Role of textural analysis parameters derived from FDG PET/CT in differentiating hepatocellular carcinoma and hepatic metastases

Abstract

Methods 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.Results 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). ConclusionTextural analysis parameters could successfully differentiate HCC and hepatic metastasis non-invasively. Larger multi-center studies are needed for better clinical prognostication of these parameters. Nucl Med Commun

Artificial intelligence strategies based on run length matrix and wavelet analyses for detection of subtle alterations in hepatocyte chromatin organization following exposure to iron oxide nanoparticles

“…Alternatively, and yet relatively uninvestigated, approaches to textural analysis of cell structure include Run Length Matrix analysis (RLM), which can be particularly useful for providing information on intensity and spatial relationships of micrograph components. Run Length Matrix analysis, also known as Gray-Level Run Length Matrix (GLRLM) analysis, can extract measures of cell texture such as Short Run Emphasis (SRE), Long Run Emphasis (LRE), and Gray Level Non-Uniformity (GLN), quantifiers that are closely related to two-dimensional heterogeneity and the level of textural disorder 10,11 . A vector of these features can later be used as valuable input for supervised ML training in classification and regression tasks.…”

Role of textural analysis parameters derived from FDG PET/CT in differentiating hepatocellular carcinoma and hepatic metastases: Erratum

“…The authors of the article [1] provided an incorrect listing of the authors. The authors should have been listed as follows:…”