Combining evidence from multiple imaging modalities: a feature-analysis method

IMPROVED DIAGNOSIS OF FOOT OSTEOARTHRITIS IN ELEPHANTS (ELEPHAS MAXIMUS, LOXODONTA AFRICANA) USING STEREORADIOGRAPHY

Diagnosis of foot disease in elephants is challenging. Owing to their large size, the available diagnostic tools and the expense of imaging are diagnostically limiting. Stereoradiography is the preparation of paired radiographs that form a three-dimensional (3D) image when viewed stereoscopically. Clinicians and veterinary students graded osteoarthritis in the feet of African (Loxodonta africana) and Asian (Elephas maximus) elephants taken postmortem with standard 2D radiographs, as well as 3D stereoradiographs. These gradings were compared with the actual gross pathology identified in the specimens. Although veterinary students diagnoses were no better than chance from 2D radiographs, 83.6% of the students could correctly differentiate severity between joints on stereoradiography; this is an absolute improvement of 30.1% (95% confidence interval [CI] = 19.6%-40.6%). Overall, participants were 27.4% (95% CI = 18.4%-36.3%) more successful at diagnosing pathology on stereoradiographs. Half of participants were shown standard 2D radiographs first, the others stereoradiographs first, but the difference in gradings between the two groups was not statistically significant. Stereoradiography appears to hold the potential to improve diagnosis of osteoarthritis in elephant feet, particularly by less experienced clinicians, and the technique is low-cost and applicable under field conditions.

Diagnóstico de las lesiones de la calota. Selección de variables por redes neuronales y regresión logística

OBJECTIVES

To establish the minimun set of features needed in the diagnosis of calvarial lesions using computed tomography (CT) and to assess the accuracy of logistic regression (LR) and artificial neural networks (NN) for their diagnosis.

MATERIAL AND METHODS

167 patients with calvarial lesions as the only known disease were enrolled. The clinical and CT data were used for LR and NN models. Both models were tested with the jacknife method. The final results of each model were compared using the area under ROC curves (A 2 ).

RESULTS

The lesions were 73.1 % benign and 26.9% malignant. There was no statistically significant difference between LR and NN in differentiating malignancy. In characterizing the histologic diagnoses, NN was statistically superior to LR. Important NN features needed for malignancy classification were age and edge definition, and for the histologic diagnoses matrix, marginal sclerosis and age.

CONCLUSIONS

A minimum four features is needed to diagnose these lesions, not being important patients' symptoms. NNs offer wide possibilities over statistics for the calvarial lesions study besides a superior diagnostic performance.

Multimodality diagnosis of liver tumors: feature analysis with CT, liver-specific and contrast-enhanced MR, and a computer model

RATIONALE AND OBJECTIVES

The purpose of this study was to measure and to clarify the diagnostic contributions of image-based features in differentiating benign from malignant and hepatocyte-containing from non-hepatocyte-containing liver lesions.

MATERIALS AND METHODS

Six experienced abdominal radiologists each read images from 146 cases (including a contrast material-enhanced computed tomographic [CT] scan and contrast-enhanced and unenhanced magnetic resonance [MR] images) following a checklist-questionnaire requiring them to rate quantitatively each of as many as 131 image features and then reported on each of the two differentiations. The diagnostic value of each feature was assessed, and linear discriminant analysis was used to develop statistical prediction rules (SPRs) for merging feature data into computerized "second opinions." For the two differentiations, accuracy (area under the receiver operating characteristic curve [Az]) was then determined for the radiologists' readings by themselves and for each of three SPRs.

RESULTS

Thirty-seven candidate features had diagnostic value for each of the two differentiations (a slightly different feature set for each). Radiologists' performance at both differentiations was excellent (Az = 0.929 [benign vs malignant] and 0.926 [hepatocyte-containing vs non-hepatocyte-containing]). Performance of the SPR that operated on the features from all modalities together was better than that of radiologists (Az = 0.936 [benign vs malignant] and 0.951 [hepatocyte-containing vs non-hepatocyte-containing]), but this difference was of marginal statistical significance (P = .11). Contrast-enhanced MR imaging and contrast-enhanced CT each made significant adjunctive contributions to accuracy compared with unenhanced MR imaging alone.

CONCLUSION

Many CT- and MR imaging-based features have diagnostic value in differentiating benign from malignant and hepatocyte-containing from non-hepatocyte-containing liver lesions. Radiologists could also benefit from the fully informed SPR's "second opinions."

Invited review: computer aids for decision-making in diagnostic radiology--a literature review

This review looks at a variety of different ways in which computers can be used to assist in the interpretation of radiological images and in radiological decision-making. The issues involved in the design of computerized decision aids are introduced and four criteria proposed for evaluating such aids: need, practicality, veracity and relevance. These criteria are used to assess research into decision aids based on: image databases, numerical methods, expert systems, image processing and image understanding systems. Possible directions for research leading to aids of practical value are discussed in the conclusion.