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. 2020 Jan 9;9:1485.
doi: 10.3389/fonc.2019.01485. eCollection 2019.

The Potential of Radiomics Nomogram in Non-invasively Prediction of Epidermal Growth Factor Receptor Mutation Status and Subtypes in Lung Adenocarcinoma

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Free PMC article

The Potential of Radiomics Nomogram in Non-invasively Prediction of Epidermal Growth Factor Receptor Mutation Status and Subtypes in Lung Adenocarcinoma

Wei Zhao et al. Front Oncol. .
Free PMC article

Abstract

Purpose: Up to 50% of Asian patients with NSCLC have EGFR gene mutations, indicating that selecting eligible patients for EGFR-TKIs treatments is clinically important. The aim of the study is to develop and validate radiomics-based nomograms, integrating radiomics, CT features and clinical characteristics, to non-invasively predict EGFR mutation status and subtypes. Materials and Methods: We included 637 patients with lung adenocarcinomas, who performed the EGFR mutations analysis in the current study. The whole dataset was randomly split into a training dataset (n = 322) and validation dataset (n = 315). A sub-dataset of EGFR-mutant lesions (EGFR mutation in exon 19 and in exon 21) was used to explore the capability of radiomic features for predicting EGFR mutation subtypes. Four hundred seventy-five radiomic features were extracted and a radiomics sore (R-score) was constructed by using the least absolute shrinkage and selection operator (LASSO) regression in the training dataset. A radiomics-based nomogram, incorporating clinical characteristics, CT features and R-score was developed in the training dataset and evaluated in the validation dataset. Results: The constructed R-scores achieved promising performance on predicting EGFR mutation status and subtypes, with AUCs of 0.694 and 0.708 in two validation datasets, respectively. Moreover, the constructed radiomics-based nomograms excelled the R-scores, clinical, CT features alone in terms of predicting EGFR mutation status and subtypes, with AUCs of 0.734 and 0.757 in two validation datasets, respectively. Conclusions: Radiomics-based nomogram, incorporating clinical characteristics, CT features and radiomic features, can non-invasively and efficiently predict the EGFR mutation status and thus potentially fulfill the ultimate purpose of precision medicine. The methodology is a possible promising strategy to predict EGFR mutation subtypes, providing the support of clinical treatment scenario.

Keywords: CT; EGFR; lung adenocarcinomas; nomogram; radiomics.

Figures

Figure 1
Figure 1
The flowchart of the study.
Figure 2
Figure 2
Feature selection using the least absolute shrinkage and selection operator (LASSO) binary logistic regression model. (A) Tuning parameter (λ) selection in the LASSO model used 10-fold cross-validation via minimum criteria. The area under the receiver operating characteristic (AUC) curve was plotted vs. log (λ). Dotted vertical lines were drawn at the optimal values by using the minimum criteria. (B) LASSO coefficient profiles of the 425 texture features. A coefficient profile plot was produced against the log (λ) sequence. Vertical line was drawn at the value selected using 10-fold cross-validation, where optimal λ (−4.497) resulted in 11 non-zero coefficients.
Figure 3
Figure 3
ROC analysis and the constructed nomogram in task a. (A,B) ROC analysis of sex, R-score, peripheral emphysema and the constructed nomogram in training and validation datasets, respectively. (C) The constructed nomogram for predicting EGFR mutation status.
Figure 4
Figure 4
ROC analysis and the constructed nomogram in task b. (A,B) ROC analysis of age, R-score* and the constructed nomogram* in training and validation datasets, respectively. (C) The constructed nomogram* for predicting EGFR mutation subtypes.

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