Nomogram based on preoperative CT imaging predicts the EGFR mutation status in lung adenocarcinoma

Zhang, Guojin; Zhang, Jing; Cao, Yuntai; Zhao, Zhiyong; Li, Shenglin; Deng, Liangna; Zhou, Junlin

doi:10.1016/j.tranon.2020.100954

Cited by 11 publications

(12 citation statements)

References 38 publications

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“…Our study showed that EGFR mutations were identified more frequently in women, and that those with EGFR mutations had a higher incidence of the presence of air bronchograms and pleural retraction than those with the wild-type. These findings are consistent with those reported in previous studies [12,13]. Compared with the wild-type, 19 deletion mutations were more frequently identified in females with a higher probability of the presence of air bronchograms and calcification.…”

Section: Discussionsupporting

confidence: 92%

Lung adenocarcinoma: development of nomograms based on PET/CT images for prediction of epidermal growth factor receptor mutation status and subtypes

Huang

Cao

Zhou

et al. 2021

Nuclear Medicine Communications

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Objective To develop nomograms that combine clinical characteristics, computed tomographic (CT) features and 18F-fluorodeoxyglucose PET (18F-FDG PET) metabolic parameters for individual prediction of epidermal growth factor receptor (EGFR) mutation status and exon 19 deletion mutation and exon 21 point mutation (21 L858R) subtypes in lung adenocarcinoma. Methods In total 124 lung adenocarcinoma patients who underwent EGFR mutation testing and whole-body 18F-FDG PET/CT were enrolled. Each patient’s clinical characteristics (age, sex, smoking history, etc.), CT features (size, location, margins, etc.) and four metabolic parameters (SUVmax, SUVmean, MTV and TLG) were recorded and analyzed. Logistic regression analyses were performed to screen for significant predictors of EGFR mutation status and subtypes, and these predictors were presented as easy-to-use nomograms. Results According to the results of multiple regression analysis, three nomograms for individualized prediction of EGFR mutation status and subtypes were constructed. The area under curve values of three nomograms were 0.852 (95% CI, 0.783–0.920), 0.857 (95% CI, 0.778–0.937) and 0.893 (95% CI, 0.819–0.968) of EGFR mutation vs. wild-type, 19 deletion mutation vs. wild-type and 21 L858R vs. wild-type, respectively. Only calcification showed significant differences between the EGFR 19 deletion and 21 L858R mutations. Conclusion EGFR 21 L858R mutation was more likely to be nonsolid texture with air bronchograms and pleural retraction on CT images. And they were more likely to be associated with lower FDG metabolic activity compared with those wild-types. The sex difference was mainly caused by the 19 deletion mutation, and calcification was more frequent in them.

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Section: Discussionsupporting

confidence: 92%

Lung adenocarcinoma: development of nomograms based on PET/CT images for prediction of epidermal growth factor receptor mutation status and subtypes

Huang

Cao

Zhou

et al. 2021

Nuclear Medicine Communications

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“…For the EGFR mutation, the performance of our model was similar to that obtained by Gevaert et al with the same dataset, and our model did not require semantic features manually specified by a radiologist. Further, our best model obtained a higher AUC than the ones presented by the most recent works that used deep learning [17] and nomograms [18].…”

Section: Discussionmentioning

confidence: 63%

“…Other recent studies have applied clinical nomograms to predict EGFR mutation status. In the work presented by Zhang et al [18], the authors combined CT features and clinical risk factors and used them to build a prediction nomogram. They obtained a 0.74 AUC on the validation cohort.…”

Section: Introductionmentioning

confidence: 99%

A Radiogenomics Ensemble to Predict EGFR and KRAS Mutations in NSCLC

et al. 2021

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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.

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“…In lung adenocarcinoma with different EGFR mutation substyles, lesions with exon 19 deletion and exon 21 L858R substitution are related to a higher GGO proportion than other mutation substyles, and the viewpoints that which of the two has higher GGO proportion are inconsistent in different studies ( 47 , 50 , 56 - 59 ). In our index, the Area under curve (AUC) values of conventional CT images for predicting EGFR mutation status were mostly lower than 0.8 ( 46 , 51 , 59 ). Moreover, studies have showed that conventional CT imaging features of lung adenocarcinoma combined with clinical variables could better classify EGFR mutation status than clinical variables alone ( 25 , 53 , 59 ).…”

Section: Conventional Ct Imaging Features and Ct Image-based Radiomic Features Predict Egfr Its Subtypes And Drug Resistance Gene Mutatiomentioning

confidence: 81%

“…In lung adenocarcinoma, conventional CT imaging features ground-glass opacity (GGO), and a small tumour size indicate better survival (39)(40)(41)(42)(43)(44)(45). Most have shown that GGOs and smaller tumour volumes are the most prominent imaging features of lung adenocarcinoma with EGFR mutations than wild-type EGFR mutations (25,(46)(47)(48)(49)(50)(51)(52)(53)(54)(55). In lung adenocarcinoma with different EGFR mutation substyles, lesions with exon 19 deletion and exon 21 L858R substitution are related to a higher GGO proportion than other mutation substyles, and the viewpoints that which of the two has higher GGO proportion are inconsistent in different studies (47,50,(56)(57)(58)(59).…”

Section: Conventional Ct Imaging Features and Ct Image-based Radiomic Features Predict Egfr Its Subtypes And Drug Resistance Gene Mutatiomentioning

confidence: 99%

Molecular typing of lung adenocarcinoma with computed tomography and CT image-based radiomics: a narrative review of research progress and prospects

Ma¹,

Li²

2021

Transl Cancer Res TCR

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Objective The purpose of this paper was to perform a narrative review of current research evidence on conventional computed tomography (CT) imaging features and CT image-based radiomic features for predicting gene mutations in lung adenocarcinoma and discuss how to translate the research findings to guide future practice. Background Lung cancer, especially lung adenocarcinoma, is the leading cause of cancer-related deaths. With advances in the diagnosis and treatment of lung adenocarcinoma with the emergence of molecular testing, the prediction of oncogenes and even drug resistance gene mutations have become key to individualized and precise clinical treatment in order to prolong survival and improve quality of life. The progress of imageological examination includes the development of CT and radiomics are promising quantitative methods for predicting different gene mutations in lung adenocarcinoma, especially common mutations, such as epidermal growth factor receptor (EGFR) mutation, anaplastic lymphoma kinase (ALK) mutation and Kirsten rat sarcoma viral oncogene (KRAS) mutation. Methods The PubMed electronic database was searched along with a set of terms specific to lung adenocarcinoma, radiomics (including texture analysis), CT, computed tomography, EGFR, ALK, KRAS, rearranging transfection (RET) rearrangement and c-ros oncogene 1 (ROS-1), v-raf murine sarcoma viral oncogene homolog B1 (BRAF), and human epidermal growth factor receptor 2 (HER2) mutations et al. This review has been reported in compliance with the Narrative Review checklist guidelines. From each full-text article, information was extracted regarding a set of terms above. Conclusions Research on the application of conventional CT features and CT image-based radiomic features for predicting the gene mutation status of lung adenocarcinoma is still in a preliminary stage. Noninvasively determination of mutation status in lung adenocarcinoma before targeted therapy with conventional CT features and CT image-based radiomic features remains both hopes and challenges. Before radiomics could be applied in clinical practice, more work needs to be done.

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Nomogram based on preoperative CT imaging predicts the EGFR mutation status in lung adenocarcinoma

Cited by 11 publications

References 38 publications

Lung adenocarcinoma: development of nomograms based on PET/CT images for prediction of epidermal growth factor receptor mutation status and subtypes

Lung adenocarcinoma: development of nomograms based on PET/CT images for prediction of epidermal growth factor receptor mutation status and subtypes

A Radiogenomics Ensemble to Predict EGFR and KRAS Mutations in NSCLC

Molecular typing of lung adenocarcinoma with computed tomography and CT image-based radiomics: a narrative review of research progress and prospects

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