BRAF-V600E mutations in plasma and peripheral blood mononuclear cells correlate with prognosis of pediatric Langerhans cell histiocytosis treated with first-line therapy.

BACKGROUND: The clinical relevance of BRAF-V600E alleles in peripheral blood mononuclear cells (PBMCs) and the prognostic impact of the mutants in cell-free (cf) and PBMC DNAs of Langerhans cell histiocytosis (LCH) have not been fully clarified in pediatric LCH. METHODS: We retrospectively determined the levels of BRAF-V600E mutation in paired plasma and PBMC samples at the time of diagnosis of LCH. Subsequently, we performed a separate or combined analysis of the clinical and prognostic impact of the mutants. RESULTS: We assessed BRAF-V600E mutation in peripheral blood from 94 patients of childhood LCH. Our data showed that cfBRAF-V600E was related to young age, multiple-system (MS) disease, involvements of organs with high risk, increased risk of relapse, and worse progression-free survival (PFS) of patients. We also observed that the presence of BRAF-V600E in PBMCs at baseline was significantly associated with MS LCH with risk organ involvement, younger age, and disease progression or relapse. The coexisting of plasma(+)/PBMC(+) identified 36.2% of the patients with the worst outcome, and the hazard ratio was more significant than either of the two alone or neither, indicating that combined analysis of the mutation in plasma and PBMCs was more accurate to predict relapse than evaluation of either one. CONCLUSIONS: Concurrent assessment of BRAF-V600E mutation in plasma and PBMCs significantly impacted the prognosis of children with LCH. Further prospective studies with larger cohorts need to validate the results of this study.

Development of a Deep-Learning Model for Diagnosing Lumbar Spinal Stenosis Based on CT Images.

STUDY DESIGN: Retrospective study. OBJECTIVES: This study aimed to develop an initial deep-learning (DL) model based on computerized tomography (CT) scans for diagnosing lumbar spinal stenosis. SUMMARY OF BACKGROUND DATA: Magnetic resonance imaging is commonly used for diagnosing lumbar spinal stenosis due to its high soft tissue resolution, but CT is more portable, cost-effective, and has wider regional coverage. Using DL models to improve the accuracy of CT diagnosis can effectively reduce missed diagnoses and misdiagnoses in clinical practice. MATERIALS AND METHODS: Axial lumbar spine CT scans obtained between March 2022 and September 2023 were included. The data set was divided into a training set (62.3%), a validation set (22.9%), and a control set (14.8%). All data were labeled by two spine surgeons using the widely accepted grading system for lumbar spinal stenosis. The training and validation sets were used to annotate the regions of interest by the two spine surgeons. First, a region of interest detection model and a convolutional neural network classifier were trained using the training set. After training, the model was preliminarily evaluated using a validation set. Finally, the performance of the DL model was evaluated on the control set, and a comparison was made between the model and the classification performance of specialists with varying levels of experience. RESULTS: The central stenosis grading accuracies of DL Model Version 1 and DL Model Version 2 were 88% and 83%, respectively. The lateral recess grading accuracies of DL Model Version 1 and DL Model Version 2 were 75% and 71%, respectively. CONCLUSIONS: Our preliminarily developed DL system for assessing the degree of lumbar spinal stenosis in CT, including the central canal and lateral recess, has shown similar accuracy to experienced specialist physicians. This holds great value for further development and clinical application.