Classification of Brain Metastases Prognostic Groups Utilizing Artificial Neural Network Approaches

George Rodrigues,Ben J Slotman,Glenn Bauman,Frank Lagerwaard

doi:10.7759/cureus.115

Abstract

Objective: The purpose of this investigation is to explore the performance of an artificial neural network (ANN) based prognostic index compared to traditional logistic regression (LR) modeling and other published prognostic indices (PI) in classifying survival among patients with brain metastases treated with stereotactic radiotherapy. Methods: A database of 460 patients having received either stereotactic radiosurgery or fractionated stereotactic radiation therapy brain radiotherapy was utilized and divided into three sub-databases for ANN/LR analysis: a testing dataset, n=276 (65%); a cross-validation dataset for training, n=69 (15%); and a validation dataset, n=115 (25%). The primary endpoint of survival was classified into one of three categories: unfavorable survival ( six months) endpoint classifications. ANNs were optimized in terms of model structure, complexity, and a cost optimization algorithm and then compared to both LR and published PIs in terms of classification accuracy (CA) and total major misclassification rates (TMMR) according to the three category survival scheme. Results: CA and TMMR for the nine published PIs for the total database (n=460) ranged from 3453% and 4-11%, respectively. Both the LR and ANN approaches (in the validation database) were over 10% superior to the best existing PI system in terms of CA (LR/ANN 62.6%, published prognostic indices 27-49%) with a similar rate of TMMR (LR 7.8%, ANN 6.1%, published prognostic indices 2-17%).

Highlights

How to cite this article Rodrigues G, Bauman G, Slotman B J., et al (May 14, 2013) Classification of Brain Metastases Prognostic Groups Utilizing Artificial Neural Network Approaches
While a modest improvement over published prognostic indices (PI) was noted, use of various artificial neural network (ANN) model structures, nodal complexity, and cost function optimization algorithms did not lead to a significant improvement in survival classification when compared to logistic regression (LR)
Multiple prognostic factors have been shown to be related to patient survival in the context of brain metastases which include: performance status, extracranial disease, age, controlled primary, primary site, interval between primary disease and brain metastases, number/volume of brain metastases, and clinical response to steroids [1,2,3,4,5,6,7,8,9,10,11]

Summary

Introduction

How to cite this article Rodrigues G, Bauman G, Slotman B J., et al (May 14, 2013) Classification of Brain Metastases Prognostic Groups Utilizing Artificial Neural Network Approaches. Validated prognostic factors and indices can be used to assist the clinician in patient counseling and treatment decision-making. Such indices can support the conduct of prospective clinical trials by defining patient eligibility and stratification criteria. The Radiation Therapy Oncology Group (RTOG) Recursive Partitioning Analysis (RPA) brain metastases prognostic index is the oldest system currently in use [2, 12,13,14,15,16]. The utility of the system has been limited by the large relative proportion of patients within the intermediate-risk group, as has been previously highlighted by several investigators [17,18]

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Conclusion