Magnetic resonance imaging of soft tissue tumors

Abstract

This article outlines the ability of MR imaging in staging, grading, tissue characterization, and posttherapeutic surveillance of soft tissue tumors. Well-known staging parameters, such as extent, relationship with adjacent structures, and detection of intralesional necrosis, are used in the MR protocol for locoregional staging. Bone scintigraphy and high-resolution CT scan of the lungs are best methods for ruling out metastatic spread. A variety of (solitary or combinations of) grading parameters are described in the radiological literature. The role of MR imaging is to afford recognition of these lesions that need further aggressive work-up, excluding all others. Despite controversial reports, the definite role of MR imaging in grading of soft tissue tumors seems to become established. As for grading, a lot of individual imaging characteristics used for tissue characterization have low sensitivity, but combinations of parameters (age, site, signal intensities) are more useful and often allow to predict a specific diagnosis or to narrow down the list of differential diagnoses. Local recurrences of soft tissue tumors are frequent and can be detected accurately by an easy-to-use MR algorithm.

Classification of Soft Tissue Tumors by Machine Learning Algorithms

“…MR imaging is currently regarded as the standard diagnostic tool for detection and grading of soft tissue tumors (STT ) (De Schepper et al (2005)). Soft tissue is a term describing all the supporting, connecting or tissues surrounding other structures and organs of the body such as fat, muscle, blood vessels, deep skin tissues, nerves and the tissues around joints (synovial tissues).…”

“…Radiologists often look for certain features in the MR image to differentiate benign from malignant STT tumors (Juan et al (2004); Mutlu et al (2006)). Although the signal characteristics of both benign and malignant tumors frequently overlap, some MR image features are more highly correlated to the benign or the malignant types of STT , see De Schepper et al (2000) and De Schepper & Bloem (2007). For example, the most commonly used individual parameters for predicting malignancy are the inhomogeneity (texture) and the intensity (gray level) of the MRI signal with different pulse sequences (De Schepper et al (2005); Hermann et al (1992)).…”

“…Although the signal characteristics of both benign and malignant tumors frequently overlap, some MR image features are more highly correlated to the benign or the malignant types of STT , see De Schepper et al (2000) and De Schepper & Bloem (2007). For example, the most commonly used individual parameters for predicting malignancy are the inhomogeneity (texture) and the intensity (gray level) of the MRI signal with different pulse sequences (De Schepper et al (2005); Hermann et al (1992)). Inhomogeneity of the tumor region on T1-weighted MR images is a very good indicator of the malignancy of the tumor because 90% of malignant tumors are inhomogeneous and show a disorganized textured pattern of the MRI signal intensity (Weatherall (1995)).…”

MRI Texture Analysis Predicts p53 Status in Head and Neck Squamous Cell Carcinoma

“…[15][16][17][18] In addition to anatomic details, the analysis of MR images provides additional metabolic and biologic information in tumors. 19 Mathematic techniques that quantify image characteristics have been applied to a vast array of pathologies, from multiple sclerosis, 20 attention deficit/hyperactivity disorder, 21 and Alzheimer disease 22 to breast cancer, 23 cervical cancer, 24 and brain tumors.…”

Magnetic Resonance Imaging features of masses in the region of nose, sinuses and adjacent bones: Retrospective Analyses of 32 Horses

“…Eine charakteristische Morphologie, die sie von anderen Veränderungen leicht unterscheidet, wird in der Humanmedizin bei folgenden Tumoren beschrieben: lipomatöse Tumoren (Lipom, Liposarkom), vaskuläre Läsionen (Hämangiom, Lymphangiom), einige neurogene Tumoren, seltene chondrogene Tumoren der Weichteile, praktisch alle Gelenktumoren und das Elastofibrom (de Schepper 2000, Kransdorf et al 1993. Lipome stellen sich auf T1w homogen signalintensiv und auf T2w mäßig hyperintens bis isointens dar.…”