Value of pre-therapy 18F-FDG PET/CT radiomics in predicting EGFR mutation status in patients with non-small cell lung cancer.
Purpose: To assess the predictive power of pre-therapy 18F-FDG PET/CT-based radiomic features for epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer. Methods: Two hundred and forty-eight lung cancer patients underwent pre-therapy diagnostic 18F-FDG PET/CT scans and...
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 47; no. 5; pp. 1137 - 1147 |
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| Autores principales: | , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
May2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142435960&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142435960 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: May2020 vid: 47 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142435960 10.1007/s00259-019-04592-1 142435960 ppf: 1137 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Value of pre-therapy 18F-FDG PET/CT radiomics in predicting EGFR mutation status in patients with non-small cell lung cancer. aug: au: Zhang, Jianyuan Zhao, Xinming Zhao, Yan Zhang, Jingmian Zhang, Zhaoqi Wang, Jianfang Wang, Yingchen Dai, Meng Han, Jingya affil: Department of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, 050011, Shijiazhuang, Hebei, China sug: ab: Purpose: To assess the predictive power of pre-therapy 18F-FDG PET/CT-based radiomic features for epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer. Methods: Two hundred and forty-eight lung cancer patients underwent pre-therapy diagnostic 18F-FDG PET/CT scans and were tested for genetic mutations. The LIFEx package was used to extract 47 PET and 45 CT radiomic features reflecting tumor heterogeneity and phenotype. The least absolute shrinkage and selection operator (LASSO) algorithm was used to select radiomic features and develop a radiomics signature. We compared the predictive performance of models established by radiomics signature, clinical variables, and their combinations using receiver operating curves (ROCs). In addition, a nomogram based on the radiomics signature score (rad-score) and clinical variables was developed. Results: The patients were divided into a training set (n = 175) and a validation set (n = 73). Ten radiomic features were selected to build the radiomics signature model. The model showed a significant ability to discriminate between EGFR mutation and EGFR wild type, with area under the ROC curve (AUC) equal to 0.79 in the training set, and 0.85 in the validation set, compared with 0.75 and 0.69 for the clinical model. When clinical variables and radiomics signature were combined, the AUC increased to 0.86 (95% CI [0.80–0.91]) in the training set and 0.87 (95% CI [0.79–0.95]) in the validation set, thus showing better performance in the prediction of EGFR mutations. Conclusion: The PET/CT-based radiomic features showed good performance in predicting EGFR mutation in non-small cell lung cancer, providing a useful method for the choice of targeted therapy in a clinical setting. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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