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

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 Imag...

Full description

Saved in:
Bibliographic Details
Published in:Journal of digital imaging 2021-10, Vol.34 (5), p.1086-1098
Main Authors: Khodabakhshi, Zahra, Amini, Mehdi, Mostafaei, Shayan, Haddadi Avval, Atlas, Nazari, Mostafa, Oveisi, Mehrdad, Shiri, Isaac, Zaidi, Habib
Format: Article
Language:English
Subjects:
Biomarkers
Cancer
Cell survival
Computed tomography
Density
Failure times
Feature extraction
Flatness
Heterogeneity
Imaging
Kidney cancer
Malignancy
Medical imaging
Medicine
Medicine & Public Health
Nephrectomy
Patients
Radiology
Radiomics
Renal cell carcinoma
Survival
Tumors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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  
ISSN:0897-1889
1618-727X
DOI:10.1007/s10278-021-00500-y