Feasibility study of preoperative precise graded prediction model for renal clear cell carcinoma based on quantitative imaging biomarkers
SHU Enfen, KONG Chunli, XIA Haihong, GAO Yang, WU Xulu, XIE Liangjun, ZHAO Xuemiao, JIANG Chunyan, CHEN Chunmiao, ZHOU Yongjin, JI Jiansong.
Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Department of Radiology, Lishui Central Hospital, Lishui 323000, China
SHU Enfen,KONG Chunli,XIA Haihong, et al. Feasibility study of preoperative precise graded prediction model for renal clear cell carcinoma based on quantitative imaging biomarkers[J]. JOURNAL OF WEZHOU MEDICAL UNIVERSITY, 2020, 50(11): 896-900.
Abstract：Objective: To explore the feasibility of constructing accurate prediction model of WHO/ISUP classification for clear cell renal cell carcinoma (ccRCC) by screened quantitative imaging biomarkers via CT imaging based radiomics. Methods: Retrospective collection of 72 patients with ccRCC who were confirmed by surgery and pathology in Lishui Central Hospital from January 2009 to October 2018. All patients were underwent abdominal CT scanning and enhanced three-phase scanning before surgery, who were divided as highdifferentiation group (I+II grade, 52 cases) and low-differentiation group (III+IV grade, 20 cases).Preoperative arterial CT images of the enrolled patients were collected for screening imaging biomarkers by radiomics analysis and then trained predictive models.Firstly, the ITK-SNAP software was used to manually delineate the target lesion (maximum lesion) edge into the whole lesion three-dimensional region of interest (VOI); secondly, the texture parameters were extracted as imaging biomarkers by A.K software, and the characteristic texture parameters are screened based on R language. The Rad-score of each patient was calculated based on the above texture parameters, and the accurate hierarchical prediction model of renal clear cell carcinoma was finally constructed. Results: A total of 396 texture features were extracted from this study, and five characteristic texture parameters of the CT image of the arterial phase were screened by Lasso reduction dimension combined with10-fold cross-validation method as the predictive biomarkers, which were root mean square (RMS), kurtosis,
correlation, entropy, inertia, and calculated the corresponding Rad-score for each patient. The preoperative grading prediction model of renal clear cell carcinoma was constructed based on rad-score, and it was found that the area under the curve (ROC) was 0.891 (95%CI=0.797-0.952), and the sensitivity and specificity were high up to 84.6% and 85.3%, respectively. Conclusion: The preoperative precise grading prediction model constructed by angiography based on arterial CT images has high accuracy, specificity and sensitivity, which is feasible for grading prediction of renal clear cell carcinoma
