Extraskeletal myxoid chondrosarcomas (EMC) are extremely rare and are usually located in the deep soft tissues of the lower extremities. Less than 10 cases of intracranial EMC have been reported in the literature, making their management and early diagnosis difficult. We present a new case of intracranial EMC occurring in a 70-year-old woman presenting with a right frontal mass initially assumed to be a brain metastasis from breast adenocarcinoma. The optimal management of these tumours is also discussed. Analysis from the literature suggests that complete resection should be recommended, whenever feasible. Although the high risk for relapse after surgery encourages postoperative treatments, relative resistance to both radio-therapy and chemotherapy characterizes EMC. Future perspectives might include multimodal treatments with highly conformal radiotherapy modalities for dose escalation strategies or use of new molecules. Knowledge of these unusual malignant tumours will be the first step for improving patients' outcome.

Background Older individuals receiving home assistance are at high risk for emergency visits and unplanned hospitalization. Anticipating their health difficulties could prevent these events. This study investigated the effectiveness of an at-home monitoring method using social workers’ observations to predict risk for 7- and 14-day emergency department (ED) visits. Methods This was a prospective cohort study of persons ≥75 years, living at home and receiving assistance from home care aides (HCA) at 6 French facilities. After each home visit, HCAs reported on participants’ functional status using a smartphone application that recorded 27 functional items about each participant (e.g., ability to stand, move, eat, mood, loneliness). We recorded ED visits. Finally, we used machine learning techniques (i.e., leveraging random forest predictors) to develop a 7- and 14-day predictive algorithm for the risk of ED visit. Results The study included 301 participants, and the HCA made 9,987 observations. Over the mean 10-month follow-up, 97 participants (32%) had at least one ED visit. Modeling techniques identified 9 contributory factors from the longitudinal records of the HCA and developed a predictive algorithm for the risk of ED visit. The predictive performance (i.e., the area under the ROC curve) was 0.70 at 7 days and 0.67 at 14 days. Interpretation For frail elders receiving in-home care, information on functional status collected by HCA helps predict the risk of ED visits 7 to 14 days in advance. A survey system for real-time identification of risks could be developed using this exploratory work.