MP50-06 UTILIZATION OF RADIOMIC MARKERS IN LOCALIZED RENAL CELL CARCINOMA TO PREDICT STAGE

Abstract

INTRODUCTION AND OBJECTIVE: Incidental pT3a upstaging following partial nephrectomy (PN) for cT1 renal masses is associated with inferior oncologic outcomes. The ability to identify tumor characteristics associated with pathologic upstaging using current imaging protocols is limited. The inclusion of quantitative radiomic imaging characteristics in pre-operative staging may improve the detection of adverse pathologic features. We sought to evaluate whether any radiomic features were associated with pathologic upstaging following PN for cT1 renal masses. METHODS: Pre-operative CT imaging from a total of 130 patients with cT1 renal masses and final pathology demonstrating either pT1 (n=92) or pT3a (n=45) renal cell carcinoma were retrospectively reviewed. T1 patients were matched by age and tumor size with pT3a patients in a 2:1 ratio. An Auto-Initialized Cascaded Level Set (AI-CALS) system was used to segment 139 renal lesions. 49 radiomic descriptors including morphological features (volume, shape) and gray level features were extracted from segmented lesions. A predictive model was developed using automatic feature selection and linear discriminant analysis classifier. A leave-one-lesion out cross validation method was used for model training and performance testing. The area under the receiver operating characteristic curve (AUC) was calculated for the predictor model to evaluate its performance in predicting cancer stage (pT1 or pT3a). RESULTS: We identified one gray level and 4 shape features of significance in our predictive model. Features related to surface irregularity of the renal mass appeared to be highly predictive in distinguishing between pT1a and pT3a cancers. The training AUC was 0.72 and the test AUC was 0.70. The results from the test AUC were graphed. The AUC for our training set was 0.72 dropping only slightly to 0.70 in our validation set (figure). CONCLUSIONS: Machine learning techniques show promise in decision support for renal cell carcinoma staging. The ability to predict pathologic stage prior to intervention could help guide treatment selection or identify patients for future neoadjuvant trials. Future work will focus on tool and feature optimization and incorporating clinical and biopsy data into the predictive model.Source of Funding: None