Document Type : Research Articles
Authors
1
Medical Study Program, Faculty of Medicine and Health, Sepuluh November Institute of Technology, Surabaya, East Java, Indonesia.
2
Department of Internal Medicine, Faculty of Medicine, Udayana University / Prof IGNG Ngoerah General Hospital, Denpasar, Bali, Indonesia.
3
Department of Pulmonary Medicine, Faculty of Medicine, Udayana University / Prof IGNG Ngoerah General Hospital, Denpasar, Bali, Indonesia.
Abstract
Introduction: The examination of epidermal growth factor receptor (EGFR) mutations may not be routinely available to all patients due to the limited availability and the expensive price of the examination, especially in area with limited resources such as in Indonesia. Therefore, we aimed to build a nomogram to predict the EGFR mutation in patients with lung adenocarcinoma by incorporating significant clinical and radiological parameters. Methods: We conducted an age-matched case–control study using 160 treatment-naïve patients [80 patients with EGFR-mutated (EGFRmut) and 80 with EGFR-wild-type (EGFRwt)] with pathologically confirmed lung adenocarcinomas with tumor specimens available for genetic analysis taken from 2017 through 2021 in Bali, Indonesia. Radiomics features were extracted from contrast CT images. The cut-off of the tumor diameter was defined using Receiver Operating Characteristic Curve. A conditional logistic regression model was constructed to identify significant risk factors, and a nomogram was developed for predicting the risk of EGFR mutation. A cohort was done to validate the nomogram. Result: Being female, never-smoker, having a smaller tumor diameter (<48.5mm), located in the upper lobe, have bubble-like lucency and air-bronchogram in the chest CT scan were identified as independent risk factors of EGFR mutation at the multivariate logistic regression model. The forming normogram model produced an area under the curve of 0.993 (95 % CI = 0.98−1.00) and 0.91 (95 % CI = 0.84−0.99) in development and validation group, respectively. The calibration curve showed good agreement between predicted and actual probability. At the cut-off point of the normogram score 246 shows a sensitivity of 97.5%, a specificity of 98.8%, a positive predictive value of 99.0%, and a negative predictive value of 96.8%. Conclusion: Our study indicated that the EGFR Mutation Normogram could provide a non-invasive way to predict the risk of EGFR mutation in patients with lung adenocarcinoma in clinical practice. This normogram need to be validated in other area in Indonesia.
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