China emerges as a major country in nuclear energy development and the application of nuclear and radiologic technology. The diagnosis and treatment of acute radiation syndrom (ARS) caused by external irradiation represent a core function in the country′s medical rescue of nuclear and radiological emergencies. Clinically, ARS manifests hematopoietic, gastrointestinal, cutaneous, and central nervous system syndromes, with specific clinical manifestations, signs, severity, and prognosis strongly correlated with radiation dose. China has established a number of national and provincial centers for treating radiation-induced damage. Nevertheless, most medical staff have limited experience in ARS treatment. This consensus presents a summary of recent experience in treating ARS of China. In combination with recommendations from international organizations such as the World Health Organization (WHO), this consensus proposes key evidence of critical clinical issues of ARS, covering all links in the rescue of external irradiation-induced ARS. Initially, clinical diagnosis, syndromes, and severe degrees should be determined based on clinical symptoms and dose estimates. It is necessary to normalize clinical treatment measures for hematopoietic recovery, gastrointestinal injury treatment, infection control, symptomatic treatment, and multi-organ function preservation. To this end, this consensus offers cautions. This consensus provides principles of treatment with traditional Chinese medicine, psychological intervention, and follow-up. Additionally, it highlights multidisciplinary collaboration. It is recommended that this consensus be applied in relevant treatment centers.

Objective:To develop and validate machine learning-based radiomics models using preoperative CT images for individualized prediction of mitotic index (MI) in patients with gastrointestinal stromal tumors (GIST).Methods:The study was a case-control study. The data of 348 GIST patients confirmed by pathology were retrospectively collected from two independent medical centers: the Affiliated Hospital of Qingdao University (center 1) and Shandong Provincial Hospital Affiliated to Shandong First Medical University (center 2), covering the period from January 2013 to June 2018. Patients from center 1 were divided into a training cohort (176 cases) and an internal validation cohort (75 cases) at a ratio of 7∶3 using random sampling. Patients from center 2 served as an independent external validation cohort (97 cases). The primary endpoint was MI, categorized into high MI (145 cases) and low MI (203 cases) groups. Radiomic features were extracted from the portal venous phase images of preoperative contrast-enhanced CT scans. Five machine learning algorithms, including logistic regression, support vector machine, random forest, decision tree, and extreme gradient boosting (XGBoost),were employed to construct MI prediction models. The optimal model was identified using receiver operating characteristic curves. An individualized prediction model was developed by integrating the the optimal machine learning model combined with selected independent clinical factors, and the importance of features was visualized using Shapley Additive Explanation (SHAP) analysis. Patients were followed up, and Kaplan-Meier curves along with log-rank tests were used to evaluate recurrence-free survival (RFS) differences between the predicted high MI and low MI groups.Results:Among the five constructed machine learning models, the XGBoost model demonstrated the best predictive performance, with area under the curve (AUC) of 0.809 (95% CI 0.738-0.872), 0.693 (95% CI 0.571-0.809), and 0.718 (95% CI 0.605-0.822) in the training cohort, internal validation cohort, and external validation cohort, respectively. An individualized prediction model combining the XGBoost model with independent clinical factors (tumor location and tumor size) was developed. The model achieved AUC of 0.843 (95% CI 0.785-0.899), 0.791 (95% CI 0.680-0.894), and 0.777 (95% CI 0.678-0.861) in the training cohort, internal validation cohort, and external validation cohort, respectively. SHAP analysis indicated that radiomic features had the highest predictive impact. In both the training cohort and internal validation cohort, the RFS of patients predicted to be in the high MI group was lower than that of the low MI group, with statistically significant differences ( χ2=14.58, 9.52, both P<0.001). However, there was no statistically significant difference in RFS in the external validation set ( χ2=6.18, P=0.080). Conclusions:The optimal XGBoost model based on radiomic features extracted from preoperative portal venous phase CT images, when combined with clinical factors, can effectively predict the MI of GIST patients.

China emerges as a major country in nuclear energy development and the application of nuclear and radiologic technology. The diagnosis and treatment of acute radiation syndrom (ARS) caused by external irradiation represent a core function in the country′s medical rescue of nuclear and radiological emergencies. Clinically, ARS manifests hematopoietic, gastrointestinal, cutaneous, and central nervous system syndromes, with specific clinical manifestations, signs, severity, and prognosis strongly correlated with radiation dose. China has established a number of national and provincial centers for treating radiation-induced damage. Nevertheless, most medical staff have limited experience in ARS treatment. This consensus presents a summary of recent experience in treating ARS of China. In combination with recommendations from international organizations such as the World Health Organization (WHO), this consensus proposes key evidence of critical clinical issues of ARS, covering all links in the rescue of external irradiation-induced ARS. Initially, clinical diagnosis, syndromes, and severe degrees should be determined based on clinical symptoms and dose estimates. It is necessary to normalize clinical treatment measures for hematopoietic recovery, gastrointestinal injury treatment, infection control, symptomatic treatment, and multi-organ function preservation. To this end, this consensus offers cautions. This consensus provides principles of treatment with traditional Chinese medicine, psychological intervention, and follow-up. Additionally, it highlights multidisciplinary collaboration. It is recommended that this consensus be applied in relevant treatment centers.

This paper summarized the current status of infection and quality control of medical flexible endoscope (abbreviation:endoscope),which can identify that defect of the quality control of endoscopic forceps channels was a major cause of nosocomial infections of endoscopy. Based on this,a multifunctional quality control workstation with forceps channel of detecting flexible endoscope,and precision components included top ends for medical endoscopes has been developed,which can clearly display residual contaminants and damages in the forceps channels and precision components after the endoscope was reprocessed. It is contribute to enhance the quality control of reprocessing endoscope,and reduce cross-infection of endoscope.

This paper summarized the current status of infection and quality control of medical flexible endoscope (abbreviation:endoscope),which can identify that defect of the quality control of endoscopic forceps channels was a major cause of nosocomial infections of endoscopy. Based on this,a multifunctional quality control workstation with forceps channel of detecting flexible endoscope,and precision components included top ends for medical endoscopes has been developed,which can clearly display residual contaminants and damages in the forceps channels and precision components after the endoscope was reprocessed. It is contribute to enhance the quality control of reprocessing endoscope,and reduce cross-infection of endoscope.

Objective:To develop and validate machine learning-based radiomics models using preoperative CT images for individualized prediction of mitotic index (MI) in patients with gastrointestinal stromal tumors (GIST).Methods:The study was a case-control study. The data of 348 GIST patients confirmed by pathology were retrospectively collected from two independent medical centers: the Affiliated Hospital of Qingdao University (center 1) and Shandong Provincial Hospital Affiliated to Shandong First Medical University (center 2), covering the period from January 2013 to June 2018. Patients from center 1 were divided into a training cohort (176 cases) and an internal validation cohort (75 cases) at a ratio of 7∶3 using random sampling. Patients from center 2 served as an independent external validation cohort (97 cases). The primary endpoint was MI, categorized into high MI (145 cases) and low MI (203 cases) groups. Radiomic features were extracted from the portal venous phase images of preoperative contrast-enhanced CT scans. Five machine learning algorithms, including logistic regression, support vector machine, random forest, decision tree, and extreme gradient boosting (XGBoost),were employed to construct MI prediction models. The optimal model was identified using receiver operating characteristic curves. An individualized prediction model was developed by integrating the the optimal machine learning model combined with selected independent clinical factors, and the importance of features was visualized using Shapley Additive Explanation (SHAP) analysis. Patients were followed up, and Kaplan-Meier curves along with log-rank tests were used to evaluate recurrence-free survival (RFS) differences between the predicted high MI and low MI groups.Results:Among the five constructed machine learning models, the XGBoost model demonstrated the best predictive performance, with area under the curve (AUC) of 0.809 (95% CI 0.738-0.872), 0.693 (95% CI 0.571-0.809), and 0.718 (95% CI 0.605-0.822) in the training cohort, internal validation cohort, and external validation cohort, respectively. An individualized prediction model combining the XGBoost model with independent clinical factors (tumor location and tumor size) was developed. The model achieved AUC of 0.843 (95% CI 0.785-0.899), 0.791 (95% CI 0.680-0.894), and 0.777 (95% CI 0.678-0.861) in the training cohort, internal validation cohort, and external validation cohort, respectively. SHAP analysis indicated that radiomic features had the highest predictive impact. In both the training cohort and internal validation cohort, the RFS of patients predicted to be in the high MI group was lower than that of the low MI group, with statistically significant differences ( χ2=14.58, 9.52, both P<0.001). However, there was no statistically significant difference in RFS in the external validation set ( χ2=6.18, P=0.080). Conclusions:The optimal XGBoost model based on radiomic features extracted from preoperative portal venous phase CT images, when combined with clinical factors, can effectively predict the MI of GIST patients.

Objective To explore the value of CT-derived fractional flow reserve(CT-FFR)and pericoronary fat attenuation index(FAI)combined with clinical and coronary CT angiography(CCTA)characteristics for predicting major adverse cardiovascular events(MACE)after aortic valve replacement(AVR).Methods Data of 139 patients with aortic stenosis who underwent AVR were retrospectively analyzed.According to occurrence of MACE or not during follow-up,the patients were divided into MACE group and non-MACE group.Cox proportional hazard regression was used to analyze clinical and CCTA data,as well as CT-FFR and FAI to screen independent predictors of MACE after AVR,and nested models based on clinical data,CCTA characteristics,CT-FFR and right coronary artery(RCA)FAI were constructed.Receiver operating characteristic(ROC)curves were drawn,the area under the curve(AUC)and Harrell C index(C-index)were calculated to assess the diagnostic efficacy of each model,and their goodness of fit were evaluated.Results There were 22 cases in MACE group and 117 in non-MACE group.CT-FFR(HR=3.683)and RCA-FAI(HR=3.261)were both independent predictors of MACE in patients after AVR.The AUC of clinical model,modelclinical+CCTA,modelclinical+CCTA+CT-FFR and modelclinical+CCTA+CT-FFR+RCA-FAI was 0.636,0.730,0.758 and 0.817,and the C-index was 0.614,0.707,0.733 and 0.782,respectively.The predicted results of modelclinical+CCTA+CT-FFR+RCA-FAI were most consistent with actual results,with the best goodness of fit.Conclusion CT-FFR and RCA-FAI combined with clinical and CCTA characteristics could effectively predict MACE in patients after AVR.

BACKGROUND: Pneumonia-like primary pulmonary lymphoma (PPL) was commonly misdiagnosed as infectious pneumonia, leading to delayed treatment. The purpose of this study was to establish a computed tomography (CT)-based radiomics model to differentiate pneumonia-like PPL from infectious pneumonia. METHODS: In this retrospective study, 79 patients with pneumonia-like PPL and 176 patients with infectious pneumonia from 12 medical centers were enrolled. Patients from center 1 to center 7 were assigned to the training or validation cohort, and the remaining patients from other centers were used as the external test cohort. Radiomics features were extracted from CT images. A three-step procedure was applied for radiomics feature selection and radiomics signature building, including the inter- and intra-class correlation coefficients (ICCs), a one-way analysis of variance (ANOVA), and least absolute shrinkage and selection operator (LASSO). Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and construct a clinical factor model. Two radiologists reviewed the CT images for the external test set. Performance of the radiomics model, clinical factor model, and each radiologist were assessed by receiver operating characteristic, and area under the curve (AUC) was compared. RESULTS: A total of 144 patients (44 with pneumonia-like PPL and 100 infectious pneumonia) were in the training cohort, 38 patients (12 with pneumonia-like PPL and 26 infectious pneumonia) were in the validation cohort, and 73 patients (23 with pneumonia-like PPL and 50 infectious pneumonia) were in the external test cohort. Twenty-three radiomics features were selected to build the radiomics model, which yielded AUCs of 0.95 (95% confidence interval [CI]: 0.94-0.99), 0.93 (95% CI: 0.85-0.98), and 0.94 (95% CI: 0.87-0.99) in the training, validation, and external test cohort, respectively. The AUCs for the two readers and clinical factor model were 0.74 (95% CI: 0.63-0.83), 0.72 (95% CI: 0.62-0.82), and 0.73 (95% CI: 0.62-0.84) in the external test cohort, respectively. The radiomics model outperformed both the readers' interpretation and clinical factor model ( P <0.05). CONCLUSIONS The CT-based radiomics model may provide an effective and non-invasive tool to differentiate pneumonia-like PPL from infectious pneumonia, which might provide assistance for clinicians in tailoring precise therapy.
Humans ; Retrospective Studies ; Pneumonia/diagnostic imaging* ; Analysis of Variance ; Tomography, X-Ray Computed ; Lymphoma/diagnostic imaging*

OBJECTIVE: To compare the biomechanical differences among the five internal fixation modes in treatment of Day type Ⅱ crescent fracture dislocation of pelvis (CFDP), and find an internal fixation mode which was the most consistent with mechanical principles. METHODS: Based on the pelvic CT data of a healthy adult male volunteer, a Day type Ⅱ CFDP finite element model was established by using Mimics 17.0, ANSYS 12.0-ICEM, Abaqus 2020, and SolidWorks 2012 softwares. After verifying the validity of the finite element model by comparing the anatomical parameters with the three-dimensional reconstruction model and the mechanical validity verification, the fracture and dislocated joint of models were fixed with S ₁ sacroiliac screw combined with 1 LC-Ⅱ screw (S ₁+LC-Ⅱ group), S ₁ sacroiliac screw combined with 2 LC-Ⅱ screws (S ₁+2LC-Ⅱ group), S ₁ sacroiliac screw combined with 2 posterior iliac screws (S ₁+2PIS group), S ₁ and S ₂ sacroiliac screws combined with 1 LC-Ⅱ screw (S ₁+S ₂+LC-Ⅱ group), S ₂-alar-iliac (S ₂AI) screw combined with 1 LC-Ⅱ screw (S ₂AI+LC-Ⅱ group), respectively. After each internal fixation model was loaded with a force of 600 N in the standing position, the maximum displacement of the crescent fracture fragments, the maximum stress of the internal fixation (the maximum stress of the screw at the ilium fracture and the maximum stress of the screw at the sacroiliac joint), sacroiliac joint displacement, and bone stress distribution around internal fixation were observed in 5 groups. RESULTS: The finite element model in this study has been verified to be effective. After loading 600 N stress, there was a certain displacement of the crescent fracture of pelvis in each internal fixation model, among which the S ₁+LC-Ⅱ group was the largest, the S ₁+2LC-Ⅱ group and the S ₁+2PIS group were the smallest. The maximum stress of the internal fixation mainly concentrated at the sacroiliac joint and the fracture line of crescent fracture. The maximum stress of the screw at the sacroiliac joint was the largest in the S ₁+LC-Ⅱ group and the smallest in the S ₂AI+LC-Ⅱ group. The maximum stress of the screw at the ilium fracture was the largest in the S ₁+2PIS group and the smallest in the S ₁+2LC-Ⅱ group. The displacement of the sacroiliac joint was the largest in the S ₁+LC-Ⅱ group and the smallest in the S ₁+S ₂+LC-Ⅱ group. In each internal fixation model, the maximum stress around the sacroiliac screws concentrated on the contact surface between the screw and the cortical bone, the maximum stress around the screws at the iliac bone concentrated on the cancellous bone of the fracture line, and the maximum stress around the S ₂AI screw concentrated on the cancellous bone on the iliac side. The maximum bone stress around the screws at the sacroiliac joint was the largest in the S ₁+LC-Ⅱ group and the smallest in the S ₂AI+LC-Ⅱ group. The maximum bone stress around the screws at the ilium was the largest in the S ₁+2PIS group and the smallest in the S ₁+LC-Ⅱ group. CONCLUSION For the treatment of Day type Ⅱ CFDP, it is recommended to choose S ₁ sacroiliac screw combined with 1 LC-Ⅱ screw for internal fixation, which can achieve a firm fixation effect without increasing the number of screws.
Adult ; Male ; Humans ; Finite Element Analysis ; Fracture Fixation, Internal/methods* ; Fractures, Bone/surgery* ; Pelvis ; Spinal Fractures/surgery* ; Fracture Dislocation/surgery* ; Joint Dislocations/surgery* ; Biomechanical Phenomena

Bone defects caused by different causes such as trauma, severe bone infection and other factors are common in clinic and difficult to treat. Usually, bone substitutes are required for repair. Current bone grafting materials used clinically include autologous bones, allogeneic bones, xenografts, and synthetic materials, etc. Other than autologous bones, the major hurdles of rest bone grafts have various degrees of poor biological activity and lack of active ingredients to provide osteogenic impetus. Bone marrow contains various components such as stem cells and bioactive factors, which are contributive to osteogenesis. In response, the technique of bone marrow enrichment, based on the efficient utilization of components within bone marrow, has been risen, aiming to extract osteogenic cells and factors from bone marrow of patients and incorporate them into 3D scaffolds for fabricating bone grafts with high osteoinductivity. However, the scientific guidance and application specification are lacked with regard to the clinical scope, approach, safety and effectiveness. In this context, under the organization of Chinese Orthopedic Association, the Expert consensus for the clinical application of autologous bone marrow enrichment technique for bone repair ( version 2023) is formulated based on the evidence-based medicine. The consensus covers the topics of the characteristics, range of application, safety and application notes of the technique of autologous bone marrow enrichment and proposes corresponding recommendations, hoping to provide better guidance for clinical practice of the technique.