title: An online evidence-based decision support system for distinguishing benign from malignant vertebral compression fractures by magnetic resonance imaging feature analysis.
abstract: Decision support systems have been used to promote the practice of evidence-based medicine. Computer-assisted diagnosis can serve as one element of evidence-based radiology. One area where such tools may provide benefit is analysis of vertebral compression fractures (VCFs), which can be a challenge in MRI interpretation. VCFs may be benign or malignant in etiology, and several MRI features may help to make this important distinction. We describe a web-based decision support system for discriminating benign from malignant VCFs as a prototype for a more general diagnostic decision support framework for radiologists. The system has three components: a feature checklist with an image gallery derived from proven reference cases, a prediction model, and a reporting mechanism. The website allows users to input the findings for a case to be interpreted using a structured feature checklist. The image gallery complements the checklist, for clarity and training purposes. The input from the checklist is then used to calculate the likelihood of malignancy by a logistic regression prediction model. Standardized report text is generated that summarizes pertinent positive and negative findings. This computer-assisted diagnosis system demonstrates the integration of three areas where diagnostic decision support can aid radiologists: first, in image interpretation, through feature checklists and illustrative image galleries; second, in feature-based prediction modeling; and third, in structured reporting. We present a diagnostic decision support tool that provides radiologists with evidence-based guidance for discriminating benign from malignant VCF. This model may be useful in other difficult-diagnosis situations and requires further clinical testing.
authors.affiliation: Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, 601 North Caroline Street, Baltimore, MD 21287, USA. kenwang@stanfordalumni.org
authors.forename: Kenneth C
authors.initials: KC
authors.lastname: Wang
authors.forename: Anthony
authors.initials: A
authors.lastname: Jeanmenne
authors.forename: Griffin M
authors.initials: GM
authors.lastname: Weber
authors.forename: Shrey K
authors.initials: SK
authors.lastname: Thawait
authors.forename: Shrey
authors.initials: S
authors.lastname: Thawait
authors.forename: John A
authors.initials: JA
authors.lastname: Carrino
mesh_terms: D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D003937:Diagnosis, Differential; D019317:Evidence-Based Medicine / Q000379:methods*; D050815:Fractures, Compression / Q000473:pathology*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009862:Online Systems; D016103:Spinal Fractures / Q000473:pathology*; D013125:Spinal Neoplasms / Q000473:pathology*; D013904:Thoracic Vertebrae / Q000473:pathology