MRI radiomics-based machine-learning classification of bone chondrosarcoma.

Purpose: To evaluate the diagnostic performance of machine learning for discrimination between low-grade and high-grade cartilaginous bone tumors based on radiomic parameters extracted from unenhanced magnetic resonance imaging (MRI).

Methods: We retrospectively enrolled 58 patients with histologically-proven low-grade/atypical cartilaginous tumor of the appendicular skeleton (n = 26) or higher-grade chondrosarcoma (n = 32, including 16 appendicular and 16 axial lesions). They were randomly divided into training (n = 42) and test (n = 16) groups for model tuning and testing, respectively.

Superb microvascular imaging (SMI) in the evaluation of musculoskeletal disorders: a systematic review.

Objectives: To systematically review the current literature concerning the role of superb microvascular imaging (SMI), a novel Doppler technique that enables detection of fine vessels and slow blood flow, in the evaluation of musculoskeletal disorders.

Methods: An online search of the literature was conducted for the period 2013 to April 2019 and included original articles written in English language. A data analysis was performed at the end of the literature search.

Contrast agents in diagnostic imaging: Present and future.

Specific contrast agents have been developed for x ray examinations (mainly CT), sonography and Magnetic Resonance Imaging. Most of them are extracellular agents which create different enhancement on basis of different vascularization or on basis of different interstitial network in tissues, but some can be targeted to a particular cell line (e.g.