。6P模型和TNT的相关性最低（r ≤ 0.84）。在预测治疗期间[(18)F]FDG摄取随时间的变化方面，SUV最不准确。对于所有方法，骨转移与NLR的相关性显著低于其他（原发性或转移性）肿瘤病变（P < 0.05）。总之，三种不同复杂程度的方法似乎是用于测量乳腺癌葡萄糖代谢的有前景的NLR替代方法：Patlak、SKM和SUV（根据LBM进行归一化并对血浆葡萄糖进行校正）。
