4.6 Article

Prediction of World Health Organization /International Society of Urological Pathology (WHO/ISUP) Pathological Grading of Clear Cell Renal Cell Carcinoma by Dual-Layer Spectral CT

Journal

ACADEMIC RADIOLOGY
Volume 30, Issue 10, Pages 2321-2328

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1016/j.acra.2022.12.002

Keywords

Clear cell renal cell carcinoma; Dual-layer spectral CT; WHO/ISUP grading; Normalized iodine concentration

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Quantitative indicators in dual-layer spectral computed tomography urography (DL-CTU) images can help predict the WHO/ISUP grade of clear cell renal cell carcinoma (ccRCC).
Rationale and objectives: To evaluate whether the dual-layer spectral computed tomography urography (DL-CTU) images could predict WHO/ISUP pathological grading of clear cell renal cell carcinoma (ccRCC).Materials and methods: We retrospectively included patients (n = 50) with pathologically confirmed ccRCC who underwent preoperative DL-CTU (from October 2017 to February 2021). They were divided into low-grade (WHO/ISUP 1/2, n = 30) and high-grade groups (WHO/ISUP 3/4, n = 20). The lesion size, attenuation (HU), iodine concentration (IC), normalized IC(NIC), and other quantitative characteristics were compared between the two groups. HU, IC, and NIC were obtained by plotting ROI with two different methods (circular ROI in the solid component or irregular ROI along the tumor edge containing tumor necrotic components). Receiver operating characteristic curves and multivariable model were used to evaluate the ability of parameters to predict WHO/ISUP grade.Results: Transverse diameter (TD) of low-grade tumors was smaller, and HU in the non-contrast phase of the second method (HU-U-2) was lower than that of high-grade tumors (34.21 +/- 15.14 mm vs. 46.50 +/- 20.68 mm, 27.33 +/- 6.65 HU vs. 31.36 +/- 6.09 HU, p< 0.05). The NIC in the nephrographic phase by the two methods (NIC-N-1 and NIC-N-2) of low-grade was higher than that of the high-grade group (0.78 +/- 0.19 vs.0.58 +/- 0.22, 0.73 +/- 0.42 vs. 0.46 +/- 0.22, p< 0.05). The final multivariable model composed of TD, HU-U-2, and NIC-N-1 could predict ccRCC grade with the area under the curve, sensitivity, specificity, and accuracy of 0.852, 70%, 90%, and 82%.Conclusion: Quantitative indicators in DL-CTU images could help predict the WHO/ISUP grade of ccRCC.

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