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Developing outcome prediction models for acute intracerebral hemorrhage patients: evaluation of a Support Vector Machine based method A. Jakab1, L. Lánczi1, L. Csiba2, I. Széll2, P. Molnár3, E. Berényi1 University of Debrecen Medical School and Health Science Center Faculty of Medicine 1: Department of Biomedical Laboratory and Imaging Science, 2: Department of Neurology, 3: Institute of Pathology 1 Introduction Intracranial hemorrhages, clinical scales PATHOLOGY Ichemic stroke primary intracerebral hemorrhage (ICH) subarachnoid hemorrhage (SAH) undetermined stroke DIAGNOSTIC IMAGING (nonenhanced CT scan) CLINICAL RECORDS Size of the hematoma Location of the hematoma Expansion rate of the hematoma Mass effect Time from onset to examination Patient age GCS BP, electrolytes, etc. Adaptation of a prognostic / predictive model (scoring system) Assesment of clinical outcome (30-day mortality) or therapy decisions 2 Introduction Technical advancement and challenges ROLE OF NEUROIMAGING Challenge: How to measure hematoma volume? Answer 1: ABC/2 Method Answer 2: Manual image segmentation Answer 3: Automatic image segmentation, parcellation (location, ventricular extension, etc.) ABC/2: quick, accurate in most cases (ellipsoid method) Image segmentation: advantageous in complex geometry, intraventricular component 3 Introduction Clinical scales and DSS „Classical clinical scales” Data used: • quick assessment of neuroimaging findings • patient data, basic lab findings • a limited number of variables Evaluation of clinical outcome • adding the scores or simple equations • logistic regression functions ICH Score (Hemphill et. al., 2001) GCS, patient age, volume, infratentorial, intraventricular Decision support systems (DSS) using Computer aided diagnosis (CAD) Data used: • CAD: computer aided definition of imaging findings, image segmentation, ROI analysis • patient data, lab results • many variables (no limit) Evaluation: • logistic regression functions • more complex, „non-linear” methods: • Artificial neural networks •Bayesian classifier •Nearest-neighbor rule •Support Vector Machines 4 Introduction Support vector machines „..a set of related supervised learning methods which analyze data and recognize patterns, used for statistical classification and regression analysis” • Fitting n-dimensional hyperplanes to examples in feature space • Complex, but reproducible mathematical function! (unlike neural networks) 5 Objectives AIM of our study WAS: AIM of our study WAS NOT: • To use semi-automatic image segmentation for determining useful neuroradiological parameters • To use many clinical parameters AND neuroradiological data to assess 30-day ICH mortality • To assess the feasibility of Support Vector Machines in the selection of variables and creation of a prognostic model • To compare efficiency with the results using „conventional” classifier methods (logistic regression analysis) • To validate the feasibility of image segmentation methods or compare to the efficiency of ABC/2 method • To evaluate the clinical and neuroradiological factors of ICH mortality • To introduce a new commercial Diagnostic Support System approach or product 6 Methods Patient database, image segmentation Patient population: 125 consecutive patients, Department of Neurology (ICU) Neuroimaging: Acute, non-enhanced CT scans (two devices: GE CT-e Dual and GE Lightspeed16, GE Medical Systems, Milwaukee, WI, USA). Image segmentation, CT volumetry: Intracranial space (cm3) Parenchymal and ventricular extension of hemorrhage (cm3) SLICER 3D SOFTWARE, SEMI-AUTOMATIC SEGMENTATION. Normalize variables to intracranial space. Effacement of prepontine cistern (mm) IMAGEJ SOFTWARE Manual measurement 7 Methods Statistical workup, SVM application Radiological variables Intracranial space Normalized parenchymal hematoma vol. Normalized intraventricular hematoma vol. Prepontine cistern effacement VARIABLE SELECTION Clinical variables Age, sex, onset, lab findings (Na, K), RR (BPsyst, Bpdiast), pulse history of IHD, mRs, etc.! VARIABLE SELECTION TRAINING Support Vector Machine Classifier TESTING Software, classifier training: WEKA Free, open-source environment for data mining applications http://www.cs.waikato.ac.nz/ml/weka/ Support Vector Machine algorithm: LibSVM Clinical outcomes of training dataset Validation on testing dataset 8 Results Assessment of clinical outcome Questions to evaluate: 1. Assesment of ICH mortality with SVM method without prospective evaluation (Training success %) 2. Test the method on a different patient population (Testing accuracy %) 3. Calculate the sensitivity, specificity, AUC, error rate 4. What clinical variables are the most important, i.e. what if many clinical variables are included in the model? 5. Is the SVM method more accurate than the logistic regression model? 8 Results Assessment of clinical outcome 9 Discussion 1. Semi-automatic segmentation of acute, ICU CT images could determine useful volumetric data 2. In our experimental evaluation (prospective testing: 75% of patients as training, 25% as test) SVM-based model could correctly prognosticate poor outcome (30-d mortality) in 90,3% of the test cases, the method had higher sensitivity than logistic regression did. 3. To achieve feasible results, all neuroimaging variables were used, plus clinical parameters. 4. The „model” was saved and could be used for further prospective analysis 10 Further plans To integrate and automate these functions • Automation: segmentation, clinical data mining • Integration: „internal” database with previous outcomes, continuous refinement of model. • All-in-one software packages are needed • Health technology assessment for the benifits of a decision support system using these algorithms Thank you for your attention!