CT prediction of the Fuhrman grade of clear cell renal cell carcinoma (RCC): towards the development of computer-assisted diagnostic method
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There are distinct quantifiable features characterizing renal cell carcinomas on contrast-enhanced CT examinations, such as peak tumor enhancement, tumor heterogeneity, and percent contrast washout. While qualitative visual impressions often suffice for diagnosis, quantitative metrics if developed and validated can add to the information available from standard of care diagnostic imaging. The purpose of this study is to assess the use of quantitative enhancement metrics in predicting the Fuhrman grade of clear cell RCC.
Materials and methods
65 multiphase CT examinations with clear cell RCCs were utilized, 44 tumors with Fuhrman grades 1 or 2 and 21 tumors with grades 3 or 4. After tumor segmentation, the following data were extracted: histogram analysis of voxel-based whole lesion attenuation in each phase, enhancement and washout using mean, median, skewness, kurtosis, standard deviation, and interquartile range.
Statistically significant difference was observed in 4 measured parameters between grades 1–2 and grades 3–4: interquartile range of nephrographic attenuation values, standard deviation of absolute enhancement, as well as interquartile range and standard deviation of residual nephrographic enhancement. Interquartile range of nephrographic attenuation values was 292.86 HU for grades 1–2 and 241.19 HU for grades 3–4 (p value 0.02). Standard deviation of absolute enhancement was 41.26 HU for grades 1–2 and 34.66 HU for grades 3–4 (p value 0.03). Interquartile range was 297.12 HU for residual nephrographic enhancement for grades 1–2 and 235.57 HU for grades 3–4 (p value 0.02), and standard deviation of the same was 42.45 HU for grades 1–2 and 37.11 for grades 3–4 (p value 0.04).
Our results indicate that absolute enhancement is more heterogeneous for lower grade tumors and that attenuation and residual enhancement in nephrographic phase is more heterogeneous for lower grade tumors. This represents an important step in devising a predictive non-invasive model to predict the nucleolar grade.
KeywordsRenal cell carcinoma Quantitative imaging Computer-assisted diagnosis Computed tomography (CT)
This project has received funding from the Whittier foundation. The project described was supported in part by Award Number P30CA014089 from the National Cancer Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health.
Hannu Huhdanpaa—primary author of the paper, data processing and analysis; Darryl Hwang—development of Matlab algorithms for data processing; Steven (Yong) Cen—statistical analysis; Brian Quinn—segmenting and registering data; Megha Nayyar—segmenting and organizing data; Xuejun Zhang—Algorithm development and review; Frank Chen—reviewing data and image analysis; Bhushan Desai—drafting, coordination, and statistical review; Gangning Liang—pathological correlation; Inderbir Gill—clinical correlation and analysis; and Vinay Duddalwar—primary radiology faculty mentor, overall guidance, coordination, and review.
Hannu Huhdanpaa, Darryl Hwang, Steven (Yong) Cen, Brian Quinn, Megha Nayyar, Xuejun Zhang, Frank Chen, Bhushan Desai, Gangning Liang, Inderbir Gill, Vinay Duddalwar have nothing to disclose.
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