Endoscopic laser ablation of clival chordoma with magnetic resonance-guided laser induced thermal therapy

Background: Chordomas are rare malignant tumors that are difficult to treat and have high recurrence rates despite aggressive therapy. Objectives: We present the first case of a patient with a clival chordoma in which complete tumor ablation was achieved using Magnetic Resonance guided Laser Induced Thermal Therapy (LITT) delivered via an endoscopic endonasal approach. We analyzed the safety and feasibility of this approach and quantified the response of this pathology to thermal energy. This novel technique is intended to provide chordoma patients with an alternative to surgery and radiotherapy. Methods: A 54 year-old female with a newly diagnosed clival chordoma elected MRI- guided LITT. She underwent placement of the laser catheter into the chordoma via an endoscopic endonasal approach. With real-time MR thermometry monitoring, laser-generated thermal energy was delivered to the tumor. We defined several parameters to quantify the thermal ablation response: the thermal damage ratio and the thermal ablation constant. Results: Post procedure contrast-enhanced MRI demonstrated a complete thermal ablation of the mass. The patient tolerated the procedure well and is being followed with serial imaging. The tumor continues to regress beyond 4 months. Additionally, chordoma cells seem to be sensitive to LITT, as indicated by a complete ablation in less than 60 s. Conclusion: The endoscopic endonasal approach to MRI-guided laser ablation is both technically feasible and safe. As a result, this therapy may be a useful alternative in hard-to-reach chordomas, or in recurrent cases that have failed other conventional treatment modalities

A Comparison of Neural Network, Statistical Methods, and Variable Choice for Life Insurers' Financial Distress Prediction

This study examines the effect of the statistical/mathematical model selected and the variable set considered on the ability to identify financially troubled life insurers. Models considered are two artificial neural network methods (back-propagation and learning vector quantization (LVQ)) and two more standard statistical methods (multiple discriminant analysis and logistic regression analysis). The variable sets considered are the insurance regulatory information system (IRIS) variables, the financial analysis solvency tracking (FAST) variables, and Texas early warning information system (EWIS) variables, and a data set consisting of twenty-two variables selected by us in conjunction with the research staff at TDI and a review of the insolvency prediction literature. The results show that the back-propagation (BP) and LVQ outperform the traditional statistical approaches for all four variable sets with a consistent superiority across the two different evaluation criteria (total misclassification cost and resubstitution risk criteria), and that the twenty-two variables and the Texas EWIS variable sets are more efficient than the IRIS and the FAST variable sets for identification of financially troubled life insurers in most comparisons. Copyright The Journal of Risk and Insurance, 2006.