Combining Washout and Noncontrast Data From Adrenal Protocol CT: Improving Diagnostic Performance.

RATIONALE AND OBJECTIVES: To determine if combination of washout and noncontrast data from delayed adrenal computed tomography (CT) improves diagnostic performance, and demonstration of an optimizing analytical framework. MATERIALS AND METHODS: This retrospective study consisted of 97 adrenal lesions, in 96 patients, with pathologically proven adrenal lesions (75 benign; 22 malignant), who had undergone noncontrast, portal- and approximate 15-minute delayed-phase CT. Lesion CT attenuations (Hounsfield units [HU]) during each phase, and "absolute" and "relative" percent enhancement washouts (APEW and RPEW) were assessed. The optimum combination of sequential parameters and thresholds was determined by recursive partitioning analysis; resultant diagnostic performance was compared to commonly applied single-parameter criteria for malignancy (noncontrast > 10 HU, APEW < 60%, RPEW < 40%). RESULTS: The above single-parameter criteria yielded sensitivities, specificities, and accuracies for malignancy of 100.0%, 41.3%, and 54.6%; 97.9%, 61.3%, and 69.1%; and 96.6%, 74.7%, and 78.4%, respectively. Recursive partitioning analysis identified noncontrast ≥24.75 HU, with subsequent APEW ≤63.49%, as the optimum sequential parameter-threshold combination, which yielded increased sensitivity, specificity, and accuracy of 100.0%, 85.3%, and 90.7%, respectively. Discrimination using the combined sequential classifier yielded statistically significant improvements in accuracy when compared to the above conventional single-parameter criteria (all P ≤ .039). CONCLUSION: Sequential application of noncontrast and washout criteria from delayed contrast-enhanced adrenal CT can improve diagnostic performance beyond that of commonly applied single-parameter criteria. Validation of the sequential ordering and refinement of the specific threshold values warrant further study.

Differentiation of Malignant and Benign Adrenal Lesions With Delayed CT: Multivariate Analysis and Predictive Models.

OBJECTIVE: The purpose of this study is to identify imaging and patient parameters that affect the diagnostic performance of delayed contrast-enhanced CT for distinguishing malignant from benign adrenal lesions larger than 1 cm in adult patients and to derive predictive models. MATERIALS AND METHODS: This retrospective study assessed 97 pathologically proven adrenal lesions that had undergone unenhanced, portal venous, and 15-minute delayed CT. Quantitatively, single-parameter evaluations of lesion attenuation (in Hounsfield units) and absolute percentage enhancement washout (APEW) and relative percentage enhancement washout (RPEW) were performed. In addition, descriptive CT features (lesion size, margin definition, heterogeneity vs homogeneity, fat, and calcification) and patients' demographic characteristics and medical history of malignancy were evaluated for association with lesion status using multiple logistic regression with stepwise model selection. Areas under the ROC curve (Az) were determined for univariate and multivariate analyses. Leave-one-lesion-out cross-validation was applied to ascertain the predictive performance of single-parameter and multivariate evaluations. RESULTS: The Az values for unenhanced attenuation, portal venous attenuation, delayed attenuation, APEW, and RPEW were 0.835, 0.534, 0.847, 0.792, and 0.871, respectively. Multivariate analyses revealed that portal venous attenuation, delayed attenuation, and APEW were significant features, with an Az of 0.923 when combined. The addition of the descriptive CT features increased the Az to 0.938; patient age and a history of malignancy were additional significant factors, increasing the Az to 0.956 and 0.972, respectively. The combined predictive classifier yielded 89% accuracy under cross-validation, compared with the best commonly applied single-parameter evaluation (77% for RPEW < 40%). CONCLUSION: Multivariate imaging evaluation applied to delayed contrast-enhanced CT alone, with or without patient characteristics, improves diagnostic performance for characterizing adrenal lesions beyond those of single-parameter evaluations. Predictive formulas assessing the probabilities of lesion benignity or malignancy are provided.