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

Abstract

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.

Systematic Review on the Association of Radiomics with Tumor Biological Endpoints

“…In total, 33 studies investigating radiomics and biological endpoints for lung lesions were identified. The imaging modalities employed were CT ( n = 22) [ 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 ], PET/CT ( n = 8) [ 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 ], PET ( n = 2) [ 100 , 101 ] and MRI ( n = 1) [ 102 ]. One study investigated radiomics from metastases, while all other studies associated tissue biomarkers with radiomics of the primary tumor.…”

Integrating Liquid Biopsy and Radiomics to Monitor Clonal Heterogeneity of EGFR-Positive Non-Small Cell Lung Cancer

“…Besides, radiomics has emerged as a novel field of research (14), dealing with the extraction and analysis of specific features from diagnostic images (15), and potentially reflecting the pathophysiological processes and the heterogeneity of tumors genetics (16). Recent data has shown that also texture analysis of radiological images can identify NSCLCs bearing EGFR mutations (17,18).…”

The Role of Radiomics in Lung Cancer: From Screening to Treatment and Follow-Up

“…One study by Zhao et al. ( 47 ) aimed at predicting EGFR mutation status and subtypes, in particular the two most common ones (exon 19 deletion and exon 21 L858R mutations). A radiomics score (R-score) based on 11 radiomics features was calculated for each lesion.…”

“…The highest performing signature was capable of distinguishing between EGFR+ and KRAS+ cases (AUC 0.80) and, when combined with a clinical model (AUC 0.81), substantially improved its performance (AUC 0.86). One study by Zhao et al(47) aimed at predicting EGFR mutation status and subtypes, in particular the two most common ones (exon 19 deletion and exon 21 L858R mutations). A radiomics score (Rscore) based on 11 radiomics features was calculated for each lesion.…”