Radiomic signatures to estimate survival in patients with advanced hepatocellular carcinoma treated with sorafenib: Cancer and Leukemia Group B 80802 (Alliance).
Authors
Affiliations (17)
Affiliations (17)
- Department of Radiology, New York-Presbyterian/Columbia University Irving Medical Center, New York, USA; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA. Electronic address: [email protected].
- Alliance Statistics and Data Management Center, Mayo Clinic, Rochester, USA.
- Duke Cancer Institute, Duke University, Durham, USA.
- Division of Pharmacotherapy and Experimental Therapeutics, University of North Carolina, Chapel Hill, USA.
- Department of Radiology, New York-Presbyterian/Columbia University Irving Medical Center, New York, USA.
- Department of Physics, NewYork-Presbyterian/Columbia University Irving Medical Center, New York, USA.
- Department of Radiology, New York-Presbyterian/Columbia University Irving Medical Center, New York, USA; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA.
- Alliance Protocol Operations Office, University of Chicago, Chicago, USA.
- Department of Radiology, New York-Presbyterian/Columbia University Irving Medical Center, New York, USA; Department of Radiology, Toulouse Rangueil Hospital, Toulouse, France.
- Department of Radiology, Toulouse Rangueil Hospital, Toulouse, France.
- Department of Radiology, New York-Presbyterian/Columbia University Irving Medical Center, New York, USA; McGovern Medical School at University of Texas Health Science Center Houston, Houston, USA.
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, USA.
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, USA; Department of Medicine, Weill Medical College of Cornell University, New York, USA.
- UCSF Helen Diller Family Comprehensive Cancer Center, San Francisco, USA.
- USC Norris Comprehensive Cancer Center, Los Angeles, USA.
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, USA; Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, USA.
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, USA; Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, USA.
Abstract
Current methods to evaluate therapeutic response in patients with hepatocellular carcinoma (HCC) rely on tumor size and density, which do not always correlate well with survival. We used pretreatment clinical and radiomics variables to predict overall survival (OS) in the randomized phase III CALGB 80802 (Alliance) trial, investigating the efficacy of sorafenib + doxorubicin versus sorafenib alone. Using machine learning, we analyzed baseline and first follow-up computed tomography (CT) images and associated clinical metadata from patients imaged in February 2010-May 2015, up to November 2015 with follow-up. Adult patients with HCC (n = 129) were randomly assigned to training (n = 92) and validation (n = 37) sets. We assessed the performance of a signature combining CT imaging features and clinical variables using hazard ratios to estimate OS after week 10 (first follow-up). Most patients were male (86.6%) and had bilirubin <2 mg/dl (98.4%), albumin >3.5 g/dl (69.0%), and moderately differentiated HCC (34.1%). Median (interquartile range) age: 58 years (63-71 years), alpha-fetoprotein (AFP): 2.5 ng/ml (26-282 ng/ml), international normalized ratio: 1 (1.1-1.2), Child-Pugh score: 5 (5-6). The highest-performing parsimonious training set signature combined clinical and radiomics features at baseline and week 10. In the validation set, the hazard ratio was 2398 (95% confidence interval 121-47 371) (P < 0.001). The signature's variables, ranked by importance, included baseline clinical features [albumin (1), AFP (2), Child-Pugh (4)], baseline radiomics features [component 17 (3), component 1 (5), component 9 (7), tumor volume (8)], and week 10 radiomics features [delta tumor volume (6)]. OS can be accurately predicted in patients with HCC receiving sorafenib by combining certain radiomics features with clinical metadata, centered primarily on baseline characteristics.