Aim: Accurate severity grading of lumbar spine disease by magnetic resonance images (MRIs) plays an important role in selecting appropriate treatment for the disease. However, interpreting these complex MRIs is a repetitive and time-consuming workload for clinicians, especially radiologists. Here, we aim to develop a multi-task classification model based on artificial intelligence for automated grading of lumbar disc herniation (LDH), lumbar central canal stenosis (LCCS) and lumbar nerve roots compression (LNRC) at lumbar axial MRIs. Methods: Total 15254 lumbar axial T2W MRIs as the internal dataset obtained from the Fifth Affiliated Hospital of Sun Yat-sen University from January 2015 to May 2019 and 1273 axial T2W MRIs as the external test dataset obtained from the Third Affiliated Hospital of Southern Medical University from June 2016 to December 2017 were analyzed in this retrospective study. Two clinicians annotated and graded all MRIs using the three international classification systems. In agreement, these results served as the reference standard; In disagreement, outcomes were adjudicated by an expert surgeon to establish the reference standard. The internal dataset was randomly split into an internal training set (70%), validation set (15%) and test set (15%). The multi-task classification model based on ResNet-50 consists of a backbone network for feature extraction and three fully-connected (FC) networks for classification and performs the classification tasks of LDH, LCCS, and LNRC at lumbar MRIs. Precision, accuracy, sensitivity, specificity, F1 scores, confusion matrices, receiver-operating characteristics and interrater agreement (Gwet k) were utilized to assess the model’s performance on the internal test dataset and external test datasets. Results: A total of 1115 patients, including 1015 patients from the internal dataset and 100 patients from the external test dataset [mean age, 49 years ± 15 (standard deviation); 543 women], were evaluated in this study. The overall accuracies of grading for LDH, LCCS and LNRC were 84.17% (74.16%), 86.99% (79.65%) and 81.21% (74.16%) respectively on the internal (external) test dataset. Internal and external testing of three spinal diseases showed substantial to the almost perfect agreement (k, 0.67 - 0.85) for the multi-task classification model. Conclusion: The multi-task classification model has achieved promising performance in the automated grading of LDH, LCCS and LNRC at lumbar axial T2W MRIs.

Fully automated radiomic screening pipeline for osteoporosis and abnormal bone density with a deep learning-based segmentation using a short lumbar mDixon sequence Yinxia Zhao, Tianyun Zhao, Shenglan Chen, Xintao Zhang, Mario Serrano Sosa, Jin Liu, Xianfu Mo, Xiaojun Chen, Mingqian Huang, Shaolin Li, Xiaodong Zhang, Chuan Huang

The collagen density is not detected in the patellar tendon (PT), posterior cruciate ligament (PCL), and anterior cruciate ligament (ACL) in clinic. We assess the technical feasibility of three-dimension multiecho fat saturated ultrashort echo time cones (3D FS-UTE-Cones) acquisitions for single- and bicomponent T2⁎analysis of bound and free water pools in PT, PCL, and ACL in clinic. The knees of five healthy volunteers and six knee joint samples from cadavers were scanned via 3D multiecho FS-UTE-Cones acquisitions on a clinical scanner. Single-component fitting of T2⁎Mand bicomponent fitting of short T2⁎(T2⁎S), long T2⁎(T2⁎L), short T2⁎fraction (Frac_S), and long T2⁎fraction (Frac_L) were performed within tendons and ligaments. Our results showed that biexponential fitting was superior to single-exponential fitting in PT, PCL, and ACL. For knee joint samples, there was no statistical difference among all data in PT, PCL, and ACL. For volunteers, all parameters of bicomponent fitting were statistically different across PT, PCL, and ACL, except for T2⁎S, T2⁎L, and T2⁎Mresulting in flawed measurements due to the magic angle effect. 3D multiecho FS-UTE-Cones acquisition allows high resolution T2⁎mapping in PT, PCL, and ACL of keen joint samples and PT and PCL of volunteers. The T2⁎values and their fractions can be characterized by bicomponent T2⁎analysis that is superior to single-component T2⁎analysis, except for ACL of volunteers.

Objective. To develop and externally validate a CT‐based radiomics nomogram for pretreatment prediction of relapse in osteosarcoma patients within one year. Materials and Methods. In this multicenter retrospective study, a total of 80 patients (training cohort: 63 patients from three hospitals; validation cohort: 17 patients from three other hospitals) with osteosarcoma, undergoing pretreatment CT between August 2010 and December 2018, were identified from multicenter databases. Radiomics features were extracted and selected from tumor regions on CT image, and then, the radiomics signature was constructed. The radiomics nomogram that incorporated the radiomics signature and clinical‐based risk factors was developed to predict relapse risk with a multivariate Cox regression model using the training cohort and validated using the external validation cohort. The performance of the nomogram was assessed concerning discrimination, calibration, reclassification, and clinical usefulness. Results. Kaplan‐Meier curves based on the radiomics signature showed a significant difference between the high‐risk and the low‐risk groups in both training and validation cohorts (P < 0.001 and P = 0.015, respectively). The radiomics nomogram achieved good discriminant results in the training cohort (C‐index: 0.779) and the validation cohort (C‐index: 0.710) as well as good calibration. Decision curve analysis revealed that the proposed model significantly improved the clinical benefit compared with the clinical‐based nomogram (P < 0.001). Conclusions. This multicenter study demonstrates that a radiomics nomogram incorporated the radiomics signature and clinical‐based risk factors can increase the predictive value of the osteosarcoma relapse risk, which supports the clinical application in different institutions.