title: Bayesian pretest probability estimation for primary malignant bone tumors based on the Surveillance, Epidemiology and End Results Program (SEER) database.
abstract: OBJECTIVE
  In the diagnostic process of primary bone tumors, patient age, tumor localization and to a lesser extent sex affect the differential diagnosis. We therefore aim to develop a pretest probability calculator for primary malignant bone tumors based on population data taking these variables into account.
  
  
  METHODS
  We access the SEER (Surveillance, Epidemiology and End Results Program of the National Cancer Institute, 2015 release) database and analyze data of all primary malignant bone tumors diagnosed between 1973 and 2012. We record age at diagnosis, tumor localization according to the International Classification of Diseases (ICD-O-3) and sex. We take relative probability of the single tumor entity as a surrogate parameter for unadjusted pretest probability. We build a probabilistic (naïve Bayes) classifier to calculate pretest probabilities adjusted for age, tumor localization and sex.
  
  
  RESULTS
  We analyze data from 12,931 patients (647 chondroblastic osteosarcomas, 3659 chondrosarcomas, 1080 chordomas, 185 dedifferentiated chondrosarcomas, 2006 Ewing's sarcomas, 281 fibroblastic osteosarcomas, 129 fibrosarcomas, 291 fibrous malignant histiocytomas, 289 malignant giant cell tumors, 238 myxoid chondrosarcomas, 3730 osteosarcomas, 252 parosteal osteosarcomas, 144 telangiectatic osteosarcomas). We make our probability calculator accessible at http://ebm-radiology.com/bayesbone/index.html . We provide exhaustive tables for age and localization data. Results from tenfold cross-validation show that in 79.8 % of cases the pretest probability is correctly raised.
  
  
  CONCLUSIONS
  Our approach employs population data to calculate relative pretest probabilities for primary malignant bone tumors. The calculator is not diagnostic in nature. However, resulting probabilities might serve as an initial evaluation of probabilities of tumors on the differential diagnosis list.
authors.affiliation: Department of Radiology, University Hospital Freiburg, Hugstetter Straße 55, 79106, Freiburg, Germany. matthias.benndorf@uniklinik-freiburg.de.
authors.forename: Matthias
authors.initials: M
authors.lastname: Benndorf
authors.affiliation: Department of Radiology, University Hospital Freiburg, Hugstetter Straße 55, 79106, Freiburg, Germany.
authors.forename: Jakob
authors.initials: J
authors.lastname: Neubauer
authors.affiliation: Department of Radiology, University Hospital Freiburg, Hugstetter Straße 55, 79106, Freiburg, Germany.
authors.forename: Mathias
authors.initials: M
authors.lastname: Langer
authors.affiliation: Department of Radiology, University Hospital Freiburg, Hugstetter Straße 55, 79106, Freiburg, Germany.
authors.forename: Elmar
authors.initials: E
authors.lastname: Kotter
mesh_terms: D001499:Bayes Theorem; D001859:Bone Neoplasms / Q000175:diagnosis / Q000000981:diagnostic imaging*; D002813:Chondrosarcoma / Q000175:diagnosis / Q000000981:diagnostic imaging; D002817:Chordoma / Q000175:diagnosis / Q000000981:diagnostic imaging*; D016208:Databases, Factual; D003937:Diagnosis, Differential; D005260:Female; D005354:Fibrosarcoma / Q000175:diagnosis / Q000000981:diagnostic imaging; D018212:Giant Cell Tumor of Bone / Q000175:diagnosis / Q000000981:diagnostic imaging*; D051677:Histiocytoma, Malignant Fibrous / Q000175:diagnosis / Q000000981:diagnostic imaging; D006801:Humans; D008297:Male; D015233:Models, Statistical; D012516:Osteosarcoma / Q000175:diagnosis / Q000000981:diagnostic imaging; D011336:Probability; D011859:Radiography; D018426:SEER Program; D012509:Sarcoma / Q000175:diagnosis / Q000000981:diagnostic imaging*; D012512:Sarcoma, Ewing / Q000175:diagnosis / Q000000981:diagnostic imaging; D012737:Sex Factors
keywords: Bayes classifier; Bayesian; Bone tumor; Epidemiology