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.

No abstract

BackgroundIdiopathic congenital talipes equinovarus (ICTEV) is a congenital limb deformity. Based on extended transmission disequilibrium testing, Gli-Kruppel family member 3 (Gli3) has been identified as a candidate gene for ICTEV. Here, we verify the role of Gli3 in ICTEV development.MethodsUsing the rat ICTEV model, we analyzed the differences in Gli3 expression levels between model rats and normal control rats. We used luciferase reporter gene assays and ChIP/EMSA assays to analyze the regulatory elements of Gli3.ResultsGli3 showed higher expression levels in ICTEV model rats compared to controls (P < 0.05). We identified repressor and activator regions in the rat Gli3 promoter. The Gli3 promoter also contains two putative Hoxd13 binding sites. Using EMSA, the Hoxd13 binding site 2 was found to directly interact with Hoxd13 in vitro. ChIP assays of the Hoxd13-Gli3 promoter complex from a developing limb confirmed that endogenous Hoxd13 interacts with this region in vivo.ConclusionOur findings suggest that HoxD13 directly interacts with the promoter of Gli3. The increase of Gli3 expression in ICTEV model animal might result from the low expression of HoxD13.

The aim of the present study was to perform comprehensive prenatal diagnosis using various detection techniques on a fetus in a high-risk pregnant woman, and to provide genetic counseling for the patient and her family so as to avoid birth defects. The routine karyotype analysis via amniocentesis, fluorescence in situ hybridization, and whole genome microarray technique were performed for the prenatal diagnosis of the fetus. The fetal karyotype was 46,X,ish der(X) inv(X)(p22.3q28)t(X;Y)(q28;q11.2)(XYqter+,SRY-,DXZ1+, RP11-64L19+,STS+,XYpter+); namely, one fetal X chromosome belonged to the derivative imbalanced chromosome and this chromosome demonstrated complex chromosomal rearrangements involving inversion, translocation and deletion. Notably, pericentric inversion between Xp22.3 and Xq28 was identified, and the chromosomal microarray technique confirmed that the long arm q28 of the derivative X chromosome had a 1.241-Mb deletion in Xq28, which included Online Mendelian Inheritance in Man genes such as coagulation factor VIII, glucose-6-phosphate dehydrogenase, inhibitor of nuclear factor-κB kinase subunit γ, trimethyllysine hydroxylase ε, Ras-related protein Rab-39B and chloride intracellular channel 2. In addition, this chromosome also exhibited the local translocation of fragment Yq11.21-q11.23, which did not include the sex determining region Y gene. This fetus demonstrated deletion, inversion and translocation syndrome, and may exhibit the corresponding clinical phenotypes (e.g., intellectual disability or general delayed development) (1) of such chromosome abnormalities after birth. Therefore, in prenatal diagnosis, a variety of genetic diagnostic techniques should be comprehensively used based on specific clinical situations, which may accurately reveal the nature, sources and manifestations of the derivative chromosome abnormalities and avoid the birth of children with defects.