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.

Fig 1

Feature-based decision support systems for radiologic diagnosis include two primary components: a set of relevant imaging findings and a prediction model to aggregate these findings. Relevant findings in a given clinical case are summarized using a feature checklist. A gallery of reference images provides guidance in evaluating individual features. These features are used as input to a prediction model, which may be based on any of several modeling methods including logistic regression (LR), neural networks (NN), recursive partitioning (RP), Bayesian networks (B), and case-based reasoning (CR). Model parameters are derived using a set of training cases. The model may then be used to calculate predictions in clinical cases (e.g., the probability of a given outcome). In addition, the structured nature of the feature checklist lends itself to automatic generation of standardized report text.

Fig 2

The primary screen of the vertebral compression fracture decision support website presents a feature checklist to the user. The majority of these features are dichotomous in nature, shown as checkboxes. A few are non-dichotomous discrete variables, shown as pop-up menus. If a particular feature is unknown to the user, clicking the adjacent question mark will display an annotated illustration demonstrating that feature (Fig. 4).

Fig 3

MRI features of vertebral compression fractures are illustrated using a series of images. These may be browsed in a gallery format, shown here, accessed using the “image gallery” link toward the top of the main page (Fig. 2). Each thumbnail in this gallery is labeled with a feature description. Clicking a particular thumbnail leads to a larger, annotated image with text-based description (Fig. 4).

Fig 4

A detailed, annotated image or set of images is available for each of the MRI features listed in the checklist of the main page (Fig. 2). A combination of image marks and text-based explanations summarize the findings which constitute a given feature, promoting a uniform understanding of these features and providing a learning resource for trainees.

Fig 5

Once the feature checklist has been completed, clicking the “submit” button towards the bottom of the main page triggers the prediction model probability calculation and template-based report text generation, both shown below the checklist items. These results are displayed respectively as a probability of malignancy and as a block of text available for cut-and-paste incorporation into the user’s reporting system.