Shen Wei-Chih, Chen Shang-Wen, Liang Ji-An, Hsieh Te-Chun, Yen Kuo-Yang, Kao Chia-Hung
Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan.
Department of Radiation Oncology, China Medical University Hospital, Taichung, Taiwan.
Eur J Nucl Med Mol Imaging. 2017 Sep;44(10):1721-1731. doi: 10.1007/s00259-017-3697-1. Epub 2017 Apr 14.
In this study, we investigated the correlation between the lymph node (LN) status or histological types and textural features of cervical cancers on F-fluorodeoxyglucose positron emission tomography/computed tomography.
We retrospectively reviewed the imaging records of 170 patients with International Federation of Gynecology and Obstetrics stage IB-IVA cervical cancer. Four groups of textural features were studied in addition to the maximum standardized uptake value (SUV), metabolic tumor volume, and total lesion glycolysis (TLG). Moreover, we studied the associations between the indices and clinical parameters, including the LN status, clinical stage, and histology. Receiver operating characteristic curves were constructed to evaluate the optimal predictive performance among the various textural indices. Quantitative differences were determined using the Mann-Whitney U test. Multivariate logistic regression analysis was performed to determine the independent factors, among all the variables, for predicting LN metastasis.
Among all the significant indices related to pelvic LN metastasis, homogeneity derived from the gray-level co-occurrence matrix (GLCM) was the sole independent predictor. By combining SUV, the risk of pelvic LN metastasis can be scored accordingly. The TLG was the independent feature of positive para-aortic LNs. Quantitative differences between squamous and nonsquamous histology can be determined using short-zone emphasis (SZE) from the gray-level size zone matrix (GLSZM).
This study revealed that in patients with cervical cancer, pelvic or para-aortic LN metastases can be predicted by using textural feature of homogeneity from the GLCM and TLG respectively. SZE from the GLSZM is the sole feature associated with quantitative differences between squamous and nonsquamous histology.
在本研究中,我们调查了氟脱氧葡萄糖正电子发射断层扫描/计算机断层扫描上宫颈癌的淋巴结(LN)状态或组织学类型与纹理特征之间的相关性。
我们回顾性分析了170例国际妇产科联盟(FIGO)分期为IB-IVA期宫颈癌患者的影像记录。除了最大标准化摄取值(SUV)、代谢肿瘤体积和总病变糖酵解(TLG)外,还研究了四组纹理特征。此外,我们研究了这些指标与临床参数之间的关联,包括LN状态、临床分期和组织学。构建受试者工作特征曲线以评估各种纹理指标中的最佳预测性能。使用Mann-Whitney U检验确定定量差异。进行多变量逻辑回归分析以确定所有变量中预测LN转移的独立因素。
在所有与盆腔LN转移相关的显著指标中,来自灰度共生矩阵(GLCM)的均匀性是唯一的独立预测因子。通过结合SUV,可以相应地对盆腔LN转移的风险进行评分。TLG是腹主动脉旁LN阳性的独立特征。使用来自灰度大小区域矩阵(GLSZM)的短区域强调(SZE)可以确定鳞状和非鳞状组织学之间的定量差异。
本研究表明,在宫颈癌患者中,分别使用GLCM的均匀性纹理特征和TLG可以预测盆腔或腹主动脉旁LN转移。GLSZM的SZE是与鳞状和非鳞状组织学之间的定量差异相关的唯一特征。