Diagnostic hierarchy of radiological features in soft tissue tumours and proposition of a simple diagnostic algorithm to estimate malignant potential of an unknown mass.

OBJECTIVE

To quantify the diagnostic utility of imaging features in soft tissue masses (STMs) and to provide a ranked list of predictors for malignancy.

SUBJECTS AND METHODS

Imaging features in 260 cases of STMs with verified histology were assessed. Diagnostic properties including sensitivity, specificity, positive and negative predictive values, likelihood/odds ratios (OR) and normalized variance (NV) via random forest analysis were calculated. The diagnostic utility of an 8-item checklist consisting of the highest-ranked features was evaluated through a receiver-operating-characteristics (ROC) curve.

RESULTS

The most predictive features (NV/OR in parentheses) were heterogeneous contrast-enhancement in ultrasound (297.9/15.1) and MRI (197.3/11.9), lesion roundness (209.8/5.5), diffusion restriction (175.8/9.3), cystic/necrotic intralesional areas (167.1/8.3), higher patient age (159.0/2.6), surrounding oedema (155.4/6.5) and intralesional Doppler hypervascularity (134.4/5.1). A simple 8-item checklist was highly predictive of malignancy in cases with at least 75% positive features (0.90 area under the ROC curve, 87.0% sensitivity, 84.5% specificity, 59.5% positive and 96.1% negative predictive value, 36.5 odds ratio) even in cases with only partial feature availability.

CONCLUSION

Features vary widely in their diagnostic value in STMs; an 8-item checklist based on the eight most decisive features can be a simple tool to assess the likelihood for malignancy in unknown soft tissue masses, even though a stratified approach is certainly still advisable when first confronted with an STM.