Bone Tumor Diagnosis Using a Naïve Bayesian Model of Demographic and Radiographic Features
- 513 Downloads
Because many bone tumors have a variety of appearances and are uncommon, few radiologists develop sufficient expertise to guide optimal management. Bayesian inference can guide decision-making by computing probabilities of multiple diagnoses to generate a differential. We built and validated a naïve Bayes machine (NBM) that processes 18 demographic and radiographic features. We reviewed over 1664 analog radiographic cases of bone tumors and selected 811 cases (66 diagnoses) for annotation using a quantitative imaging platform. Leave-one-out cross validation was performed. Primary accuracy was defined as the correct pathological diagnosis as the top machine prediction. Differential accuracy was defined as whether the correct pathological diagnosis was within the top three predictions. For the 29 most common diagnoses (710 cases), primary accuracy was 44%, and differential accuracy was 60%. For the top 10 most common diagnoses (478 cases), primary accuracy was 62%, and differential accuracy was 80%. The machine returned relevant diagnoses for the majority of unknown test cases and may be a feasible alternative to machine learning approaches such as deep neural networks or support vector machines that typically require larger training data (our model required a minimum of five samples per diagnosis) and are “black boxes” (our model can provide details of probability calculations to identify features that most significantly contribute to truth diagnoses). Finally, our Bayes model was designed to scale and “learn” from external data, enabling incorporation of outside knowledge such as Dahlin’s Bone Tumors, a reference of anatomic and demographic statistics of more than 10,000 tumors.
KeywordsNaïve Bayes model Bone tumor diagnosis
Compliance with Ethical Standards
Institutional review board approval was obtained. The requirement for informed consent was waived as this was a retrospective review of de-identified radiologic images with only age, gender, and brief clinical notes/diagnosis.
- 5.Lodwick GS et al.: Computer diagnosis of primary bone tumors: a preliminary report. Radiology 2(80):3, 1963Google Scholar
- 9.Richardson ML: Bayesian bone tumor diagnosis. 2005; Available from: http://uwmsk.org/bayes/bonetumor.html
- 11.Rubin DL et al.: iPad: Semantic annotation and markup of radiological images. AMIA Annu Symp Proc:626–630, 2008Google Scholar
- 23.Rokach L, Maimon O: Data mining with decision trees: theory and applications. In: Series in Machine Perception and Artificial Intelligence, 2nd edition. Singapore: World Scientific Publishing Company, 2014, p. 328Google Scholar
- 24.Unni KK, Inwards CY: Dahlin’s Bone Tumors: General Aspects and Data on 10,165 Cases. Philadelphia: LWW, 2010, p. 416Google Scholar