Establish of a simple predictive model based on CT imaging histology in the differential diagnosis of extra-small renal cell carcinoma with rich blood supply and angiomyolipoma with minimal fat

Objective To explore the differential value of a simple prediction model based on CT imaging histology in the diagnosis of ultra-small renal cell carcinoma (usRCC) with rich blood supply and angiomyolipoma with minimal fat (mfAML).

Methods The clinical data of 71 patients with ultra-small renal tumor (diameter ≤2 cm) with rich blood supply were collected. According to the postoperative pathological types, they were divided into usRCC group (n=33) and mfAML group (n=38). Clinical data, CT imaging manifestations, and related CT quantitative parameters were compared, independent influencing factors with differential significance for usRCC and mfAML were screened using binary Logistic regression, and a simple predictive model based on CT imaging histology was constructed. Receiver operating characteristic (ROC) curves were drawn to evaluate differential value of relevant CT quantitative parameters and predictive model for usRCC and mfAML.

Results The proportion of cystic necrosis, pseudocapsule sign and parenchymal phase heterogeneous enhancement in the usRCC group was higher than that in the mfAML group (P < 0.05). The CT value of cortical phase, enhanced CT value of cortical phase and parenchymal phase in the usRCC group were also significantly higher than those in the mfAML group (P < 0.05). The areas under the curve (AUCs) of differential diagnosis of usRCC and mfAML by CT value of cortical phase, enhanced CT value of cortical phase and parenchymal phase were 0.702, 0.718 and 0.803, respectively. Cystic necrosis (OR=2.537; 95% CI, 1.125 to 4.358), parenchymal enhancement uniformity (OR=3.872; 95% CI, 1.327 to 7.259), and parenchymal net enhancement CT value (OR=3.593; 95% CI, 1.290 to 7.518) were independent influencing factors for differentiated diagnosis of usRCC and mfAML (P < 0.05), thus a simple prediction model was constructed based on the three CT image omics variables. The ROC curve showed that the AUC of the model for differential diagnosis of usRCC and mfAML was 0.890 (95% CI, 0.804 to 0.976), the sensitivity was 87.888, and the specificity was 76.32%.

Conclusion The simple prediction model based on CT imaging histology has a good value in differential diagnosis of usRCC and mfAML, and provides an important reference for clinical diagnosis and treatment of small renal tumors.