Lipoblastomatosis Extended into the Lumbar Spinal Canal in a Child: A Case Report.

INTRODUCTION: Lipoblastoma and lipoblastomatosis are rare benign mesenchymal adipose tumors that originate from embryonic white adipocytes and occur most commonly in infancy and early childhood. Lipoblastomas occur in the extremities and trunk, including the retroperitoneum and peritoneal cavity. Therefore, infiltration into the spinal canal has rarely been reported. CASE PRESENTATION: A 4-year-old girl presented to our clinic because of difficulty sitting on the floor with her legs straight. She also complained of enuresis and constipation for the past 6 months with persistent headaches and back pain evoked by body anteflexion. A magnetic resonance imaging revealed a massive lesion of the psoas major muscle, retroperitoneal, and subcutaneous spaces, extending into the spinal epidural space between L2 and S1. The patient underwent surgery which resulted in gross total removal of the tumor from the spinal canal. The mass was yellowish, soft, lobulated, fatty, and easily removed from the surrounding structures. Pathology confirmed the diagnosis of lipoblastoma. The postoperative course was uneventful, and the patient was discharged without any signs of neurological deficit. CONCLUSION: We herein discuss a rare case of lipoblastoma extending into the spinal canal, resulting in neurological symptoms. Although this tumor is benign with no potential for metastasis, it is prone to local recurrence. Therefore, close postoperative observation should be performed.

Statistical and machine learning approaches to predict the necessity for computed tomography in children with mild traumatic brain injury.

BACKGROUND: Minor head trauma in children is a common reason for emergency department visits, but the risk of traumatic brain injury (TBI) in those children is very low. Therefore, physicians should consider the indication for computed tomography (CT) to avoid unnecessary radiation exposure to children. The purpose of this study was to statistically assess the differences between control and mild TBI (mTBI). In addition, we also investigate the feasibility of machine learning (ML) to predict the necessity of CT scans in children with mTBI. METHODS AND FINDINGS: The study enrolled 1100 children under the age of 2 years to assess pre-verbal children. Other inclusion and exclusion criteria were per the PECARN study. Data such as demographics, injury details, medical history, and neurological assessment were used for statistical evaluation and creation of the ML algorithm. The number of children with clinically important TBI (ciTBI), mTBI on CT, and controls was 28, 30, and 1042, respectively. Statistical significance between the control group and clinically significant TBI requiring hospitalization (csTBI: ciTBI+mTBI on CT) was demonstrated for all nonparametric predictors except severity of the injury mechanism. The comparison between the three groups also showed significance for all predictors (p<0.05). This study showed that supervised ML for predicting the need for CT scan can be generated with 95% accuracy. It also revealed the significance of each predictor in the decision tree, especially the "days of life." CONCLUSIONS: These results confirm the role and importance of each of the predictors mentioned in the PECARN study and show that ML could discriminate between children with csTBI and the control group.