RESUMEN
Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors.
RESUMEN
OBJECTIVE: Diffusion tensor imaging (DTI) tractography provides useful information that can be used to optimize surgical planning and help avoid injury during subcortical dissection of eloquent tracts. The objective is to provide a safe, timely, and affordable algorithm for preoperative DTI language reconstruction for intrinsic frontotemporal diseases. METHODS: We reviewed a prospectively acquired database of preoperative DTI reconstruction for resection of left frontotemporal lesions over 3 years at Hospital de San José and Hospital Infantil Universitario San José, Fundación Universitaria de Ciencias de la Salud, Bogota, Colombia. Preoperative and postoperative clinical and radiographic features were determined from retrospective chart review. A comprehensive review of the structural and functional anatomy of the language tracts was performed. Separate reconstruction of both ventral (semantic) and dorsal (phonologic) stream pathways is described: arcuate fasciculus, superior longitudinal fasciculus, inferior fronto-occipital fasciculus, uncinate fasciculus, and inferior longitudinal fasciculus. RESULTS: Between January 2015 and January 2018, 44 tumor cases were found to be resected with preoperative fiber tracking planning and neuronavigation-guided surgery. Ten patients (7 women, 3 men) aged 28-65 years underwent resection of an intrinsic frontotemporal lesion with preoperative DTI tractography reconstruction of language tracts. Eight cases (80%) were high-grade gliomas and 2 (20%) were cavernous malformations. In 5 cases (50%), the lesion was in the frontal lobe and in 5 (50%), it was in the temporal lobe. The extent of resection was classified as gross total resection (100%), subtotal resection (>90%), or partial resection (<90%). Gross total resection was achieved in 5 cases (50%), subtotal resection was achieved in 4 cases (40%), and partial resection in the remaining case (10%). Compromised tracts included superior longitudinal fasciculus in 7 (70%), inferior longitudinal fasciculus in 4 (40%), the arcuate fasciculus in 3 (30%), and uncinate fasciculus in 1 (10%). Language function was unchanged or improved in 90% of patients. New-onset postoperative language decline occurred in 1 patient, who recovered transient phonemic paraphasias 1 month after resection. The mean follow-up time was 7 months (range, 4-12 months). Residual tumors were treated with radiation and/or with chemotherapy as indicated in an outpatient setting. CONCLUSIONS: We present a safe and efficacious preoperative DTI language reconstruction algorithm that could be used as a feasible treatment strategy in a challenging subset of tumors in low- to middle-income countries.
