Objective To observe the value of intratumoral and peritumoral ROI-based CT radiomics combined with clinical and imaging features for preoperatively predicting renal capsule invasion of clear cell renal cell carcinoma(ccRCC).Methods Totally 105 ccRCC patients were retrospectively collected and divided into invasion group(n=70)and non-invasion group(n=35)according to post operation pathology,also divided into training set(n=84,including 56 cases of invasion group and 28 of non-invasion group)and test set(n=21,including 14 cases of invasion group and 7 of non-invasion group)at a ratio of 8∶2.A clinical-imaging model was constructed based on clinical and CT features being significantly different between groups.Radiomics features related to renal capsule invasion were extracted and selected from intratumoral and of 1-6 mm peritumoral ROI on unenhanced phase(UP),corticomedullary phase(CMP)and nephrographic phase(NP)CT images,respectively.The optimal algorithm was selected among 6 machine learning algorisms based on CMP intratumoral ROI.With the optimal and selected features,single intratumoral or peritumoral models,combined intratumoral and peritumoral models within the same phase and combined pairwise models within the same range across different phases images were established.The best performing radiomics model was chosen and integrated with clinical and imaging features to form a combined model.Receiver operating characteristic(ROC)curves were drawn,the area under the curve(AUC)was calculated to evaluate the efficacy of model for predicting renal capsule invasion of ccRCC,which were compared using DeLong's test.Results Hypertension,presence of clinical symptoms and high enhancement degree on CMP images were all independent predicting factors for renal capsule invasion of ccRCC,which were used to establish clinical-imaging model.Support vector machine(SVM)was the optimal algorithm.CMP peritumoral 3 mm model,CMP intratumoral model,NP peritumoral 4 mm model,NP intratumoral+peritumoral 4 mm model and CMP peritumoral 3 mm+NP peritumoral 3 mm model showed higher performance than the others,with AUC being not significantly different(all P＞0.05).CMP peritumoral 3 mm model was the optimal radiomics model,with the highest AUC(0.898)in test set.The combined model demonstrated superior AUC(0.979)in training set compared to both clinical-imaging model and the optimal radiomics model alone(both P＜0.05),while in test set(AUC 0.918)showed comparable performance with the latter two(both P＞0.05).Conclusion CT-based peritumoral radiomics models were equally effective for preoperatively predicting renal capsule invasion of ccRCC.Combining with clinical and imaging features might further enhance diagnostic performance.

Objective To observe the value of intratumoral and peritumoral ROI-based CT radiomics combined with clinical and imaging features for preoperatively predicting renal capsule invasion of clear cell renal cell carcinoma(ccRCC).Methods Totally 105 ccRCC patients were retrospectively collected and divided into invasion group(n=70)and non-invasion group(n=35)according to post operation pathology,also divided into training set(n=84,including 56 cases of invasion group and 28 of non-invasion group)and test set(n=21,including 14 cases of invasion group and 7 of non-invasion group)at a ratio of 8∶2.A clinical-imaging model was constructed based on clinical and CT features being significantly different between groups.Radiomics features related to renal capsule invasion were extracted and selected from intratumoral and of 1-6 mm peritumoral ROI on unenhanced phase(UP),corticomedullary phase(CMP)and nephrographic phase(NP)CT images,respectively.The optimal algorithm was selected among 6 machine learning algorisms based on CMP intratumoral ROI.With the optimal and selected features,single intratumoral or peritumoral models,combined intratumoral and peritumoral models within the same phase and combined pairwise models within the same range across different phases images were established.The best performing radiomics model was chosen and integrated with clinical and imaging features to form a combined model.Receiver operating characteristic(ROC)curves were drawn,the area under the curve(AUC)was calculated to evaluate the efficacy of model for predicting renal capsule invasion of ccRCC,which were compared using DeLong's test.Results Hypertension,presence of clinical symptoms and high enhancement degree on CMP images were all independent predicting factors for renal capsule invasion of ccRCC,which were used to establish clinical-imaging model.Support vector machine(SVM)was the optimal algorithm.CMP peritumoral 3 mm model,CMP intratumoral model,NP peritumoral 4 mm model,NP intratumoral+peritumoral 4 mm model and CMP peritumoral 3 mm+NP peritumoral 3 mm model showed higher performance than the others,with AUC being not significantly different(all P＞0.05).CMP peritumoral 3 mm model was the optimal radiomics model,with the highest AUC(0.898)in test set.The combined model demonstrated superior AUC(0.979)in training set compared to both clinical-imaging model and the optimal radiomics model alone(both P＜0.05),while in test set(AUC 0.918)showed comparable performance with the latter two(both P＞0.05).Conclusion CT-based peritumoral radiomics models were equally effective for preoperatively predicting renal capsule invasion of ccRCC.Combining with clinical and imaging features might further enhance diagnostic performance.

Spinal cord injury (SCI) is a disabling and destructive central nervous system injury that has not yet been successfully treated at this stage. Three-dimensional (3D) bioprinting has become a promising method to produce more biologically complex microstructures, which fabricate living neural constructs with anatomically accurate complex geometries and spatial distributions of neural stem cells, and this is critical in the treatment of SCI. With the development of 3D printing technology and the deepening of research, neural tissue engineering research using different printing methods, bio-inks, and cells to repair SCI has achieved certain results. Although satisfactory results have not yet been achieved, they have provided novel ideas for the clinical treatment of SCI. Considering the potential impact of 3D bioprinting technology on neural studies, this review focuses on 3D bioprinting methods widely used in SCI neural tissue engineering, and the latest technological applications of bioprinting of nerve tissues for the repair of SCI are discussed. In addition to introducing the recent progress, this work also describes the existing limitations and highlights emerging possibilities and future prospects in this field.

Objective To evaluate specific clinical and imaging features of osteogenesis imperfecta V and to improve diagnostic accuracy of this disease. Methods Data of 15 patients with osteogenesis imperfecta type V were retrospectively analyzed for their clinical and imaging features. There were 10 males and 5 females, aged from 1 year and 30 years old (median age,12.5 years ). All 15 patients had plain X-ray, and 4 of 15 had CT. All data were analyzed by 3 experienced deputy chief doctors in OI according to OI V standard. Results X-ray:calcification of the interosseous membrane between radius-ulna was detected in all patients and calcification of the interosseous membrane between tibia-fibula was detected in 2 of 15 patients. Dislocation of the radial head was seen in 13 of 15 patients,bilateral in 9 and utilateral in 4.All patients showed restriction in the pronation and supination of the forearm and restricton in the flexion and extention of the elbow joint. Patients with dislocation of raidal head were associated with large coronoid process and olecranon of the ulna. Hyperplastic callus of the extremities were detected in 7 of 15 patients (7 at femur , 3 at humerus, 1 at tibia.In early stage, hyperplastic callus showed thin cortice, and clear boundares with the diaphysis showing and low density, irregular, mesh-like lamellation inside. In the later stage, there were dense calcification inside hyperplastic callus, and no difference in density with the diaphysis. Diaphysis surrounded by hyperplastic callus had clear boundaries with the hyperplastic callus. No cortical destruction was detected. CT:there were sparse needle-dot calcification inside hyperplastic callus, with the patterns of granular, ring-and-arch,irregular streaky mineralization. The cross section of proximal femoral shaft showed irregular shape , flat square shape and tiny medullary cavity, with no calcification on the edge of hyperplastic callus. CT value:-91 HU inside hyperplastic callus; 283 HU in femoral shaft. Conclusions Interosseous membrane between radius-ulna or tibia-fibula, hyperplastic callus ,dislocation of the radial head are specific features in osteogenesis imperfecta V. X-ray can make a definitive diagnosis of osteogenesis imperfecta V. CT scan is helpful in the differential diagnosis of osteogenesis imperfecta V from osteosarcoma.