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Published on: April 29, 2014
MRI-Based Habitat Radiomics for Predicting Postoperative Recurrence in Nonmetastatic Clear Cell Renal Cell Carcinoma
Sicheng Yi1, Tongyu Jia2, Xu Bai3
1Department of Radiology, First Medical Center of the Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing 100853, China (S.Y., H.K., B.L., C.L., X.W., H.X., X.N., M.C., S.Z., Y.M., H.W.).
Abstract:
RATIONALE AND OBJECTIVES: This study aimed to develop and validate a magnetic resonance imaging (MRI)-based habitat radiomics model to predict recurrence-free survival (RFS) in patients with nonmetastatic clear cell renal cell carcinoma (ccRCC) after surgical resection.
Materials And Methods:
A retrospective cohort of 630 patients with nonmetastatic ccRCC who underwent surgical resection at the First Medical Center of Chinese PLA General Hospital (2011-2019) was included. Preoperative T2-weighted imaging (T2WI) and contrast-enhanced corticomedullary phase (CP) MRI were used to cluster tumor voxels into homogeneous habitats via K-means algorithm based on signal intensity. Radiomic features were extracted from habitats; after feature selection, these features were integrated with clinicopathological indicators to build a Cox proportional hazards regression model. Model performance was assessed via receiver operating characteristic (ROC) curves, concordance index (C-index), calibration curves, and decision curve analysis (DCA).
Results:
Three distinct tumor habitat regions were identified through clustering, from which 13 recurrence-related radiomic features were selected to construct a Habitat Signature (HS). Multivariate Cox regression analysis demonstrated that age (HR = 1.039, 95% CI: 1.016-1.063, P<0.001), sex (male vs female, HR = 2.608, 95% CI: 1.291-5.270, P=0.008), and pathological T stage (T3 vs T1, HR = 4.284, 95% CI: 1.997-9.193, P < 0.001) served as independent predictors of postoperative recurrence. Constructed by combining clinicopathological predictors with the HS score, the clinical-habitat combined model yielded AUC values for 3-year and 5-year postoperative recurrence prediction of 0.80/0.81 in the training set and 0.85/0.81 in the test set, along with C-indices of 0.80 and 0.81, respectively.
Conclusion:
The predictive model constructed by combining MRI-based HS score and clinical-pathological features has predictive value for recurrence of nonmetastatic ccRCC.
