Paper Info
Title
Immunotherapy treatment outcome prediction in metastatic melanoma through an automated multi-objective delta-radiomics model
Abstract
Based on recent studies, immunotherapy led by immune checkpoint inhibitors has significantly improved the patient survival rate and effectively reduced the recurrence risk. However, immunotherapy has different therapeutic effects for different patients, leading to difficulties in predicting the treatment response. Conversely, delta-radiomic features, which measure the difference between pre-and post-treatment through quantitative image features, have proven to be promising descriptors for treatment outcome prediction. Consequently, we developed an effective model termed as the automated multi-objective delta-radiomics (Auto-MODR) model for the prediction of immunotherapy response in metastatic melanoma. In Auto-MODR, delta-radiomic features and traditional radiomic features were used as inputs. Furthermore, a novel automated multi-objective model was developed to obtain more reliable and balanced results between sensitivity and specificity. We conducted extensive comparisons with existing studies on treatment outcome prediction. Our method achieved an area under the curve (AUC) of 0.86 in a cross-validation study and an AUC of 0.73 in an independent study. Compared with the model using conventional radiomic features (pre-and post-treatment) only, better performance can be obtained when conventional radiomic and delta-radiomic features are combined. Furthermore, Auto-MODR outperformed the currently available radiomic strategies.
Year
DOI
Venue
2021
10.1016/j.compbiomed.2021.104916
COMPUTERS IN BIOLOGY AND MEDICINE
Keywords
DocType
Volume
Outcome prediction, Delta radiomics, Ensemble learning, Genetic algorithms, Multi-objective learning
Journal
138
ISSN
Citations 
PageRank 
0010-4825
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Xi Chen133.62
Meijuan Zhou200.34
Zhilong Wang300.34
Si Lu400.34
Shaojie Chang500.34
Zhi-Guo Zhou61119.47