CAJ | 학술논문

目的通过水模研究,探讨影响18F-脱氧葡萄糖(FDG)PET图像靶区勾画阈值(TH%)的因素,建立计算阈值的公式,为不同生物学特征的肿瘤选取不同阈值.方法对特制的拥有5个不同大小靶区、7种不同靶区本底放射性比值的水模进行PET/CT图像采集,由图像得到每个靶区的最大标准摄取值(SUVmax)、靶区边界处的SUV(SUVborder)、本底中1 cm×1 cm大小感兴趣区的平均SUV(SUVbg)以及靶区内径(D)等,使用SPSS 13.0的曲线估计和线性回归分析方法,得到计算阈值的公式,并以此对29个经病理检查确诊的肺癌原发灶或转移淋巴结进行肿瘤靶区勾画,求出体积,比较PET和CT图像勾画的体积间的差别.结果通过分析水模数据,得出阈值与靶区的大小呈负相关,与靶区的SUVmax呈负相关,与SUVbg呈正相关,阈值的计算公式为TH%=33.1%+46.8%×SUVbg/SUVmax+13.9%/D,r=0.994.通过对29个病灶进行比较,发现PET和CT勾画的平均大体肿瘤体积(GTV)分别是(7.36±1.62)ml和(8.31±2.05)ml,两者间差异无统计学意义(t=-1.26,P＞0.05).结论靶区的大小、SUVmax及SUVbg均会影响靶区勾画阈值;通过公式TH%=33.1%+46.8%×SUVbg/SUVmax+13.9%/D可对不同肿瘤计算合适的阈值.按此公式PET和CT勾画的靶区无明显差异.
목적 통과수모연구,탐토영향18F-탈양포도당(FDG)PET도상파구구화역치(TH%)적인소,건립계산역치적공식,위불동생물학특정적종류선취불동역치.방법 대특제적옹유5개불동대소파구、7충불동파구본저방사성비치적수모진행PET/CT도상채집,유도상득도매개파구적최대표준섭취치(SUVmax)、파구변계처적SUV(SUVborder)、본저중1 cm×1 cm대소감흥취구적평균SUV(SUVbg)이급파구내경(D)등,사용SPSS 13.0적곡선고계화선성회귀분석방법,득도계산역치적공식,병이차대29개경병리검사학진적폐암원발조혹전이림파결진행종류파구구화,구출체적,비교PET화CT도상구화적체적간적차별.결과 통과분석수모수거,득출역치여파구적대소정부상관,여파구적SUVmax정부상관,여SUVbg정정상관,역치적계산공식위TH%=33.1%+46.8%×SUVbg/SUVmax+13.9%/D,r=0.994.통과대29개병조진행비교,발현PET화CT구화적평균대체종류체적(GTV)분별시(7.36±1.62)ml화(8.31±2.05)ml,량자간차이무통계학의의(t=-1.26,P＞0.05).결론 파구적대소、SUVmax급SUVbg균회영향파구구화역치;통과공식TH%=33.1%+46.8%×SUVbg/SUVmax+13.9%/D가대불동종류계산합괄적역치.안차공식PET화CT구화적파구무명현차이.
Objective To explore an algorithm to define the threshold value for tumor contouring on 18F-fluorodexyglucose (FDG) PET imaging. Methods A National Electrical Manufacturing Association (NEMA)NU 2 1994 PET phantom with 5 spheres of different diameters were filled with 18F-FDG. Seven different sphere-to-background ratios were obtained and the phantom was scanned by Discovery LS 4. For each sphere-to-background ratio, the maximum standardized uptake value ( SUVmax ) of each sphere, the SUV of the border of each sphere ( SUVborder ), the mean SUV of a 1 cm region of background (SUVbg) and the diameter (D) of each sphere were measured. SPSS 13.0 software was used for curve fitting and regression analysis to obtain the threshold algorithm. The calculated thresholds were applied to delineate 29 pathologically confirmed lung cancer lesions on PET images and the obtained volumes were compared with the volumes contoured on CT images in lung window. Results The algorithm for defining contour threshold is TH% = 33.1% + 46.8% SUVbg/SUVmax + 13.9%/D ( r = 0.994) by phantom studies. For 29 lung cancer lesions, the average gross tumor volumes ( GTV ) delineated on PET and CT are ( 7.36 ± 1.62 ) ml and (8.31 ±2.05) ml, respectively (t = -1.26, P＞0.05). Conclusion The proposed threshold algorithm for tumor contouring on PET image could provide comparable GTV with CT.