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Open AccessArticle

A Radiogenomics Ensemble to Predict EGFR and KRAS Mutations in NSCLC

1
Systems Engineering, Universidad Simon Bolivar, Barranquilla 080001, Colombia
2
Systems Engineering, Universidad del Norte, Atlántico 080001, Colombia
3
Computer Science and Engineering, University of South Florida, Tampa, FL 33620, USA
4
Cancer Epidemiology, Moffit Cancer Center, Tampa, FL 33617, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Brian D. Ross
Tomography 2021, 7(2), 154-168; https://doi.org/10.3390/tomography7020014
Received: 6 April 2021 / Revised: 23 April 2021 / Accepted: 27 April 2021 / Published: 29 April 2021
Lung cancer causes more deaths globally than any other type of cancer. To determine the best treatment, detecting EGFR and KRAS mutations is of interest. However, non-invasive ways to obtain this information are not available. Furthermore, many times there is a lack of big enough relevant public datasets, so the performance of single classifiers is not outstanding. In this paper, an ensemble approach is applied to increase the performance of EGFR and KRAS mutation prediction using a small dataset. A new voting scheme, Selective Class Average Voting (SCAV), is proposed and its performance is assessed both for machine learning models and CNNs. For the EGFR mutation, in the machine learning approach, there was an increase in the sensitivity from 0.66 to 0.75, and an increase in AUC from 0.68 to 0.70. With the deep learning approach, an AUC of 0.846 was obtained, and with SCAV, the accuracy of the model was increased from 0.80 to 0.857. For the KRAS mutation, both in the machine learning models (0.65 to 0.71 AUC) and the deep learning models (0.739 to 0.778 AUC), a significant increase in performance was found. The results obtained in this work show how to effectively learn from small image datasets to predict EGFR and KRAS mutations, and that using ensembles with SCAV increases the performance of machine learning classifiers and CNNs. The results provide confidence that as large datasets become available, tools to augment clinical capabilities can be fielded. View Full-Text
Keywords: radiogenomics; NSCLC; machine learning; EGFR; KRAS; ensembles; CNN radiogenomics; NSCLC; machine learning; EGFR; KRAS; ensembles; CNN
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MDPI and ACS Style

Moreno, S.; Bonfante, M.; Zurek, E.; Cherezov, D.; Goldgof, D.; Hall, L.; Schabath, M. A Radiogenomics Ensemble to Predict EGFR and KRAS Mutations in NSCLC. Tomography 2021, 7, 154-168. https://doi.org/10.3390/tomography7020014

AMA Style

Moreno S, Bonfante M, Zurek E, Cherezov D, Goldgof D, Hall L, Schabath M. A Radiogenomics Ensemble to Predict EGFR and KRAS Mutations in NSCLC. Tomography. 2021; 7(2):154-168. https://doi.org/10.3390/tomography7020014

Chicago/Turabian Style

Moreno, Silvia; Bonfante, Mario; Zurek, Eduardo; Cherezov, Dmitry; Goldgof, Dmitry; Hall, Lawrence; Schabath, Matthew. 2021. "A Radiogenomics Ensemble to Predict EGFR and KRAS Mutations in NSCLC" Tomography 7, no. 2: 154-168. https://doi.org/10.3390/tomography7020014

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