Background: Preoperative prediction of epidermal growth factor receptor (EGFR) mutation status in patients with spinal bone metastases (SBM) from primary lung adenocarcinoma is potentially important for treatment decisions. Purpose: To develop and validate multiparametric magnetic resonance imaging (MRI)-based radiomics methods for preoperative prediction of EGFR mutation based on MRI of SBM. Study Type: Retrospective. Population: A total of 97 preoperative patients with lumbar SBM from lung adenocarcinoma (77 in training set and 20 in validation set). Field Strength/Sequence: T1-weighted, T2-weighted, and T2-weighted fat-suppressed fast spin echo sequences at 3.0 T. Assessment: Radiomics handcrafted and deep learning-based features were extracted and selected from each MRI sequence. The abilities of the features to predict EGFR mutation status were analyzed and compared. A radiomics nomogram was constructed integrating the selected features. Statistical Tests: The Mann-Whitney U test and χ 2 test were employed for evaluating associations between clinical characteristics and EGFR mutation status for continuous and discrete variables, respectively. Least absolute shrinkage and selection operator was used for selection of predictive features. Sensitivity (SEN), specificity (SPE), and area under the receiver operating characteristic curve (AUC) were used to evaluate the ability of radiomics models to predict the EGFR mutation. Calibration and decision curve analysis (DCA) were performed to assess and validate nomogram results. Results: The radiomics signature comprised five handcrafted and one deep learning-based features and achieved good performance for predicting EGFR mutation status, with AUCs of 0.891 (95% confidence interval [CI], 0.820-0.962, SEN = 0.913, SPE = 0.710) in the training group and 0.771 (95% CI, 0.551-0.991, SEN = 0.750, SPE = 0.875) in the validation group. DCA confirmed the potential clinical usefulness of the radiomics models. Data Conclusion: Multiparametric MRI-based radiomics is potentially clinical valuable for predicting EGFR mutation status in patients with SBM from lung adenocarcinoma. Level of Evidence: 3 Technical Efficacy: 2

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 January 2016 and March 2019 as a primary cohort. A time‐independent validation cohort was conducted containing 52 patients consecutively enrolled from July 2019 to April 2021. The patients were pathologically diagnosed with 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) cohorts. 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.

This study aimed to develop and validate nomograms predicting the survival of osteosarcoma patients from the SEER database and our hospital. Data of 1,066 osteosarcoma patients from the SEER database were randomly divided into a development cohort (n=800) and validation cohort one (n=266). Another cohort of 126 patients from our hospital was utilized as validation cohort two. Univariate and multivariate Cox analyses were performed to identify the independent prognostic factors for overall survival (OS) and cancer-specific survival (CSS). Nomograms predicting the 3-and 5-year OS and CSS probability were constructed and validated. The predictive performances of the established nomograms were evaluated by the concordance index (C-index) and the calibration plot. Variables of age, surgical stage, surgery, grade, tumor site, and tumor size were identified as independent prognosticators for OS and CSS in Cox analyses. The C-indexes for OS and CSS in the development cohort were 0.818 and 0.829. Comparatively, the C-indexes for OS and CSS were 0.843 and 0.834, 0.736 and 0.782 for validation cohort one and two, respectively. Calibration plots showed excellent consistency between nomogram prediction and actual survival. Nomograms based on the SEER database are of high accuracy and can serve as a reliable tool for individualized consultation and survival prediction in osteosarcoma patients.