Abstract
ObjectivesEGFR testing is a mandatory step before targeted therapy for non-small cell lung cancer patients. Combining some quantifiable features to establish a predictive model of EGFR expression status, break the limitations of tissue biopsy.Materials and MethodsWe retrospectively analyzed 1074 patients of non-small cell lung cancer with complete reports of EGFR gene testing. Then manually segmented VOI, captured the clinicopathological features, analyzed traditional radiology features, and extracted radiomic, and deep learning features. The cases were randomly divided into training and test set. We carried out feature screening; then applied the light GBM algorithm, Resnet-101 algorithm, logistic regression to develop sole models, and fused models to predict EGFR mutation conditions. The efficiency of models was evaluated by ROC and PRC curves.ResultsWe successfully established Modelclinical, Modelradiomic, ModelCNN (based on clinical-radiology, radiomic and deep learning features respectively), Modelradiomic+clinical (combining clinical-radiology and radiomic features), and ModelCNN+radiomic+clinical (combining clinical-radiology, radiomic, and deep learning features). Among the prediction models, ModelCNN+radiomic+clinical showed the highest performance, followed by ModelCNN, and then Modelradiomic+clinical. All three models were able to accurately predict EGFR mutation with AUC values of 0.751, 0.738, and 0.684, respectively. There was no significant difference in the AUC values between ModelCNN+radiomic+clinical and ModelCNN. Further analysis showed that ModelCNN+radiomic+clinical effectively improved the efficacy of Modelradiomic+clinical and showed better efficacy than ModelCNN. The inclusion of clinical-radiology features did not effectively improve the efficacy of Modelradiomic.ConclusionsEither deep learning or radiomic signature-based models can provide a fairly accurate non-invasive prediction of EGFR expression status. The model combined both features effectively enhanced the performance of radiomic models and provided marginal enhancement to deep learning models. Collectively, fusion models offer a novel and more reliable way of providing the efficacy of currently developed prediction models, and have far-reaching potential for the optimization of noninvasive EGFR mutation status prediction methods.
Highlights
Lung cancer is the leading cause of cancer-related deaths, with incidence and mortality rates of approximately 11.4% and 18%, respectively, and is the second-highest incidence rate in the world [1]
We aimed to investigate and validate whether a prediction model incorporating deep learning features and radiomic features can improve the performance of the current mainstream models for the non-invasive prediction of Epidermal Growth Factor Receptor (EGFR) mutations
A total of 1074 eligible non-small cell lung cancer cases were enrolled in this study, including 527 wild-type EGFR cases and 547 EGFR mutant cases; there were 443 males and 631 females
Summary
Lung cancer is the leading cause of cancer-related deaths, with incidence and mortality rates of approximately 11.4% and 18%, respectively, and is the second-highest incidence rate in the world [1]. Non-small cell lung cancer is the main pathological form and accounts for approximately 80-90% of all lung cancers [2]. Targeted therapy has become one of the first-line standard treatments for non-small cell lung cancer patients; because this form of treatment can effectively improve their prognosis, prolong the PFS and OS, compared with traditional means of treatment, like chemotherapy [3,4,5,6]. In patients with non-small cell lung cancer, EGFR is responsible for approximately 10-20% of all and is the most predominant driver mutations target for targeted therapy [7]. EGFR-TKI therapy plays a pivotal role in the targeted therapy of patients with non-small cell lung cancer. There are several methods that can be used to detect EGFR mutations, including tissue biopsy, liquid biopsy, and radiogenomics
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