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Article: Deep learning for predicting epidermal growth factor receptor mutations of non-small cell lung cancer on PET/CT images

TitleDeep learning for predicting epidermal growth factor receptor mutations of non-small cell lung cancer on PET/CT images
Authors
KeywordsEfficientNet-V2 Model
epidermal growth factor receptor (EGFR)
positron emission tomography/computed tomography (PET/CT)
Issue Date6-Feb-2023
PublisherAME Publishing
Citation
Quantitative Imaging in Medicine and Surgery, 2023, v. 13, n. 3, p. 1286-1299 How to Cite?
Abstract

Background: Predicting the mutation status of the epidermal growth factor receptor (EGFR) gene based on an integrated positron emission tomography/computed tomography (PET/CT) image of non-small cell lung cancer (NSCLC) is a noninvasive, low-cost method which is valuable for targeted therapy. Although deep learning has been very successful in robotic vision, it is still challenging to predict gene mutations in PET/CT-derived studies because of the small amount of medical data and the different parameters of PET/ CT devices.Methods: We used the advanced EfficientNet-V2 model to predict the EGFR mutation based on fused PET/CT images. First, we extracted 3-dimensional (3D) pulmonary nodules from PET and CT as regions of interest (ROIs). We then fused each single PET and CT image. The network model was used to predict the mutation status of lung nodules by the new data after fusion, and the model was weighted adaptively. The EfficientNet-V2 model used multiple channels to represent nodules comprehensively.Results: We trained the EfficientNet-V2 model through our PET/CT fusion algorithm using a dataset of 150 patients. The prediction accuracy of EGFR and non-EGFR mutations was 86.25% in the training dataset, and the accuracy rate was 81.92% in the validation set.Conclusions: Combined with experiments, the demonstrated PET/CT fusion algorithm outperformed radiomics methods in predicting EGFR and non-EGFR mutations in NSCLC.


Persistent Identifierhttp://hdl.handle.net/10722/331421
ISSN
2023 Impact Factor: 2.9
2023 SCImago Journal Rankings: 0.746
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorXiao, ZH-
dc.contributor.authorCai, HH-
dc.contributor.authorWang, Y-
dc.contributor.authorCui, RX-
dc.contributor.authorHuo, L-
dc.contributor.authorLee, EYP-
dc.contributor.authorLiang, Y-
dc.contributor.authorLi, XM-
dc.contributor.authorHu, ZL-
dc.contributor.authorChen, L-
dc.contributor.authorZhang, N-
dc.date.accessioned2023-09-21T06:55:33Z-
dc.date.available2023-09-21T06:55:33Z-
dc.date.issued2023-02-06-
dc.identifier.citationQuantitative Imaging in Medicine and Surgery, 2023, v. 13, n. 3, p. 1286-1299-
dc.identifier.issn2223-4292-
dc.identifier.urihttp://hdl.handle.net/10722/331421-
dc.description.abstract<p></p><p>Background: Predicting the mutation status of the epidermal growth factor receptor (EGFR) gene based on an integrated positron emission tomography/computed tomography (PET/CT) image of non-small cell lung cancer (NSCLC) is a noninvasive, low-cost method which is valuable for targeted therapy. Although deep learning has been very successful in robotic vision, it is still challenging to predict gene mutations in PET/CT-derived studies because of the small amount of medical data and the different parameters of PET/ CT devices.Methods: We used the advanced EfficientNet-V2 model to predict the EGFR mutation based on fused PET/CT images. First, we extracted 3-dimensional (3D) pulmonary nodules from PET and CT as regions of interest (ROIs). We then fused each single PET and CT image. The network model was used to predict the mutation status of lung nodules by the new data after fusion, and the model was weighted adaptively. The EfficientNet-V2 model used multiple channels to represent nodules comprehensively.Results: We trained the EfficientNet-V2 model through our PET/CT fusion algorithm using a dataset of 150 patients. The prediction accuracy of EGFR and non-EGFR mutations was 86.25% in the training dataset, and the accuracy rate was 81.92% in the validation set.Conclusions: Combined with experiments, the demonstrated PET/CT fusion algorithm outperformed radiomics methods in predicting EGFR and non-EGFR mutations in NSCLC.<br></p>-
dc.languageeng-
dc.publisherAME Publishing-
dc.relation.ispartofQuantitative Imaging in Medicine and Surgery-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectEfficientNet-V2 Model-
dc.subjectepidermal growth factor receptor (EGFR)-
dc.subjectpositron emission tomography/computed tomography (PET/CT)-
dc.titleDeep learning for predicting epidermal growth factor receptor mutations of non-small cell lung cancer on PET/CT images-
dc.typeArticle-
dc.identifier.doi10.21037/qims-22-760-
dc.identifier.scopuseid_2-s2.0-85149013817-
dc.identifier.volume13-
dc.identifier.issue3-
dc.identifier.spage1286-
dc.identifier.epage1299-
dc.identifier.eissn2223-4306-
dc.identifier.isiWOS:000927492100001-
dc.identifier.issnl2223-4306-

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