Hiwatashi Akio, Togao Osamu, Yamashita Koji, Kikuchi Kazufumi, Yoshimoto Koji, Mizoguchi Masahiro, Suzuki Satoshi O, Yoshiura Takashi, Honda Hiroshi
Department of clinical radiology, Graduate School of medical sciences, Kyushu university, 3-1-1, Maidashi, Higashi-ku, 812-8582 Fukuoka, Japan.
Department of clinical radiology, Graduate School of medical sciences, Kyushu university, 3-1-1, Maidashi, Higashi-ku, 812-8582 Fukuoka, Japan.
J Neuroradiol. 2016 Jul;43(4):266-72. doi: 10.1016/j.neurad.2016.01.147.
Correction of contrast leakage is recommended when enhancing lesions during perfusion analysis. The purpose of this study was to assess the diagnostic performance of computed tomography perfusion (CTP) with a delay-invariant singular-value decomposition algorithm (SVD+) and a Patlak plot in differentiating glioblastomas from lymphomas.
This prospective study included 17 adult patients (12 men and 5 women) with pathologically proven glioblastomas (n=10) and lymphomas (n=7). CTP data were analyzed using SVD+ and a Patlak plot. The relative tumor blood volume and flow compared to contralateral normal-appearing gray matter (rCBV and rCBF derived from SVD+, and rBV and rFlow derived from the Patlak plot) were used to differentiate between glioblastomas and lymphomas. The Mann-Whitney U test and receiver operating characteristic (ROC) analyses were used for statistical analysis.
Glioblastomas showed significantly higher rFlow (3.05±0.49, mean±standard deviation) than lymphomas (1.56±0.53; P<0.05). There were no statistically significant differences between glioblastomas and lymphomas in rBV (2.52±1.57 vs. 1.03±0.51; P>0.05), rCBF (1.38±0.41 vs. 1.29±0.47; P>0.05), or rCBV (1.78±0.47 vs. 1.87±0.66; P>0.05). ROC analysis showed the best diagnostic performance with rFlow (Az=0.871), followed by rBV (Az=0.771), rCBF (Az=0.614), and rCBV (Az=0.529).
CTP analysis with a Patlak plot was helpful in differentiating between glioblastomas and lymphomas, but CTP analysis with SVD+ was not.
在灌注分析中增强病变时，建议校正对比剂渗漏。本研究的目的是评估采用延迟不变奇异值分解算法（SVD+）和Patlak图的计算机断层扫描灌注（CTP）在鉴别胶质母细胞瘤与淋巴瘤方面的诊断性能。
本前瞻性研究纳入了17例成年患者（12例男性和5例女性），其中胶质母细胞瘤患者10例，淋巴瘤患者7例，均经病理证实。使用SVD+和Patlak图对CTP数据进行分析。将肿瘤相对血容量和血流量与对侧正常灰质进行比较（分别从SVD+得出rCBV和rCBF，从Patlak图得出rBV和rFlow），以鉴别胶质母细胞瘤和淋巴瘤。采用Mann-Whitney U检验和受试者操作特征（ROC）分析进行统计学分析。
胶质母细胞瘤的rFlow（3.05±0.49，平均值±标准差）显著高于淋巴瘤（1.56±0.53；P<0.05）。胶质母细胞瘤与淋巴瘤在rBV（2.52±1.57对1.03±0.51；P>0.05）、rCBF（1.38±0.41对1.29±0.47；P>0.05）或rCBV（1.78±0.47对1.87±0.66；P>0.05）方面无统计学显著差异。ROC分析显示，rFlow的诊断性能最佳（Az=0.871），其次是rBV（Az=0.771）、rCBF（Az=0.614）和rCBV（Az=0.529）。
采用Patlak图的CTP分析有助于鉴别胶质母细胞瘤和淋巴瘤，但采用SVD+的CTP分析则无此作用。
