Objective A screening survey for osteoporotic fractures in men and women in Hong Kong represents the first large-scale prospective population-based study on bone health in elderly (≥65 years) Chinese men and women. This study aims to identify the prevalence and potential risk factors of lumbar spondylolisthesis in these subjects. Methods The lateral lumbar radiographs of 1,994 male and 1,996 female patients were analysed using the Meyerding classification. Results Amongst the men, 380 (19.1 %) had at least one spondylolisthesis and 43 (11.3 %) had slips at two or more levels; 283 had anterolisthesis, 85 had retrolisthesis, whereas 12 subjects had both anterolisthesis and retrolisthesis. Amongst the women, 499 (25.0 %) had at least one spondylolisthesis and 69 (13.8 %) had slips at two or more levels; 459 had anterolisthesis, 34 had retrolisthesis, whereas 6 subjects had both anterolisthesis and retrolisthesis. Advanced age, short height, higher body mass index (BMI), higher bone mineral density (BMD) and degenerative arthritis are associated with spondylolisthesis. Lower Physical Activity Scale for the Elderly (PASE) score was associated with spondylolisthesis in men; higher body weight, angina and lower grip strength were associated with spondylolisthesis in women. Conclusion The male/female ratio of lumbar spondylolisthesis prevalence was 1:1.3 in elderly Chinese. Men are more likely to have retrolisthesis.

ObjectiveTo investigate whether radiomic features can be surrogate biomarkers for epidermal growth factor receptor (EGFR) mutation statuses.Materials and methodsTwo hundred ninety six consecutive patients, who underwent CT examinations before operation within 3 months and had EGFR mutations tested, were enrolled in this retrospective study. CT texture features were extracted using an open-source software with whole volume segmentation. The association between CT texture features and EGFR mutation statuses were analyzed.ResultsIn the 296 patients, there were 151 patients with EGFR mutations (51%). Logistic analysis identified that lower age (Odds Ratio[OR]: 0.968,95% confidence interval [CI]:0.946~0.990, p = 0.005) and a radiomic feature named GreyLevelNonuniformityNormalized (OR: 0.012, 95% CI:0.000~0.352, p = 0.01) were predictors for exon 19 mutation; higher age (OR: 1.027, 95%CI:1.003~1.052,p = 0.025), female sex (OR: 2.189, 95%CI:1.264~3.791, p = 0.005) and a radiomic feature named Maximum2DDiameterColumn (OR: 0.968, 95%CI:0.946~0.990], p = 0.005) for exon 21 mutation; and female sex (OR: 1.883,95%CI:1.064~3.329, p = 0.030), non-smoking status (OR: 2.070, 95%CI:1.090~3.929, p = 0.026) and a radiomic feature termed SizeZone NonUniformityNormalized (OR: 0.010, 95% CI:0.0001~0.852, p = 0.042) for EGFR mutations. Areas under the curve (AUCs) of combination with clinical and radiomic features to predict exon 19 mutation, exon 21 mutation and EGFR mutations were 0.655, 0.675 and 0.664, respectively.ConclusionSeveral radiomic features are associated with EGFR mutation statuses of lung adenocarcinoma. Combination with clinical files, moderate diagnostic performance can be obtained to predict EGFR mutation status of lung adenocarcinoma. Radiomic features might harbor potential surrogate biomarkers for identification of EGRF mutation statuses.

Highlights d Purification of biologically functional human IGF-1R in fulllength d Cryo-EM structures of insulin or IGF-1 bound human IGF-1R d Hormone induces the formation of active IGF-1R assembly

Background Lymphovascular space invasion (LVSI) of endometrial carcinoma (EMC) is one of the important prognostic factors, which is not usually visible subjectively. Therefore, clinicians lack imaging‐based evidence about LVSI for preoperative treatment decision‐making. Purpose To develop a multiparametric MRI (mpMRI)‐based radiomics nomogram for predicting LVSI in EMC and provide decision‐making support to clinicians. Study Type Retrospective. Population In all, 144 patients with histologically confirmed EMC, 101 patients in a training cohort, and 43 patients in a test cohort. Field Strength/Sequence T2WI, contrast enhanced‐T1WI, and diffusion‐weighted imaging (DWI) at 3.0T MRI. Assessment Tumors were independently segmented images by two radiologists. Two pathologists reviewed the tissue specimens of the tumors to identify the existence of LVSI in consensus. Statistical Tests The intraclass correlation coefficient was used to test the reliability and least absolute shrinkage and selection operator (LASSO) regression for features selection and then developed a radiomics signature named Rad‐score. A nomogram was developed in the training cohort. The diagnostic performance of the nomogram model was assessed by area under the curve (AUC) of the receiver operator characteristic (ROC) in the training and test cohort, respectively. Results LVSI was identified in 32 patients (22.2%). Older age and high grade were correlated with LVSI at univariate analysis. There were five radiomics features that were identified as independent risk factors for LVSI by LASSO regression. Based on age, grade, and Rad‐score, the AUC values of the nomogram model to predict LVSI in the training and test cohort were 0.820 (95% confidence interval [CI]: 0.725, 0.916; sensitivity: 82.6%, specificity: 72.9%), 0.807 (95% CI: 0.673, 0.941; sensitivity: 77.8%, specificity: 78.6%), respectively. Data Conclusion The radiomic‐based machine‐learning model using a nomogram algorithm achieved high diagnostic performance for predicting LVSI of EMC preoperatively, which could enhance risk stratification and provide support for therapeutic decision‐making. Level of Evidence 2. Technical Efficacy Stage 3. J. Magn. Reson. Imaging 2020;52:1257–1262.