Pellegrino Sara, Panico Mariarosaria, Bologna Roberto, Morra Rocco, Servetto Alberto, Bianco Roberto, Del Vecchio Silvana, Fonti Rosa
Department of Advanced Biomedical Sciences, University Federico II, 80131 Naples, Italy.
Institute of Biostructures and Bioimages, National Research Council, 80145 Naples, Italy.
Biomedicines. 2025 May 23;13(6):1286. doi: 10.3390/biomedicines13061286.
: The aim of our study is to evaluate whether texture analysis of 68Ga-DOTATOC PET/CT images can predict clinical outcome in patients with neuroendocrine tumors (NET). : Forty-seven NET patients who had undergone 68Ga-DOTATOC PET/CT were studied. Primary tumors were localized in the gastroenteropancreatic (n = 35), bronchopulmonary (n = 8), and other (n = 4) districts. NET lesions were segmented using an automated contouring program and subjected to texture analysis, thus obtaining the conventional parameters SUVmax and SUVmean, volumetric parameters of the primary lesion, such as Receptor-Expressing Tumor Volume (RETV) and Total Lesion Receptor Expression (TLRE), volumetric parameters of the lesions in the whole-body, such as wbRETV and wbTLRE, and texture features such as Coefficient of Variation (CoV), HISTO Skewness, HISTO Kurtosis, HISTO Entropy-log, GLCM Entropy-log, GLCM Dissimilarity, and NGLDM Coarseness. Patients were subjected to a mean follow-up period of 17 months, and survival analysis was performed using the Kaplan-Meier method and log-rank tests. : Forty-seven primary lesions were analyzed. Survival analysis was performed, including clinical variables along with conventional, volumetric, and texture imaging features. At univariate analysis, overall survival (OS) was predicted by age ( = 0.0079), grading ( = 0.0130), SUVmax ( = 0.0017), SUVmean ( = 0.0011), CoV ( = 0.0037), HISTO Entropy-log ( = 0.0039), GLCM Entropy-log ( = 0.0044), and GLCM Dissimilarity ( = 0.0063). At multivariate analysis, only GLCM Entropy-log was retained in the model (χ = 7.7120, = 0.0055). Kaplan-Meier curves showed that patients with GLCM Entropy-log >1.28 had a significantly better OS than patients with GLCM Entropy-log ≤1.28 (χ = 10.6063, = 0.0011). : Texture analysis of 68Ga-DOTATOC PET/CT images, by revealing the heterogeneity of somatostatin receptor expression, can predict the clinical outcome of NET patients.
我们研究的目的是评估68Ga-DOTATOC PET/CT图像的纹理分析能否预测神经内分泌肿瘤（NET）患者的临床结局。对47例已接受68Ga-DOTATOC PET/CT检查的NET患者进行了研究。原发性肿瘤位于胃肠胰（n = 35）、支气管肺（n = 8）和其他（n = 4）区域。使用自动轮廓程序对NET病变进行分割并进行纹理分析，从而获得传统参数SUVmax和SUVmean、原发性病变的体积参数，如受体表达肿瘤体积（RETV）和总病变受体表达（TLRE）、全身病变的体积参数，如wbRETV和wbTLRE，以及纹理特征，如变异系数（CoV）、HISTO偏度、HISTO峰度、HISTO熵对数、GLCM熵对数、GLCM差异和NGLDM粗糙度。患者的平均随访期为17个月，并使用Kaplan-Meier方法和对数秩检验进行生存分析。对47个原发性病变进行了分析。进行了生存分析，包括临床变量以及传统、体积和纹理成像特征。单因素分析显示，总生存期（OS）可由年龄（P = 0.0079）、分级（P = 0.0130）、SUVmax（P = 0.0017）、SUVmean（P = 0.0011）、CoV（P = 0.0037）、HISTO熵对数（P = 0.0039）、GLCM熵对数（P = 0.0044）和GLCM差异（P = 0.0063）预测。多因素分析显示，模型中仅保留了GLCM熵对数（χ = 7.7120，P = 0.0055）。Kaplan-Meier曲线显示，GLCM熵对数>1.28的患者的OS明显优于GLCM熵对数≤1.28的患者（χ = 10.6063，P = 0.0011）。68Ga-DOTATOC PET/CT图像的纹理分析通过揭示生长抑素受体表达的异质性，可以预测NET患者的临床结局。
