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

• Published in: Canadian Association of Radiologists journal Vol. 72; no. 1; pp. 109 - 119
• Main Authors: Nair, Jay Kumar Raghavan, Saeed, Umar Abid, McDougall, Connor C., Sabri, Ali, Kovacina, Bojan, Raidu, B. V. S., Khokhar, Riaz Ahmed, Probst, Stephan, Hirsh, Vera, Chankowsky, Jeffrey, Van Kempen, Léon C., Taylor, Jana
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01-02-2021; SAGE PUBLICATIONS, INC
• Subjects: Accuracy; Aged; Carcinoma, Non-Small-Cell Lung - diagnostic imaging; Carcinoma, Non-Small-Cell Lung - genetics; Computed tomography; Contouring; Epidermal growth factor; Epidermal growth factor receptors; ErbB Receptors - genetics; Feature extraction; Female; Fluorodeoxyglucose F18; Growth factors; Humans; Image Interpretation, Computer-Assisted - methods; Imaging Genomics - methods; Learning algorithms; Lung - diagnostic imaging; Lung cancer; Lung Neoplasms - diagnostic imaging; Lung Neoplasms - genetics; Machine Learning; Male; Medical imaging; Mutation; Mutation - genetics; Non-small cell lung carcinoma; Positron emission; Positron emission tomography; Positron Emission Tomography Computed Tomography - methods; Predictive Value of Tests; Radiopharmaceuticals; Regression analysis; Reproducibility of Results; Retrospective Studies; Sensitivity; Sensitivity and Specificity; Signatures; Small cell lung carcinoma; Texture; Tomography; Tumors; radiomics; machine-learning; non-small cell lung cancer ( NSCLC); epidermal growth factor receptor (EGFR)
• ISSN: 0846-5371; 1488-2361
• DOI: 10.1177/0846537119899526

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.