Fledelius Joan, Winther-Larsen Anne, Khalil Azza A, Bylov Catharina M, Hjorthaug Karin, Bertelsen Aksel, Frøkiær Jørgen, Meldgaard Peter
Department of Nuclear Medicine, Herning Regional Hospital, Herning, Denmark
Department of Clinical Biochemistry, Aarhus University Hospital, Aarhus, Denmark.
J Nucl Med. 2017 Dec;58(12):1931-1937. doi: 10.2967/jnumed.117.193003. Epub 2017 May 10.
The purpose of this study was to determine which method for early response evaluation with F-FDG PET/CT performed most optimally for the prediction of response on a later CT scan in erlotinib-treated non-small cell lung cancer patients. F-FDG PET/CT scans were obtained before and after 7-10 d of erlotinib treatment in 50 non-small cell lung cancer patients. The scans were evaluated using a qualitative approach and various semiquantitative methods including percentage change in SUVs, lean body mass-corrected (SUL) SUL, SUL, and total lesion glycolysis (TLG). The PET parameters and their corresponding response categories were compared with the percentage change in the sum of the longest diameter in target lesions and the resulting response categories from a CT scan obtained after 9-11 wk of erlotinib treatment using receiver-operating-characteristic analysis, linear regression, and quadratic-weighted κ. TLG delineation according to the PERCIST showed the strongest correlation to sum of the longest diameter ( = 0.564, < 0.001), compared with SUL ( = 0.298, = 0.039) and SUL ( = 0.402, = 0.005). For predicting progression on CT, receiver-operating-characteristic analysis showed area under the curves between 0.79 and 0.92, with the highest area under the curve of 0.92 (95% confidence interval [CI], 0.84-1.00) found for TLG (PERCIST). Furthermore, the use of a cutoff of 25% change in TLG (PERCIST) for both partial metabolic response and progressive metabolic disease, which is the best predictor of the CT response categories, showed a κ-value of 0.53 (95% CI, 0.31-0.75). This method identifies 41% of the later progressive diseases on CT, with no false-positives. Visual evaluation correctly categorized 50%, with a κ-value of 0.47 (95% CI, 0.24-0.70). TLG (PERCIST) was the optimal predictor of response on later CT scans, outperforming both SUL and SUL The use of TLG (PERCIST) with a 25% cutoff after 1-2 wk of treatment allows us to safely identify 41% of the patients who will not benefit from erlotinib and stop the treatment at this time.
本研究的目的是确定在接受厄洛替尼治疗的非小细胞肺癌患者中,哪种使用F-FDG PET/CT进行早期反应评估的方法在预测后续CT扫描的反应方面表现最为理想。对50例非小细胞肺癌患者在厄洛替尼治疗7 - 10天前后进行了F-FDG PET/CT扫描。采用定性方法和多种半定量方法对扫描结果进行评估,包括SUVs的变化百分比、瘦体重校正后的标准化摄取值(SUL)、SUL以及总病变糖酵解(TLG)。使用受试者工作特征分析、线性回归和二次加权κ值,将PET参数及其相应的反应类别与目标病变最长径总和的变化百分比以及厄洛替尼治疗9 - 11周后获得的CT扫描结果的反应类别进行比较。根据实体瘤疗效评价标准(PERCIST)进行的TLG描绘显示与最长径总和的相关性最强(r = 0.564,P < 0.001),相比之下SUL的相关性为(r = 0.298,P = 0.039),SUL为(r = 0.402,P = 0.005)。对于预测CT上的疾病进展,受试者工作特征分析显示曲线下面积在0.79至0.92之间,TLG(PERCIST)的曲线下面积最高,为0.92(95%置信区间[CI],0.84 - 1.00)。此外,将TLG(PERCIST)变化25%作为部分代谢反应和进行性代谢疾病的截断值,这是CT反应类别的最佳预测指标,κ值为0.53(95% CI,0.31 - 0.75)。该方法可识别出41%的后续CT上的进行性疾病,且无假阳性。视觉评估正确分类率为50%时,κ值为0.47(95% CI,0.24 - 0.70)。TLG(PERCIST)是后续CT扫描反应的最佳预测指标,优于SUL和SUL。在治疗1 - 2周后使用截断值为25%的TLG(PERCIST),使我们能够安全地识别出41%不会从厄洛替尼治疗中获益的患者,并在此阶段停止治疗。