| Literature DB >> 24778839 |
Larisa Sandulescu1, V Padureanu2, Cristina Dumitrescu2, Natalia Braia2, C T Streba1, D I Gheonea1, S Cazacu1, T Ciurea1, I Rogoveanu1, A Saftoiu1.
Abstract
INTRODUCTION: Real time-sonoelastography (RTE) is a new developed technique that reveals the physical properties of the tissue by characterizing the difference in hardness between diseased tissue and surrounding tissue. Elasticity measurements have been already reported to be useful for the diagnosis and differentiation of many tumors: breast lesions, prostate cancer, lymph nodes and pancreatic masses but there are only few studies for the focal liver lesions. The aim of the study was to analyze whether computer enhanced dynamic analysis of elastography images is able to better characterize and differentiate benign and malignant liver lesions.Entities:
Keywords: focal liver lesions; hepatocellular carcinoma; real-time sonoelastography
Year: 2012 PMID: 24778839 PMCID: PMC3945262
Source DB: PubMed Journal: Curr Health Sci J
Fig.1Real time transabdominal sonoelastography to a patient 65 years old with cholangiocarcinoma. The aspects in RT-E examination is predominant blue (average histogram= 221.81) suggest a hardness of the lesion. Figure take a elastography image during fine needle aspiration guided by endoscopic ultrasound
Fig.2Appearance of liver metastases in RT-E by transabdominal approach (2a) and endoscopic ultrasound (2b). The images illustrate the hard aspect of lesions predominantly blue. In Fig 2a RT-E demarcates very well a liver metastasis which was very difficult visible in standard ultrasound
Fig.3Haemangioma to a young patient. RT-E reveal inhomogeneous pattern with dominant green areas. Average histogram was in this case 147.73
Fig.4Graph shows box plots of average histogram to type of tumors. From examined tumors cholangiocarcinoma and liver metastases had the highest hardness
Fig.5Graph shows box plots of average histogram for benign versus malignant tumors
Fig.6ROC analysis used for the differentiation between benign and malignant focal liver lesions based on pattern analysis of elastography images