Overall Survival Prediction in Renal Cell Carcinoma Patients Using Computed Tomography Radiomic and Clinical Information

Abstract

The aim of this work is to investigate the applicability of radiomic features alone and in combination with clinical information for the prediction of renal cell carcinoma (RCC) patients’ overall survival after partial or radical nephrectomy. Clinical studies of 210 RCC patients from The Cancer Imaging Archive (TCIA) who underwent either partial or radical nephrectomy were included in this study. Regions of interest (ROIs) were manually defined on CT images. A total of 225 radiomic features were extracted and analyzed along with the 59 clinical features. An elastic net penalized Cox regression was used for feature selection. Accelerated failure time (AFT) with the shared frailty model was used to determine the effects of the selected features on the overall survival time. Eleven radiomic and twelve clinical features were selected based on their non-zero coefficients. Tumor grade, tumor malignancy, and pathology t-stage were the most significant predictors of overall survival (OS) among the clinical features (p < 0.002, < 0.02, and < 0.018, respectively). The most significant predictors of OS among the selected radiomic features were flatness, area density, and median (p < 0.02, < 0.02, and < 0.05, respectively). Along with important clinical features, such as tumor heterogeneity and tumor grade, imaging biomarkers such as tumor flatness, area density, and median are significantly correlated with OS of RCC patients.

A Clinical-Radiomics Nomogram Based on the Apparent Diffusion Coefficient (ADC) for Individualized Prediction of the Risk of Early Relapse in Advanced Sinonasal Squamous Cell Carcinoma: A 2-Year Follow-Up Study

“…This result is consistent with a recent study by Khodabakhshi et al. ( 18 ), which reported a significant correlation between higher values of flatness and better survival outcomes in renal cell carcinoma patients. However, these above features could only reflect one aspect of tumor information.…”

COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

“…We included 14,339 patients along with their CT images, segmented the lungs, and extracted distinct radiomics features. As there is no “one fits all” machine learning approaches for radiomic studies, given that their performance is task-dependent and there is large variability across models [ [49] , [50] , [51] , [52] , [53] , [54] , [55] ], we tested the cross-combination of four feature selectors and seven classifiers, which resulted in twenty-eight different combinations of algorithms to find the best performing model. Since the dataset was gathered from different centers, we applied the ComBat Harmonization algorithm that has been successfully applied in radiomics studies over the extracted features [ 25 , 55 , 56 ].…”

Predicting the WHO/ISUP Grade of Clear Cell Renal Cell Carcinoma Through CT-Based Tumoral and Peritumoral Radiomics

“…High-grade ccRCCs have a poorer prognosis compared with low-grade ccRCCs in terms of biologic behavior and prognostic factors which are related to pathological grades (23). Precisely predicting the pathological grades of ccRCCs through a noninvasive method of radiomics based on medical images is of great significance (24), allowing not only the assessment and characterization of ccRCCs but also the identification of patients with poorer prognosis who may benefit from early surveillance (25). We identified that the radiomics analysis of tumor and peritumor may aid in the preoperative prediction of the WHO/ ISUP grades of ccRCCs with AUCs of 0.802 and 0.788 in the training cohort and 0.796 and 0.787 in the validation cohort.…”