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A generic support vector machine model for preoperative glioma survival associations
Kyrre E Emblem1, Marco C Pinho, Frank G Zöllner
1From the Intervention Centre (K.E.E., A.B.), Department of Radiology (P.D.T., J.K.H.), and Department of Neurosurgery (T.R.M.), Oslo University Hospital, N-0027 Sognsvannsveien 20, 0372 Oslo, Norway; Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Mass (K.E.E., M.C.P., O.R.); Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Tex (M.C.P.); Department of Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany (F.G.Z., L.R.S.); and Department of Physics, University of Oslo, Oslo, Norway (A.B.).
Purpose:
To develop a generic support vector machine (SVM) model by using magnetic resonance (MR) imaging-based blood volume distribution data for preoperative glioma survival associations and to prospectively evaluate the diagnostic effectiveness of this model in autonomous patient data.
Materials And Methods:
Institutional and regional medical ethics committees approved the study, and all patients signed a consent form. Two hundred thirty-five preoperative adult patients from two institutions with a subsequent histologically confirmed diagnosis of glioma after surgery were included retrospectively. An SVM learning technique was applied to MR imaging-based whole-tumor relative cerebral blood volume (rCBV) histograms. SVM models with the highest diagnostic accuracy for 6-month and 1-, 2-, and 3-year survival associations were trained on 101 patients from the first institution. With Cox survival analysis, the diagnostic effectiveness of the SVM models was tested on independent data from 134 patients at the second institution.
Results:
were adjusted for known survival predictors, including patient age, tumor size, neurologic status, and postsurgery treatment, and were compared with survival associations from an expert reader.
Results:
Compared with total qualitative assessment by an expert reader, the whole-tumor rCBV-based SVM model was the strongest parameter associated with 6-month and 1-, 2-, and 3-year survival in the independent patient data (area under the receiver operating characteristic curve, 0.794-0.851; hazard ratio, 5.4-21.2).
Discussion:
Machine learning by means of SVM in combination with whole-tumor rCBV histogram analysis can be used to identify early patient survival in aggressive gliomas. The SVM model returned higher diagnostic accuracy values than an expert reader, and the model appears to be insensitive to patient, observer, and institutional variations.
