PET/CT Radiomic Features: A Potential Biomarker for EGFR Mutation Status and Survival Outcome Prediction in NSCLC Patients Treated With TKIs

Abstract

BackgroundsEpidermal growth factor receptor (EGFR) mutation profiles play a vital role in treatment strategy decisions for non–small cell lung cancer (NSCLC). The purpose of this study was to evaluate the predictive efficacy of baseline 18F-FDG PET/CT-based radiomics analysis for EGFR mutation status, mutation site, and the survival benefit of targeted therapy.MethodsA sum of 313 NSCLC patients with pre-treatment 18F-FDG PET/CT scans and genetic mutations detection were retrospectively studied. Clinical and PET metabolic parameters were incorporated into independent predictors of determining mutation status and mutation site. The dataset was randomly allocated into the training and the validation sets in a 7:3 ratio. Three-dimensional (3D) radiomics features were extracted from each PET- and CT-volume of interests (VOI) singularly, and then a radiomics signature (RS) associated with EGFR mutation profiles is built by feature selection. Three different prediction models based on support vector machine (SVM), decision tree (DT), and random forest (RF) classifiers were established. Furthermore, nomograms for estimation of overall survival (OS) and progression-free survival (PFS) were established by integrating PET/CT radiomics score (Rad-score), metabolic parameters, and clinical factors. Predictive performance was assessed by the receiver operating characteristic (ROC) analysis and the calibration curve analysis. The decision curve analysis (DCA) was applied to estimate and compare the clinical usefulness of nomograms.ResultsThree hundred thirteen NSCLC patients were classified into a training set (n=218) and a validation set (n=95). Multivariate analysis demonstrated that SUVmax and sex were independent indicators of EGFR mutation status and mutation site. Eight CT-derived RS, six PET-derived RS, and two clinical factors were retained to develop integrated models, which exhibited excellent ability to distinguish between EGFR wild type (EGFR-WT), EGFR 19 mutation type (EGFR-19-MT), and EGFR 21 mutation type (EGFR-21-MT). The SVM model outperformed the RF model and the DT model, yielding training area under the curves (AUC) of EGFR-WT, EGFR-19-WT, and EGFR-21-WT, with 0.881, 0.851, and 0.849, respectively, and validation AUCs of 0.926, 0.805 and 0.859, respectively. For prediction of OS, the integrated nomogram is superior to the clinical nomogram and the radiomics nomogram, with C-indexes of 0.80 in the training set and 0.83 in the validation set, respectively.ConclusionsThe PET/CT-based radiomics analysis might provide a novel approach to predict EGFR mutation status and mutation site in NSCLC patients and could serve as useful predictors for the patients’ survival outcome of targeted therapy in clinical practice.

EfficientNet-Based System for Detecting EGFR-Mutant Status and Predicting Prognosis of Tyrosine Kinase Inhibitors in Patients with NSCLC

“…To further clarify the value of CT images in this context, we constructed radiomics models for comparison and fusion. In predicting EGFR mutation status, the radiomics results (Table 2 ) demonstrated good performance, which confirmed that medical images contain substantial information for detecting EFGR mutation-derived variations [ 46 , 47 ]. Therefore, a fusion model containing EME and radiomics models was constructed.…”

The predictive value of [18F]FDG PET/CT radiomics combined with clinical features for EGFR mutation status in different clinical staging of lung adenocarcinoma

“…In recent years, using machine learning methods with high prediction efficiency and strong feasibility to assess radiomic features and predict EGFR mutation status has become a research “hot spot” [ 5 , 19 – 32 ]. However, most of these studies had small sample sizes, with a total sample number of no more than 200 cases [ 19 – 26 ], and the TNM stages of the enrolled patients varied greatly [ 21 , 27 , 31 ]. These factors significantly affected the stability of the results.…”

“…Compared to SUV max , radiomics features can better reflect the spatial distribution of tumors and more comprehensively evaluate tumor heterogeneity. In recent years, using machine learning methods with high prediction efficiency and strong feasibility to assess radiomic features and predict EGFR mutation status has become a research "hot spot" [5,[19][20][21][22][23][24][25][26][27][28][29][30][31][32]. However, most of these studies had small sample sizes, with a total sample number of no more than 200 cases [19][20][21][22][23][24][25][26], and the TNM stages of the enrolled patients varied greatly [21,27,31].…”

New research progress on 18F-FDG PET/CT radiomics for EGFR mutation prediction in lung adenocarcinoma: a review

“…Yang et al. ( 49 ) retained the radiomics features and clinical factors to establish integrated models. The SVM model exhibited a stronger performance than the RF and DT models, yielding an AUC value of 0.926 in the testing set.…”

“…Also, Yang et al. ( 49 ) revealed that the SVM model was superior to the RF model and the DT model, achieving AUC values of 0.805 and 0.859 for predicting the 19 del and 21 L858R mutations in the testing set, respectively. Based on the results of subtype prediction, the predictive performance for the exon 21 mutation was more optimal than that for the exon 19 mutation, and the overall results for MLR were also superior to those of TSR.…”