Developing outcome prediction models for acute intracerebral hemorrhage patients: evaluation of a Support Vector Machine based method A. Jakab 1, L. Lánczi 1, L. Csiba 2, I. Széll 2, P. Molnár 3, E. Berényi 1 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 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. DIAGNOSTIC IMAGING (nonenhanced CT scan) 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 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 ICH Score (Hemphill et. al., 2001) GCS, patient age, volume, infratentorial, intraventricular

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: 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) AIM of our study WAS NOT: 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 (cm 3 ) Parenchymal and ventricular extension of hemorrhage (cm 3 ) Effacement of pre- pontine cistern (mm) SLICER 3D SOFTWARE, SEMI-AUTOMATIC SEGMENTATION. Normalize variables to intracranial space. IMAGEJ SOFTWARE Manual measurement

7 Methods Statistical workup, SVM application Support Vector Machine Classifier Radiological variables Intracranial space Normalized parenchymal hematoma vol. Normalized intraventricular hematoma vol. Prepontine cistern effacement Clinical variables Age, sex, onset, lab findings (Na, K), RR (BPsyst, Bpdiast), pulse history of IHD, mRs, etc.! Software, classifier training: WEKA Free, open-source environment for data mining applications Support Vector Machine algorithm: LibSVM Clinical outcomes of training dataset TRAINING VARIABLE SELECTION Validation on testing dataset TESTING

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

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