Radiogenomic Models Using Machine Learning Techniques to Predict EGFR Mutations in Non-Small Cell Lung Cancer

Abstract

Background: The purpose of this study was to build radiogenomics models from texture signatures derived from computed tomography (CT) and 18F-FDG PET-CT (FDG PET-CT) images of non-small cell lung cancer (NSCLC) with and without epidermal growth factor receptor ( EGFR) mutations. Methods: Fifty patients diagnosed with NSCLC between 2011 and 2015 and with known EGFR mutation status were retrospectively identified. Texture features extracted from pretreatment CT and FDG PET-CT images by manual contouring of the primary tumor were used to develop multivariate logistic regression (LR) models to predict EGFR mutations in exon 19 and exon 20. Results: An LR model evaluating FDG PET-texture features was able to differentiate EGFR mutant from wild type with an area under the curve (AUC), sensitivity, specificity, and accuracy of 0.87, 0.76, 0.66, and 0.71, respectively. The model derived from CT texture features had an AUC, sensitivity, specificity, and accuracy of 0.83, 0.84, 0.73, and 0.78, respectively. FDG PET-texture features that could discriminate between mutations in EGFR exon 19 and 21 demonstrated AUC, sensitivity, specificity, and accuracy of 0.86, 0.84, 0.73, and 0.78, respectively. Based on CT texture features, the AUC, sensitivity, specificity, and accuracy were 0.75, 0.81, 0.69, and 0.75, respectively. Conclusion: Non-small cell lung cancer texture analysis using FGD-PET and CT images can identify tumors with mutations in EGFR. Imaging signatures could be valuable for pretreatment assessment and prognosis in precision therapy.

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.…”

Artificial Intelligence Applications to Improve the Treatment of Locally Advanced Non-Small Cell Lung Cancers

“…For example, a ML model using CT radiomics and clinical features achieved a diagnostic accuracy of 88.3% in the external validation dataset for predicting EGFR mutant NSCLC [ 59 ]. Additionally, PET-imaging derived radiomic features have also been used to predict EGFR mutation status with accuracies around 75–78% [ 60 , 61 ]. The development of the T790M mutation in EGFR, which can occur during treatment with first-generation EGFR tyrosine kinase inhibitors (gefitinib and erlotinib) is an important mechanism of resistance.…”

CARJ 2021: Year in Review

“…The application of quantitative imaging to identify epidermal growth factor receptor mutation status was assessed for non-small cell lung cancer. 11 This study found that CT texture analysis could be used to differentiate wild type and mutant epidermal growth factor receptor (EGFR) variants. Both CT texture analysis and FDG-PET texture analysis could be used to differentiate EGFR wild type and mutant, with entropy and kurtosis being the most important texture analysis features.…”

“…However, FDG-PET texture analysis showed superior performance for differentiating between types of EGFR mutations. 11 Tumors with EGFR mutations are more likely to respond to tyrosine kinase inhibitor therapy and obtaining this information pre-surgery could accelerate initiation of systemic treatment.…”