Agarwal, Amit

Cauda Equina Neuroendocrine Tumors (CE-NET), previously referred to as paragangliomas are a rare subset of spinal tumors, with limited data on imaging. Herein, we present a retrospective review of clinical and imaging findings of CE-NETs in ten patients who were evaluated at our institution over the past two decades. All patients had well-defined intradural lesions in the lumbar spine which demonstrated slow growth. A review of imaging findings revealed the presence of an eccentric vascular pedicle along the dorsal aspect of the tumor in 8 of the 10 patients (eccentric vessel sign), a distinctive finding that has not previously been reported with this tumor and may help improve the accuracy of imaging-based diagnosis. In all cases, a gross-total resection was performed, with resolution of symptoms in most of the cases.

Introduction Survival prediction in glioblastoma remains challenging, and identification of robust imaging markers could help with this relevant clinical problem. We evaluated multiparametric magnetic resonance imaging–derived radiomics to assess prediction of overall survival (OS) and progression-free survival (PFS). Methodology A retrospective, institutional review board–approved study was performed. There were 93 eligible patients, of which 55 underwent gross tumor resection and chemoradiation (GTR-CR). Overall survival and PFS were assessed in the entire cohort and the GTR-CR cohort using multiple machine learning pipelines. A model based on multiple clinical variables was also developed. Survival prediction was assessed using the radiomics-only, clinical-only, and the radiomics and clinical combined models. Results For all patients combined, the clinical feature–derived model outperformed the best radiomics model for both OS (C-index, 0.706 vs 0.597; P < 0.0001) and PFS prediction (C-index, 0.675 vs 0.588; P < 0.001). Within the GTR-CR cohort, the radiomics model showed nonstatistically improved performance over the clinical model for predicting OS (C-index, 0.638 vs 0.588; P = 0.4). However, the radiomics model outperformed the clinical feature model for predicting PFS in GTR-CR cohort (C-index, 0.641 vs 0.550; P = 0.004). Combined clinical and radiomics model did not yield superior prediction when compared with the best model in each case. Conclusions When considering all patients, regardless of therapy, the radiomics-derived prediction of OS and PFS is inferior to that from a model derived from clinical features alone. However, in patients with GTR-CR, radiomics-only model outperforms clinical feature–derived model for predicting PFS.