title: Support vector machine-based prediction of local tumor control after stereotactic body radiation therapy for early-stage non-small cell lung cancer.
abstract: BACKGROUND
  Several prognostic factors for local tumor control probability (TCP) after stereotactic body radiation therapy (SBRT) for early stage non-small cell lung cancer (NSCLC) have been described, but no attempts have been undertaken to explore whether a nonlinear combination of potential factors might synergistically improve the prediction of local control.
  
  
  METHODS
  We investigated a support vector machine (SVM) for predicting TCP in a cohort of 399 patients treated at 13 German and Austrian institutions. Among 7 potential input features for the SVM we selected those most important on the basis of forward feature selection, thereby evaluating classifier performance by using 10-fold cross-validation and computing the area under the ROC curve (AUC). The final SVM classifier was built by repeating the feature selection 10 times with different splitting of the data for cross-validation and finally choosing only those features that were selected at least 5 out of 10 times. It was compared with a multivariate logistic model that was built by forward feature selection.
  
  
  RESULTS
  Local failure occurred in 12% of patients. Biologically effective dose (BED) at the isocenter (BED(ISO)) was the strongest predictor of TCP in the logistic model and also the most frequently selected input feature for the SVM. A bivariate logistic function of BED(ISO) and the pulmonary function indicator forced expiratory volume in 1 second (FEV1) yielded the best description of the data but resulted in a significantly smaller AUC than the final SVM classifier with the input features BED(ISO), age, baseline Karnofsky index, and FEV1 (0.696 ± 0.040 vs 0.789 ± 0.001, P<.03). The final SVM resulted in sensitivity and specificity of 67.0% ± 0.5% and 78.7% ± 0.3%, respectively.
  
  
  CONCLUSIONS
  These results confirm that machine learning techniques like SVMs can be successfully applied to predict treatment outcome after SBRT. Improvements over traditional TCP modeling are expected through a nonlinear combination of multiple features, eventually helping in the task of personalized treatment planning.
authors.affiliation: Department of Radiation Oncology, University of Würzburg, Germany; Department of Radiotherapy and Radiation Oncology, Leopoldina Hospital, Schweinfurt, Germany. Electronic address: rainer_klement@gmx.de.
authors.forename: Rainer J
authors.initials: RJ
authors.lastname: Klement
authors.affiliation: Department of Radiotherapy, Barmherzige Brüder Regensburg, Regensburg, Germany.
authors.forename: Michael
authors.initials: M
authors.lastname: Allgäuer
authors.affiliation: Department of Radiation Oncology, Technische Universität Dresden, Germany.
authors.forename: Steffen
authors.initials: S
authors.lastname: Appold
authors.affiliation: Department of Radiotherapy, Medical University of Vienna, Austria.
authors.forename: Karin
authors.initials: K
authors.lastname: Dieckmann
authors.affiliation: Department of Radiotherapy, University Hospital Münster, Germany.
authors.forename: Iris
authors.initials: I
authors.lastname: Ernst
authors.affiliation: Department of Radiation Oncology, Ludwigs-Maximilians-University Munich, München, Germany.
authors.forename: Ute
authors.initials: U
authors.lastname: Ganswindt
authors.affiliation: Department of Radiation Oncology, RWTH Aachen University, Aachen, Germany.
authors.forename: Richard
authors.initials: R
authors.lastname: Holy
authors.affiliation: Department of Radiation Oncology, University Hospital Freiburg, Freiburg i Br, Germany.
authors.forename: Ursula
authors.initials: U
authors.lastname: Nestle
authors.affiliation: Department of Therapeutic Radiology and Oncology, Innsbruck Medical University, Austria.
authors.forename: Meinhard
authors.initials: M
authors.lastname: Nevinny-Stickel
authors.affiliation: Department of Radiation Oncology, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany.
authors.forename: Sabine
authors.initials: S
authors.lastname: Semrau
authors.affiliation: Department of Radiation Oncology, University Hospital Heidelberg, Germany.
authors.forename: Florian
authors.initials: F
authors.lastname: Sterzing
authors.affiliation: Department of Radiotherapy and Radiation Oncology, Philipps-University Marburg, Germany.
authors.forename: Andrea
authors.initials: A
authors.lastname: Wittig
authors.affiliation: Department of Radiation Oncology, Technische Universität München, Germany.
authors.forename: Nicolaus
authors.initials: N
authors.lastname: Andratschke
authors.affiliation: Department of Radiation Oncology, University of Würzburg, Germany.
authors.forename: Matthias
authors.initials: M
authors.lastname: Guckenberger
mesh_terms: D019540:Area Under Curve; D001185:Artificial Intelligence; D001317:Austria; D002289:Carcinoma, Non-Small-Cell Lung / Q000473:pathology / Q000503:physiopathology / Q000601:surgery*; D005541:Forced Expiratory Volume / Q000502:physiology; D005858:Germany; D006801:Humans; D016015:Logistic Models; D008175:Lung Neoplasms / Q000473:pathology / Q000601:surgery*; D011336:Probability; D011379:Prognosis; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D016896:Treatment Outcome