Sarcopenia is a progressive, generalized skeletal muscle disorder characterized by reduction of muscle mass and strength. It is associated with increased adverse outcomes including falls, fractures, physical disability, and mortality, particularly, in elderly patients. Nowadays, sarcopenia has become a specific imaging biomarker able to predict clinical outcomes of patients. Muscle fibre reduction has shown to be an unfavourable pre-operative predictive factor in patients with cancer, and is associated with worse clinical outcomes in terms of postoperative complications, morbidity, mortality, and lower tolerance of chemoradiation therapy. Several imaging modalities, including dual-energy X-ray absorptiometry, CT, MRI, and US can be used to estimate muscle mass and quality to reach the diagnosis of sarcopenia. This article reviews the clinical implications of sarcopenia, how this condition can be assessed through different imaging modalities, and future perspectives of imaging of sarcopenia.

Plantar fascia (PF) disorders commonly cause heel pain and disability in the general population. Imaging is often required to confirm diagnosis. This review article aims to provide simple and systematic guidelines for imaging assessment of PF disease, focussing on key findings detectable on plain radiography, ultrasound and magnetic resonance imaging (MRI). Sonographic characteristics of plantar fasciitis include PF thickening, loss of fibrillar structure, perifascial collections, calcifications and hyperaemia on Doppler imaging. Thickening and signal changes in the PF as well as oedema of adjacent soft tissues and bone marrow can be assessed on MRI. Radiographic findings of plantar fasciitis include PF thickening, cortical irregularities and abnormalities in the fat pad located deep below the PF. Plantar fibromatosis appears as well-demarcated, nodular thickenings that are iso-hypoechoic on ultrasound and show low-signal intensity on MRI. PF tears present with partial or complete fibre interruption on both ultrasound and MRI. Imaging description of further PF disorders, including xanthoma, diabetic fascial disease, foreign-body reactions and plantar infections, is detailed in the main text. Ultrasound and MRI should be considered as first- and second-line modalities for assessment of PF disorders, respectively. Indirect findings of PF disease can be ruled out on plain radiography. Teaching Points • PF disorders commonly cause heel pain and disability in the general population. • Imaging is often required to confirm diagnosis or reveal concomitant injuries. • Ultrasound and MRI respectively represent the first- and second-line modalities for diagnosis. • Indirect findings of PF disease can be ruled out on plain radiography.

Background Lower muscle mass is a known predictor of unfavorable outcome, but its prognostic impact on COVID-19 patients is unknown. Purpose To investigate the contribution of CT-derived muscle sta...

Atypical cartilaginous tumour (ACT) and grade II chondrosarcoma (CS2) of long bones are respectively managed with watchful waiting or curettage and wide resection. Preoperatively, imaging diagnosis can be challenging due to interobserver variability and biopsy suffers from sample errors. The aim of this study is to determine diagnostic performance of MRI radiomics-based machine learning in differentiating ACT from CS2 of long bones. One-hundred-fifty-eight patients with surgically treated and histology-proven cartilaginous bone tumours were retrospectively included at two tertiary bone tumour centres. The training cohort consisted of 93 MRI scans from centre 1 (n=74 ACT; n=19 CS2). The external test cohort consisted of 65 MRI scans from centre 2 (n=45 ACT; n=20 CS2). Bidimensional segmentation was performed on T1-weighted MRI. Radiomic features were extracted. After dimensionality reduction and class balancing in centre 1, a machine-learning classifier (Extra Trees Classifier) was tuned on the training cohort using 10-fold cross-validation and tested on the external test cohort. In centre 2, its performance was compared with an experienced musculoskeletal oncology radiologist using McNemar's test. After tuning on the training cohort (AUC=0.88), the machine-learning classifier had 92% accuracy (60/65, AUC=0.94) in identifying the lesions in the external test cohort. Its accuracies in correctly classifying ACT and CS2 were 98% (44/45) and 80% (16/20), respectively. The radiologist had 98% accuracy (64/65) with no difference compared to the classifier (p=0.134). Machine learning showed high accuracy in classifying ACT and CS2 of long bones based on MRI radiomic features. ESSR Young Researchers Grant.