Interobserver agreement issues in radiology.

Agreement between observers (i.e., inter-rater agreement) can be quantified with various criteria but their appropriate selections are critical. When the measure is qualitative (nominal or ordinal), the proportion of agreement or the kappa coefficient should be used to evaluate inter-rater consistency (i.e., inter-rater reliability). The kappa coefficient is more meaningful that the raw percentage of agreement, because the latter does not account for agreements due to chance alone. When the measures are quantitative, the intraclass correlation coefficient (ICC) should be used to assess agreement but this should be done with care because there are different ICCs so that it is important to describe the model and type of ICC being used. The Bland-Altman method can be used to assess consistency and conformity but its use should be restricted to comparison of two raters.

Prediction of tumor grade and lymphovascular space invasion in endometrial adenocarcinoma with MR imaging-based radiomic analysis.

PURPOSE: To evaluate the capabilities of two-dimensional magnetic resonance imaging (MRI)-based texture analysis features, tumor volume, tumor short axis and apparent diffusion coefficient (ADC) in predicting histopathological high-grade and lymphovascular space invasion (LVSI) in endometrial adenocarcinoma. MATERIALS AND METHODS: Seventy-three women (mean age: 66±11.5 [SD] years; range: 45-88 years) with endometrial adenocarcinoma who underwent MRI of the pelvis at 1.5-T before hysterectomy were retrospectively included. Texture analysis was performed using TexRAD® software on T2-weighted images and ADC maps. Primary outcomes were high-grade and LVSI prediction using histopathological analysis as standard of reference. After data reduction using ascending hierarchical classification analysis, a predictive model was obtained by stepwise multivariate logistic regression and performances were assessed using cross-validated receiver operator curve (ROC). RESULTS: A total of 72 texture features per tumor were computed. Texture model yielded 52% sensitivity and 75% specificity for the diagnosis of high-grade tumor (areas under ROC curve [AUC]=0.64) and 71% sensitivity and 59% specificity for the diagnosis of LVSI (AUC=0.59). Volumes and tumor short axis were greater for high-grade tumors (P=0.0002 and P=0.004, respectively) and for patients with LVSI (P=0.004 and P=0.0279, respectively). No differences in ADC values were found between high-grade and low-grade tumors and for LVSI. A tumor short axis≥20mm yielded 95% sensitivity and 75% specificity for the diagnosis of high-grade tumor (AUC=0.86). CONCLUSION: MRI-based texture analysis is of limited value to predict high grade and LVSI of endometrial adenocarcinoma. A tumor short axis≥20mm is the best predictor of high grade and LVSI.