Does Computed Tomography Still Have Limitations to Distinguish Benign from Malignant Renal Tumors for Radiologists?

Abstract

Objectives: To evaluate the current accuracy of CT for diagnosing benign renal tumors. Materials and Methods: We retrospectively reviewed 905 patients who underwent preoperative CT followed by surgical resection. The final pathology was benign in 156 patients (17%). After exclusions, 140 patients with 163 benign tumors were included and 3 sets of the CT interpretations by radiologists with varying levels of experience were analyzed. Results: The histological breakdown was as follows: oncocytomas (54.6%), angiomyolipomas (AMLs; 30.7%), renal cysts (8.0%), other miscellaneous benign tumors (6.7%). The sensitivities of diagnosing oncocytomas were 3.4, 9.0, and 13.5% in primary radiological reports, second blinded reviews, and third non-blinded reviews, respectively (p = 0.055). The sensitivities of diagnosing AMLs were 46.0, 58.0, and 62.0% in the 3-sets of CT interpretations, respectively (p = 0.246). As for renal cysts, the sensitivities were 69.2, 92.3, and 100% in the 3-sets of CT interpretations, respectively (p = 0.051). In primary reports, the positive predictive values were 95.8% in lipid poor (lp)-AMLs, 60.0% in oncocytomas, 69.2% in renal cysts, respectively (p < 0.05). Conclusions: Current conventional CT imaging still has limitations in differentiating oncocytomas and lp-AMLs from renal cell carcinomas, even when images were re-examined by experienced radiologists.

A Decision-Support Tool for Renal Mass Classification

“…At a high-level, these results show that RFGB consistently outperforms several standard ML algorithms on all three evaluation metrics significantly at p = 0.05. Finally, note that visual classification by experts only achieves AUC-ROC of 0.65 using the pathological gold standard [50], which suggests that many ML approaches can already outperform human diagnostic baselines on this difficult task.…”

Multiple angiomyolipomas mimicking metastases of concurrent clear cell renal cell carcinoma

“…CT is unable to detect fat components in approximately 4.5% of AMLs that are defined as lipid‐poor AMLs . The sensitivity of CT for diagnosis of AML is reported to be only 46.0%, and most of the missed cases are lipid‐poor AMLs . A homogeneous enhancement pattern and high unenhanced attenuation on CT are reported to be sensitive and specific for lipid‐poor AML .…”

“…It is well known that AML is the most common benign tumor of the kidney whereas ccRCC is the most common malignant one . Concurrence of RCC and AML is quite rare, and even the latest imaging modalities still have limited efficacy for distinguishing lipid‐poor AML from ccRCC . In general, the probability of synchronous metastasis of RCC increases with tumor size, and the median diameter of RCC with synchronous metastasis is 8.0 cm .…”

Differentiating solid, non-macroscopic fat containing, enhancing renal masses using fast Fourier transform analysis of multiphase CT

“…In addition, even non-blinded third reviews by an experienced abdominal radiologist found classic CT findings only in 13.5% of oncocytomas. 37 Despite the identification of tumor features such as central scar, segmental enhancement inversion etc. which are characteristic of an oncocytoma, to date, there are no reliable features that can differentiate oncocytoma from RCC.…”