Objective To investigate the aggressive signs of benign spinal lesions appearing on medical imaging and their impact on diagnosis.Methods 139 cases of benign spinal lesions with aggressive signs confirmed by pathology of needle aspiration or surgery were reviewed,including 18 cases of osteoblastoma(OB),12 cases of aneurysmal bone cyst(ABC),14 cases of osteoenchondroma (OC),19 cases of Langerhans cell histiocytosis (LCH),15 cases of hemangioma (HA),34 cases of tuberculous spondylitis (TS),and 27 cases of pyogenic spondylitis (PS).All patients underwent radiography,119 cases CT plain scan,75 cases MRI scan,and 57 cases performed all the three imaging modalities.The aggressive signs,including bulging of posterior margin of the vertebral body,pathological compression fractures,ill-defined boundary,abnormal soft tissue mass,bone marrow and soft tissue edema were showed.The benign and malignant misdiagnosis rate,the consistent rate of diagnosis with pathology were statistically analysed.Results Bulging of posterior margin of the vertebral body were found in 2 cases of OB,1 case ABC,3 cases LCH,1 case OC,6 cases HA,6 cases TS,2 cases PS.Pathological compression fracture were found in 6 cases of OB,10 cases ABC,16 cases LCH,4 cases HA,21 cases TS,16 cases PS.Ill defined boundary were found in 3 cases of OB,8 cases HA,34 cases TS,27 cases PS.The abnormal soft tissue around spine were found in 6 cases of OB,2 cases ABC,15 cases LCH,10 cases TS,15 cases PS.Bone marrow and soft tissue edema were found in 5 cases of OB,4 cases ABC,10 cases LCH,4 cases HA,30 cases TS,27 cases PS.For benign and malignant misdiagnosis rate,MRI was better than CT(P＜ 0.05).For accuracy of the consistent rate with pathology,CT was better than MRI(P＜0.05).The integrated application of the three imaging methods could significantly improve diagnostic accuracy (P＜0.05).Conclusion The imaging features benign spinal lesions are various,which may be associated with aggressive signs.A comprehensive method combined with three kinds of imaging methods,is a simple and feasible way to avoid the misdiagnosis.

Objective:To investigate the value of an MRI and digital pathology images based omics nomogram for the prediction of recurrence risk in soft tissue sarcoma (STS).Methods:This was a retrospective cohort study. From January 2016 to March 2021, 192 patients with STS confirmed by pathology in the Affiliated Hospital of Qingdao University were enrolled, among which 112 patients in the Laoshan campus were enrolled as training set, and 80 patients in the Shinan campus were enrolled as validation set. The patients were divided into recurrence group ( n=87) and no recurrence group ( n=105) during follow-up. The clinical and MRI features of patients were collected. The radiomics features based on fat saturated T 2WI images and pathomics features based on digital pathology images of the lesions were extracted respectively. The clinical model, radiomics model, pathomics model, radiomics-pathomics combined model, and omics nomogram which combined the optimal prediction model and the clinical model were established by multivariate Cox regression analysis. The concordance index (C index) and time-dependent area under the receiver operating characteristic curve (t-AUC) were used to evaluate the performance of each model in predicting STS postoperative recurrence. The DeLong test was used for comparison of t-AUC between every two models. The X-tile software was used to determine the cut-off value of the omics nomogram, then the patients were divided into low risk ( n=106), medium risk ( n=64), and high risk ( n=22) groups. Three groups′ cumulative recurrence-free survival (RFS) rates were calculated and compared by the Kaplan-Meier survival curve and log-rank test. Results:The performance of the radiomics-pathomics combined model was superior to the radiomics model and pathomics model, with C index of 0.727 (95% CI 0.632-0.823) and medium t-AUC value of 0.737 (95% CI0.584-0.891) in the validation set. The omics nomogram was established by combining the clinical model and the radiomics-pathomics combined model, with C index of 0.763 (95% CI 0.685-0.842) and medium t-AUC value of 0.783 (95% CI0.639-0.927) in the validation set. The t-AUC value of omics nomogram was significantly higher than that of clinical model, TNM model, radiomics model, and pathomics model in the validation set ( Z=3.33, 2.18, 2.08, 2.72, P=0.001, 0.029, 0.037, 0.007). There was no statistical difference in t-AUC between the omics nomogram and radiomics-pathomics combined model ( Z=0.70, P=0.487). In the validation set, the 1-year RFS rates of STS patients in the low, medium, and high recurrence risk groups were 92.0% (95% CI 81.5%-100%), 55.9% (95% CI 40.8%-76.6%), and 37.5% (95% CI 15.3%-91.7%). In the training and validation sets, there were statistically significant in cumulative RFS rates among the low, medium, and high groups of STS patients (training set χ2=73.90, P<0.001; validation set χ2=18.70, P<0.001). Conclusion:The omics nomogram based on MRI and digital pathology images has favorable performance for the prediction of STS recurrence risk.