Automatic Feature Construction Based on Genetic Programming for Survival Prediction in Lung Cancer Using CT Images
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Scalco:2022:EMBC,
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author = "Elisa Scalco and Giovanna Rizzo and
Wilfrido Gomez-Flores",
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title = "Automatic Feature Construction Based on Genetic
Programming for Survival Prediction in Lung Cancer
Using {CT} Images",
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booktitle = "2022 44th Annual International Conference of the IEEE
Engineering in Medicine \& Biology Society (EMBC)",
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year = "2022",
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pages = "3797--3800",
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abstract = "In the radiomics workflow, machine learning builds
classification models from a set of input features.
However, some features can be irrelevant and redundant,
reducing the classification performance. This paper
proposes using the Genetic Programming (GP) algorithm
to automatically construct a reduced number of
independent and relevant radiomic features. The
proposed method is applied to patients affected by
Non-Small Cell Lung Cancer (NSCLC) with pre-operative
computed tomography (CT) images to predict the two-year
survival by the use of linear classifiers. The model
built using GP features is compared with benchmark
models built using traditional features. The use of the
GP algorithm increased classification performance: AUC
=0.69 for the proposed model vs. AUC =0.66 and 0.64 for
the benchmark models. Hence, the proposed approach
better stratifies patients at high and low risk
according to their overall postoperative survival
time.",
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keywords = "genetic algorithms, genetic programming, Machine
learning algorithms, Computed tomography, Computational
modeling, Biological system modeling, Lung cancer,
Benchmark testing",
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DOI = "doi:10.1109/EMBC48229.2022.9871039",
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ISSN = "2694-0604",
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month = jul,
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notes = "Also known as \cite{9871039}",
- }
Genetic Programming entries for
Elisa Scalco
Giovanna Rizzo
Wilfrido Gomez-Flores
Citations