Purpose: To establish thresholds for contrast enhancement-based attenuation (CM) and iodine concentration (IOD) for the quantitative evaluation of enhancement in renal lesions on single-phase split-filter dual-energy CT (tbDECT) and combine measurements in a machine learning algorithm to potentially improve performance.
Material: 126 patients with incidental renal cysts (both hypo- and hyperdense cysts) or high suspicion for renal cell carcinoma (312 total lesions) undergoing abdominal, portal venous phase tbDECT were initially included in this retrospective study. Gold standard was pathological confirmation or follow-up imaging (MRI or multiphasic CT). CM, IOD, and ROI size were recorded. Thresholds for CM and IOD were identified using Youden-Index of the empirical ROC curves. Decision tree (DTC) and random forest classifier (RFC) were trained. Sensitivities, specificities, and AUCs were compared using McNemar and DeLong test.
Results: The final study cohort comprised 40 enhancing and 113 non-enhancing renal lesions. Optimal thresholds for quantitative iodine measurements and contrast enhancement-based attenuation were 1.0 ± 0.0 mg/ml and 23.6 ± 0.3 HU, respectively. Single DECT parameters (IOD, CM) showed similar overall performance with an AUC of 0.894 and 0.858 (p = 0.541) (sensitivity 90 and 80%, specificity 88 and 92%, respectively). While overall performance for the DTC (AUC 0.944) was higher than RFC (AUC 0.886), this difference (p = 0.409) and comparison to CM (p = 0.243) and IOD (p = 0.353) was not statistically significant.
Conclusions: Enhancement in incidental renal lesions on single-phase tbDECT can be classified with up to 87.5% sensitivity and 94.6% specificity. Algorithms combining DECT parameters did not increase overall performance.
Keywords: Artificial intelligence; Computer-assisted radiographic image interpretation; Dual-energy X-ray computed tomography; Kidney diseases; Supervised machine learning.