LAUSR

PURPOSE This study aims to develop and evaluate multi-parametric MRI-based radiomics for preoperative identification of epidermal growth factor receptor (EGFR) mutation, which is important in treatment planning for patients with thoracic spinal metastases from primary lung adenocarcinoma. METHODS A total of 110 patients were enrolled between Jan. 2016 and Mar. 2019 as a primary cohort. A time-independent validation cohort was conducted containing 52 patients consecutively enrolled from Jul. 2019 to Apr. 2021. The patients were pathologically diagnosed thoracic spinal metastases from primary lung adenocarcinoma, all underwent T1-weighted (T1W), T2-weighted (T2W) and T2-weighted fat-suppressed (T2FS) MRI scans of the thoracic spinal. Handcrafted and deep learning-based features were extracted and selected from each MRI modality, and used to build the radiomics signature. Various machine learning classifiers were developed and compared. A clinical-radiomics nomogram integrating the combined rad signature and the most important clinical factor was constructed with receiver operating characteristic (ROC), calibration and decision curves analysis (DCA) to evaluate the prediction performance. RESULTS The combined radiomics signature derived from the joint of three modalities can effectively classify EGFR mutation and EGFR wild-type patients, with an area under the ROC curve (AUC) of 0.886 (95% Confidence Interval [CI]: 0.826 - 0.947, SEN = 0.935, SPE = 0.688) in the training group and 0.803 (95% CI: 0.682 - 0.924, SEN = 0.700, SPE = 0.818) in the time-independent validation group. The nomogram incorporating the combined radiomics signature and smoking status achieved the best prediction performance in the training (AUC = 0.888, 95% CI: 0.849 - 0.958, SEN = 0.839, SPE = 0.792) and time-independent validation (AUC = 0.821, 95% CI: 0.692 - 0.929, SEN = 0.667, SPE = 0.909) cohort. The DCA confirmed potential clinical usefulness of our nomogram. CONCLUSION Our study demonstrated the potential of multi-parametric MRI-based radiomics on preoperatively predicting the EGFR mutation. The proposed nomogram model can be considered as a new biomarker to guide the selection of individual treatment strategies for patients with thoracic spinal metastases from primary lung adenocarcinoma.