Ravina Mudalsha, Mishra Ajit, Kote Rutuja, Kumar Amit, Kashyap Yashwant, Dasgupta Subhajit, Reddy Moulish
Department of Nuclear Medicine, AIl India Institute of Medical Sciences.
Department of Surgical Gastroenterology, DKS Multispeciality Hospital.
Nucl Med Commun. 2023 May 1;44(5):381-389. doi: 10.1097/MNM.0000000000001676. Epub 2023 Feb 27.
Texture and radiomic analysis characterize the tumor's phenotype and evaluate its microenvironment in quantitative terms. The aim of this study was to investigate the role of textural features of 18F-FDG PET/computed tomography (CT) images in differentiating hepatocellular carcinoma (HCC) and hepatic metastasis in patients with suspected liver tumors.
This is a retrospective, single-center study of 30 patients who underwent FDG PET/CT for the characterization of liver lesions or for staging a suspected liver tumor. The histological diagnosis of either primary or metastatic tumor was obtained from CT-guided biopsy, ultrasound-guided biopsy, or surgical removal of a liver lesion. The PET/CT images were then processed in commercially available textural analysis software. Region of interest was drawn over the primary tumor with a 40% threshold and was processed further to derive 42 textural and radiomic parameters. These parameters were then compared between HCC group and hepatic metastases group. Receiver-operating characteristic (ROC) curves were used to identify cutoff values for textural features with a P value <0.05 for statistical significance.
A retrospective study of 30 patients with suspected liver tumors was done. After undergoing PET/CT, the histological diagnosis of these lesions was confirmed. Among these 30 patients, 15 patients had HCC, and 15 patients had hepatic metastases from various primary sites. Seven textural analysis parameters were significant in differentiating HCC from liver metastasis. Cutoff values were calculated for these parameters according to the ROC curves, standardized uptake value (SUV) Skewness (0.705), SUV Kurtosis (3.65), SUV Excess Kurtosis (0.653), gray-level zone length matrix_long zone emphasis (349.2), gray-level zone length matrix_long zone low gray-level emphasis (1.6), gray-level run length matrix_long run emphasis (1.38) and gray-level co-occurrence matrix_Homogeneity (0.406).
Textural analysis parameters could successfully differentiate HCC and hepatic metastasis non-invasively. Larger multi-center studies are needed for better clinical prognostication of these parameters.
纹理分析和放射组学分析可对肿瘤表型进行特征描述，并从定量角度评估其微环境。本研究的目的是探讨18F-FDG PET/计算机断层扫描（CT）图像的纹理特征在鉴别疑似肝肿瘤患者的肝细胞癌（HCC）和肝转移瘤中的作用。
这是一项回顾性单中心研究，纳入30例因肝病变特征性诊断或疑似肝肿瘤分期而接受FDG PET/CT检查的患者。原发性或转移性肿瘤的组织学诊断通过CT引导下活检、超声引导下活检或肝病变手术切除获得。然后使用商用纹理分析软件对PET/CT图像进行处理。在原发性肿瘤上绘制感兴趣区域，阈值为40%，并进一步处理以得出42个纹理和放射组学参数。然后比较HCC组和肝转移瘤组之间的这些参数。采用受试者操作特征（ROC）曲线确定纹理特征的截断值，P值<0.05具有统计学意义。
对30例疑似肝肿瘤患者进行了回顾性研究。接受PET/CT检查后，确诊了这些病变的组织学诊断。在这30例患者中，15例患有HCC，15例患有来自不同原发部位的肝转移瘤。七个纹理分析参数在鉴别HCC和肝转移瘤方面具有显著意义。根据ROC曲线计算这些参数的截断值，标准化摄取值（SUV）偏度（0.705）、SUV峰度（3.65）、SUV超额峰度（0.653）、灰度区域长度矩阵_长区域强调（349.2）、灰度区域长度矩阵_长区域低灰度强调（1.6）、灰度游程长度矩阵_长游程强调（1.38）和灰度共生矩阵_同质性（0.406）。
纹理分析参数可以成功地无创鉴别HCC和肝转移瘤。需要开展更大规模的多中心研究以更好地对这些参数进行临床预后评估。
